diff --git a/README.md b/README.md index 720e487..52855d6 100644 --- a/README.md +++ b/README.md @@ -1,6 +1,6 @@ # Isaac ROS Pose Estimation -Deep learned, hardware-accelerated 3D object pose estimation +Deep learned, NVIDIA-accelerated 3D object pose estimation
image
@@ -8,18 +8,34 @@ Deep learned, hardware-accelerated 3D object pose estimation [Isaac ROS Pose Estimation](https://github.com/NVIDIA-ISAAC-ROS/isaac_ros_pose_estimation) contains ROS 2 packages to predict the pose of an object. +`isaac_ros_foundationpose` estimates the object’s pose using the 3D +bounding cuboid dimensions of unknown object from an input image. `isaac_ros_dope` estimates the object’s pose using the 3D bounding cuboid dimensions of a known object in an input image. `isaac_ros_centerpose` estimates the object’s pose using the 3D bounding cuboid dimensions of unknown object instances in a known -category of objects from an input image. `isaac_ros_dope` and -`isaac_ros_centerpose` use GPU acceleration for DNN inference to +category of objects from an input image. +Those packages use GPU acceleration for DNN inference to estimate the pose of an object. The output prediction can be used by perception functions when fusing with the corresponding depth to provide the 3D pose of an object and distance for navigation or manipulation.
image
+`isaac_ros_foundationpose` is used in a graph of nodes to estimate the pose of +a novel object using 3D bounding cuboid dimensions. It’s developed on top of +[FoundationPose](https://github.com/NVlabs/FoundationPose) model, which is +a pre-trained deep learning model developed by NVLabs. FoundationPose +is capable for both pose estimation and tracking on unseen objects without requiring fine-tuning, +and its accuracy outperforms existing state-of-art methods. + +FoundationPose comprises two distinct models: the refine model and the score model. +The refine model processes initial pose hypotheses, iteratively refining them, then +passes these refined hypotheses to the score model, which selects and finalizes the pose estimation. +Additionally, the refine model can serve for tracking, that updates the pose estimation based on +new image inputs and the previous frame’s pose estimate. This tracking process is more efficient +compared to pose estimation, which speeds exceeding 120 FPS on the Jetson Orin platform. + `isaac_ros_dope` is used in a graph of nodes to estimate the pose of a known object with 3D bounding cuboid dimensions. To produce the estimate, a [DOPE](https://github.com/NVlabs/Deep_Object_Pose) (Deep @@ -65,10 +81,11 @@ which can also be found at [Isaac ROS DNN Inference](https://github.com/NVIDIA-I ## Performance -| Sample Graph

| Input Size

| AGX Orin

| Orin NX

| Orin Nano 8GB

| x86_64 w/ RTX 4060 Ti

| -|-----------------------------------------------------------------------------------------------------------------------------------------------------------------|-------------------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------------------------------------------------------------------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------| -| [DOPE Pose Estimation Graph](https://github.com/NVIDIA-ISAAC-ROS/isaac_ros_benchmark/blob/main/scripts/isaac_ros_dope_graph.py)



| VGA



| [39.8 fps](https://github.com/NVIDIA-ISAAC-ROS/isaac_ros_benchmark/blob/main/results/isaac_ros_dope_graph-agx_orin.json)


33 ms

| [17.3 fps](https://github.com/NVIDIA-ISAAC-ROS/isaac_ros_benchmark/blob/main/results/isaac_ros_dope_graph-orin_nx.json)


120 ms

| –



| [89.2 fps](https://github.com/NVIDIA-ISAAC-ROS/isaac_ros_benchmark/blob/main/results/isaac_ros_dope_graph-nuc_4060ti.json)


15 ms

| -| [Centerpose Pose Estimation Graph](https://github.com/NVIDIA-ISAAC-ROS/isaac_ros_benchmark/blob/main/scripts/isaac_ros_centerpose_graph.py)



| VGA



| [36.1 fps](https://github.com/NVIDIA-ISAAC-ROS/isaac_ros_benchmark/blob/main/results/isaac_ros_centerpose_graph-agx_orin.json)


5.7 ms

| [19.4 fps](https://github.com/NVIDIA-ISAAC-ROS/isaac_ros_benchmark/blob/main/results/isaac_ros_centerpose_graph-orin_nx.json)


7.4 ms

| [13.8 fps](https://github.com/NVIDIA-ISAAC-ROS/isaac_ros_benchmark/blob/main/results/isaac_ros_centerpose_graph-orin_nano.json)


12 ms

| [50.2 fps](https://github.com/NVIDIA-ISAAC-ROS/isaac_ros_benchmark/blob/main/results/isaac_ros_centerpose_graph-nuc_4060ti.json)


14 ms

| +| Sample Graph

| Input Size

| AGX Orin

| Orin NX

| Orin Nano 8GB

| x86_64 w/ RTX 4060 Ti

| x86_64 w/ RTX 4090

| +|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|--------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------| +| [FoundationPose Pose Estimation Node](https://github.com/NVIDIA-ISAAC-ROS/isaac_ros_benchmark/blob/main/benchmarks/isaac_ros_foundationpose_benchmark/scripts/isaac_ros_foundationpose_node.py)



| 720p



| [1.72 fps](https://github.com/NVIDIA-ISAAC-ROS/isaac_ros_benchmark/blob/main/results/isaac_ros_foundationpose_node-agx_orin.json)


690 ms @ 30Hz

| –



| –



| –



| [7.02 fps](https://github.com/NVIDIA-ISAAC-ROS/isaac_ros_benchmark/blob/main/results/isaac_ros_foundationpose_node-x86_4090.json)


170 ms @ 30Hz

| +| [DOPE Pose Estimation Graph](https://github.com/NVIDIA-ISAAC-ROS/isaac_ros_benchmark/blob/main/benchmarks/isaac_ros_dope_benchmark/scripts/isaac_ros_dope_graph.py)



| VGA



| [41.3 fps](https://github.com/NVIDIA-ISAAC-ROS/isaac_ros_benchmark/blob/main/results/isaac_ros_dope_graph-agx_orin.json)


42 ms @ 30Hz

| [17.5 fps](https://github.com/NVIDIA-ISAAC-ROS/isaac_ros_benchmark/blob/main/results/isaac_ros_dope_graph-orin_nx.json)


76 ms @ 30Hz

| –



| [85.2 fps](https://github.com/NVIDIA-ISAAC-ROS/isaac_ros_benchmark/blob/main/results/isaac_ros_dope_graph-nuc_4060ti.json)


24 ms @ 30Hz

| [199 fps](https://github.com/NVIDIA-ISAAC-ROS/isaac_ros_benchmark/blob/main/results/isaac_ros_dope_graph-x86_4090.json)


14 ms @ 30Hz

| +| [Centerpose Pose Estimation Graph](https://github.com/NVIDIA-ISAAC-ROS/isaac_ros_benchmark/blob/main/benchmarks/isaac_ros_centerpose_benchmark/scripts/isaac_ros_centerpose_graph.py)



| VGA



| [36.3 fps](https://github.com/NVIDIA-ISAAC-ROS/isaac_ros_benchmark/blob/main/results/isaac_ros_centerpose_graph-agx_orin.json)


4.8 ms @ 30Hz

| [19.7 fps](https://github.com/NVIDIA-ISAAC-ROS/isaac_ros_benchmark/blob/main/results/isaac_ros_centerpose_graph-orin_nx.json)


4.9 ms @ 30Hz

| [13.8 fps](https://github.com/NVIDIA-ISAAC-ROS/isaac_ros_benchmark/blob/main/results/isaac_ros_centerpose_graph-orin_nano.json)


7.4 ms @ 30Hz

| [50.2 fps](https://github.com/NVIDIA-ISAAC-ROS/isaac_ros_benchmark/blob/main/results/isaac_ros_centerpose_graph-nuc_4060ti.json)


23 ms @ 30Hz

| [50.2 fps](https://github.com/NVIDIA-ISAAC-ROS/isaac_ros_benchmark/blob/main/results/isaac_ros_centerpose_graph-x86_4090.json)


20 ms @ 30Hz

| --- @@ -82,7 +99,6 @@ Please visit the [Isaac ROS Documentation](https://nvidia-isaac-ros.github.io/re * [`isaac_ros_centerpose`](https://nvidia-isaac-ros.github.io/repositories_and_packages/isaac_ros_pose_estimation/isaac_ros_centerpose/index.html) * [Quickstart](https://nvidia-isaac-ros.github.io/repositories_and_packages/isaac_ros_pose_estimation/isaac_ros_centerpose/index.html#quickstart) - * [Use Different Models](https://nvidia-isaac-ros.github.io/repositories_and_packages/isaac_ros_pose_estimation/isaac_ros_centerpose/index.html#use-different-models) * [Troubleshooting](https://nvidia-isaac-ros.github.io/repositories_and_packages/isaac_ros_pose_estimation/isaac_ros_centerpose/index.html#troubleshooting) * [API](https://nvidia-isaac-ros.github.io/repositories_and_packages/isaac_ros_pose_estimation/isaac_ros_centerpose/index.html#api) * [`isaac_ros_dope`](https://nvidia-isaac-ros.github.io/repositories_and_packages/isaac_ros_pose_estimation/isaac_ros_dope/index.html) @@ -91,7 +107,12 @@ Please visit the [Isaac ROS Documentation](https://nvidia-isaac-ros.github.io/re * [Use Different Models](https://nvidia-isaac-ros.github.io/repositories_and_packages/isaac_ros_pose_estimation/isaac_ros_dope/index.html#use-different-models) * [Troubleshooting](https://nvidia-isaac-ros.github.io/repositories_and_packages/isaac_ros_pose_estimation/isaac_ros_dope/index.html#troubleshooting) * [API](https://nvidia-isaac-ros.github.io/repositories_and_packages/isaac_ros_pose_estimation/isaac_ros_dope/index.html#api) +* [`isaac_ros_foundationpose`](https://nvidia-isaac-ros.github.io/repositories_and_packages/isaac_ros_pose_estimation/isaac_ros_foundationpose/index.html) + * [Quickstart](https://nvidia-isaac-ros.github.io/repositories_and_packages/isaac_ros_pose_estimation/isaac_ros_foundationpose/index.html#quickstart) + * [Try More Examples](https://nvidia-isaac-ros.github.io/repositories_and_packages/isaac_ros_pose_estimation/isaac_ros_foundationpose/index.html#try-more-examples) + * [Troubleshooting](https://nvidia-isaac-ros.github.io/repositories_and_packages/isaac_ros_pose_estimation/isaac_ros_foundationpose/index.html#troubleshooting) + * [API](https://nvidia-isaac-ros.github.io/repositories_and_packages/isaac_ros_pose_estimation/isaac_ros_foundationpose/index.html#api) ## Latest -Update 2023-10-18: Adding NITROS CenterPose decoder. +Update 2024-05-30: Added FoundationPose pose estimation package diff --git a/isaac_ros_centerpose/CMakeLists.txt b/isaac_ros_centerpose/CMakeLists.txt index 5c43432..b6ed263 100644 --- a/isaac_ros_centerpose/CMakeLists.txt +++ b/isaac_ros_centerpose/CMakeLists.txt @@ -1,5 +1,5 @@ # SPDX-FileCopyrightText: NVIDIA CORPORATION & AFFILIATES -# Copyright (c) 2021-2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# Copyright (c) 2021-2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. @@ -29,19 +29,22 @@ ament_auto_find_build_dependencies() ament_auto_add_library(centerpose_decoder_node SHARED src/centerpose_decoder_node.cpp) rclcpp_components_register_nodes(centerpose_decoder_node "nvidia::isaac_ros::centerpose::CenterPoseDecoderNode") set(node_plugins "${node_plugins}nvidia::isaac_ros::centerpose::CenterPoseDecoderNode;$\n") +set_target_properties(centerpose_decoder_node PROPERTIES + BUILD_WITH_INSTALL_RPATH TRUE + BUILD_RPATH_USE_ORIGIN TRUE + INSTALL_RPATH_USE_LINK_PATH TRUE +) # Visualizer node ament_auto_add_library(centerpose_visualizer_node SHARED src/centerpose_visualizer_node.cpp) rclcpp_components_register_nodes(centerpose_visualizer_node "nvidia::isaac_ros::centerpose::CenterPoseVisualizerNode") set(node_plugins "${node_plugins}nvidia::isaac_ros::centerpose::CenterPoseVisualizerNode;$\n") +set_target_properties(centerpose_visualizer_node PROPERTIES + BUILD_WITH_INSTALL_RPATH TRUE + BUILD_RPATH_USE_ORIGIN TRUE + INSTALL_RPATH_USE_LINK_PATH TRUE +) -### Install extensions built from source - -# CenterPose -add_subdirectory(gxf/centerpose) -install(TARGETS gxf_centerpose DESTINATION share/${PROJECT_NAME}/gxf/lib/centerpose) - -### End extensions if(BUILD_TESTING) find_package(ament_lint_auto REQUIRED) ament_lint_auto_find_test_dependencies() diff --git a/isaac_ros_centerpose/config/centerpose_node.yaml b/isaac_ros_centerpose/config/centerpose_node.yaml index a1fcd43..a572575 100644 --- a/isaac_ros_centerpose/config/centerpose_node.yaml +++ b/isaac_ros_centerpose/config/centerpose_node.yaml @@ -22,17 +22,14 @@ components: type: nvidia::gxf::DoubleBufferReceiver parameters: capacity: 12 - policy: 0 - name: camera_model_input type: nvidia::gxf::DoubleBufferReceiver parameters: capacity: 12 - policy: 0 - name: output type: nvidia::gxf::DoubleBufferTransmitter parameters: capacity: 12 - policy: 0 - name: allocator type: nvidia::gxf::UnboundedAllocator - name: centerpose_decoder @@ -64,12 +61,10 @@ components: type: nvidia::gxf::DoubleBufferReceiver parameters: capacity: 12 - policy: 0 - name: output type: nvidia::gxf::DoubleBufferTransmitter parameters: capacity: 12 - policy: 0 - name: centerpose_decoder_to_isaac type: nvidia::isaac::centerpose::CenterPoseDetectionToIsaac parameters: @@ -91,7 +86,6 @@ components: type: nvidia::gxf::DoubleBufferReceiver parameters: capacity: 2 - policy: 0 - type: nvidia::gxf::MessageAvailableSchedulingTerm parameters: receiver: signal diff --git a/isaac_ros_centerpose/config/centerpose_visualizer_node.yaml b/isaac_ros_centerpose/config/centerpose_visualizer_node.yaml index 4a07eb8..77b8fe3 100644 --- a/isaac_ros_centerpose/config/centerpose_visualizer_node.yaml +++ b/isaac_ros_centerpose/config/centerpose_visualizer_node.yaml @@ -22,22 +22,18 @@ components: type: nvidia::gxf::DoubleBufferReceiver parameters: capacity: 12 - policy: 0 - name: detections_input type: nvidia::gxf::DoubleBufferReceiver parameters: capacity: 12 - policy: 0 - name: camera_model_input type: nvidia::gxf::DoubleBufferReceiver parameters: capacity: 12 - policy: 0 - name: output type: nvidia::gxf::DoubleBufferTransmitter parameters: capacity: 12 - policy: 0 - name: allocator type: nvidia::gxf::UnboundedAllocator - name: centerpose_visualizer @@ -73,7 +69,6 @@ components: type: nvidia::gxf::DoubleBufferReceiver parameters: capacity: 2 - policy: 0 - type: nvidia::gxf::MessageAvailableSchedulingTerm parameters: receiver: signal diff --git a/isaac_ros_centerpose/gxf/centerpose/CMakeLists.txt b/isaac_ros_centerpose/gxf/centerpose/CMakeLists.txt deleted file mode 100644 index ff61a79..0000000 --- a/isaac_ros_centerpose/gxf/centerpose/CMakeLists.txt +++ /dev/null @@ -1,68 +0,0 @@ -# SPDX-FileCopyrightText: NVIDIA CORPORATION & AFFILIATES -# Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# -# SPDX-License-Identifier: Apache-2.0 - -project(gxf_centerpose LANGUAGES C CXX) - -# Dependencies -find_package(CUDAToolkit REQUIRED) -find_package (Eigen3 3.3 REQUIRED) -find_package(GXF ${ISAAC_ROS_GXF_VERSION} MODULE REQUIRED - COMPONENTS - cuda - multimedia - std - isaac_messages -) -find_package(OpenCV REQUIRED) -find_package(yaml-cpp) -find_package(isaac_ros_nitros_detection3_d_array_type REQUIRED) - -# Centerpose extension -add_library(gxf_centerpose SHARED - components/centerpose_postprocessor.cpp - components/centerpose_postprocessor.hpp - components/cuboid3d.hpp - components/cuboid3d.cpp - components/cuboid_pnp_solver.cpp - components/cuboid_pnp_solver.hpp - components/centerpose_detection.hpp - components/centerpose_detection_to_isaac.hpp - components/centerpose_detection_to_isaac.cpp - components/soft_nms_nvidia.cpp - components/soft_nms_nvidia.hpp - components/centerpose_visualizer.cpp - components/centerpose_visualizer.hpp - components/video_buffer_utils.hpp - centerpose_ext.cpp -) -target_include_directories(gxf_centerpose - PUBLIC - ${OpenCV_INCLUDE_DIRS} - ${isaac_ros_nitros_detection3_d_array_type_INCLUDE_DIRS} -) -target_link_libraries(gxf_centerpose - PUBLIC - CUDA::cudart - Eigen3::Eigen - GXF::cuda - GXF::isaac_messages - GXF::multimedia - GXF::std - ${OpenCV_LIBS} - yaml-cpp - ${isaac_ros_nitros_detection3_d_array_type_LIBRARIES} -) diff --git a/isaac_ros_centerpose/include/isaac_ros_centerpose/centerpose_decoder_node.hpp b/isaac_ros_centerpose/include/isaac_ros_centerpose/centerpose_decoder_node.hpp index 479ff70..55004e3 100644 --- a/isaac_ros_centerpose/include/isaac_ros_centerpose/centerpose_decoder_node.hpp +++ b/isaac_ros_centerpose/include/isaac_ros_centerpose/centerpose_decoder_node.hpp @@ -1,5 +1,5 @@ // SPDX-FileCopyrightText: NVIDIA CORPORATION & AFFILIATES -// Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +// Copyright (c) 2023-2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved. // // Licensed under the Apache License, Version 2.0 (the "License"); // you may not use this file except in compliance with the License. diff --git a/isaac_ros_centerpose/include/isaac_ros_centerpose/centerpose_visualizer_node.hpp b/isaac_ros_centerpose/include/isaac_ros_centerpose/centerpose_visualizer_node.hpp index 350f321..dcc5836 100644 --- a/isaac_ros_centerpose/include/isaac_ros_centerpose/centerpose_visualizer_node.hpp +++ b/isaac_ros_centerpose/include/isaac_ros_centerpose/centerpose_visualizer_node.hpp @@ -1,5 +1,5 @@ // SPDX-FileCopyrightText: NVIDIA CORPORATION & AFFILIATES -// Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +// Copyright (c) 2023-2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved. // // Licensed under the Apache License, Version 2.0 (the "License"); // you may not use this file except in compliance with the License. diff --git a/isaac_ros_centerpose/launch/isaac_ros_centerpose_core.launch.py b/isaac_ros_centerpose/launch/isaac_ros_centerpose_core.launch.py new file mode 100644 index 0000000..a4c1155 --- /dev/null +++ b/isaac_ros_centerpose/launch/isaac_ros_centerpose_core.launch.py @@ -0,0 +1,203 @@ +# SPDX-FileCopyrightText: NVIDIA CORPORATION & AFFILIATES +# Copyright (c) 2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +# SPDX-License-Identifier: Apache-2.0 + +import os +from typing import Any, Dict + +from ament_index_python.packages import get_package_share_directory +from isaac_ros_examples import IsaacROSLaunchFragment +import launch +from launch.actions import DeclareLaunchArgument, IncludeLaunchDescription +from launch.launch_description_sources import PythonLaunchDescriptionSource +from launch.substitutions import LaunchConfiguration +from launch_ros.descriptions import ComposableNode + + +class IsaacROSCenterPoseLaunchFragment(IsaacROSLaunchFragment): + + @staticmethod + def get_composable_nodes(interface_specs: Dict[str, Any]) -> Dict[str, ComposableNode]: + # Triton parameters + model_name = LaunchConfiguration('model_name') + model_repository_paths = LaunchConfiguration('model_repository_paths') + max_batch_size = LaunchConfiguration('max_batch_size') + input_tensor_names = LaunchConfiguration('input_tensor_names') + input_binding_names = LaunchConfiguration('input_binding_names') + input_tensor_formats = LaunchConfiguration('input_tensor_formats') + output_tensor_names = LaunchConfiguration('output_tensor_names') + output_binding_names = LaunchConfiguration('output_binding_names') + output_tensor_formats = LaunchConfiguration('output_tensor_formats') + + # Centerpose Decoder parameters + output_field_size = LaunchConfiguration('output_field_size') + cuboid_scaling_factor = LaunchConfiguration('cuboid_scaling_factor') + score_threshold = LaunchConfiguration('score_threshold') + object_name = LaunchConfiguration('object_name') + + return { + 'centerpose_inference_node': ComposableNode( + name='centerpose_inference', + package='isaac_ros_triton', + plugin='nvidia::isaac_ros::dnn_inference::TritonNode', + parameters=[{ + 'model_name': model_name, + 'model_repository_paths': model_repository_paths, + 'max_batch_size': max_batch_size, + 'input_tensor_names': input_tensor_names, + 'input_binding_names': input_binding_names, + 'input_tensor_formats': input_tensor_formats, + 'output_tensor_names': output_tensor_names, + 'output_binding_names': output_binding_names, + 'output_tensor_formats': output_tensor_formats, + }] + ), + 'centerpose_decoder_node': ComposableNode( + name='centerpose_decoder_node', + package='isaac_ros_centerpose', + plugin='nvidia::isaac_ros::centerpose::CenterPoseDecoderNode', + parameters=[{ + 'output_field_size': output_field_size, + 'cuboid_scaling_factor': cuboid_scaling_factor, + 'score_threshold': score_threshold, + 'object_name': object_name, + }], + remappings=[ + ('camera_info', 'camera_info_rect'), + ], + ) + } + + @staticmethod + def get_launch_actions(interface_specs: Dict[str, Any]) -> \ + Dict[str, launch.actions.OpaqueFunction]: + + # DNN Image Encoder parameters + network_image_width = LaunchConfiguration('network_image_width') + network_image_height = LaunchConfiguration('network_image_height') + encoder_image_mean = LaunchConfiguration('encoder_image_mean') + encoder_image_stddev = LaunchConfiguration('encoder_image_stddev') + + encoder_dir = get_package_share_directory('isaac_ros_dnn_image_encoder') + + tensor_names = [ + 'bboxes', + 'scores', + 'kps', + 'clses', + 'obj_scale', + 'kps_displacement_mean', + 'kps_heatmap_mean', + ] + tensor_names_str = '[' + for tensor_name in tensor_names: + tensor_names_str += f'"{tensor_name}"' + if tensor_name != tensor_names[-1]: + tensor_names_str += ',' + tensor_names_str += ']' + + return { + 'network_image_width': DeclareLaunchArgument( + 'network_image_width', + default_value='512', + description='The input image width that the network expects'), + 'network_image_height': DeclareLaunchArgument( + 'network_image_height', + default_value='512', + description='The input image height that the network expects'), + 'encoder_image_mean': DeclareLaunchArgument( + 'encoder_image_mean', + default_value='[0.408, 0.447, 0.47]', + description='The mean for image normalization'), + 'encoder_image_stddev': DeclareLaunchArgument( + 'encoder_image_stddev', + default_value='[0.289, 0.274, 0.278]', + description='The standard deviation for image normalization'), + 'model_name': DeclareLaunchArgument( + 'model_name', + default_value='', + description='The name of the model'), + 'model_repository_paths': DeclareLaunchArgument( + 'model_repository_paths', + default_value='', + description='The absolute path to the repository of models'), + 'max_batch_size': DeclareLaunchArgument( + 'max_batch_size', + default_value='0', + description='The maximum allowed batch size of the model'), + 'input_tensor_names': DeclareLaunchArgument( + 'input_tensor_names', + default_value='["input_tensor"]', + description='A list of tensor names to bound to the specified input binding names' + ), + 'input_binding_names': DeclareLaunchArgument( + 'input_binding_names', + default_value='["input"]', + description='A list of input tensor binding names (specified by model)'), + 'input_tensor_formats': DeclareLaunchArgument( + 'input_tensor_formats', + default_value='["nitros_tensor_list_nchw_rgb_f32"]', + description='The nitros format of the input tensors'), + 'output_tensor_names': DeclareLaunchArgument( + 'output_tensor_names', + default_value=tensor_names_str, + description='A list of tensor names to bound to the specified output binding names' + ), + 'output_binding_names': DeclareLaunchArgument( + 'output_binding_names', + default_value=tensor_names_str, + description='A list of output tensor binding names (specified by model)'), + 'output_tensor_formats': DeclareLaunchArgument( + 'output_tensor_formats', + default_value='["nitros_tensor_list_nhwc_rgb_f32"]', + description='The nitros format of the output tensors'), + 'output_field_size': DeclareLaunchArgument( + 'output_field_size', + default_value='[128, 128]', + description='The size of the 2D keypoint decoding from the network output'), + 'cuboid_scaling_factor': DeclareLaunchArgument( + 'cuboid_scaling_factor', + default_value='1.0', + description='Scales the cuboid used for calculating detected objects size'), + 'score_threshold': DeclareLaunchArgument( + 'score_threshold', + default_value='0.3', + description='The threshold for scores values to discard.'), + 'object_name': DeclareLaunchArgument( + 'object_name', + default_value='Ketchup', + description='The object class that the DOPE network is detecting'), + 'centerpose_encoder_launch': IncludeLaunchDescription( + PythonLaunchDescriptionSource( + [os.path.join(encoder_dir, 'launch', 'dnn_image_encoder.launch.py')] + ), + launch_arguments={ + 'input_image_width': str(interface_specs['camera_resolution']['width']), + 'input_image_height': str(interface_specs['camera_resolution']['height']), + 'network_image_width': network_image_width, + 'network_image_height': network_image_height, + 'image_mean': encoder_image_mean, + 'image_stddev': encoder_image_stddev, + 'attach_to_shared_component_container': 'True', + 'component_container_name': '/isaac_ros_examples/container', + 'dnn_image_encoder_namespace': 'centerpose_encoder', + 'image_input_topic': '/image_rect', + 'camera_info_input_topic': '/camera_info_rect', + 'tensor_output_topic': '/tensor_pub', + 'keep_aspect_ratio': 'False' + }.items(), + ), + } diff --git a/isaac_ros_centerpose/launch/isaac_ros_centerpose_tensor_rt.launch.py b/isaac_ros_centerpose/launch/isaac_ros_centerpose_tensor_rt.launch.py index 90cd607..a453fbe 100644 --- a/isaac_ros_centerpose/launch/isaac_ros_centerpose_tensor_rt.launch.py +++ b/isaac_ros_centerpose/launch/isaac_ros_centerpose_tensor_rt.launch.py @@ -1,5 +1,5 @@ # SPDX-FileCopyrightText: NVIDIA CORPORATION & AFFILIATES -# Copyright (c) 2021-2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# Copyright (c) 2021-2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. @@ -15,8 +15,12 @@ # # SPDX-License-Identifier: Apache-2.0 +import os + +from ament_index_python.packages import get_package_share_directory from launch import LaunchDescription -from launch.actions import DeclareLaunchArgument +from launch.actions import DeclareLaunchArgument, IncludeLaunchDescription +from launch.launch_description_sources import PythonLaunchDescriptionSource from launch.substitutions import LaunchConfiguration from launch_ros.actions import ComposableNodeContainer from launch_ros.descriptions import ComposableNode @@ -164,20 +168,6 @@ def generate_launch_description(): show_axes = LaunchConfiguration('show_axes') bounding_box_color = LaunchConfiguration('bounding_box_color') - centerpose_encoder_node = ComposableNode( - name='centerpose_encoder', - package='isaac_ros_dnn_image_encoder', - plugin='nvidia::isaac_ros::dnn_inference::DnnImageEncoderNode', - parameters=[{ - 'input_image_width': input_image_width, - 'input_image_height': input_image_height, - 'network_image_width': network_image_width, - 'network_image_height': network_image_height, - 'image_mean': encoder_image_mean, - 'image_stddev': encoder_image_stddev, - }], - remappings=[('encoded_tensor', 'tensor_pub')]) - centerpose_inference_node = ComposableNode( name='centerpose_inference', package='isaac_ros_tensor_rt', @@ -223,10 +213,30 @@ def generate_launch_description(): package='rclcpp_components', executable='component_container_mt', composable_node_descriptions=[ - centerpose_encoder_node, centerpose_inference_node, - centerpose_decoder_node, centerpose_visualizer_node], + centerpose_inference_node, centerpose_decoder_node, centerpose_visualizer_node], output='screen', ) - final_launch_container = launch_args + [rclcpp_container] + encoder_dir = get_package_share_directory('isaac_ros_dnn_image_encoder') + centerpose_encoder_launch = IncludeLaunchDescription( + PythonLaunchDescriptionSource( + [os.path.join(encoder_dir, 'launch', 'dnn_image_encoder.launch.py')] + ), + launch_arguments={ + 'input_image_width': input_image_width, + 'input_image_height': input_image_height, + 'network_image_width': network_image_width, + 'network_image_height': network_image_height, + 'image_mean': encoder_image_mean, + 'image_stddev': encoder_image_stddev, + 'attach_to_shared_component_container': 'True', + 'component_container_name': 'centerpose_container', + 'dnn_image_encoder_namespace': 'centerpose_encoder', + 'image_input_topic': '/image', + 'camera_info_input_topic': '/camera_info', + 'tensor_output_topic': '/tensor_pub', + }.items(), + ) + + final_launch_container = launch_args + [rclcpp_container, centerpose_encoder_launch] return LaunchDescription(final_launch_container) diff --git a/isaac_ros_centerpose/launch/isaac_ros_centerpose_triton.launch.py b/isaac_ros_centerpose/launch/isaac_ros_centerpose_triton.launch.py index f4a299c..fc800cc 100644 --- a/isaac_ros_centerpose/launch/isaac_ros_centerpose_triton.launch.py +++ b/isaac_ros_centerpose/launch/isaac_ros_centerpose_triton.launch.py @@ -1,5 +1,5 @@ # SPDX-FileCopyrightText: NVIDIA CORPORATION & AFFILIATES -# Copyright (c) 2021-2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# Copyright (c) 2021-2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. @@ -15,8 +15,12 @@ # # SPDX-License-Identifier: Apache-2.0 +import os + +from ament_index_python.packages import get_package_share_directory from launch import LaunchDescription -from launch.actions import DeclareLaunchArgument +from launch.actions import DeclareLaunchArgument, IncludeLaunchDescription +from launch.launch_description_sources import PythonLaunchDescriptionSource from launch.substitutions import LaunchConfiguration from launch_ros.actions import ComposableNodeContainer from launch_ros.descriptions import ComposableNode @@ -159,20 +163,6 @@ def generate_launch_description(): show_axes = LaunchConfiguration('show_axes') bounding_box_color = LaunchConfiguration('bounding_box_color') - centerpose_encoder_node = ComposableNode( - name='centerpose_encoder', - package='isaac_ros_dnn_image_encoder', - plugin='nvidia::isaac_ros::dnn_inference::DnnImageEncoderNode', - parameters=[{ - 'input_image_width': input_image_width, - 'input_image_height': input_image_height, - 'network_image_width': network_image_width, - 'network_image_height': network_image_height, - 'image_mean': encoder_image_mean, - 'image_stddev': encoder_image_stddev, - }], - remappings=[('encoded_tensor', 'tensor_pub')]) - centerpose_inference_node = ComposableNode( name='centerpose_inference', package='isaac_ros_triton', @@ -217,10 +207,31 @@ def generate_launch_description(): package='rclcpp_components', executable='component_container_mt', composable_node_descriptions=[ - centerpose_encoder_node, centerpose_inference_node, - centerpose_decoder_node, centerpose_visualizer_node], + centerpose_inference_node, centerpose_decoder_node, + centerpose_visualizer_node], output='screen', ) - final_launch_container = launch_args + [rclcpp_container] + encoder_dir = get_package_share_directory('isaac_ros_dnn_image_encoder') + centerpose_encoder_launch = IncludeLaunchDescription( + PythonLaunchDescriptionSource( + [os.path.join(encoder_dir, 'launch', 'dnn_image_encoder.launch.py')] + ), + launch_arguments={ + 'input_image_width': input_image_width, + 'input_image_height': input_image_height, + 'network_image_width': network_image_width, + 'network_image_height': network_image_height, + 'image_mean': encoder_image_mean, + 'image_stddev': encoder_image_stddev, + 'attach_to_shared_component_container': 'True', + 'component_container_name': 'centerpose_container', + 'dnn_image_encoder_namespace': 'centerpose_encoder', + 'image_input_topic': '/image', + 'camera_info_input_topic': '/camera_info', + 'tensor_output_topic': '/tensor_pub', + }.items(), + ) + + final_launch_container = launch_args + [rclcpp_container, centerpose_encoder_launch] return LaunchDescription(final_launch_container) diff --git a/isaac_ros_centerpose/launch/isaac_ros_centerpose_visualizer_core.launch.py b/isaac_ros_centerpose/launch/isaac_ros_centerpose_visualizer_core.launch.py new file mode 100644 index 0000000..54f9610 --- /dev/null +++ b/isaac_ros_centerpose/launch/isaac_ros_centerpose_visualizer_core.launch.py @@ -0,0 +1,64 @@ +# SPDX-FileCopyrightText: NVIDIA CORPORATION & AFFILIATES +# Copyright (c) 2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +# SPDX-License-Identifier: Apache-2.0 + +from typing import Any, Dict + +from isaac_ros_examples import IsaacROSLaunchFragment +import launch +from launch.actions import DeclareLaunchArgument +from launch.substitutions import LaunchConfiguration +from launch_ros.descriptions import ComposableNode + + +class IsaacROSCenterPoseVisualizerLaunchFragment(IsaacROSLaunchFragment): + + @staticmethod + def get_composable_nodes(interface_specs: Dict[str, Any]) -> Dict[str, ComposableNode]: + # Centerpose visualization parameters + show_axes = LaunchConfiguration('show_axes') + bounding_box_color = LaunchConfiguration('bounding_box_color') + + return { + 'centerpose_visualizer_node': ComposableNode( + name='centerpose_visualizer', + package='isaac_ros_centerpose', + plugin='nvidia::isaac_ros::centerpose::CenterPoseVisualizerNode', + parameters=[{ + 'show_axes': show_axes, + 'bounding_box_color': bounding_box_color, + }], + remappings=[ + ('image', 'image_rect'), + ('camera_info', 'camera_info_rect') + ], + ), + } + + @staticmethod + def get_launch_actions(interface_specs: Dict[str, Any]) -> \ + Dict[str, launch.actions.OpaqueFunction]: + + return { + 'show_axes': DeclareLaunchArgument( + 'show_axes', + default_value='True', + description='Whether to show axes or not for visualization'), + 'bounding_box_color': DeclareLaunchArgument( + 'bounding_box_color', + default_value='0x000000ff', + description='The color of the bounding box for visualization'), + } diff --git a/isaac_ros_centerpose/package.xml b/isaac_ros_centerpose/package.xml index ebb5da9..34edcd6 100644 --- a/isaac_ros_centerpose/package.xml +++ b/isaac_ros_centerpose/package.xml @@ -21,10 +21,10 @@ isaac_ros_centerpose - 2.1.0 + 3.0.0 CenterPose: Pose Estimation using Deep Learning - Kajanan Chinniah + Isaac ROS Maintainers Apache-2.0 https://developer.nvidia.com/isaac-ros-gems/ Ethan Yu @@ -57,6 +57,8 @@ isaac_ros_common isaac_ros_gxf + gxf_isaac_centerpose + ament_lint_auto ament_lint_common isaac_ros_test diff --git a/isaac_ros_centerpose/src/centerpose_decoder_node.cpp b/isaac_ros_centerpose/src/centerpose_decoder_node.cpp index 51eb3dc..fe2a416 100644 --- a/isaac_ros_centerpose/src/centerpose_decoder_node.cpp +++ b/isaac_ros_centerpose/src/centerpose_decoder_node.cpp @@ -1,5 +1,5 @@ // SPDX-FileCopyrightText: NVIDIA CORPORATION & AFFILIATES -// Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +// Copyright (c) 2023-2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved. // // Licensed under the Apache License, Version 2.0 (the "License"); // you may not use this file except in compliance with the License. @@ -52,7 +52,7 @@ const std::vector> EXTENSIONS = { {"isaac_ros_gxf", "gxf/lib/multimedia/libgxf_multimedia.so"}, {"isaac_ros_gxf", "gxf/lib/serialization/libgxf_serialization.so"}, {"isaac_ros_gxf", "gxf/lib/cuda/libgxf_cuda.so"}, - {"isaac_ros_centerpose", "gxf/lib/centerpose/libgxf_centerpose.so"}, + {"gxf_isaac_centerpose", "gxf/lib/libgxf_isaac_centerpose.so"}, }; const std::vector PRESET_EXTENSION_SPEC_NAMES = { diff --git a/isaac_ros_centerpose/src/centerpose_visualizer_node.cpp b/isaac_ros_centerpose/src/centerpose_visualizer_node.cpp index f99b181..bb80a99 100644 --- a/isaac_ros_centerpose/src/centerpose_visualizer_node.cpp +++ b/isaac_ros_centerpose/src/centerpose_visualizer_node.cpp @@ -1,5 +1,5 @@ // SPDX-FileCopyrightText: NVIDIA CORPORATION & AFFILIATES -// Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +// Copyright (c) 2023-2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved. // // Licensed under the Apache License, Version 2.0 (the "License"); // you may not use this file except in compliance with the License. @@ -56,7 +56,7 @@ const std::vector> EXTENSIONS = { {"isaac_ros_gxf", "gxf/lib/multimedia/libgxf_multimedia.so"}, {"isaac_ros_gxf", "gxf/lib/serialization/libgxf_serialization.so"}, {"isaac_ros_gxf", "gxf/lib/cuda/libgxf_cuda.so"}, - {"isaac_ros_centerpose", "gxf/lib/centerpose/libgxf_centerpose.so"}, + {"gxf_isaac_centerpose", "gxf/lib/libgxf_isaac_centerpose.so"}, }; const std::vector PRESET_EXTENSION_SPEC_NAMES = { diff --git a/isaac_ros_centerpose/test/test_centerpose_pol.py b/isaac_ros_centerpose/test/test_centerpose_pol.py index edb486e..a181203 100644 --- a/isaac_ros_centerpose/test/test_centerpose_pol.py +++ b/isaac_ros_centerpose/test/test_centerpose_pol.py @@ -1,5 +1,5 @@ # SPDX-FileCopyrightText: NVIDIA CORPORATION & AFFILIATES -# Copyright (c) 2021-2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# Copyright (c) 2021-2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. @@ -26,7 +26,10 @@ import pathlib import time +from ament_index_python.packages import get_package_share_directory from isaac_ros_test import IsaacROSBaseTest, JSONConversion +from launch.actions import IncludeLaunchDescription +from launch.launch_description_sources import PythonLaunchDescriptionSource from launch_ros.actions import ComposableNodeContainer from launch_ros.descriptions import ComposableNode @@ -36,7 +39,7 @@ from sensor_msgs.msg import CameraInfo, Image from vision_msgs.msg import Detection3DArray -MODEL_GENERATION_TIMEOUT_SEC = 300 +MODEL_GENERATION_TIMEOUT_SEC = 600 INIT_WAIT_SEC = 10 MODEL_PATH = '/tmp/centerpose_trt_engine.plan' @@ -53,20 +56,24 @@ def generate_test_description(): model_dir_path = launch_dir_path / 'models' model_file_name = 'centerpose_shoe.onnx' - centerpose_encoder_node = ComposableNode( - name='centerpose_encoder', - package='isaac_ros_dnn_image_encoder', - plugin='nvidia::isaac_ros::dnn_inference::DnnImageEncoderNode', - namespace=IsaacROSCenterPosePOLTest.generate_namespace(), - parameters=[{ - 'input_image_width': 600, - 'input_image_height': 800, - 'network_image_width': 512, - 'network_image_height': 512, - 'image_mean': [0.408, 0.447, 0.47], - 'image_stddev': [0.289, 0.274, 0.278] - }], - remappings=[('encoded_tensor', 'tensor_pub')]) + encoder_dir = get_package_share_directory('isaac_ros_dnn_image_encoder') + centerpose_encoder_launch = IncludeLaunchDescription( + PythonLaunchDescriptionSource( + [os.path.join(encoder_dir, 'launch', 'dnn_image_encoder.launch.py')] + ), + launch_arguments={ + 'input_image_width': '600', + 'input_image_height': '800', + 'network_image_width': '512', + 'network_image_height': '512', + 'image_mean': '[0.408, 0.447, 0.47]', + 'image_stddev': '[0.289, 0.274, 0.278]', + 'attach_to_shared_component_container': 'True', + 'component_container_name': 'rclcpp_container', + 'dnn_image_encoder_namespace': IsaacROSCenterPosePOLTest.generate_namespace(), + 'tensor_output_topic': 'tensor_pub', + }.items(), + ) centerpose_inference_node = ComposableNode( name='centerpose_inference', @@ -110,13 +117,13 @@ def generate_test_description(): package='rclcpp_components', executable='component_container_mt', composable_node_descriptions=[ - centerpose_encoder_node, centerpose_inference_node, - centerpose_decoder_node, + centerpose_inference_node, centerpose_decoder_node, ], output='screen', ) - return IsaacROSCenterPosePOLTest.generate_test_description([rclcpp_container]) + return IsaacROSCenterPosePOLTest.generate_test_description( + [rclcpp_container, centerpose_encoder_launch]) class IsaacROSCenterPosePOLTest(IsaacROSBaseTest): diff --git a/isaac_ros_centerpose/test/test_centerpose_pol_triton.py b/isaac_ros_centerpose/test/test_centerpose_pol_triton.py index f874b4f..229f71c 100644 --- a/isaac_ros_centerpose/test/test_centerpose_pol_triton.py +++ b/isaac_ros_centerpose/test/test_centerpose_pol_triton.py @@ -1,5 +1,5 @@ # SPDX-FileCopyrightText: NVIDIA CORPORATION & AFFILIATES -# Copyright (c) 2021-2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# Copyright (c) 2021-2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. @@ -26,7 +26,10 @@ import subprocess import time +from ament_index_python.packages import get_package_share_directory from isaac_ros_test import IsaacROSBaseTest, JSONConversion +from launch.actions import IncludeLaunchDescription +from launch.launch_description_sources import PythonLaunchDescriptionSource from launch_ros.actions import ComposableNodeContainer from launch_ros.descriptions import ComposableNode @@ -71,20 +74,24 @@ def generate_test_description(): f'stderr:\n' + result.stderr.decode('utf-8') ) - centerpose_encoder_node = ComposableNode( - name='centerpose_encoder', - package='isaac_ros_dnn_image_encoder', - plugin='nvidia::isaac_ros::dnn_inference::DnnImageEncoderNode', - namespace=IsaacROSCenterPosePOLTest.generate_namespace(), - parameters=[{ - 'input_image_width': 600, - 'input_image_height': 800, - 'network_image_width': 512, - 'network_image_height': 512, - 'image_mean': [0.408, 0.447, 0.47], - 'image_stddev': [0.289, 0.274, 0.278] - }], - remappings=[('encoded_tensor', 'tensor_pub')]) + encoder_dir = get_package_share_directory('isaac_ros_dnn_image_encoder') + centerpose_encoder_launch = IncludeLaunchDescription( + PythonLaunchDescriptionSource( + [os.path.join(encoder_dir, 'launch', 'dnn_image_encoder.launch.py')] + ), + launch_arguments={ + 'input_image_width': '600', + 'input_image_height': '800', + 'network_image_width': '512', + 'network_image_height': '512', + 'image_mean': '[0.408, 0.447, 0.47]', + 'image_stddev': '[0.289, 0.274, 0.278]', + 'attach_to_shared_component_container': 'True', + 'component_container_name': 'rclcpp_container', + 'dnn_image_encoder_namespace': IsaacROSCenterPosePOLTest.generate_namespace(), + 'tensor_output_topic': 'tensor_pub', + }.items(), + ) centerpose_inference_node = ComposableNode( name='centerpose_inference', @@ -127,13 +134,14 @@ def generate_test_description(): package='rclcpp_components', executable='component_container_mt', composable_node_descriptions=[ - centerpose_encoder_node, centerpose_inference_node, + centerpose_inference_node, centerpose_decoder_node, ], output='screen', ) - return IsaacROSCenterPosePOLTest.generate_test_description([rclcpp_container]) + return IsaacROSCenterPosePOLTest.generate_test_description( + [rclcpp_container, centerpose_encoder_launch]) class IsaacROSCenterPosePOLTest(IsaacROSBaseTest): diff --git a/isaac_ros_dope/CMakeLists.txt b/isaac_ros_dope/CMakeLists.txt index 3eebd38..b7d0ffe 100644 --- a/isaac_ros_dope/CMakeLists.txt +++ b/isaac_ros_dope/CMakeLists.txt @@ -1,5 +1,5 @@ # SPDX-FileCopyrightText: NVIDIA CORPORATION & AFFILIATES -# Copyright (c) 2021-2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# Copyright (c) 2021-2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. @@ -29,14 +29,14 @@ ament_auto_find_build_dependencies() ament_auto_add_library(dope_decoder_node SHARED src/dope_decoder_node.cpp) rclcpp_components_register_nodes(dope_decoder_node "nvidia::isaac_ros::dope::DopeDecoderNode") set(node_plugins "${node_plugins}nvidia::isaac_ros::dope::DopeDecoderNode;$\n") - -### Install extensions built from source - -# DOPE -add_subdirectory(gxf/dope) -install(TARGETS gxf_dope DESTINATION share/${PROJECT_NAME}/gxf/lib/dope) - -### End extensions +set_target_properties(dope_decoder_node PROPERTIES + BUILD_WITH_INSTALL_RPATH TRUE + BUILD_RPATH_USE_ORIGIN TRUE + INSTALL_RPATH_USE_LINK_PATH TRUE +) + +ament_python_install_package(${PROJECT_NAME}) +install(PROGRAMS scripts/dope_converter.py DESTINATION lib/${PROJECT_NAME}) if(BUILD_TESTING) find_package(ament_lint_auto REQUIRED) diff --git a/isaac_ros_dope/config/dope_node.yaml b/isaac_ros_dope/config/dope_node.yaml index 618af8c..e6b7b3c 100644 --- a/isaac_ros_dope/config/dope_node.yaml +++ b/isaac_ros_dope/config/dope_node.yaml @@ -22,12 +22,10 @@ components: type: nvidia::gxf::DoubleBufferReceiver parameters: capacity: 12 - policy: 0 - name: posearray_out type: nvidia::gxf::DoubleBufferTransmitter parameters: capacity: 12 - policy: 0 - name: allocator type: nvidia::gxf::UnboundedAllocator - name: dope_decoder @@ -54,7 +52,6 @@ components: type: nvidia::gxf::DoubleBufferReceiver parameters: capacity: 2 - policy: 0 - type: nvidia::gxf::MessageAvailableSchedulingTerm parameters: receiver: signal diff --git a/isaac_ros_dope/gxf/AMENT_IGNORE b/isaac_ros_dope/gxf/AMENT_IGNORE deleted file mode 100644 index e69de29..0000000 diff --git a/isaac_ros_dope/gxf/dope/CMakeLists.txt b/isaac_ros_dope/gxf/dope/CMakeLists.txt deleted file mode 100644 index 7ab33fb..0000000 --- a/isaac_ros_dope/gxf/dope/CMakeLists.txt +++ /dev/null @@ -1,48 +0,0 @@ -# SPDX-FileCopyrightText: NVIDIA CORPORATION & AFFILIATES -# Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# -# SPDX-License-Identifier: Apache-2.0 - -project(gxf_dope LANGUAGES C CXX) - -# Dependencies -find_package(CUDAToolkit REQUIRED) -find_package (Eigen3 3.3 REQUIRED NO_MODULE) -find_package(GXF ${ISAAC_ROS_GXF_VERSION} MODULE REQUIRED - COMPONENTS - cuda - multimedia - std -) -find_package(OpenCV REQUIRED) -find_package(yaml-cpp) - -# DOPE extension -add_library(gxf_dope SHARED - dope_decoder.cpp - dope_decoder.hpp - dope_ext.cpp -) -target_include_directories(gxf_dope PUBLIC ${OpenCV_INCLUDE_DIRS}) -target_link_libraries(gxf_dope - PUBLIC - CUDA::cudart - Eigen3::Eigen - GXF::cuda - GXF::multimedia - GXF::std - ${OpenCV_LIBS} - yaml-cpp -) \ No newline at end of file diff --git a/isaac_ros_dope/include/isaac_ros_dope/dope_decoder_node.hpp b/isaac_ros_dope/include/isaac_ros_dope/dope_decoder_node.hpp index 9aa84c5..8831240 100644 --- a/isaac_ros_dope/include/isaac_ros_dope/dope_decoder_node.hpp +++ b/isaac_ros_dope/include/isaac_ros_dope/dope_decoder_node.hpp @@ -1,5 +1,5 @@ // SPDX-FileCopyrightText: NVIDIA CORPORATION & AFFILIATES -// Copyright (c) 2021-2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +// Copyright (c) 2021-2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved. // // Licensed under the Apache License, Version 2.0 (the "License"); // you may not use this file except in compliance with the License. diff --git a/isaac_ros_dope/isaac_ros_dope/__init__.py b/isaac_ros_dope/isaac_ros_dope/__init__.py new file mode 100644 index 0000000..34d9bfe --- /dev/null +++ b/isaac_ros_dope/isaac_ros_dope/__init__.py @@ -0,0 +1,16 @@ +# SPDX-FileCopyrightText: NVIDIA CORPORATION & AFFILIATES +# Copyright (c) 2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +# SPDX-License-Identifier: Apache-2.0 diff --git a/isaac_ros_dope/launch/isaac_ros_dope_core.launch.py b/isaac_ros_dope/launch/isaac_ros_dope_core.launch.py new file mode 100644 index 0000000..7155e3c --- /dev/null +++ b/isaac_ros_dope/launch/isaac_ros_dope_core.launch.py @@ -0,0 +1,174 @@ +# SPDX-FileCopyrightText: NVIDIA CORPORATION & AFFILIATES +# Copyright (c) 2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +# SPDX-License-Identifier: Apache-2.0 + +import os +from typing import Any, Dict + +from ament_index_python.packages import get_package_share_directory +from isaac_ros_examples import IsaacROSLaunchFragment +import launch +from launch.actions import DeclareLaunchArgument, IncludeLaunchDescription +from launch.launch_description_sources import PythonLaunchDescriptionSource +from launch.substitutions import LaunchConfiguration +from launch_ros.descriptions import ComposableNode + + +class IsaacROSDopeLaunchFragment(IsaacROSLaunchFragment): + + @staticmethod + def get_composable_nodes(interface_specs: Dict[str, Any]) -> Dict[str, ComposableNode]: + # Tensor RT parameters + model_file_path = LaunchConfiguration('model_file_path') + engine_file_path = LaunchConfiguration('engine_file_path') + input_tensor_names = LaunchConfiguration('input_tensor_names') + input_binding_names = LaunchConfiguration('input_binding_names') + input_tensor_formats = LaunchConfiguration('input_tensor_formats') + output_tensor_names = LaunchConfiguration('output_tensor_names') + output_binding_names = LaunchConfiguration('output_binding_names') + output_tensor_formats = LaunchConfiguration('output_tensor_formats') + tensorrt_verbose = LaunchConfiguration('tensorrt_verbose') + force_engine_update = LaunchConfiguration('force_engine_update') + + # DOPE Decoder parameters + object_name = LaunchConfiguration('object_name') + + return { + 'dope_inference_node': ComposableNode( + name='dope_inference', + package='isaac_ros_tensor_rt', + plugin='nvidia::isaac_ros::dnn_inference::TensorRTNode', + parameters=[{ + 'model_file_path': model_file_path, + 'engine_file_path': engine_file_path, + 'input_tensor_names': input_tensor_names, + 'input_binding_names': input_binding_names, + 'input_tensor_formats': input_tensor_formats, + 'output_tensor_names': output_tensor_names, + 'output_binding_names': output_binding_names, + 'output_tensor_formats': output_tensor_formats, + 'verbose': tensorrt_verbose, + 'force_engine_update': force_engine_update + }] + ), + 'dope_decoder_node': ComposableNode( + name='dope_decoder', + package='isaac_ros_dope', + plugin='nvidia::isaac_ros::dope::DopeDecoderNode', + parameters=[{ + 'object_name': object_name, + }], + remappings=[('belief_map_array', 'tensor_sub'), + ('dope/pose_array', 'poses')] + ) + } + + @staticmethod + def get_launch_actions(interface_specs: Dict[str, Any]) -> \ + Dict[str, launch.actions.OpaqueFunction]: + + # DNN Image Encoder parameters + network_image_width = LaunchConfiguration('network_image_width') + network_image_height = LaunchConfiguration('network_image_height') + encoder_image_mean = LaunchConfiguration('encoder_image_mean') + encoder_image_stddev = LaunchConfiguration('encoder_image_stddev') + + encoder_dir = get_package_share_directory('isaac_ros_dnn_image_encoder') + + return { + 'network_image_width': DeclareLaunchArgument( + 'network_image_width', + default_value='640', + description='The input image width that the network expects'), + 'network_image_height': DeclareLaunchArgument( + 'network_image_height', + default_value='480', + description='The input image height that the network expects'), + 'encoder_image_mean': DeclareLaunchArgument( + 'encoder_image_mean', + default_value='[0.5, 0.5, 0.5]', + description='The mean for image normalization'), + 'encoder_image_stddev': DeclareLaunchArgument( + 'encoder_image_stddev', + default_value='[0.5, 0.5, 0.5]', + description='The standard deviation for image normalization'), + 'model_file_path': DeclareLaunchArgument( + 'model_file_path', + default_value='', + description='The absolute file path to the ONNX file'), + 'engine_file_path': DeclareLaunchArgument( + 'engine_file_path', + default_value='', + description='The absolute file path to the TensorRT engine file'), + 'input_tensor_names': DeclareLaunchArgument( + 'input_tensor_names', + default_value='["input_tensor"]', + description='A list of tensor names to bound to the specified input binding names' + ), + 'input_binding_names': DeclareLaunchArgument( + 'input_binding_names', + default_value='["input"]', + description='A list of input tensor binding names (specified by model)'), + 'input_tensor_formats': DeclareLaunchArgument( + 'input_tensor_formats', + default_value='["nitros_tensor_list_nchw_rgb_f32"]', + description='The nitros format of the input tensors'), + 'output_tensor_names': DeclareLaunchArgument( + 'output_tensor_names', + default_value='["output"]', + description='A list of tensor names to bound to the specified output binding names' + ), + 'output_binding_names': DeclareLaunchArgument( + 'output_binding_names', + default_value='["output"]', + description='A list of output tensor binding names (specified by model)'), + 'output_tensor_formats': DeclareLaunchArgument( + 'output_tensor_formats', + default_value='["nitros_tensor_list_nhwc_rgb_f32"]', + description='The nitros format of the output tensors'), + 'tensorrt_verbose': DeclareLaunchArgument( + 'tensorrt_verbose', + default_value='False', + description='Whether TensorRT should verbosely log or not'), + 'object_name': DeclareLaunchArgument( + 'object_name', + default_value='Ketchup', + description='The object class that the DOPE network is detecting'), + 'force_engine_update': DeclareLaunchArgument( + 'force_engine_update', + default_value='False', + description='Whether TensorRT should update the TensorRT engine file or not'), + 'dope_encoder_launch': IncludeLaunchDescription( + PythonLaunchDescriptionSource( + [os.path.join(encoder_dir, 'launch', 'dnn_image_encoder.launch.py')] + ), + launch_arguments={ + 'input_image_width': str(interface_specs['camera_resolution']['width']), + 'input_image_height': str(interface_specs['camera_resolution']['height']), + 'network_image_width': network_image_width, + 'network_image_height': network_image_height, + 'image_mean': encoder_image_mean, + 'image_stddev': encoder_image_stddev, + 'attach_to_shared_component_container': 'True', + 'component_container_name': '/isaac_ros_examples/container', + 'dnn_image_encoder_namespace': 'dope_encoder', + 'image_input_topic': '/image_rect', + 'camera_info_input_topic': '/camera_info_rect', + 'tensor_output_topic': '/tensor_pub', + 'keep_aspect_ratio': 'False' + }.items(), + ), + } diff --git a/isaac_ros_dope/launch/isaac_ros_dope_tensor_rt.launch.py b/isaac_ros_dope/launch/isaac_ros_dope_tensor_rt.launch.py index 66aaf6f..5c3231a 100644 --- a/isaac_ros_dope/launch/isaac_ros_dope_tensor_rt.launch.py +++ b/isaac_ros_dope/launch/isaac_ros_dope_tensor_rt.launch.py @@ -1,5 +1,5 @@ # SPDX-FileCopyrightText: NVIDIA CORPORATION & AFFILIATES -# Copyright (c) 2021-2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# Copyright (c) 2021-2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. @@ -17,8 +17,10 @@ import os +from ament_index_python.packages import get_package_share_directory import launch -from launch.actions import DeclareLaunchArgument +from launch.actions import DeclareLaunchArgument, IncludeLaunchDescription +from launch.launch_description_sources import PythonLaunchDescriptionSource from launch.substitutions import LaunchConfiguration from launch_ros.actions import ComposableNodeContainer from launch_ros.descriptions import ComposableNode @@ -32,11 +34,11 @@ def generate_launch_description(): launch_args = [ DeclareLaunchArgument( 'input_image_width', - default_value='640', + default_value='1920', description='The input image width'), DeclareLaunchArgument( 'input_image_height', - default_value='480', + default_value='1080', description='The input image height'), DeclareLaunchArgument( 'network_image_width', @@ -101,6 +103,8 @@ def generate_launch_description(): ] # DNN Image Encoder parameters + input_image_width = LaunchConfiguration('input_image_width') + input_image_height = LaunchConfiguration('input_image_height') network_image_width = LaunchConfiguration('network_image_width') network_image_height = LaunchConfiguration('network_image_height') encoder_image_mean = LaunchConfiguration('encoder_image_mean') @@ -121,32 +125,27 @@ def generate_launch_description(): # DOPE Decoder parameters object_name = LaunchConfiguration('object_name') - image_resize_node = ComposableNode( - package='isaac_ros_image_proc', - plugin='nvidia::isaac_ros::image_proc::ResizeNode', - name='image_resize', - parameters=[{ - 'output_width': network_image_width, - 'output_height': network_image_width - }], - ) - - dope_encoder_node = ComposableNode( - name='dope_encoder', - package='isaac_ros_dnn_image_encoder', - plugin='nvidia::isaac_ros::dnn_inference::DnnImageEncoderNode', - parameters=[{ - 'input_image_width': network_image_width, - 'input_image_height': network_image_height, + encoder_dir = get_package_share_directory('isaac_ros_dnn_image_encoder') + dope_encoder_launch = IncludeLaunchDescription( + PythonLaunchDescriptionSource( + [os.path.join(encoder_dir, 'launch', 'dnn_image_encoder.launch.py')] + ), + launch_arguments={ + 'input_image_width': input_image_width, + 'input_image_height': input_image_height, 'network_image_width': network_image_width, 'network_image_height': network_image_height, 'image_mean': encoder_image_mean, 'image_stddev': encoder_image_stddev, - }], - remappings=[ - ('image', 'resize/image'), - ('encoded_tensor', 'tensor_pub') - ]) + 'attach_to_shared_component_container': 'True', + 'component_container_name': 'dope_container', + 'dnn_image_encoder_namespace': 'dope_encoder', + 'image_input_topic': '/image_rect', + 'camera_info_input_topic': '/camera_info_rect', + 'tensor_output_topic': '/tensor_pub', + 'keep_aspect_ratio': 'False' + }.items(), + ) dope_inference_node = ComposableNode( name='dope_inference', @@ -180,12 +179,10 @@ def generate_launch_description(): namespace='', package='rclcpp_components', executable='component_container_mt', - composable_node_descriptions=[ - image_resize_node, dope_encoder_node, dope_inference_node, dope_decoder_node - ], + composable_node_descriptions=[dope_inference_node, dope_decoder_node], output='screen', arguments=['--ros-args', '--log-level', 'info'] ) - final_launch_description = launch_args + [container] + final_launch_description = launch_args + [container, dope_encoder_launch] return launch.LaunchDescription(final_launch_description) diff --git a/isaac_ros_dope/launch/isaac_ros_dope_triton.launch.py b/isaac_ros_dope/launch/isaac_ros_dope_triton.launch.py index 6f6593a..7cfe98f 100644 --- a/isaac_ros_dope/launch/isaac_ros_dope_triton.launch.py +++ b/isaac_ros_dope/launch/isaac_ros_dope_triton.launch.py @@ -1,5 +1,5 @@ # SPDX-FileCopyrightText: NVIDIA CORPORATION & AFFILIATES -# Copyright (c) 2021-2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# Copyright (c) 2021-2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. @@ -15,8 +15,12 @@ # # SPDX-License-Identifier: Apache-2.0 +import os + +from ament_index_python.packages import get_package_share_directory import launch -from launch.actions import DeclareLaunchArgument +from launch.actions import DeclareLaunchArgument, IncludeLaunchDescription +from launch.launch_description_sources import PythonLaunchDescriptionSource from launch.substitutions import LaunchConfiguration from launch_ros.actions import ComposableNodeContainer from launch_ros.descriptions import ComposableNode @@ -27,11 +31,11 @@ def generate_launch_description(): launch_args = [ DeclareLaunchArgument( 'input_image_width', - default_value='640', + default_value='1920', description='The input image width'), DeclareLaunchArgument( 'input_image_height', - default_value='480', + default_value='1080', description='The input image height'), DeclareLaunchArgument( 'network_image_width', @@ -92,6 +96,8 @@ def generate_launch_description(): ] # DNN Image Encoder parameters + input_image_width = LaunchConfiguration('input_image_width') + input_image_height = LaunchConfiguration('input_image_height') network_image_width = LaunchConfiguration('network_image_width') network_image_height = LaunchConfiguration('network_image_height') encoder_image_mean = LaunchConfiguration('encoder_image_mean') @@ -111,32 +117,27 @@ def generate_launch_description(): # DOPE Decoder parameters object_name = LaunchConfiguration('object_name') - image_resize_node = ComposableNode( - package='isaac_ros_image_proc', - plugin='nvidia::isaac_ros::image_proc::ResizeNode', - name='image_resize', - parameters=[{ - 'output_width': network_image_width, - 'output_height': network_image_width - }], - ) - - dope_encoder_node = ComposableNode( - name='dope_encoder', - package='isaac_ros_dnn_image_encoder', - plugin='nvidia::isaac_ros::dnn_inference::DnnImageEncoderNode', - parameters=[{ - 'input_image_width': network_image_width, - 'input_image_height': network_image_height, + encoder_dir = get_package_share_directory('isaac_ros_dnn_image_encoder') + dope_encoder_launch = IncludeLaunchDescription( + PythonLaunchDescriptionSource( + [os.path.join(encoder_dir, 'launch', 'dnn_image_encoder.launch.py')] + ), + launch_arguments={ + 'input_image_width': input_image_width, + 'input_image_height': input_image_height, 'network_image_width': network_image_width, 'network_image_height': network_image_height, 'image_mean': encoder_image_mean, 'image_stddev': encoder_image_stddev, - }], - remappings=[ - ('image', 'resize/image'), - ('encoded_tensor', 'tensor_pub') - ]) + 'attach_to_shared_component_container': 'True', + 'component_container_name': 'dope_container', + 'dnn_image_encoder_namespace': 'dope_encoder', + 'image_input_topic': '/image_rect', + 'camera_info_input_topic': '/camera_info_rect', + 'tensor_output_topic': '/tensor_pub', + 'keep_aspect_ratio': 'False' + }.items(), + ) dope_inference_node = ComposableNode( name='dope_inference', @@ -169,11 +170,9 @@ def generate_launch_description(): namespace='', package='rclcpp_components', executable='component_container_mt', - composable_node_descriptions=[ - image_resize_node, dope_encoder_node, dope_inference_node, dope_decoder_node - ], + composable_node_descriptions=[dope_inference_node, dope_decoder_node], output='screen', ) - final_launch_description = launch_args + [container] + final_launch_description = launch_args + [container, dope_encoder_launch] return launch.LaunchDescription(final_launch_description) diff --git a/isaac_ros_dope/package.xml b/isaac_ros_dope/package.xml index bec1553..a237370 100644 --- a/isaac_ros_dope/package.xml +++ b/isaac_ros_dope/package.xml @@ -21,10 +21,10 @@ isaac_ros_dope - 2.1.0 + 3.0.0 Deep learning based pose estimation - Jaiveer Singh + Isaac ROS Maintainers Apache-2.0 https://developer.nvidia.com/isaac-ros-gems/ Ethan Yu @@ -53,6 +53,8 @@ isaac_ros_common isaac_ros_gxf + gxf_isaac_dope + ament_lint_auto ament_lint_common isaac_ros_test diff --git a/isaac_ros_dope/scripts/dope_converter.py b/isaac_ros_dope/scripts/dope_converter.py old mode 100644 new mode 100755 index d47c3ae..0656a87 --- a/isaac_ros_dope/scripts/dope_converter.py +++ b/isaac_ros_dope/scripts/dope_converter.py @@ -1,5 +1,7 @@ +#!/usr/bin/env python3 + # SPDX-FileCopyrightText: NVIDIA CORPORATION & AFFILIATES -# Copyright (c) 2021-2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# Copyright (c) 2021-2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/isaac_ros_dope/src/dope_decoder_node.cpp b/isaac_ros_dope/src/dope_decoder_node.cpp index 00d73af..3309729 100644 --- a/isaac_ros_dope/src/dope_decoder_node.cpp +++ b/isaac_ros_dope/src/dope_decoder_node.cpp @@ -1,5 +1,5 @@ // SPDX-FileCopyrightText: NVIDIA CORPORATION & AFFILIATES -// Copyright (c) 2021-2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +// Copyright (c) 2021-2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved. // // Licensed under the Apache License, Version 2.0 (the "License"); // you may not use this file except in compliance with the License. @@ -57,7 +57,7 @@ const std::vector> EXTENSIONS = { {"isaac_ros_gxf", "gxf/lib/multimedia/libgxf_multimedia.so"}, {"isaac_ros_gxf", "gxf/lib/serialization/libgxf_serialization.so"}, {"isaac_ros_gxf", "gxf/lib/cuda/libgxf_cuda.so"}, - {"isaac_ros_dope", "gxf/lib/dope/libgxf_dope.so"}, + {"gxf_isaac_dope", "gxf/lib/libgxf_isaac_dope.so"}, }; const std::vector PRESET_EXTENSION_SPEC_NAMES = { diff --git a/isaac_ros_dope/test/isaac_ros_dope_pol.py b/isaac_ros_dope/test/isaac_ros_dope_pol.py index 94fede7..6a6b342 100644 --- a/isaac_ros_dope/test/isaac_ros_dope_pol.py +++ b/isaac_ros_dope/test/isaac_ros_dope_pol.py @@ -1,5 +1,5 @@ # SPDX-FileCopyrightText: NVIDIA CORPORATION & AFFILIATES -# Copyright (c) 2021-2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# Copyright (c) 2021-2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. @@ -25,16 +25,18 @@ import pathlib import time +from ament_index_python.packages import get_package_share_directory from geometry_msgs.msg import PoseArray from isaac_ros_test import IsaacROSBaseTest, JSONConversion - +from launch.actions import IncludeLaunchDescription +from launch.launch_description_sources import PythonLaunchDescriptionSource from launch_ros.actions import ComposableNodeContainer from launch_ros.descriptions import ComposableNode import pytest import rclpy -from sensor_msgs.msg import Image +from sensor_msgs.msg import CameraInfo, Image MODEL_FILE_NAME = 'dope_ketchup_pol.onnx' @@ -45,19 +47,6 @@ @pytest.mark.rostest def generate_test_description(): - dope_encoder_node = ComposableNode( - name='dope_encoder', - package='isaac_ros_dnn_image_encoder', - plugin='nvidia::isaac_ros::dnn_inference::DnnImageEncoderNode', - namespace=IsaacROSDopePOLTest.generate_namespace(), - parameters=[{ - 'network_image_width': 640, - 'network_image_height': 480, - 'input_image_width': 852, - 'input_image_height': 480 - }], - remappings=[('encoded_tensor', 'tensor_pub')]) - dope_inference_node = ComposableNode( name='dope_inference', package='isaac_ros_tensor_rt', @@ -89,16 +78,33 @@ def generate_test_description(): remappings=[('belief_map_array', 'tensor_sub'), ('dope/pose_array', 'poses')]) + encoder_dir = get_package_share_directory('isaac_ros_dnn_image_encoder') + dope_encoder_launch = IncludeLaunchDescription( + PythonLaunchDescriptionSource( + [os.path.join(encoder_dir, 'launch', 'dnn_image_encoder.launch.py')] + ), + launch_arguments={ + 'input_image_width': '852', + 'input_image_height': '480', + 'network_image_width': '640', + 'network_image_height': '480', + 'attach_to_shared_component_container': 'True', + 'component_container_name': 'dope_container', + 'dnn_image_encoder_namespace': IsaacROSDopePOLTest.generate_namespace(), + 'tensor_output_topic': 'tensor_pub', + }.items(), + ) + container = ComposableNodeContainer( name='dope_container', namespace='', package='rclcpp_components', executable='component_container_mt', - composable_node_descriptions=[dope_encoder_node, dope_inference_node, dope_decoder_node], + composable_node_descriptions=[dope_inference_node, dope_decoder_node], output='screen', ) - return IsaacROSDopePOLTest.generate_test_description([container]) + return IsaacROSDopePOLTest.generate_test_description([container, dope_encoder_launch]) class IsaacROSDopePOLTest(IsaacROSBaseTest): @@ -126,11 +132,14 @@ def test_pol(self): received_messages = {} - self.generate_namespace_lookup(['image', 'poses']) + self.generate_namespace_lookup(['image', 'camera_info', 'poses']) image_pub = self.node.create_publisher( Image, self.namespaces['image'], self.DEFAULT_QOS) + camera_info_pub = self.node.create_publisher( + CameraInfo, self.namespaces['camera_info'], self.DEFAULT_QOS) + # The current DOPE decoder outputs PoseArray subs = self.create_logging_subscribers( [('poses', PoseArray)], received_messages) @@ -140,12 +149,17 @@ def test_pol(self): image = JSONConversion.load_image_from_json(json_file) image.header.stamp = self.node.get_clock().now().to_msg() + camera_info = CameraInfo() + camera_info.header = image.header + camera_info.distortion_model = 'plumb_bob' + TIMEOUT = 60 end_time = time.time() + TIMEOUT done = False while time.time() < end_time: image_pub.publish(image) + camera_info_pub.publish(camera_info) rclpy.spin_once(self.node, timeout_sec=(0.1)) if 'poses' in received_messages: done = True diff --git a/isaac_ros_foundationpose/CMakeLists.txt b/isaac_ros_foundationpose/CMakeLists.txt new file mode 100644 index 0000000..991614d --- /dev/null +++ b/isaac_ros_foundationpose/CMakeLists.txt @@ -0,0 +1,78 @@ +# SPDX-FileCopyrightText: NVIDIA CORPORATION & AFFILIATES +# Copyright (c) 2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +# SPDX-License-Identifier: Apache-2.0 + +cmake_minimum_required(VERSION 3.22.1) +project(isaac_ros_foundationpose LANGUAGES C CXX) + +if(CMAKE_COMPILER_IS_GNUCXX OR CMAKE_CXX_COMPILER_ID MATCHES "Clang") + add_compile_options(-Wall -Wextra -Wpedantic) +endif() + +find_package(ament_cmake_auto REQUIRED) +ament_auto_find_build_dependencies() + +# FoundationPose node +ament_auto_add_library(foundationpose_node SHARED src/foundationpose_node.cpp) +rclcpp_components_register_nodes(foundationpose_node "nvidia::isaac_ros::foundationpose::FoundationPoseNode") +set(node_plugins "${node_plugins}nvidia::isaac_ros::foundationpose::FoundationPoseNode;$\n") + +# FoundationPose Tracking node +ament_auto_add_library(foundationpose_tracking_node SHARED src/foundationpose_tracking_node.cpp) +rclcpp_components_register_nodes(foundationpose_tracking_node "nvidia::isaac_ros::foundationpose::FoundationPoseTrackingNode") +set(node_plugins + "${node_plugins}nvidia::isaac_ros::foundationpose::FoundationPoseTrackingNode;$\n") + +# Detection2DToMask utils node +ament_auto_add_library(detection2_d_to_mask SHARED src/detection2_d_to_mask.cpp) +rclcpp_components_register_nodes(detection2_d_to_mask "nvidia::isaac_ros::foundationpose::Detection2DToMask") +set(node_plugins "${node_plugins}nvidia::isaac_ros::foundationpose::Detection2DToMask;$\n") + +# Tracking selector node +ament_auto_add_library(foundationpose_selector_node SHARED src/foundationpose_selector_node.cpp) +rclcpp_components_register_nodes(foundationpose_selector_node "nvidia::isaac_ros::foundationpose::Selector") +set(node_plugins "${node_plugins}nvidia::isaac_ros::foundationpose::Selector;$\n") + +set_target_properties(foundationpose_node PROPERTIES + BUILD_WITH_INSTALL_RPATH TRUE + BUILD_RPATH_USE_ORIGIN TRUE + INSTALL_RPATH_USE_LINK_PATH TRUE +) + +set_target_properties(foundationpose_selector_node PROPERTIES + BUILD_WITH_INSTALL_RPATH TRUE + BUILD_RPATH_USE_ORIGIN TRUE + INSTALL_RPATH_USE_LINK_PATH TRUE +) + +if(BUILD_TESTING) + find_package(ament_lint_auto REQUIRED) + set(AMENT_LINT_AUTO_FILE_EXCLUDE scripts/models/network_modules.py) + ament_lint_auto_find_test_dependencies() + + # The FindPythonInterp and FindPythonLibs modules are removed + if(POLICY CMP0148) + cmake_policy(SET CMP0148 OLD) + endif() + + find_package(launch_testing_ament_cmake REQUIRED) + add_launch_test(test/isaac_ros_foundationpose_pol.py TIMEOUT "900") + add_launch_test(test/isaac_ros_foundationpose_bbox_pol.py TIMEOUT "900") + add_launch_test(test/isaac_ros_foundationpose_iterative_refine_pol.py TIMEOUT "900") + add_launch_test(test/isaac_ros_foundationpose_tracking_pol.py TIMEOUT "900") +endif() + +ament_auto_package(INSTALL_TO_SHARE config launch rviz) diff --git a/isaac_ros_foundationpose/config/foundationpose_model_config.yaml b/isaac_ros_foundationpose/config/foundationpose_model_config.yaml new file mode 100644 index 0000000..086bf15 --- /dev/null +++ b/isaac_ros_foundationpose/config/foundationpose_model_config.yaml @@ -0,0 +1,28 @@ +%YAML 1.2 +# SPDX-FileCopyrightText: NVIDIA CORPORATION & AFFILIATES +# Copyright (c) 2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +# SPDX-License-Identifier: Apache-2.0 +--- +foundationpose: + ros__parameters: + resized_image_height: 160 + resized_image_width: 160 + max_hypothesis: 252 + refine_crop_ratio: 1.2 + score_crop_ratio: 1.1 + rot_normalizer: 0.349065850398865 + trans_normalizer: [0.019999999552965164, 0.019999999552965164, 0.05000000074505806] + diff --git a/isaac_ros_foundationpose/config/namespace_injector_rule.yaml b/isaac_ros_foundationpose/config/namespace_injector_rule.yaml new file mode 100644 index 0000000..df7506f --- /dev/null +++ b/isaac_ros_foundationpose/config/namespace_injector_rule.yaml @@ -0,0 +1,34 @@ +%YAML 1.2 +# SPDX-FileCopyrightText: NVIDIA CORPORATION & AFFILIATES +# Copyright (c) 2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +# SPDX-License-Identifier: Apache-2.0 +--- +name: Tensor RT Inference Namespace Injector Rule +operation: namespace_injector +body: + components: + - type: nvidia::gxf::TensorRtInference + path_parameter_keys: [cuda_stream_pool, clock] + - type: nvidia::isaac_ros::FoundationposeSampling + path_parameter_keys: [cuda_stream_pool] + - type: nvidia::isaac_ros::FoundationposeRender + path_parameter_keys: [cuda_stream_pool] + - type: nvidia::isaac_ros::FoundationposeTransformation + path_parameter_keys: [cuda_stream_pool] + - type: nvidia::isaac_ros::FoundationposeDecoder + path_parameter_keys: [cuda_stream_pool] + - type: nvidia::isaac_ros::RenderReceptiveSchedulingTerm + path_parameter_keys: [render_impl] \ No newline at end of file diff --git a/isaac_ros_foundationpose/config/nitros_foundationpose_node.yaml b/isaac_ros_foundationpose/config/nitros_foundationpose_node.yaml new file mode 100644 index 0000000..468e035 --- /dev/null +++ b/isaac_ros_foundationpose/config/nitros_foundationpose_node.yaml @@ -0,0 +1,1100 @@ +%YAML 1.2 +# SPDX-FileCopyrightText: NVIDIA CORPORATION & AFFILIATES +# Copyright (c) 2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +# SPDX-License-Identifier: Apache-2.0 +--- +name: sync +components: +- name: rgb_image_receiver + type: nvidia::gxf::DoubleBufferReceiver + parameters: + capacity: 12 # Synchronization queue size set to 12 to allow for one out of order message +- type: nvidia::gxf::MessageAvailableSchedulingTerm + parameters: + receiver: rgb_image_receiver + min_size: 1 +- name: camera_model_receiver + type: nvidia::gxf::DoubleBufferReceiver + parameters: + capacity: 12 # Synchronization queue size set to 12 to allow for one out of order message +- type: nvidia::gxf::MessageAvailableSchedulingTerm + parameters: + receiver: camera_model_receiver + min_size: 1 +- name: mask_receiver + type: nvidia::gxf::DoubleBufferReceiver + parameters: + capacity: 12 # Synchronization queue size set to 12 to allow for one out of order message +- type: nvidia::gxf::MessageAvailableSchedulingTerm + parameters: + receiver: mask_receiver + min_size: 1 +- name: depth_receiver + type: nvidia::gxf::DoubleBufferReceiver + parameters: + capacity: 12 # Synchronization queue size set to 12 to allow for one out of order message +- type: nvidia::gxf::MessageAvailableSchedulingTerm + parameters: + receiver: depth_receiver + min_size: 1 +- name: rgb_image_transmitter + type: nvidia::gxf::DoubleBufferTransmitter + parameters: + capacity: 1 +- type: nvidia::gxf::DownstreamReceptiveSchedulingTerm + parameters: + transmitter: rgb_image_transmitter + min_size: 1 +- name: camera_model_transmitter + type: nvidia::gxf::DoubleBufferTransmitter + parameters: + capacity: 1 +- type: nvidia::gxf::DownstreamReceptiveSchedulingTerm + parameters: + transmitter: camera_model_transmitter + min_size: 1 +- name: mask_transmitter + type: nvidia::gxf::DoubleBufferTransmitter + parameters: + capacity: 1 +- type: nvidia::gxf::DownstreamReceptiveSchedulingTerm + parameters: + transmitter: mask_transmitter + min_size: 1 +- name: depth_transmitter + type: nvidia::gxf::DoubleBufferTransmitter + parameters: + capacity: 1 +- type: nvidia::gxf::DownstreamReceptiveSchedulingTerm + parameters: + transmitter: depth_transmitter + min_size: 1 +- type: nvidia::gxf::Synchronization + parameters: + inputs: [rgb_image_receiver, camera_model_receiver, mask_receiver, depth_receiver] + outputs: [rgb_image_transmitter, camera_model_transmitter, mask_transmitter, depth_transmitter] +--- +name: depth_message_compositor +components: +- name: video_buffer_input + type: nvidia::gxf::DoubleBufferReceiver + parameters: + capacity: 1 +- type: nvidia::gxf::MessageAvailableSchedulingTerm + parameters: + receiver: video_buffer_input + min_size: 1 +- name: camera_model_input + type: nvidia::gxf::DoubleBufferReceiver + parameters: + capacity: 1 +- type: nvidia::gxf::MessageAvailableSchedulingTerm + parameters: + receiver: camera_model_input + min_size: 1 +- name: depth_message_output + type: nvidia::gxf::DoubleBufferTransmitter + parameters: + capacity: 1 +- type: nvidia::gxf::DownstreamReceptiveSchedulingTerm + parameters: + transmitter: depth_message_output + min_size: 1 +- name: depth_message_compositor + type: nvidia::isaac::CameraMessageCompositor + parameters: + video_buffer_input: video_buffer_input + camera_model_input: camera_model_input + camera_message_output: depth_message_output +--- +name: depth_broadcaster +components: +- name: depth_input + type: nvidia::gxf::DoubleBufferReceiver + parameters: + capacity: 1 +- type: nvidia::gxf::MessageAvailableSchedulingTerm + parameters: + receiver: depth_input + min_size: 1 +- type: nvidia::gxf::Broadcast + parameters: + source: depth_input +- name: depth_output_1 + type: nvidia::gxf::DoubleBufferTransmitter + parameters: + capacity: 1 +- type: nvidia::gxf::DownstreamReceptiveSchedulingTerm + parameters: + transmitter: depth_output_1 + min_size: 1 +- name: depth_output_2 + type: nvidia::gxf::DoubleBufferTransmitter + parameters: + capacity: 1 +- type: nvidia::gxf::DownstreamReceptiveSchedulingTerm + parameters: + transmitter: depth_output_2 + min_size: 1 +--- +name: depth_to_pointcloud +components: +- name: depth_receiver + type: nvidia::gxf::DoubleBufferReceiver + parameters: + capacity: 1 +- type: nvidia::gxf::MessageAvailableSchedulingTerm + parameters: + receiver: depth_receiver + min_size: 1 +- name: point_cloud_transmitter + type: nvidia::gxf::DoubleBufferTransmitter + parameters: + capacity: 1 +- type: nvidia::gxf::DownstreamReceptiveSchedulingTerm + parameters: + transmitter: point_cloud_transmitter + min_size: 1 +- name: allocator + type: nvidia::gxf::BlockMemoryPool + parameters: + storage_type: 1 + block_size: 33177600 # Image_shape(1920*1440) * points_per_pixel(3) * data_type (4) + num_blocks: 10 +- name: depth_to_pointcloud + type: nvidia::isaac_ros::depth_image_proc::DepthToPointCloud + parameters: + allocator: allocator + depth_receiver: depth_receiver + point_cloud_transmitter: point_cloud_transmitter + skip: 1 +- type: nvidia::gxf::MemoryAvailableSchedulingTerm + parameters: + allocator: allocator + min_blocks: 1 +--- +name: sampling +components: +- name: point_cloud_input + type: nvidia::gxf::DoubleBufferReceiver + parameters: + capacity: 1 +- name: depth_input + type: nvidia::gxf::DoubleBufferReceiver + parameters: + capacity: 1 +- name: segmentation_input + type: nvidia::gxf::DoubleBufferReceiver + parameters: + capacity: 1 +- name: rgb_input + type: nvidia::gxf::DoubleBufferReceiver + parameters: + capacity: 1 +- name: output + type: nvidia::gxf::DoubleBufferTransmitter + parameters: + # Sampling will publish 6 message (sliced) per input + # Need keep enough space for the transmitter + capacity: 6 +- name: point_cloud_output + type: nvidia::gxf::DoubleBufferTransmitter + parameters: + capacity: 6 +- name: rgb_output + type: nvidia::gxf::DoubleBufferTransmitter + parameters: + capacity: 6 +- name: camera_model_output + type: nvidia::gxf::DoubleBufferTransmitter + parameters: + capacity: 6 +- type: nvidia::gxf::MessageAvailableSchedulingTerm + parameters: + receiver: point_cloud_input + min_size: 1 +- type: nvidia::gxf::MessageAvailableSchedulingTerm + parameters: + receiver: depth_input + min_size: 1 +- type: nvidia::gxf::MessageAvailableSchedulingTerm + parameters: + receiver: segmentation_input + min_size: 1 +- type: nvidia::gxf::MessageAvailableSchedulingTerm + parameters: + receiver: rgb_input + min_size: 1 +- type: nvidia::gxf::DownstreamReceptiveSchedulingTerm + parameters: + transmitter: output + # Sampling will publish 6 message per input + # Therefore the downstream should at least have 6 available space to avoid frame drop + min_size: 6 +- type: nvidia::gxf::DownstreamReceptiveSchedulingTerm + parameters: + transmitter: point_cloud_output + min_size: 6 +- type: nvidia::gxf::DownstreamReceptiveSchedulingTerm + parameters: + transmitter: rgb_output + min_size: 6 +- type: nvidia::gxf::DownstreamReceptiveSchedulingTerm + parameters: + transmitter: camera_model_output + min_size: 6 +- name: allocator + type: nvidia::gxf::BlockMemoryPool + parameters: + storage_type: 1 + block_size: 16128 # Max_batch_size(252) * pose_matrix_size(4*4) * score_type(4) + num_blocks: 60 # 6 blocks per frame +- type: nvidia::isaac_ros::FoundationposeSampling + parameters: + point_cloud_input: point_cloud_input + depth_input: depth_input + rgb_input: rgb_input + segmentation_input: segmentation_input + output: output + point_cloud_output: point_cloud_output + rgb_output: rgb_output + camera_model_output: camera_model_output + allocator: allocator + cuda_stream_pool: utils/stream + min_depth: 0.1 +- type: nvidia::gxf::MemoryAvailableSchedulingTerm + parameters: + allocator: allocator + min_blocks: 6 +- type: nvidia::isaac_ros::RenderReceptiveSchedulingTerm + parameters: + render_impl: render/render_refine +--- +name: pose_broadcaster +components: +- name: poses_input + type: nvidia::gxf::DoubleBufferReceiver + parameters: + capacity: 6 +- type: nvidia::gxf::MessageAvailableSchedulingTerm + parameters: + receiver: poses_input + min_size: 1 +- type: nvidia::gxf::Broadcast + parameters: + source: poses_input +- name: broadcast_poses_1 + type: nvidia::gxf::DoubleBufferTransmitter + parameters: + capacity: 1 +- type: nvidia::gxf::DownstreamReceptiveSchedulingTerm + parameters: + transmitter: broadcast_poses_1 + min_size: 1 +- name: broadcast_poses_2 + type: nvidia::gxf::DoubleBufferTransmitter + parameters: + capacity: 1 +- type: nvidia::gxf::DownstreamReceptiveSchedulingTerm + parameters: + transmitter: broadcast_poses_2 + min_size: 1 +--- +name: rgb_broadcaster +components: +- name: rgb_input + type: nvidia::gxf::DoubleBufferReceiver + parameters: + capacity: 6 +- type: nvidia::gxf::MessageAvailableSchedulingTerm + parameters: + receiver: rgb_input + min_size: 1 +- type: nvidia::gxf::Broadcast + parameters: + source: rgb_input +- name: rgb_output_1 + type: nvidia::gxf::DoubleBufferTransmitter + parameters: + capacity: 1 +- type: nvidia::gxf::DownstreamReceptiveSchedulingTerm + parameters: + transmitter: rgb_output_1 + min_size: 1 +- name: rgb_output_2 + type: nvidia::gxf::DoubleBufferTransmitter + parameters: + capacity: 1 +- type: nvidia::gxf::DownstreamReceptiveSchedulingTerm + parameters: + transmitter: rgb_output_2 + min_size: 1 +--- +name: camera_model_broadcaster +components: +- name: camera_model_input + type: nvidia::gxf::DoubleBufferReceiver + parameters: + capacity: 6 +- type: nvidia::gxf::MessageAvailableSchedulingTerm + parameters: + receiver: camera_model_input + min_size: 1 +- type: nvidia::gxf::Broadcast + parameters: + source: camera_model_input +- name: camera_model_output_1 + type: nvidia::gxf::DoubleBufferTransmitter + parameters: + capacity: 1 +- type: nvidia::gxf::DownstreamReceptiveSchedulingTerm + parameters: + transmitter: camera_model_output_1 + min_size: 1 +- name: camera_model_output_2 + type: nvidia::gxf::DoubleBufferTransmitter + parameters: + capacity: 1 +- type: nvidia::gxf::DownstreamReceptiveSchedulingTerm + parameters: + transmitter: camera_model_output_2 + min_size: 1 +--- +name: xyz_broadcaster +components: +- name: xyz_input + type: nvidia::gxf::DoubleBufferReceiver + parameters: + capacity: 6 +- type: nvidia::gxf::MessageAvailableSchedulingTerm + parameters: + receiver: xyz_input + min_size: 1 +- type: nvidia::gxf::Broadcast + parameters: + source: xyz_input +- name: xyz_output_1 + type: nvidia::gxf::DoubleBufferTransmitter + parameters: + capacity: 1 +- type: nvidia::gxf::DownstreamReceptiveSchedulingTerm + parameters: + transmitter: xyz_output_1 + min_size: 1 +- name: xyz_output_2 + type: nvidia::gxf::DoubleBufferTransmitter + parameters: + capacity: 1 +- type: nvidia::gxf::DownstreamReceptiveSchedulingTerm + parameters: + transmitter: xyz_output_2 + min_size: 1 +--- +name: render +components: +- name: pose_array_input + type: nvidia::gxf::DoubleBufferReceiver + parameters: + # Accumulate 6 sliced input from sampling + capacity: 6 +- name: point_cloud_input + type: nvidia::gxf::DoubleBufferReceiver + parameters: + capacity: 6 +- name: rgb_input + type: nvidia::gxf::DoubleBufferReceiver + parameters: + capacity: 6 +- name: camera_model_input + type: nvidia::gxf::DoubleBufferReceiver + parameters: + capacity: 6 +- name: iterative_pose_array_input + type: nvidia::gxf::DoubleBufferReceiver + parameters: + # The iterative input capacity should be N (regular input capaciy) + 1 + # Because it will accumulate N inputs to itselft while consuming the regular input + # Then it needs one extra space to tick this codelet + capacity: 7 +- name: iterative_point_cloud_input + type: nvidia::gxf::DoubleBufferReceiver + parameters: + capacity: 7 +- name: iterative_rgb_input + type: nvidia::gxf::DoubleBufferReceiver + parameters: + capacity: 7 +- name: iterative_camera_model_input + type: nvidia::gxf::DoubleBufferReceiver + parameters: + capacity: 7 +- name: output + type: nvidia::gxf::DoubleBufferTransmitter + parameters: + capacity: 1 +- name: iterative_point_cloud_output + type: nvidia::gxf::DoubleBufferTransmitter + parameters: + capacity: 1 +- name: iterative_rgb_output + type: nvidia::gxf::DoubleBufferTransmitter + parameters: + capacity: 1 +- name: iterative_camera_model_output + type: nvidia::gxf::DoubleBufferTransmitter + parameters: + capacity: 1 +- type: nvidia::gxf::MultiMessageAvailableSchedulingTerm + parameters: + receivers: [pose_array_input, iterative_pose_array_input] + min_sum: 1 +- type: nvidia::gxf::MultiMessageAvailableSchedulingTerm + parameters: + receivers: [point_cloud_input, iterative_point_cloud_input] + min_sum: 1 +- type: nvidia::gxf::MultiMessageAvailableSchedulingTerm + parameters: + receivers: [rgb_input, iterative_rgb_input] + min_sum: 1 +- type: nvidia::gxf::MultiMessageAvailableSchedulingTerm + parameters: + receivers: [camera_model_input, iterative_camera_model_input] + min_sum: 1 +- type: nvidia::gxf::DownstreamReceptiveSchedulingTerm + parameters: + transmitter: output + min_size: 1 +- type: nvidia::gxf::DownstreamReceptiveSchedulingTerm + parameters: + transmitter: iterative_point_cloud_output + min_size: 1 +- type: nvidia::gxf::DownstreamReceptiveSchedulingTerm + parameters: + transmitter: iterative_rgb_output + min_size: 1 +- type: nvidia::gxf::DownstreamReceptiveSchedulingTerm + parameters: + transmitter: iterative_camera_model_output + min_size: 1 +- name: allocator + type: nvidia::gxf::BlockMemoryPool + parameters: + storage_type: 1 + block_size: 25804800 # batch_size(42) * input_tensor_shape(160*160*6) * data_type(4) + num_blocks: 2 # two blocks per frame +- name: render_refine + type: nvidia::isaac_ros::FoundationposeRender + parameters: + pose_array_input: pose_array_input + point_cloud_input: point_cloud_input + rgb_input: rgb_input + camera_model_input: camera_model_input + mesh_file_path: /workspaces/isaac_ros-dev/ros_ws/src/isaac_ros_pose_estimation/isaac_ros_foundationpose/textured_simple.obj + texture_path: /workspaces/isaac_ros-dev/ros_ws/src/isaac_ros_pose_estimation/isaac_ros_foundationpose/texture_map.png + mode: refine + output: output + allocator: allocator + min_depth: 0.1 + max_depth: 4.0 + refine_iterations: 1 + cuda_stream_pool: utils/stream + #optional, only used for iterative refinements + iterative_pose_array_input: iterative_pose_array_input + iterative_point_cloud_input: iterative_point_cloud_input + iterative_rgb_input: iterative_rgb_input + iterative_camera_model_input: iterative_camera_model_input + iterative_point_cloud_output: iterative_point_cloud_output + iterative_rgb_output: iterative_rgb_output + iterative_camera_model_output: iterative_camera_model_output +- type: nvidia::gxf::MemoryAvailableSchedulingTerm + parameters: + allocator: allocator + min_blocks: 2 +--- +name: refine_inference +components: +- name: tx + type: nvidia::gxf::DoubleBufferTransmitter + parameters: + capacity: 1 +- type: nvidia::gxf::DownstreamReceptiveSchedulingTerm + parameters: + transmitter: tx + min_size: 1 +- type: nvidia::gxf::MessageAvailableSchedulingTerm + parameters: + receiver: rx + min_size: 1 +- name: rx + type: nvidia::gxf::DoubleBufferReceiver + parameters: + capacity: 1 +- name: pool + type: nvidia::gxf::BlockMemoryPool + parameters: + storage_type: 1 + block_size: 16128 # Max_batch_size(252) * pose_matrix_size(4*4) * data_type(4) + num_blocks: 20 # two blocks per frame +- type: nvidia::gxf::TensorRtInference + parameters: + model_file_path: /tmp/refine_model.onnx + engine_file_path: /tmp/refine_trt_engine.plan + input_tensor_names: + - [input_tensor1, input_tensor2] + input_binding_names: + - [input1, input2] + output_tensor_names: + - [output_tensor1, output_tensor2] + output_binding_names: + - [output1, output2] + pool: pool + cuda_stream_pool: utils/stream + dummy_rx: rx + rx: [rx] + tx: tx + force_engine_update: false + verbose: true + clock: utils/clock + max_workspace_size: 6710886400 + enable_fp16: true + max_batch_size: 42 +- type: nvidia::gxf::MemoryAvailableSchedulingTerm + parameters: + allocator: pool + min_blocks: 2 +--- +name: refine_stream_sync +components: +- name: rx + type: nvidia::gxf::DoubleBufferReceiver + parameters: + capacity: 1 +- name: tx + type: nvidia::gxf::DoubleBufferTransmitter + parameters: + capacity: 1 +- type: nvidia::gxf::MessageAvailableSchedulingTerm + parameters: + receiver: rx + min_size: 1 +- type: nvidia::gxf::DownstreamReceptiveSchedulingTerm + parameters: + transmitter: tx + min_size: 1 +- type: nvidia::gxf::CudaStreamSync + parameters: + rx: rx + tx: tx +--- +name: transform +components: +- name: refined_poses_input + type: nvidia::gxf::DoubleBufferReceiver + parameters: + capacity: 1 +- name: poses_input + type: nvidia::gxf::DoubleBufferReceiver + parameters: + capacity: 6 +- name: iterative_poses_input + type: nvidia::gxf::DoubleBufferReceiver + parameters: + capacity: 7 +- name: sliced_output + type: nvidia::gxf::DoubleBufferTransmitter + parameters: + capacity: 1 +- name: batched_output + type: nvidia::gxf::DoubleBufferTransmitter + parameters: + capacity: 1 +- name: batched_output + type: nvidia::gxf::DoubleBufferTransmitter + parameters: + capacity: 1 +- name: iterative_sliced_output + type: nvidia::gxf::DoubleBufferTransmitter + parameters: + capacity: 1 +- type: nvidia::gxf::MessageAvailableSchedulingTerm + parameters: + receiver: refined_poses_input + min_size: 1 +- type: nvidia::gxf::MultiMessageAvailableSchedulingTerm + parameters: + receivers: [poses_input, iterative_poses_input] + min_sum: 1 +- type: nvidia::gxf::DownstreamReceptiveSchedulingTerm + parameters: + transmitter: sliced_output + min_size: 1 +- type: nvidia::gxf::DownstreamReceptiveSchedulingTerm + parameters: + transmitter: batched_output + min_size: 1 +- type: nvidia::gxf::DownstreamReceptiveSchedulingTerm + parameters: + transmitter: iterative_sliced_output + min_size: 1 +- name: allocator + type: nvidia::gxf::BlockMemoryPool + parameters: + storage_type: 1 + block_size: 16128 # Max_batch_size(252) * pose_matrix_size(4*4) * data_type(4) + num_blocks: 20 # two blocks per frame +- type: nvidia::isaac_ros::FoundationposeTransformation + parameters: + poses_input: poses_input + refined_poses_input: refined_poses_input + iterative_poses_input: iterative_poses_input + iterative_sliced_output: iterative_sliced_output + sliced_output: sliced_output + batched_output: batched_output + mesh_file_path: /workspaces/isaac_ros-dev/ros_ws/src/isaac_ros_pose_estimation/isaac_ros_foundationpose/textured_simple.obj + allocator: allocator + mode: detection + refine_iterations: 1 + cuda_stream_pool: utils/stream +- type: nvidia::gxf::MemoryAvailableSchedulingTerm + parameters: + allocator: allocator + min_blocks: 1 +--- +name: iterative_pose_broadcaster +components: +- name: poses_input + type: nvidia::gxf::DoubleBufferReceiver + parameters: + capacity: 1 +- type: nvidia::gxf::MessageAvailableSchedulingTerm + parameters: + receiver: poses_input + min_size: 1 +- type: nvidia::gxf::Broadcast + parameters: + source: poses_input +- name: broadcast_poses_1 + type: nvidia::gxf::DoubleBufferTransmitter + parameters: + capacity: 1 +- type: nvidia::gxf::DownstreamReceptiveSchedulingTerm + parameters: + transmitter: broadcast_poses_1 + min_size: 1 +- name: broadcast_poses_2 + type: nvidia::gxf::DoubleBufferTransmitter + parameters: + capacity: 1 +- type: nvidia::gxf::DownstreamReceptiveSchedulingTerm + parameters: + transmitter: broadcast_poses_2 + min_size: 1 +--- +name: render_score +components: +- name: pose_array_input + type: nvidia::gxf::DoubleBufferReceiver + parameters: + capacity: 6 +- name: point_cloud_input + type: nvidia::gxf::DoubleBufferReceiver + parameters: + capacity: 6 +- name: rgb_input + type: nvidia::gxf::DoubleBufferReceiver + parameters: + capacity: 6 +- name: camera_model_input + type: nvidia::gxf::DoubleBufferReceiver + parameters: + capacity: 6 +- name: output + type: nvidia::gxf::DoubleBufferTransmitter + parameters: + capacity: 1 +- type: nvidia::gxf::MessageAvailableSchedulingTerm + parameters: + receiver: pose_array_input + min_size: 1 +- type: nvidia::gxf::MessageAvailableSchedulingTerm + parameters: + receiver: point_cloud_input + min_size: 1 +- type: nvidia::gxf::MessageAvailableSchedulingTerm + parameters: + receiver: rgb_input + min_size: 1 +- type: nvidia::gxf::MessageAvailableSchedulingTerm + parameters: + receiver: camera_model_input + min_size: 1 +- type: nvidia::gxf::DownstreamReceptiveSchedulingTerm + parameters: + transmitter: output + min_size: 1 +- name: allocator + type: nvidia::gxf::BlockMemoryPool + parameters: + storage_type: 1 + block_size: 154828800 # batch_size(252) * input_tensor_shape(160*160*6) * data_type(4) + num_blocks: 2 # two blocks per frame +- type: nvidia::isaac_ros::FoundationposeRender + parameters: + pose_array_input: pose_array_input + point_cloud_input: point_cloud_input + rgb_input: rgb_input + camera_model_input: camera_model_input + mesh_file_path: /workspaces/isaac_ros-dev/ros_ws/src/isaac_ros_pose_estimation/isaac_ros_foundationpose/textured_simple.obj + texture_path: /workspaces/isaac_ros-dev/ros_ws/src/isaac_ros_pose_estimation/isaac_ros_foundationpose/texture_map.png + output: output + allocator: allocator + mode: score + min_depth: 0.1 + max_depth: 4.0 + cuda_stream_pool: utils/stream +- type: nvidia::gxf::MemoryAvailableSchedulingTerm + parameters: + allocator: allocator + min_blocks: 2 +--- +name: score_inference +components: +- name: tx + type: nvidia::gxf::DoubleBufferTransmitter + parameters: + capacity: 1 +- type: nvidia::gxf::DownstreamReceptiveSchedulingTerm + parameters: + transmitter: tx + min_size: 1 +- type: nvidia::gxf::MessageAvailableSchedulingTerm + parameters: + receiver: rx + min_size: 1 +- name: rx + type: nvidia::gxf::DoubleBufferReceiver + parameters: + capacity: 1 +- name: pool + type: nvidia::gxf::BlockMemoryPool + parameters: + storage_type: 1 + block_size: 16128 # Max_batch_size(252) * pose_matrix_size(4*4) * data_type(4) + num_blocks: 10 +- type: nvidia::gxf::TensorRtInference + parameters: + model_file_path: /tmp/score_model.onnx + engine_file_path: /tmp/score_trt_engine.plan + input_tensor_names: + - input_tensor1, input_tensor2 + input_binding_names: + - input1, input2 + output_tensor_names: + - output_tensor1 + output_binding_names: + - output1 + pool: pool + cuda_stream_pool: utils/stream + dummy_rx: rx + rx: [rx] + tx: tx + force_engine_update: false + verbose: true + clock: utils/clock + max_workspace_size: 6710886400 + enable_fp16: true + max_batch_size: 252 +- type: nvidia::gxf::MemoryAvailableSchedulingTerm + parameters: + allocator: pool + min_blocks: 1 +--- +name: score_stream_sync +components: +- name: rx + type: nvidia::gxf::DoubleBufferReceiver + parameters: + capacity: 1 +- name: tx + type: nvidia::gxf::DoubleBufferTransmitter + parameters: + capacity: 1 +- type: nvidia::gxf::MessageAvailableSchedulingTerm + parameters: + receiver: rx + min_size: 1 +- type: nvidia::gxf::DownstreamReceptiveSchedulingTerm + parameters: + transmitter: tx + min_size: 1 +- type: nvidia::gxf::CudaStreamSync + parameters: + rx: rx + tx: tx +--- +name: decoder +components: +- name: pose_array_input + type: nvidia::gxf::DoubleBufferReceiver + parameters: + capacity: 1 +- name: pose_scores_input + type: nvidia::gxf::DoubleBufferReceiver + parameters: + capacity: 1 +- name: output + type: nvidia::gxf::DoubleBufferTransmitter + parameters: + capacity: 1 +- name: pose_matrix_output + type: nvidia::gxf::DoubleBufferTransmitter + parameters: + capacity: 1 +- type: nvidia::gxf::MessageAvailableSchedulingTerm + parameters: + receiver: pose_array_input + min_size: 1 +- type: nvidia::gxf::MessageAvailableSchedulingTerm + parameters: + receiver: pose_scores_input + min_size: 1 +- type: nvidia::gxf::DownstreamReceptiveSchedulingTerm + parameters: + transmitter: output + min_size: 1 +- type: nvidia::gxf::DownstreamReceptiveSchedulingTerm + parameters: + transmitter: pose_matrix_output + min_size: 1 +- name: allocator + type: nvidia::gxf::BlockMemoryPool + parameters: + storage_type: 1 + block_size: 64 # Pose_shape(4*4) * data_type(4) + num_blocks: 20 # two blocks per frame +- type: nvidia::isaac_ros::FoundationposeDecoder + parameters: + pose_array_input: pose_array_input + pose_scores_input: pose_scores_input + output: output + pose_matrix_output: pose_matrix_output + mesh_file_path: /workspaces/isaac_ros-dev/ros_ws/src/isaac_ros_pose_estimation/isaac_ros_foundationpose/textured_simple.obj + allocator: allocator + mode: pose_estimation + cuda_stream_pool: utils/stream +- type: nvidia::gxf::MemoryAvailableSchedulingTerm + parameters: + allocator: allocator + min_blocks: 1 +--- +name: pose_sink +components: +- name: signal + type: nvidia::gxf::DoubleBufferReceiver + parameters: + capacity: 1 +- type: nvidia::gxf::MessageAvailableSchedulingTerm + parameters: + receiver: signal + min_size: 1 +- name: sink + type: nvidia::isaac_ros::MessageRelay + parameters: + source: signal +--- +name: pose_matrix_sink +components: +- name: signal + type: nvidia::gxf::DoubleBufferReceiver + parameters: + capacity: 1 +- type: nvidia::gxf::MessageAvailableSchedulingTerm + parameters: + receiver: signal + min_size: 1 +- name: sink + type: nvidia::isaac_ros::MessageRelay + parameters: + source: signal +--- +name: connections +components: +- type: nvidia::gxf::Connection + parameters: + source: sync/rgb_image_transmitter + target: sampling/rgb_input +- type: nvidia::gxf::Connection + parameters: + source: sync/camera_model_transmitter + target: depth_message_compositor/camera_model_input +- type: nvidia::gxf::Connection + parameters: + source: sync/mask_transmitter + target: sampling/segmentation_input +- type: nvidia::gxf::Connection + parameters: + source: sync/depth_transmitter + target: depth_message_compositor/video_buffer_input +- type: nvidia::gxf::Connection + parameters: + source: depth_message_compositor/depth_message_output + target: depth_broadcaster/depth_input +- type: nvidia::gxf::Connection + parameters: + source: depth_broadcaster/depth_output_1 + target: depth_to_pointcloud/depth_receiver +- type: nvidia::gxf::Connection + parameters: + source: depth_broadcaster/depth_output_2 + target: sampling/depth_input +- type: nvidia::gxf::Connection + parameters: + source: sampling/rgb_output + target: rgb_broadcaster/rgb_input +- type: nvidia::gxf::Connection + parameters: + source: rgb_broadcaster/rgb_output_1 + target: render/rgb_input +- type: nvidia::gxf::Connection + parameters: + source: rgb_broadcaster/rgb_output_2 + target: render_score/rgb_input +- type: nvidia::gxf::Connection + parameters: + source: depth_to_pointcloud/point_cloud_transmitter + target: sampling/point_cloud_input +- type: nvidia::gxf::Connection + parameters: + source: sampling/output + target: pose_broadcaster/poses_input +- type: nvidia::gxf::Connection + parameters: + source: pose_broadcaster/broadcast_poses_1 + target: render/pose_array_input +- type: nvidia::gxf::Connection + parameters: + source: sampling/point_cloud_output + target: xyz_broadcaster/xyz_input +- type: nvidia::gxf::Connection + parameters: + source: xyz_broadcaster/xyz_output_1 + target: render/point_cloud_input +- type: nvidia::gxf::Connection + parameters: + source: render/output + target: refine_inference/rx +- type: nvidia::gxf::Connection + parameters: + source: render/iterative_rgb_output + target: render/iterative_rgb_input +- type: nvidia::gxf::Connection + parameters: + source: render/iterative_camera_model_output + target: render/iterative_camera_model_input +- type: nvidia::gxf::Connection + parameters: + source: render/iterative_point_cloud_output + target: render/iterative_point_cloud_input +- type: nvidia::gxf::Connection + parameters: + source: pose_broadcaster/broadcast_poses_2 + target: transform/poses_input +- type: nvidia::gxf::Connection + parameters: + source: camera_model_broadcaster/camera_model_output_1 + target: render_score/camera_model_input +- type: nvidia::gxf::Connection + parameters: + source: camera_model_broadcaster/camera_model_output_2 + target: render/camera_model_input +- type: nvidia::gxf::Connection + parameters: + source: sampling/camera_model_output + target: camera_model_broadcaster/camera_model_input +- type: nvidia::gxf::Connection + parameters: + source: refine_inference/tx + target: refine_stream_sync/rx +- type: nvidia::gxf::Connection + parameters: + source: refine_stream_sync/tx + target: transform/refined_poses_input +- type: nvidia::gxf::Connection + parameters: + source: transform/sliced_output + target: render_score/pose_array_input +- type: nvidia::gxf::Connection + parameters: + source: transform/iterative_sliced_output + target: iterative_pose_broadcaster/poses_input +- type: nvidia::gxf::Connection + parameters: + source: iterative_pose_broadcaster/broadcast_poses_1 + target: transform/iterative_poses_input +- type: nvidia::gxf::Connection + parameters: + source: iterative_pose_broadcaster/broadcast_poses_2 + target: render/iterative_pose_array_input +- type: nvidia::gxf::Connection + parameters: + source: xyz_broadcaster/xyz_output_2 + target: render_score/point_cloud_input +- type: nvidia::gxf::Connection + parameters: + source: render_score/output + target: score_inference/rx +- type: nvidia::gxf::Connection + parameters: + source: score_inference/tx + target: score_stream_sync/rx +- type: nvidia::gxf::Connection + parameters: + source: score_stream_sync/tx + target: decoder/pose_scores_input +- type: nvidia::gxf::Connection + parameters: + source: transform/batched_output + target: decoder/pose_array_input +- type: nvidia::gxf::Connection + parameters: + source: decoder/output + target: pose_sink/signal +- type: nvidia::gxf::Connection + parameters: + source: decoder/pose_matrix_output + target: pose_matrix_sink/signal +--- +name: utils +components: +- name: clock + type: nvidia::gxf::RealtimeClock +- type: nvidia::gxf::MultiThreadScheduler + parameters: + clock: clock + stop_on_deadlock: false + check_recession_period_ms: 1 + worker_thread_number: 1 +- type: nvidia::gxf::JobStatistics + parameters: + clock: clock +- name: stream + type: nvidia::gxf::CudaStreamPool + parameters: + dev_id: 0 + stream_flags: 0 + stream_priority: 0 + reserved_size: 1 + max_size: 8 \ No newline at end of file diff --git a/isaac_ros_foundationpose/config/nitros_foundationpose_tracking_node.yaml b/isaac_ros_foundationpose/config/nitros_foundationpose_tracking_node.yaml new file mode 100644 index 0000000..8d01718 --- /dev/null +++ b/isaac_ros_foundationpose/config/nitros_foundationpose_tracking_node.yaml @@ -0,0 +1,556 @@ +%YAML 1.2 +# SPDX-FileCopyrightText: NVIDIA CORPORATION & AFFILIATES +# Copyright (c) 2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +# SPDX-License-Identifier: Apache-2.0 +--- +name: sync +components: +- name: rgb_image_receiver + type: nvidia::gxf::DoubleBufferReceiver + parameters: + capacity: 1 # No messages should be queued for tracking, otherwise it will dirty the input during reset +- type: nvidia::gxf::MessageAvailableSchedulingTerm + parameters: + receiver: rgb_image_receiver + min_size: 1 +- name: camera_model_receiver + type: nvidia::gxf::DoubleBufferReceiver + parameters: + capacity: 1 # No messages should be queued for tracking, otherwise it will dirty the input during reset +- type: nvidia::gxf::MessageAvailableSchedulingTerm + parameters: + receiver: camera_model_receiver + min_size: 1 +- name: depth_receiver + type: nvidia::gxf::DoubleBufferReceiver + parameters: + capacity: 1 # No messages should be queued for tracking, otherwise it will dirty the input during reset +- type: nvidia::gxf::MessageAvailableSchedulingTerm + parameters: + receiver: depth_receiver + min_size: 1 +- name: rgb_image_transmitter + type: nvidia::gxf::DoubleBufferTransmitter + parameters: + capacity: 1 +- type: nvidia::gxf::DownstreamReceptiveSchedulingTerm + parameters: + transmitter: rgb_image_transmitter + min_size: 1 +- name: camera_model_transmitter + type: nvidia::gxf::DoubleBufferTransmitter + parameters: + capacity: 1 +- type: nvidia::gxf::DownstreamReceptiveSchedulingTerm + parameters: + transmitter: camera_model_transmitter + min_size: 1 +- name: depth_transmitter + type: nvidia::gxf::DoubleBufferTransmitter + parameters: + capacity: 1 +- type: nvidia::gxf::DownstreamReceptiveSchedulingTerm + parameters: + transmitter: depth_transmitter + min_size: 1 +- type: nvidia::gxf::Synchronization + parameters: + inputs: [rgb_image_receiver, camera_model_receiver, depth_receiver] + outputs: [rgb_image_transmitter, camera_model_transmitter, depth_transmitter] +--- +name: depth_message_compositor +components: +- name: video_buffer_input + type: nvidia::gxf::DoubleBufferReceiver + parameters: + capacity: 1 +- type: nvidia::gxf::MessageAvailableSchedulingTerm + parameters: + receiver: video_buffer_input + min_size: 1 +- name: camera_model_input + type: nvidia::gxf::DoubleBufferReceiver + parameters: + capacity: 1 +- type: nvidia::gxf::MessageAvailableSchedulingTerm + parameters: + receiver: camera_model_input + min_size: 1 +- name: depth_message_output + type: nvidia::gxf::DoubleBufferTransmitter + parameters: + capacity: 1 +- type: nvidia::gxf::DownstreamReceptiveSchedulingTerm + parameters: + transmitter: depth_message_output + min_size: 1 +- name: depth_message_compositor + type: nvidia::isaac::CameraMessageCompositor + parameters: + video_buffer_input: video_buffer_input + camera_model_input: camera_model_input + camera_message_output: depth_message_output +--- +name: depth_to_pointcloud +components: +- name: depth_receiver + type: nvidia::gxf::DoubleBufferReceiver + parameters: + capacity: 1 +- type: nvidia::gxf::MessageAvailableSchedulingTerm + parameters: + receiver: depth_receiver + min_size: 1 +- name: point_cloud_transmitter + type: nvidia::gxf::DoubleBufferTransmitter + parameters: + capacity: 1 +- type: nvidia::gxf::DownstreamReceptiveSchedulingTerm + parameters: + transmitter: point_cloud_transmitter + min_size: 1 +- name: allocator + type: nvidia::gxf::BlockMemoryPool + parameters: + storage_type: 1 + block_size: 33177600 # Image_shape(1920*1440) * points_per_pixel(3) * data_type (4) + num_blocks: 10 +- name: depth_to_pointcloud + type: nvidia::isaac_ros::depth_image_proc::DepthToPointCloud + parameters: + allocator: allocator + depth_receiver: depth_receiver + point_cloud_transmitter: point_cloud_transmitter + skip: 1 +- type: nvidia::gxf::MemoryAvailableSchedulingTerm + parameters: + allocator: allocator + min_blocks: 1 +--- +name: pose_broadcaster +components: +- name: poses_input + type: nvidia::gxf::DoubleBufferReceiver + parameters: + capacity: 1 +- type: nvidia::gxf::MessageAvailableSchedulingTerm + parameters: + receiver: poses_input + min_size: 1 +- type: nvidia::gxf::Broadcast + parameters: + source: poses_input +- name: broadcast_poses_1 + type: nvidia::gxf::DoubleBufferTransmitter + parameters: + capacity: 1 +- type: nvidia::gxf::DownstreamReceptiveSchedulingTerm + parameters: + transmitter: broadcast_poses_1 + min_size: 1 +- name: broadcast_poses_2 + type: nvidia::gxf::DoubleBufferTransmitter + parameters: + capacity: 1 +- type: nvidia::gxf::DownstreamReceptiveSchedulingTerm + parameters: + transmitter: broadcast_poses_2 + min_size: 1 +--- +name: camera_model_broadcaster +components: +- name: camera_model_input + type: nvidia::gxf::DoubleBufferReceiver + parameters: + capacity: 1 +- type: nvidia::gxf::MessageAvailableSchedulingTerm + parameters: + receiver: camera_model_input + min_size: 1 +- type: nvidia::gxf::Broadcast + parameters: + source: camera_model_input +- name: camera_model_output_1 + type: nvidia::gxf::DoubleBufferTransmitter + parameters: + capacity: 1 +- type: nvidia::gxf::DownstreamReceptiveSchedulingTerm + parameters: + transmitter: camera_model_output_1 + min_size: 1 +- name: camera_model_output_2 + type: nvidia::gxf::DoubleBufferTransmitter + parameters: + capacity: 1 +- type: nvidia::gxf::DownstreamReceptiveSchedulingTerm + parameters: + transmitter: camera_model_output_2 + min_size: 1 +--- +name: render +components: +- name: pose_array_input + type: nvidia::gxf::DoubleBufferReceiver + parameters: + capacity: 1 +- name: point_cloud_input + type: nvidia::gxf::DoubleBufferReceiver + parameters: + capacity: 1 +- name: rgb_input + type: nvidia::gxf::DoubleBufferReceiver + parameters: + capacity: 1 +- name: camera_model_input + type: nvidia::gxf::DoubleBufferReceiver + parameters: + capacity: 1 +- name: output + type: nvidia::gxf::DoubleBufferTransmitter + parameters: + capacity: 1 +- type: nvidia::gxf::MessageAvailableSchedulingTerm + parameters: + receiver: pose_array_input + min_size: 1 +- type: nvidia::gxf::MessageAvailableSchedulingTerm + parameters: + receiver: point_cloud_input + min_size: 1 +- type: nvidia::gxf::MessageAvailableSchedulingTerm + parameters: + receiver: rgb_input + min_size: 1 +- type: nvidia::gxf::MessageAvailableSchedulingTerm + parameters: + receiver: camera_model_input + min_size: 1 +- type: nvidia::gxf::DownstreamReceptiveSchedulingTerm + parameters: + transmitter: output + min_size: 1 +- name: allocator + type: nvidia::gxf::BlockMemoryPool + parameters: + storage_type: 1 + block_size: 614400 # input_tensor_shape(160*160*6) * data_type(4) + num_blocks: 2 # two blocks per frame +- type: nvidia::isaac_ros::FoundationposeRender + parameters: + pose_array_input: pose_array_input + point_cloud_input: point_cloud_input + rgb_input: rgb_input + camera_model_input: camera_model_input + mesh_file_path: /workspaces/isaac_ros-dev/ros_ws/src/isaac_ros_pose_estimation/isaac_ros_foundationpose/textured_simple.obj + texture_path: /workspaces/isaac_ros-dev/ros_ws/src/isaac_ros_pose_estimation/isaac_ros_foundationpose/texture_map.png + mode: refine + output: output + allocator: allocator + min_depth: 0.1 + max_depth: 4.0 + cuda_stream_pool: utils/stream +- type: nvidia::gxf::MemoryAvailableSchedulingTerm + parameters: + allocator: allocator + min_blocks: 2 +--- +name: refine_inference +components: +- name: tx + type: nvidia::gxf::DoubleBufferTransmitter + parameters: + capacity: 1 +- type: nvidia::gxf::DownstreamReceptiveSchedulingTerm + parameters: + transmitter: tx + min_size: 1 +- type: nvidia::gxf::MessageAvailableSchedulingTerm + parameters: + receiver: rx + min_size: 1 +- name: rx + type: nvidia::gxf::DoubleBufferReceiver + parameters: + capacity: 1 +- name: pool + type: nvidia::gxf::BlockMemoryPool + parameters: + storage_type: 1 + block_size: 64 # pose_matrix_size(4*4) * data_type(4) + num_blocks: 20 # two blocks per frame +- type: nvidia::gxf::TensorRtInference + parameters: + model_file_path: /tmp/refine_model.onnx + engine_file_path: /tmp/refine_trt_engine.plan + input_tensor_names: + - [input_tensor1, input_tensor2] + input_binding_names: + - [input1, input2] + output_tensor_names: + - [output_tensor1, output_tensor2] + output_binding_names: + - [output1, output2] + pool: pool + cuda_stream_pool: utils/stream + dummy_rx: rx + rx: [rx] + tx: tx + force_engine_update: false + verbose: true + clock: utils/clock + # Estimated GPU mem usage for the engine generation + max_workspace_size: 67108864 + enable_fp16: true + max_batch_size: 1 +- type: nvidia::gxf::MemoryAvailableSchedulingTerm + parameters: + allocator: pool + min_blocks: 2 +--- +name: refine_stream_sync +components: +- name: rx + type: nvidia::gxf::DoubleBufferReceiver + parameters: + capacity: 1 +- name: tx + type: nvidia::gxf::DoubleBufferTransmitter + parameters: + capacity: 1 +- type: nvidia::gxf::MessageAvailableSchedulingTerm + parameters: + receiver: rx + min_size: 1 +- type: nvidia::gxf::DownstreamReceptiveSchedulingTerm + parameters: + transmitter: tx + min_size: 1 +- type: nvidia::gxf::CudaStreamSync + parameters: + rx: rx + tx: tx +--- +name: transform +components: +- name: refined_poses_input + type: nvidia::gxf::DoubleBufferReceiver + parameters: + capacity: 1 +- name: poses_input + type: nvidia::gxf::DoubleBufferReceiver + parameters: + capacity: 1 +- name: sliced_output + type: nvidia::gxf::DoubleBufferTransmitter + parameters: + capacity: 1 +- name: batched_output + type: nvidia::gxf::DoubleBufferTransmitter + parameters: + capacity: 1 +- type: nvidia::gxf::MessageAvailableSchedulingTerm + parameters: + receiver: refined_poses_input + min_size: 1 +- type: nvidia::gxf::MessageAvailableSchedulingTerm + parameters: + receiver: poses_input + min_size: 1 +- type: nvidia::gxf::DownstreamReceptiveSchedulingTerm + parameters: + transmitter: sliced_output + min_size: 1 +- name: allocator + type: nvidia::gxf::BlockMemoryPool + parameters: + storage_type: 1 + block_size: 64 # pose_matrix_size(4*4) * data_type(4) + num_blocks: 20 # two blocks per frame +- type: nvidia::isaac_ros::FoundationposeTransformation + parameters: + poses_input: poses_input + refined_poses_input: refined_poses_input + sliced_output: sliced_output + batched_output: batched_output + mesh_file_path: /workspaces/isaac_ros-dev/ros_ws/src/isaac_ros_pose_estimation/isaac_ros_foundationpose/textured_simple.obj + allocator: allocator + mode: tracking + cuda_stream_pool: utils/stream +- type: nvidia::gxf::MemoryAvailableSchedulingTerm + parameters: + allocator: allocator + min_blocks: 1 +--- +name: decoder +components: +- name: pose_array_input + type: nvidia::gxf::DoubleBufferReceiver + parameters: + capacity: 1 +- name: output + type: nvidia::gxf::DoubleBufferTransmitter + parameters: + capacity: 1 +- name: pose_matrix_output + type: nvidia::gxf::DoubleBufferTransmitter + parameters: + capacity: 1 +- type: nvidia::gxf::MessageAvailableSchedulingTerm + parameters: + receiver: pose_array_input + min_size: 1 +- type: nvidia::gxf::DownstreamReceptiveSchedulingTerm + parameters: + transmitter: output + min_size: 1 +- type: nvidia::gxf::DownstreamReceptiveSchedulingTerm + parameters: + transmitter: pose_matrix_output + min_size: 1 +- name: allocator + type: nvidia::gxf::BlockMemoryPool + parameters: + storage_type: 1 + block_size: 64 # Pose_shape(4*4) * data_type(4) + num_blocks: 20 # two blocks per frame +- type: nvidia::isaac_ros::FoundationposeDecoder + parameters: + pose_array_input: pose_array_input + output: output + pose_matrix_output: pose_matrix_output + mesh_file_path: /workspaces/isaac_ros-dev/ros_ws/src/isaac_ros_pose_estimation/isaac_ros_foundationpose/textured_simple.obj + allocator: allocator + mode: tracking + cuda_stream_pool: utils/stream +- type: nvidia::gxf::MemoryAvailableSchedulingTerm + parameters: + allocator: allocator + min_blocks: 1 +--- +name: pose_sink +components: +- name: signal + type: nvidia::gxf::DoubleBufferReceiver + parameters: + capacity: 1 +- type: nvidia::gxf::MessageAvailableSchedulingTerm + parameters: + receiver: signal + min_size: 1 +- name: sink + type: nvidia::isaac_ros::MessageRelay + parameters: + source: signal +--- +name: pose_matrix_sink +components: +- name: signal + type: nvidia::gxf::DoubleBufferReceiver + parameters: + capacity: 1 +- type: nvidia::gxf::MessageAvailableSchedulingTerm + parameters: + receiver: signal + min_size: 1 +- name: sink + type: nvidia::isaac_ros::MessageRelay + parameters: + source: signal +--- +name: connections +components: +- type: nvidia::gxf::Connection + parameters: + source: sync/rgb_image_transmitter + target: render/rgb_input +- type: nvidia::gxf::Connection + parameters: + source: sync/camera_model_transmitter + target: camera_model_broadcaster/camera_model_input +- type: nvidia::gxf::Connection + parameters: + source: sync/depth_transmitter + target: depth_message_compositor/video_buffer_input +- type: nvidia::gxf::Connection + parameters: + source: depth_message_compositor/depth_message_output + target: depth_to_pointcloud/depth_receiver +- type: nvidia::gxf::Connection + parameters: + source: depth_to_pointcloud/point_cloud_transmitter + target: render/point_cloud_input +- type: nvidia::gxf::Connection + parameters: + source: pose_broadcaster/broadcast_poses_1 + target: render/pose_array_input +- type: nvidia::gxf::Connection + parameters: + source: camera_model_broadcaster/camera_model_output_1 + target: render/camera_model_input +- type: nvidia::gxf::Connection + parameters: + source: camera_model_broadcaster/camera_model_output_2 + target: depth_message_compositor/camera_model_input +- type: nvidia::gxf::Connection + parameters: + source: render/output + target: refine_inference/rx +- type: nvidia::gxf::Connection + parameters: + source: pose_broadcaster/broadcast_poses_2 + target: transform/poses_input +- type: nvidia::gxf::Connection + parameters: + source: refine_inference/tx + target: refine_stream_sync/rx +- type: nvidia::gxf::Connection + parameters: + source: refine_stream_sync/tx + target: transform/refined_poses_input +- type: nvidia::gxf::Connection + parameters: + source: transform/sliced_output + target: decoder/pose_array_input +- type: nvidia::gxf::Connection + parameters: + source: decoder/output + target: pose_sink/signal +- type: nvidia::gxf::Connection + parameters: + source: decoder/pose_matrix_output + target: pose_matrix_sink/signal +--- +name: utils +components: +- name: clock + type: nvidia::gxf::RealtimeClock +- type: nvidia::gxf::MultiThreadScheduler + parameters: + clock: clock + stop_on_deadlock: false + check_recession_period_ms: 1 + worker_thread_number: 1 +- type: nvidia::gxf::JobStatistics + parameters: + clock: clock +- name: stream + type: nvidia::gxf::CudaStreamPool + parameters: + dev_id: 0 + stream_flags: 0 + stream_priority: 0 + reserved_size: 1 + max_size: 5 \ No newline at end of file diff --git a/isaac_ros_foundationpose/config/realsense.yaml b/isaac_ros_foundationpose/config/realsense.yaml new file mode 100644 index 0000000..0694003 --- /dev/null +++ b/isaac_ros_foundationpose/config/realsense.yaml @@ -0,0 +1,4 @@ +enable_infra1: false +enable_infra2: false + +align_depth.enable: true diff --git a/isaac_ros_foundationpose/include/isaac_ros_foundationpose/foundationpose_node.hpp b/isaac_ros_foundationpose/include/isaac_ros_foundationpose/foundationpose_node.hpp new file mode 100644 index 0000000..166a78b --- /dev/null +++ b/isaac_ros_foundationpose/include/isaac_ros_foundationpose/foundationpose_node.hpp @@ -0,0 +1,95 @@ +// SPDX-FileCopyrightText: NVIDIA CORPORATION & AFFILIATES +// Copyright (c) 2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. +// +// SPDX-License-Identifier: Apache-2.0 + +#ifndef ISAAC_ROS_FOUNDATIONPOSE__FOUNDATIONPOSE_NODE_HPP_ +#define ISAAC_ROS_FOUNDATIONPOSE__FOUNDATIONPOSE_NODE_HPP_ + +#include +#include +#include + +#include "isaac_ros_nitros/nitros_node.hpp" +#include "isaac_ros_tensor_list_interfaces/msg/tensor_list.hpp" +#include "rclcpp/rclcpp.hpp" + +using StringList = std::vector; + +namespace nvidia +{ +namespace isaac_ros +{ +namespace foundationpose +{ + +/** + * @class FoundationPoseNode + * @brief This node performs pose estimation of an unseen object from RGBD + */ +class FoundationPoseNode : public nitros::NitrosNode +{ +public: + explicit FoundationPoseNode(rclcpp::NodeOptions options = rclcpp::NodeOptions()); + ~FoundationPoseNode(); + + void postLoadGraphCallback() override; + + void FoundationPoseDetectionCallback(const gxf_context_t context, nitros::NitrosTypeBase & msg); + +private: + // The name of the YAML configuration file + const std::string configuration_file_; + uint32_t max_hypothesis_; + uint32_t resized_image_width_; + uint32_t resized_image_height_; + float refine_crop_ratio_; + float score_crop_ratio_; + float rot_normalizer_; + + // Path to the mesh files + const std::string mesh_file_path_; + const std::string texture_path_; + + const float min_depth_; + const float max_depth_; + const int32_t refine_iterations_; + + // Models file path + const std::string refine_model_file_path_; + const std::string refine_engine_file_path_; + const std::string score_model_file_path_; + const std::string score_engine_file_path_; + + // Input tensor names + const StringList refine_input_tensor_names_; + const StringList refine_input_binding_names_; + const StringList score_input_tensor_names_; + const StringList score_input_binding_names_; + + // Output tensor names + const StringList refine_output_tensor_names_; + const StringList refine_output_binding_names_; + const StringList score_output_tensor_names_; + const StringList score_output_binding_names_; + + const std::string tf_frame_name_; +}; + +} // namespace foundationpose +} // namespace isaac_ros +} // namespace nvidia + +#endif // ISAAC_ROS_FOUNDATIONPOSE__FOUNDATIONPOSE_NODE_HPP_ diff --git a/isaac_ros_foundationpose/include/isaac_ros_foundationpose/foundationpose_tracking_node.hpp b/isaac_ros_foundationpose/include/isaac_ros_foundationpose/foundationpose_tracking_node.hpp new file mode 100644 index 0000000..99df039 --- /dev/null +++ b/isaac_ros_foundationpose/include/isaac_ros_foundationpose/foundationpose_tracking_node.hpp @@ -0,0 +1,86 @@ +// SPDX-FileCopyrightText: NVIDIA CORPORATION & AFFILIATES +// Copyright (c) 2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. +// +// SPDX-License-Identifier: Apache-2.0 + +#ifndef ISAAC_ROS_FOUNDATIONPOSE__FOUNDATIONPOSE_TRACKING_NODE_HPP_ +#define ISAAC_ROS_FOUNDATIONPOSE__FOUNDATIONPOSE_TRACKING_NODE_HPP_ + +#include +#include +#include + +#include "isaac_ros_nitros/nitros_node.hpp" +#include "isaac_ros_tensor_list_interfaces/msg/tensor_list.hpp" +#include "rclcpp/rclcpp.hpp" + +using StringList = std::vector; + +namespace nvidia +{ +namespace isaac_ros +{ +namespace foundationpose +{ + +/** + * @class FoundationPoseTrackingNode + * @brief This node performs tracking use the FoundationPose refine network + */ +class FoundationPoseTrackingNode : public nitros::NitrosNode +{ +public: + explicit FoundationPoseTrackingNode(rclcpp::NodeOptions options = rclcpp::NodeOptions()); + ~FoundationPoseTrackingNode(); + + void postLoadGraphCallback() override; + + void FoundationPoseTrackingCallback(const gxf_context_t context, nitros::NitrosTypeBase & msg); + +private: + // The name of the YAML configuration file + const std::string configuration_file_; + uint32_t resized_image_width_; + uint32_t resized_image_height_; + float refine_crop_ratio_; + float rot_normalizer_; + + // Path to the mesh files + const std::string mesh_file_path_; + const std::string texture_path_; + + const float min_depth_; + const float max_depth_; + + // Models file path + const std::string refine_model_file_path_; + const std::string refine_engine_file_path_; + + // Input tensor names + const StringList refine_input_tensor_names_; + const StringList refine_input_binding_names_; + + // Output tensor names + const StringList refine_output_tensor_names_; + const StringList refine_output_binding_names_; + + const std::string tf_frame_name_; +}; + +} // namespace foundationpose +} // namespace isaac_ros +} // namespace nvidia + +#endif // ISAAC_ROS_FOUNDATIONPOSE__FOUNDATIONPOSE_TRACKING_NODE_HPP_ diff --git a/isaac_ros_foundationpose/launch/isaac_ros_foundationpose.launch.py b/isaac_ros_foundationpose/launch/isaac_ros_foundationpose.launch.py new file mode 100644 index 0000000..760e2b4 --- /dev/null +++ b/isaac_ros_foundationpose/launch/isaac_ros_foundationpose.launch.py @@ -0,0 +1,175 @@ +# SPDX-FileCopyrightText: NVIDIA CORPORATION & AFFILIATES +# Copyright (c) 2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +# SPDX-License-Identifier: Apache-2.0 + +import os + +from ament_index_python.packages import get_package_share_directory +import launch +from launch.actions import DeclareLaunchArgument +from launch.conditions import IfCondition, UnlessCondition +from launch.substitutions import LaunchConfiguration +from launch_ros.actions import ComposableNodeContainer, Node +from launch_ros.descriptions import ComposableNode + +MESH_FILE_NAME = '/tmp/textured_simple.obj' +TEXTURE_MAP_NAME = '/tmp/texture_map.png' +REFINE_MODEL_NAME = '/tmp/refine_model.onnx' +REFINE_ENGINE_NAME = '/tmp/refine_trt_engine.plan' +SCORE_MODEL_NAME = '/tmp/score_model.onnx' +SCORE_ENGINE_NAME = '/tmp/score_trt_engine.plan' + + +def generate_launch_description(): + """Generate launch description for testing relevant nodes.""" + rviz_config_path = os.path.join( + get_package_share_directory('isaac_ros_foundationpose'), + 'rviz', 'foundationpose.rviz') + + launch_args = [ + DeclareLaunchArgument( + 'mesh_file_path', + default_value=MESH_FILE_NAME, + description='The absolute file path to the mesh file'), + + DeclareLaunchArgument( + 'texture_path', + default_value=TEXTURE_MAP_NAME, + description='The absolute file path to the texture map'), + + DeclareLaunchArgument( + 'refine_model_file_path', + default_value=REFINE_MODEL_NAME, + description='The absolute file path to the refine model'), + + DeclareLaunchArgument( + 'refine_engine_file_path', + default_value=REFINE_ENGINE_NAME, + description='The absolute file path to the refine trt engine'), + + DeclareLaunchArgument( + 'score_model_file_path', + default_value=SCORE_MODEL_NAME, + description='The absolute file path to the score model'), + + DeclareLaunchArgument( + 'score_engine_file_path', + default_value=SCORE_ENGINE_NAME, + description='The absolute file path to the score trt engine'), + + DeclareLaunchArgument( + 'mask_height', + default_value='480', + description='The height of the mask generated from the bounding box'), + + DeclareLaunchArgument( + 'mask_width', + default_value='640', + description='The width of the mask generated from the bounding box'), + + DeclareLaunchArgument( + 'launch_bbox_to_mask', + default_value='False', + description='Flag to enable bounding box to mask converter'), + + DeclareLaunchArgument( + 'launch_rviz', + default_value='False', + description='Flag to enable Rviz2 launch'), + + ] + + mesh_file_path = LaunchConfiguration('mesh_file_path') + texture_path = LaunchConfiguration('texture_path') + refine_model_file_path = LaunchConfiguration('refine_model_file_path') + refine_engine_file_path = LaunchConfiguration('refine_engine_file_path') + score_model_file_path = LaunchConfiguration('score_model_file_path') + score_engine_file_path = LaunchConfiguration('score_engine_file_path') + mask_height = LaunchConfiguration('mask_height') + mask_width = LaunchConfiguration('mask_width') + launch_rviz = LaunchConfiguration('launch_rviz') + launch_bbox_to_mask = LaunchConfiguration('launch_bbox_to_mask') + + foundationpose_node = ComposableNode( + name='foundationpose', + package='isaac_ros_foundationpose', + plugin='nvidia::isaac_ros::foundationpose::FoundationPoseNode', + parameters=[{ + 'mesh_file_path': mesh_file_path, + 'texture_path': texture_path, + + 'refine_model_file_path': refine_model_file_path, + 'refine_engine_file_path': refine_engine_file_path, + 'refine_input_tensor_names': ['input_tensor1', 'input_tensor2'], + 'refine_input_binding_names': ['input1', 'input2'], + 'refine_output_tensor_names': ['output_tensor1', 'output_tensor2'], + 'refine_output_binding_names': ['output1', 'output2'], + + 'score_model_file_path': score_model_file_path, + 'score_engine_file_path': score_engine_file_path, + 'score_input_tensor_names': ['input_tensor1', 'input_tensor2'], + 'score_input_binding_names': ['input1', 'input2'], + 'score_output_tensor_names': ['output_tensor'], + 'score_output_binding_names': ['output1'], + }], + remappings=[ + ('pose_estimation/depth_image', 'depth_registered/image_rect'), + ('pose_estimation/image', 'rgb/image_rect_color'), + ('pose_estimation/camera_info', 'rgb/camera_info'), + ('pose_estimation/segmentation', 'segmentation'), + ('pose_estimation/output', 'output')]) + + rviz_node = Node( + package='rviz2', + executable='rviz2', + name='rviz2', + arguments=['-d', rviz_config_path], + condition=IfCondition(launch_rviz)) + + detection2_d_to_mask_node = ComposableNode( + name='detection2_d_to_mask', + package='isaac_ros_foundationpose', + plugin='nvidia::isaac_ros::foundationpose::Detection2DToMask', + parameters=[{ + 'mask_width': mask_width, + 'mask_height': mask_height + }]) + + foundationpose_bbox_container = ComposableNodeContainer( + name='foundationpose_container', + namespace='foundationpose_container', + package='rclcpp_components', + executable='component_container_mt', + composable_node_descriptions=[ + foundationpose_node, + detection2_d_to_mask_node], + output='screen', + condition=IfCondition(launch_bbox_to_mask) + ) + + foundationpose_container = ComposableNodeContainer( + name='foundationpose_container', + namespace='foundationpose_container', + package='rclcpp_components', + executable='component_container_mt', + composable_node_descriptions=[foundationpose_node], + output='screen', + condition=UnlessCondition(launch_bbox_to_mask) + ) + + return launch.LaunchDescription(launch_args + [foundationpose_container, + foundationpose_bbox_container, + rviz_node]) diff --git a/isaac_ros_foundationpose/launch/isaac_ros_foundationpose_core.launch.py b/isaac_ros_foundationpose/launch/isaac_ros_foundationpose_core.launch.py new file mode 100644 index 0000000..cb83c44 --- /dev/null +++ b/isaac_ros_foundationpose/launch/isaac_ros_foundationpose_core.launch.py @@ -0,0 +1,368 @@ +# SPDX-FileCopyrightText: NVIDIA CORPORATION & AFFILIATES +# Copyright (c) 2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +# SPDX-License-Identifier: Apache-2.0 + +from typing import Any, Dict + +from isaac_ros_examples import IsaacROSLaunchFragment +import launch +from launch.actions import DeclareLaunchArgument +from launch.substitutions import LaunchConfiguration +from launch_ros.actions import ComposableNodeContainer +from launch_ros.descriptions import ComposableNode + +# Expected number of input messages in 1 second +INPUT_IMAGES_EXPECT_FREQ = 30 +# Number of input messages to be dropped in 1 second +INPUT_IMAGES_DROP_FREQ = 28 + +# RT-DETR models expect 640x640 encoded image size +RT_DETR_MODEL_INPUT_SIZE = 640 +# RT-DETR models expect 3 image channels +RT_DETR_MODEL_NUM_CHANNELS = 3 + +VISUALIZATION_DOWNSCALING_FACTOR = 10 + +REFINE_MODEL_PATH = '/tmp/refine_model.onnx' +REFINE_ENGINE_PATH = '/tmp/refine_trt_engine.plan' +SCORE_MODEL_PATH = '/tmp/score_model.onnx' +SCORE_ENGINE_PATH = '/tmp/score_trt_engine.plan' + + +class IsaacROSFoundationPoseLaunchFragment(IsaacROSLaunchFragment): + + @staticmethod + def get_composable_nodes(interface_specs: Dict[str, Any]) -> Dict[str, ComposableNode]: + + # Drop node parameters + input_images_expect_freq = LaunchConfiguration('input_images_expect_freq') + input_images_drop_freq = LaunchConfiguration('input_images_drop_freq') + # FoundationPose parameters + mesh_file_path = LaunchConfiguration('mesh_file_path') + texture_path = LaunchConfiguration('texture_path') + refine_model_file_path = LaunchConfiguration('refine_model_file_path') + refine_engine_file_path = LaunchConfiguration('refine_engine_file_path') + score_model_file_path = LaunchConfiguration('score_model_file_path') + score_engine_file_path = LaunchConfiguration('score_engine_file_path') + # RT-DETR parameters + rt_detr_model_file_path = LaunchConfiguration('rt_detr_model_file_path') + rt_detr_engine_file_path = LaunchConfiguration('rt_detr_engine_file_path') + input_width = interface_specs['camera_resolution']['width'] + input_height = interface_specs['camera_resolution']['height'] + input_to_RT_DETR_ratio = input_width / RT_DETR_MODEL_INPUT_SIZE + return { + # Drops input_images_expect_freq out of input_images_drop_freq input messages + 'drop_node': ComposableNode( + name='drop_node', + package='isaac_ros_nitros_topic_tools', + plugin='nvidia::isaac_ros::nitros::NitrosCameraDropNode', + parameters=[{ + 'X': input_images_drop_freq, + 'Y': input_images_expect_freq, + 'mode': 'mono+depth', + 'depth_format_string': 'nitros_image_mono16' + }], + remappings=[ + ('image_1', 'image_rect'), + ('camera_info_1', 'camera_info_rect'), + ('depth_1', 'depth'), + ('image_1_drop', 'rgb/image_rect_color'), + ('camera_info_1_drop', 'rgb/camera_info'), + ('depth_1_drop', 'depth_image'), + ] + ), + + # Resize and pad input images to RT-DETR model input image size + # Resize from IMAGE_WIDTH x IMAGE_HEIGHT to + # IMAGE_WIDTH/input_TO_RT_DETR_RATIO x IMAGE_HEIGHT/input_TO_RT_DETR_RATIO + # output height constraint is not used since keep_aspect_ratio is True + 'resize_left_rt_detr_node': ComposableNode( + name='resize_left_rt_detr_node', + package='isaac_ros_image_proc', + plugin='nvidia::isaac_ros::image_proc::ResizeNode', + parameters=[{ + 'input_width': input_width, + 'input_height': input_height, + 'output_width': RT_DETR_MODEL_INPUT_SIZE, + 'output_height': RT_DETR_MODEL_INPUT_SIZE, + 'keep_aspect_ratio': True, + 'encoding_desired': 'rgb8', + 'disable_padding': True + }], + remappings=[ + ('image', 'rgb/image_rect_color'), + ('camera_info', 'rgb/camera_info'), + ('resize/image', 'color_image_resized'), + ('resize/camera_info', 'camera_info_resized') + ] + ), + # Pad the image from IMAGE_WIDTH/input_TO_RT_DETR_RATIO x + # IMAGE_HEIGHT/input_TO_RT_DETR_RATIO + # to RT_DETR_MODEL_INPUT_WIDTH x RT_DETR_MODEL_INPUT_HEIGHT + 'pad_node': ComposableNode( + name='pad_node', + package='isaac_ros_image_proc', + plugin='nvidia::isaac_ros::image_proc::PadNode', + parameters=[{ + 'output_image_width': RT_DETR_MODEL_INPUT_SIZE, + 'output_image_height': RT_DETR_MODEL_INPUT_SIZE, + 'padding_type': 'BOTTOM_RIGHT' + }], + remappings=[( + 'image', 'color_image_resized' + )] + ), + + # Convert image to tensor and reshape + 'image_to_tensor_node': ComposableNode( + name='image_to_tensor_node', + package='isaac_ros_tensor_proc', + plugin='nvidia::isaac_ros::dnn_inference::ImageToTensorNode', + parameters=[{ + 'scale': False, + 'tensor_name': 'image', + }], + remappings=[ + ('image', 'padded_image'), + ('tensor', 'normalized_tensor'), + ] + ), + + 'interleave_to_planar_node': ComposableNode( + name='interleaved_to_planar_node', + package='isaac_ros_tensor_proc', + plugin='nvidia::isaac_ros::dnn_inference::InterleavedToPlanarNode', + parameters=[{ + 'input_tensor_shape': [RT_DETR_MODEL_INPUT_SIZE, + RT_DETR_MODEL_INPUT_SIZE, + RT_DETR_MODEL_NUM_CHANNELS] + }], + remappings=[ + ('interleaved_tensor', 'normalized_tensor') + ] + ), + + 'reshape_node': ComposableNode( + name='reshape_node', + package='isaac_ros_tensor_proc', + plugin='nvidia::isaac_ros::dnn_inference::ReshapeNode', + parameters=[{ + 'output_tensor_name': 'input_tensor', + 'input_tensor_shape': [RT_DETR_MODEL_NUM_CHANNELS, + RT_DETR_MODEL_INPUT_SIZE, + RT_DETR_MODEL_INPUT_SIZE], + 'output_tensor_shape': [1, RT_DETR_MODEL_NUM_CHANNELS, + RT_DETR_MODEL_INPUT_SIZE, + RT_DETR_MODEL_INPUT_SIZE] + }], + remappings=[ + ('tensor', 'planar_tensor') + ], + ), + + 'rtdetr_preprocessor_node': ComposableNode( + name='rtdetr_preprocessor', + package='isaac_ros_rtdetr', + plugin='nvidia::isaac_ros::rtdetr::RtDetrPreprocessorNode', + remappings=[ + ('encoded_tensor', 'reshaped_tensor') + ] + ), + + # RT-DETR objection detection pipeline + 'tensor_rt_node': ComposableNode( + name='tensor_rt', + package='isaac_ros_tensor_rt', + plugin='nvidia::isaac_ros::dnn_inference::TensorRTNode', + parameters=[{ + 'model_file_path': rt_detr_model_file_path, + 'engine_file_path': rt_detr_engine_file_path, + 'output_binding_names': ['labels', 'boxes', 'scores'], + 'output_tensor_names': ['labels', 'boxes', 'scores'], + 'input_tensor_names': ['images', 'orig_target_sizes'], + 'input_binding_names': ['images', 'orig_target_sizes'], + 'force_engine_update': False + }] + ), + 'rtdetr_decoder_node': ComposableNode( + name='rtdetr_decoder', + package='isaac_ros_rtdetr', + plugin='nvidia::isaac_ros::rtdetr::RtDetrDecoderNode', + ), + + # Create a binary segmentation mask from a Detection2DArray published by RT-DETR. + # The segmentation mask is of size + # int(IMAGE_WIDTH/input_to_RT_DETR_ratio) x int(IMAGE_HEIGHT/input_to_RT_DETR_ratio) + 'detection2_d_to_mask_node': ComposableNode( + name='detection2_d_to_mask', + package='isaac_ros_foundationpose', + plugin='nvidia::isaac_ros::foundationpose::Detection2DToMask', + parameters=[{ + 'mask_width': int(input_width/input_to_RT_DETR_ratio), + 'mask_height': int(input_height/input_to_RT_DETR_ratio)}], + remappings=[('detection2_d_array', 'detections_output'), + ('segmentation', 'rt_detr_segmentation')]), + + # Resize segmentation mask to ESS model image size so it can be used by FoundationPose + # FoundationPose requires depth, rgb image and segmentation mask to be of the same size + # Resize from int(IMAGE_WIDTH/input_to_RT_DETR_ratio) x + # int(IMAGE_HEIGHT/input_to_RT_DETR_ratio) + # to ESS_MODEL_IMAGE_WIDTH x ESS_MODEL_IMAGE_HEIGHT + # output height constraint is used since keep_aspect_ratio is False + # and the image is padded + 'resize_mask_node': ComposableNode( + name='resize_mask_node', + package='isaac_ros_image_proc', + plugin='nvidia::isaac_ros::image_proc::ResizeNode', + parameters=[{ + 'input_width': int(input_width/input_to_RT_DETR_ratio), + 'input_height': int(input_height/input_to_RT_DETR_ratio), + 'output_width': input_width, + 'output_height': input_height, + 'keep_aspect_ratio': False, + 'disable_padding': False + }], + remappings=[ + ('image', 'rt_detr_segmentation'), + ('camera_info', 'camera_info_resized'), + ('resize/image', 'segmentation'), + ('resize/camera_info', 'camera_info_segmentation') + ] + ), + + 'resize_left_viz': ComposableNode( + name='resize_left_viz', + package='isaac_ros_image_proc', + plugin='nvidia::isaac_ros::image_proc::ResizeNode', + parameters=[{ + 'input_width': input_width, + 'input_height': input_height, + 'output_width': int(input_width/VISUALIZATION_DOWNSCALING_FACTOR) * 2, + 'output_height': int(input_height/VISUALIZATION_DOWNSCALING_FACTOR) * 2, + 'keep_aspect_ratio': False, + 'encoding_desired': 'rgb8', + 'disable_padding': False + }], + remappings=[ + ('image', 'rgb/image_rect_color'), + ('camera_info', 'rgb/camera_info'), + ('resize/image', 'rgb/image_rect_color_viz'), + ('resize/camera_info', 'rgb/camera_info_viz') + ] + ), + + 'foundationpose_node': ComposableNode( + name='foundationpose_node', + package='isaac_ros_foundationpose', + plugin='nvidia::isaac_ros::foundationpose::FoundationPoseNode', + parameters=[{ + 'mesh_file_path': mesh_file_path, + 'texture_path': texture_path, + + 'refine_model_file_path': refine_model_file_path, + 'refine_engine_file_path': refine_engine_file_path, + 'refine_input_tensor_names': ['input_tensor1', 'input_tensor2'], + 'refine_input_binding_names': ['input1', 'input2'], + 'refine_output_tensor_names': ['output_tensor1', 'output_tensor2'], + 'refine_output_binding_names': ['output1', 'output2'], + + 'score_model_file_path': score_model_file_path, + 'score_engine_file_path': score_engine_file_path, + 'score_input_tensor_names': ['input_tensor1', 'input_tensor2'], + 'score_input_binding_names': ['input1', 'input2'], + 'score_output_tensor_names': ['output_tensor'], + 'score_output_binding_names': ['output1'], + }], + remappings=[ + ('pose_estimation/depth_image', 'depth_image'), + ('pose_estimation/image', 'rgb/image_rect_color'), + ('pose_estimation/camera_info', 'rgb/camera_info'), + ('pose_estimation/segmentation', 'segmentation'), + ('pose_estimation/output', 'output')] + ), + } + + @staticmethod + def get_launch_actions(interface_specs: Dict[str, Any]) -> \ + Dict[str, launch.actions.OpaqueFunction]: + + return { + 'input_images_expect_freq': DeclareLaunchArgument( + 'input_images_expect_freq', + default_value=str(INPUT_IMAGES_EXPECT_FREQ), + description='Expected number of input messages in 1 second'), + + 'input_images_drop_freq': DeclareLaunchArgument( + 'input_images_drop_freq', + default_value=str(INPUT_IMAGES_DROP_FREQ), + description='Number of input messages to be dropped in 1 second'), + + 'mesh_file_path': DeclareLaunchArgument( + 'mesh_file_path', + default_value='', + description='The absolute file path to the mesh file'), + + 'texture_path': DeclareLaunchArgument( + 'texture_path', + default_value='', + description='The absolute file path to the texture map'), + + 'refine_model_file_path': DeclareLaunchArgument( + 'refine_model_file_path', + default_value=REFINE_MODEL_PATH, + description='The absolute file path to the refine model'), + + 'refine_engine_file_path': DeclareLaunchArgument( + 'refine_engine_file_path', + default_value=REFINE_ENGINE_PATH, + description='The absolute file path to the refine trt engine'), + + 'score_model_file_path': DeclareLaunchArgument( + 'score_model_file_path', + default_value=SCORE_MODEL_PATH, + description='The absolute file path to the score model'), + + 'score_engine_file_path': DeclareLaunchArgument( + 'score_engine_file_path', + default_value=SCORE_ENGINE_PATH, + description='The absolute file path to the score trt engine'), + + 'rt_detr_model_file_path': DeclareLaunchArgument( + 'rt_detr_model_file_path', + default_value='', + description='The absolute file path to the RT-DETR ONNX file'), + + 'rt_detr_engine_file_path': DeclareLaunchArgument( + 'rt_detr_engine_file_path', + default_value='', + description='The absolute file path to the RT-DETR TensorRT engine file'), + } + + +def generate_launch_description(): + foundationpose_container = ComposableNodeContainer( + package='rclcpp_components', + name='foundationpose_container', + namespace='', + executable='component_container_mt', + composable_node_descriptions=IsaacROSFoundationPoseLaunchFragment + .get_composable_nodes().values(), + output='screen' + ) + + return launch.LaunchDescription( + [foundationpose_container] + + IsaacROSFoundationPoseLaunchFragment.get_launch_actions().values()) diff --git a/isaac_ros_foundationpose/launch/isaac_ros_foundationpose_hawk.launch.py b/isaac_ros_foundationpose/launch/isaac_ros_foundationpose_hawk.launch.py new file mode 100644 index 0000000..6d5d7d0 --- /dev/null +++ b/isaac_ros_foundationpose/launch/isaac_ros_foundationpose_hawk.launch.py @@ -0,0 +1,523 @@ +# SPDX-FileCopyrightText: NVIDIA CORPORATION & AFFILIATES +# Copyright (c) 2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +# SPDX-License-Identifier: Apache-2.0 + +import os + +from ament_index_python.packages import get_package_share_directory +import launch +from launch.actions import DeclareLaunchArgument +from launch.conditions import IfCondition +from launch.substitutions import LaunchConfiguration +from launch_ros.actions import ComposableNodeContainer, Node +from launch_ros.descriptions import ComposableNode + +# Number of Hawk messages to be dropped in 1 second +HAWK_EXPECT_FREQ = 28 +# Expected number of Hawk messages in 1 second +INPUT_IMAGES_DROP_FREQ = 30 + +RT_DETR_MODEL_INPUT_WIDTH = 640 +RT_DETR_MODEL_INPUT_HEIGHT = 640 +RT_DETR_MODEL_NUM_CHANNELS = 3 # RT-DETR models expect 3 image channels + +ESS_MODEL_IMAGE_WIDTH = 480 +ESS_MODEL_IMAGE_HEIGHT = 288 + +HAWK_IMAGE_WIDTH = 1920 +HAWK_IMAGE_HEIGHT = 1200 + +VISUALIZATION_DOWNSCALING_FACTOR = 10 + +HAWK_TO_RT_DETR_RATIO = HAWK_IMAGE_WIDTH / RT_DETR_MODEL_INPUT_WIDTH + +REFINE_MODEL_PATH = '/tmp/refine_model.onnx' +REFINE_ENGINE_PATH = '/tmp/refine_trt_engine.plan' +SCORE_MODEL_PATH = '/tmp/score_model.onnx' +SCORE_ENGINE_PATH = '/tmp/score_trt_engine.plan' + + +def generate_launch_description(): + """Generate launch description for testing relevant nodes.""" + rviz_config_path = os.path.join( + get_package_share_directory('isaac_ros_foundationpose'), + 'rviz', 'foundationpose_hawk.rviz') + + launch_args = [ + DeclareLaunchArgument( + 'hawk_expect_freq', + default_value=str(HAWK_EXPECT_FREQ), + description='Number of Hawk messages to be dropped in 1 second'), + + DeclareLaunchArgument( + 'input_images_drop_freq', + default_value=str(INPUT_IMAGES_DROP_FREQ), + description='Expected number of Hawk messages in 1 second'), + + DeclareLaunchArgument( + 'mesh_file_path', + default_value='', + description='The absolute file path to the mesh file'), + + DeclareLaunchArgument( + 'texture_path', + default_value='', + description='The absolute file path to the texture map'), + + DeclareLaunchArgument( + 'refine_model_file_path', + default_value=REFINE_MODEL_PATH, + description='The absolute file path to the refine model'), + + DeclareLaunchArgument( + 'refine_engine_file_path', + default_value=REFINE_ENGINE_PATH, + description='The absolute file path to the refine trt engine'), + + DeclareLaunchArgument( + 'score_model_file_path', + default_value=SCORE_MODEL_PATH, + description='The absolute file path to the score model'), + + DeclareLaunchArgument( + 'score_engine_file_path', + default_value=SCORE_ENGINE_PATH, + description='The absolute file path to the score trt engine'), + + DeclareLaunchArgument( + 'rt_detr_model_file_path', + default_value='', + description='The absolute file path to the RT-DETR ONNX file'), + + DeclareLaunchArgument( + 'rt_detr_engine_file_path', + default_value='', + description='The absolute file path to the RT-DETR TensorRT engine file'), + + DeclareLaunchArgument( + 'ess_depth_engine_file_path', + default_value='', + description='The absolute path to the ESS engine plan.'), + + DeclareLaunchArgument( + 'ess_depth_threshold', + default_value='0.9', + description='Threshold value ranges between 0.0 and 1.0 ' + 'for filtering disparity with confidence.'), + + DeclareLaunchArgument( + 'launch_rviz', + default_value='False', + description='Flag to enable Rviz2 launch'), + + DeclareLaunchArgument( + 'container_name', + default_value='foundationpose_container', + description='Name for ComposableNodeContainer') + ] + + hawk_expect_freq = LaunchConfiguration('hawk_expect_freq') + input_images_drop_freq = LaunchConfiguration('input_images_drop_freq') + mesh_file_path = LaunchConfiguration('mesh_file_path') + texture_path = LaunchConfiguration('texture_path') + refine_model_file_path = LaunchConfiguration('refine_model_file_path') + refine_engine_file_path = LaunchConfiguration('refine_engine_file_path') + score_model_file_path = LaunchConfiguration('score_model_file_path') + score_engine_file_path = LaunchConfiguration('score_engine_file_path') + rt_detr_model_file_path = LaunchConfiguration('rt_detr_model_file_path') + rt_detr_engine_file_path = LaunchConfiguration('rt_detr_engine_file_path') + ess_depth_engine_file_path = LaunchConfiguration( + 'ess_depth_engine_file_path') + ess_depth_threshold = LaunchConfiguration('ess_depth_threshold') + launch_rviz = LaunchConfiguration('launch_rviz') + container_name = LaunchConfiguration('container_name') + + # Launch hawk + # Publishes left and right images at HAWK_IMAGE_WIDTH x HAWK_IMAGE_HEIGHT resolution + correlated_timestamp_driver_node = ComposableNode( + package='isaac_ros_correlated_timestamp_driver', + plugin='nvidia::isaac_ros::correlated_timestamp_driver::CorrelatedTimestampDriverNode', + name='correlated_timestamp_driver', + parameters=[{'use_time_since_epoch': False, + 'nvpps_dev_name': '/dev/nvpps0'}]) + hawk_node = ComposableNode( + name='hawk_node', + package='isaac_ros_hawk', + plugin='nvidia::isaac_ros::hawk::HawkNode', + parameters=[{'module_id': 5, + 'input_qos': 'SENSOR_DATA', + 'output_qos': 'SENSOR_DATA', + 'enable_statistics': True, + 'topics_list': ['left/image_raw'], + 'expected_fps_list': [30.0], + 'jitter_tolerance_us': 30000}], + remappings=[ + ('/hawk_front/correlated_timestamp', '/correlated_timestamp') + ] + ) + + # Drops hawk_expect_freq out of input_images_drop_freq Hawk messages + drop_node = ComposableNode( + name='drop_node', + package='isaac_ros_nitros_topic_tools', + plugin='nvidia::isaac_ros::nitros::NitrosCameraDropNode', + parameters=[{ + 'input_qos': 'SENSOR_DATA', + 'output_qos': 'SENSOR_DATA', + 'X': hawk_expect_freq, + 'Y': input_images_drop_freq, + 'mode': 'stereo', + 'sync_queue_size': 100 + }], + remappings=[ + ('image_1', 'left/image_raw'), + ('camera_info_1', 'left/camera_info'), + ('image_1_drop', 'left/image_raw_drop'), + ('camera_info_1_drop', 'left/camera_info_drop'), + ('image_2', 'right/image_raw'), + ('camera_info_2', 'right/camera_info'), + ('image_2_drop', 'right/image_raw_drop'), + ('camera_info_2_drop', 'right/camera_info_drop'), + ] + ) + + # Rectify left and right images to be used by ESS(depth estimation) + # and RT-DETR(object detection) + left_rectify_node = ComposableNode( + name='left_rectify_node', + package='isaac_ros_image_proc', + plugin='nvidia::isaac_ros::image_proc::RectifyNode', + parameters=[{ + 'output_width': HAWK_IMAGE_WIDTH, + 'output_height': HAWK_IMAGE_HEIGHT, + 'input_qos': 'SENSOR_DATA', + 'output_qos': 'DEFAULT' + }], + remappings=[ + ('image_raw', 'left/image_raw_drop'), + ('camera_info', 'left/camera_info_drop'), + ('image_rect', 'left/image_rect'), + ('camera_info_rect', 'left/camera_info_rect') + ] + ) + right_rectify_node = ComposableNode( + name='right_rectify_node', + package='isaac_ros_image_proc', + plugin='nvidia::isaac_ros::image_proc::RectifyNode', + parameters=[{ + 'output_width': HAWK_IMAGE_WIDTH, + 'output_height': HAWK_IMAGE_HEIGHT, + 'input_qos': 'SENSOR_DATA', + 'output_qos': 'DEFAULT' + }], + remappings=[ + ('image_raw', 'right/image_raw_drop'), + ('camera_info', 'right/camera_info_drop'), + ('image_rect', 'right/image_rect'), + ('camera_info_rect', 'right/camera_info_rect') + ] + ) + + # Resize and pad hawk images to RT-DETR model input image size + # Resize from HAWK_IMAGE_WIDTH x HAWK_IMAGE_HEIGHT to + # HAWK_IMAGE_WIDTH/HAWK_TO_RT_DETR_RATIO x HAWK_IMAGE_HEIGHT/HAWK_TO_RT_DETR_RATIO + # output height constraint is not used since keep_aspect_ratio is True + resize_left_rt_detr_node = ComposableNode( + name='resize_left_rt_detr_node', + package='isaac_ros_image_proc', + plugin='nvidia::isaac_ros::image_proc::ResizeNode', + parameters=[{ + 'input_width': HAWK_IMAGE_WIDTH, + 'input_height': HAWK_IMAGE_HEIGHT, + 'output_width': RT_DETR_MODEL_INPUT_WIDTH, + 'keep_aspect_ratio': True, + 'encoding_desired': 'rgb8', + 'disable_padding': True + }], + remappings=[ + ('image', 'left/image_rect'), + ('camera_info', 'left/camera_info_rect') + ] + ) + # Pad the image from HAWK_IMAGE_WIDTH/HAWK_TO_RT_DETR_RATIO x + # HAWK_IMAGE_HEIGHT/HAWK_TO_RT_DETR_RATIO + # to RT_DETR_MODEL_INPUT_WIDTH x RT_DETR_MODEL_INPUT_HEIGHT + pad_node = ComposableNode( + name='pad_node', + package='isaac_ros_image_proc', + plugin='nvidia::isaac_ros::image_proc::PadNode', + parameters=[{ + 'output_image_width': RT_DETR_MODEL_INPUT_WIDTH, + 'output_image_height': RT_DETR_MODEL_INPUT_HEIGHT, + 'padding_type': 'BOTTOM_RIGHT' + }], + remappings=[( + 'image', 'resize/image' + )] + ) + + # Convert image to tensor and reshape + image_to_tensor_node = ComposableNode( + name='image_to_tensor_node', + package='isaac_ros_tensor_proc', + plugin='nvidia::isaac_ros::dnn_inference::ImageToTensorNode', + parameters=[{ + 'scale': False, + 'tensor_name': 'image', + }], + remappings=[ + ('image', 'padded_image'), + ('tensor', 'normalized_tensor'), + ] + ) + interleave_to_planar_node = ComposableNode( + name='interleaved_to_planar_node', + package='isaac_ros_tensor_proc', + plugin='nvidia::isaac_ros::dnn_inference::InterleavedToPlanarNode', + parameters=[{ + 'input_tensor_shape': [RT_DETR_MODEL_INPUT_WIDTH, + RT_DETR_MODEL_INPUT_HEIGHT, + RT_DETR_MODEL_NUM_CHANNELS] + }], + remappings=[ + ('interleaved_tensor', 'normalized_tensor') + ] + ) + reshape_node = ComposableNode( + name='reshape_node', + package='isaac_ros_tensor_proc', + plugin='nvidia::isaac_ros::dnn_inference::ReshapeNode', + parameters=[{ + 'output_tensor_name': 'input_tensor', + 'input_tensor_shape': [RT_DETR_MODEL_NUM_CHANNELS, + RT_DETR_MODEL_INPUT_WIDTH, + RT_DETR_MODEL_INPUT_HEIGHT], + 'output_tensor_shape': [1, RT_DETR_MODEL_NUM_CHANNELS, + RT_DETR_MODEL_INPUT_WIDTH, + RT_DETR_MODEL_INPUT_HEIGHT] + }], + remappings=[ + ('tensor', 'planar_tensor') + ], + ) + rtdetr_preprocessor_node = ComposableNode( + name='rtdetr_preprocessor', + package='isaac_ros_rtdetr', + plugin='nvidia::isaac_ros::rtdetr::RtDetrPreprocessorNode', + remappings=[ + ('encoded_tensor', 'reshaped_tensor') + ] + ) + + # RT-DETR objection detection pipeline + tensor_rt_node = ComposableNode( + name='tensor_rt', + package='isaac_ros_tensor_rt', + plugin='nvidia::isaac_ros::dnn_inference::TensorRTNode', + parameters=[{ + 'model_file_path': rt_detr_model_file_path, + 'engine_file_path': rt_detr_engine_file_path, + 'output_binding_names': ['labels', 'boxes', 'scores'], + 'output_tensor_names': ['labels', 'boxes', 'scores'], + 'input_tensor_names': ['images', 'orig_target_sizes'], + 'input_binding_names': ['images', 'orig_target_sizes'], + 'force_engine_update': False + }] + ) + rtdetr_decoder_node = ComposableNode( + name='rtdetr_decoder', + package='isaac_ros_rtdetr', + plugin='nvidia::isaac_ros::rtdetr::RtDetrDecoderNode', + ) + + # Create a binary segmentation mask from a Detection2DArray published by RT-DETR. + # The segmentation mask is of size int(HAWK_IMAGE_WIDTH/HAWK_TO_RT_DETR_RATIO) x + # int(HAWK_IMAGE_HEIGHT/HAWK_TO_RT_DETR_RATIO) + detection2_d_to_mask_node = ComposableNode( + name='detection2_d_to_mask', + package='isaac_ros_foundationpose', + plugin='nvidia::isaac_ros::foundationpose::Detection2DToMask', + parameters=[{ + 'mask_width': int(HAWK_IMAGE_WIDTH/HAWK_TO_RT_DETR_RATIO), + 'mask_height': int(HAWK_IMAGE_HEIGHT/HAWK_TO_RT_DETR_RATIO)}], + remappings=[('detection2_d_array', 'detections_output'), + ('segmentation', 'rt_detr_segmentation')]) + + # Resize segmentation mask to ESS model image size so it can be used by FoundationPose + # FoundationPose requires depth, rgb image and segmentation mask to be of the same size + # Resize from int(HAWK_IMAGE_WIDTH/HAWK_TO_RT_DETR_RATIO) x + # int(HAWK_IMAGE_HEIGHT/HAWK_TO_RT_DETR_RATIO) to + # ESS_MODEL_IMAGE_WIDTH x ESS_MODEL_IMAGE_HEIGHT + # output height constraint is used since keep_aspect_ratio is False + # and the image is padded + resize_mask_node = ComposableNode( + name='resize_mask_node', + package='isaac_ros_image_proc', + plugin='nvidia::isaac_ros::image_proc::ResizeNode', + parameters=[{ + 'input_width': int(HAWK_IMAGE_WIDTH/HAWK_TO_RT_DETR_RATIO), + 'input_height': int(HAWK_IMAGE_HEIGHT/HAWK_TO_RT_DETR_RATIO), + 'output_width': ESS_MODEL_IMAGE_WIDTH, + 'output_height': ESS_MODEL_IMAGE_HEIGHT, + 'keep_aspect_ratio': False, + 'disable_padding': False + }], + remappings=[ + ('image', 'rt_detr_segmentation'), + ('camera_info', 'resize/camera_info'), + ('resize/image', 'segmentation'), + ('resize/camera_info', 'camera_info_segmentation') + ] + ) + + # Resize hawk images to ESS model image size so it can be used by FoundationPose + # FoundationPose requires depth, rgb image and segmentation mask to be of the same size + resize_left_ess_size = ComposableNode( + name='resize_left_ess_size', + package='isaac_ros_image_proc', + plugin='nvidia::isaac_ros::image_proc::ResizeNode', + parameters=[{ + 'input_width': HAWK_IMAGE_WIDTH, + 'input_height': HAWK_IMAGE_HEIGHT, + 'output_width': ESS_MODEL_IMAGE_WIDTH, + 'output_height': ESS_MODEL_IMAGE_HEIGHT, + 'keep_aspect_ratio': False, + 'encoding_desired': 'rgb8', + 'disable_padding': False + }], + remappings=[ + ('image', 'left/image_rect'), + ('camera_info', 'left/camera_info_rect'), + ('resize/image', 'rgb/image_rect_color'), + ('resize/camera_info', 'rgb/camera_info') + ] + ) + + # Launch ESS(depth estimation) + ess_node = ComposableNode( + name='ess_node', + package='isaac_ros_ess', + plugin='nvidia::isaac_ros::dnn_stereo_depth::ESSDisparityNode', + parameters=[{'engine_file_path': ess_depth_engine_file_path, + 'threshold': ess_depth_threshold + }], + remappings=[ + ('left/image_rect', 'left/image_rect'), + ('right/image_rect', 'right/image_rect'), + ('left/camera_info', 'left/camera_info_rect'), + ('right/camera_info', 'right/camera_info_rect') + ] + ) + disparity_to_depth_node = ComposableNode( + name='disparity_to_depth', + package='isaac_ros_stereo_image_proc', + plugin='nvidia::isaac_ros::stereo_image_proc::DisparityToDepthNode', + remappings=[( + 'depth', 'depth_image' + )] + ) + + # Launch isaac_ros_foundationpose + foundationpose_node = ComposableNode( + name='foundationpose_node', + package='isaac_ros_foundationpose', + plugin='nvidia::isaac_ros::foundationpose::FoundationPoseNode', + parameters=[{ + 'mesh_file_path': mesh_file_path, + 'texture_path': texture_path, + + 'refine_model_file_path': refine_model_file_path, + 'refine_engine_file_path': refine_engine_file_path, + 'refine_input_tensor_names': ['input_tensor1', 'input_tensor2'], + 'refine_input_binding_names': ['input1', 'input2'], + 'refine_output_tensor_names': ['output_tensor1', 'output_tensor2'], + 'refine_output_binding_names': ['output1', 'output2'], + + 'score_model_file_path': score_model_file_path, + 'score_engine_file_path': score_engine_file_path, + 'score_input_tensor_names': ['input_tensor1', 'input_tensor2'], + 'score_input_binding_names': ['input1', 'input2'], + 'score_output_tensor_names': ['output_tensor'], + 'score_output_binding_names': ['output1'], + }], + remappings=[ + ('pose_estimation/depth_image', 'depth_image'), + ('pose_estimation/image', 'rgb/image_rect_color'), + ('pose_estimation/camera_info', 'rgb/camera_info'), + ('pose_estimation/segmentation', 'segmentation'), + ('pose_estimation/output', 'output')] + ) + + resize_left_viz = ComposableNode( + name='resize_left_viz', + package='isaac_ros_image_proc', + plugin='nvidia::isaac_ros::image_proc::ResizeNode', + parameters=[{ + 'input_width': HAWK_IMAGE_WIDTH, + 'input_height': HAWK_IMAGE_HEIGHT, + 'output_width': int(HAWK_IMAGE_WIDTH/VISUALIZATION_DOWNSCALING_FACTOR), + 'output_height': int(HAWK_IMAGE_HEIGHT/VISUALIZATION_DOWNSCALING_FACTOR), + 'keep_aspect_ratio': False, + 'encoding_desired': 'rgb8', + 'disable_padding': False + }], + remappings=[ + ('image', 'rgb/image_rect_color'), + ('camera_info', 'rgb/camera_info'), + ('resize/image', 'rgb/image_rect_color_viz'), + ('resize/camera_info', 'rgb/camera_info_viz') + ] + ) + + rviz_node = Node( + package='rviz2', + executable='rviz2', + name='rviz2', + arguments=['-d', rviz_config_path], + condition=IfCondition(launch_rviz)) + + foundationpose_container = ComposableNodeContainer( + name=container_name, + namespace='', + package='rclcpp_components', + executable='component_container_mt', + composable_node_descriptions=[ + correlated_timestamp_driver_node, + hawk_node, + drop_node, + left_rectify_node, + right_rectify_node, + resize_left_rt_detr_node, + pad_node, + image_to_tensor_node, + interleave_to_planar_node, + reshape_node, + rtdetr_preprocessor_node, + tensor_rt_node, + rtdetr_decoder_node, + detection2_d_to_mask_node, + resize_mask_node, + resize_left_ess_size, + ess_node, + disparity_to_depth_node, + foundationpose_node, + resize_left_viz + ], + output='screen' + ) + + return launch.LaunchDescription(launch_args + [foundationpose_container, + rviz_node]) diff --git a/isaac_ros_foundationpose/launch/isaac_ros_foundationpose_hawk_tracking.launch.py b/isaac_ros_foundationpose/launch/isaac_ros_foundationpose_hawk_tracking.launch.py new file mode 100644 index 0000000..e0a91d2 --- /dev/null +++ b/isaac_ros_foundationpose/launch/isaac_ros_foundationpose_hawk_tracking.launch.py @@ -0,0 +1,547 @@ +# SPDX-FileCopyrightText: NVIDIA CORPORATION & AFFILIATES +# Copyright (c) 2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +# SPDX-License-Identifier: Apache-2.0 + +import os + +from ament_index_python.packages import get_package_share_directory +import launch +from launch.actions import DeclareLaunchArgument +from launch.conditions import IfCondition +from launch.substitutions import LaunchConfiguration +from launch_ros.actions import ComposableNodeContainer, Node +from launch_ros.descriptions import ComposableNode + +# Number of Hawk messages to be dropped in 1 second +HAWK_EXPECT_FREQ = 15 +# Expected number of Hawk messages in 1 second +INPUT_IMAGES_DROP_FREQ = 30 + +RT_DETR_MODEL_INPUT_WIDTH = 640 +RT_DETR_MODEL_INPUT_HEIGHT = 640 +RT_DETR_MODEL_NUM_CHANNELS = 3 # RT-DETR models expect 3 image channels + +ESS_MODEL_IMAGE_WIDTH = 480 +ESS_MODEL_IMAGE_HEIGHT = 288 + +HAWK_IMAGE_WIDTH = 1920 +HAWK_IMAGE_HEIGHT = 1200 + +VISUALIZATION_DOWNSCALING_FACTOR = 10 + +HAWK_TO_RT_DETR_RATIO = HAWK_IMAGE_WIDTH / RT_DETR_MODEL_INPUT_WIDTH + +REFINE_MODEL_PATH = '/tmp/refine_model.onnx' +REFINE_ENGINE_PATH = '/tmp/refine_trt_engine.plan' +SCORE_MODEL_PATH = '/tmp/score_model.onnx' +SCORE_ENGINE_PATH = '/tmp/score_trt_engine.plan' + + +def generate_launch_description(): + """Generate launch description for testing relevant nodes.""" + rviz_config_path = os.path.join( + get_package_share_directory('isaac_ros_foundationpose'), + 'rviz', 'foundationpose_hawk.rviz') + + launch_args = [ + DeclareLaunchArgument( + 'hawk_expect_freq', + default_value=str(HAWK_EXPECT_FREQ), + description='Number of Hawk messages to be dropped in 1 second'), + + DeclareLaunchArgument( + 'input_images_drop_freq', + default_value=str(INPUT_IMAGES_DROP_FREQ), + description='Expected number of Hawk messages in 1 second'), + + DeclareLaunchArgument( + 'mesh_file_path', + default_value='', + description='The absolute file path to the mesh file'), + + DeclareLaunchArgument( + 'texture_path', + default_value='', + description='The absolute file path to the texture map'), + + DeclareLaunchArgument( + 'refine_model_file_path', + default_value=REFINE_MODEL_PATH, + description='The absolute file path to the refine model'), + + DeclareLaunchArgument( + 'refine_engine_file_path', + default_value=REFINE_ENGINE_PATH, + description='The absolute file path to the refine trt engine'), + + DeclareLaunchArgument( + 'score_model_file_path', + default_value=SCORE_MODEL_PATH, + description='The absolute file path to the score model'), + + DeclareLaunchArgument( + 'score_engine_file_path', + default_value=SCORE_ENGINE_PATH, + description='The absolute file path to the score trt engine'), + + DeclareLaunchArgument( + 'rt_detr_model_file_path', + default_value='', + description='The absolute file path to the RT-DETR ONNX file'), + + DeclareLaunchArgument( + 'rt_detr_engine_file_path', + default_value='', + description='The absolute file path to the RT-DETR TensorRT engine file'), + + DeclareLaunchArgument( + 'ess_depth_engine_file_path', + default_value='', + description='The absolute path to the ESS engine plan.'), + + DeclareLaunchArgument( + 'ess_depth_threshold', + default_value='0.9', + description='Threshold value ranges between 0.0 and 1.0 ' + 'for filtering disparity with confidence.'), + + DeclareLaunchArgument( + 'launch_rviz', + default_value='False', + description='Flag to enable Rviz2 launch'), + + DeclareLaunchArgument( + 'container_name', + default_value='foundationpose_container', + description='Name for ComposableNodeContainer'), + ] + + hawk_expect_freq = LaunchConfiguration('hawk_expect_freq') + input_images_drop_freq = LaunchConfiguration('input_images_drop_freq') + mesh_file_path = LaunchConfiguration('mesh_file_path') + texture_path = LaunchConfiguration('texture_path') + refine_model_file_path = LaunchConfiguration('refine_model_file_path') + refine_engine_file_path = LaunchConfiguration('refine_engine_file_path') + score_model_file_path = LaunchConfiguration('score_model_file_path') + score_engine_file_path = LaunchConfiguration('score_engine_file_path') + rt_detr_model_file_path = LaunchConfiguration('rt_detr_model_file_path') + rt_detr_engine_file_path = LaunchConfiguration('rt_detr_engine_file_path') + ess_depth_engine_file_path = LaunchConfiguration( + 'ess_depth_engine_file_path') + ess_depth_threshold = LaunchConfiguration('ess_depth_threshold') + launch_rviz = LaunchConfiguration('launch_rviz') + container_name = LaunchConfiguration('container_name') + + # Launch hawk + # Publishes left and right images at HAWK_IMAGE_WIDTH x HAWK_IMAGE_HEIGHT resolution + correlated_timestamp_driver_node = ComposableNode( + package='isaac_ros_correlated_timestamp_driver', + plugin='nvidia::isaac_ros::correlated_timestamp_driver::CorrelatedTimestampDriverNode', + name='correlated_timestamp_driver', + parameters=[{'use_time_since_epoch': False, + 'nvpps_dev_name': '/dev/nvpps0'}]) + hawk_node = ComposableNode( + name='hawk_node', + package='isaac_ros_hawk', + plugin='nvidia::isaac_ros::hawk::HawkNode', + parameters=[{'module_id': 5, + 'input_qos': 'SENSOR_DATA', + 'output_qos': 'SENSOR_DATA', + 'enable_statistics': True, + 'topics_list': ['left/image_raw'], + 'expected_fps_list': [30.0], + 'jitter_tolerance_us': 30000}], + remappings=[ + ('/hawk_front/correlated_timestamp', '/correlated_timestamp') + ] + ) + + # Drops hawk_expect_freq out of input_images_drop_freq Hawk messages + drop_node = ComposableNode( + name='drop_node', + package='isaac_ros_nitros_topic_tools', + plugin='nvidia::isaac_ros::nitros::NitrosCameraDropNode', + parameters=[{ + 'input_qos': 'SENSOR_DATA', + 'output_qos': 'SENSOR_DATA', + 'X': hawk_expect_freq, + 'Y': input_images_drop_freq, + 'mode': 'stereo', + 'sync_queue_size': 100 + }], + remappings=[ + ('image_1', 'left/image_raw'), + ('camera_info_1', 'left/camera_info'), + ('image_1_drop', 'left/image_raw_drop'), + ('camera_info_1_drop', 'left/camera_info_drop'), + ('image_2', 'right/image_raw'), + ('camera_info_2', 'right/camera_info'), + ('image_2_drop', 'right/image_raw_drop'), + ('camera_info_2_drop', 'right/camera_info_drop'), + ] + ) + + # Rectify left and right images to be used by ESS(depth estimation) + # and RT-DETR(object detection) + left_rectify_node = ComposableNode( + name='left_rectify_node', + package='isaac_ros_image_proc', + plugin='nvidia::isaac_ros::image_proc::RectifyNode', + parameters=[{ + 'output_width': HAWK_IMAGE_WIDTH, + 'output_height': HAWK_IMAGE_HEIGHT, + 'input_qos': 'SENSOR_DATA', + 'output_qos': 'DEFAULT' + }], + remappings=[ + ('image_raw', 'left/image_raw_drop'), + ('camera_info', 'left/camera_info_drop'), + ('image_rect', 'left/image_rect'), + ('camera_info_rect', 'left/camera_info_rect') + ] + ) + right_rectify_node = ComposableNode( + name='right_rectify_node', + package='isaac_ros_image_proc', + plugin='nvidia::isaac_ros::image_proc::RectifyNode', + parameters=[{ + 'output_width': HAWK_IMAGE_WIDTH, + 'output_height': HAWK_IMAGE_HEIGHT, + 'input_qos': 'SENSOR_DATA', + 'output_qos': 'DEFAULT' + }], + remappings=[ + ('image_raw', 'right/image_raw_drop'), + ('camera_info', 'right/camera_info_drop'), + ('image_rect', 'right/image_rect'), + ('camera_info_rect', 'right/camera_info_rect') + ] + ) + + # Resize and pad hawk images to RT-DETR model input image size + # Resize from HAWK_IMAGE_WIDTH x HAWK_IMAGE_HEIGHT to + # HAWK_IMAGE_WIDTH/HAWK_TO_RT_DETR_RATIO x HAWK_IMAGE_HEIGHT/HAWK_TO_RT_DETR_RATIO + # output height constraint is not used since keep_aspect_ratio is True + resize_left_rt_detr_node = ComposableNode( + name='resize_left_rt_detr_node', + package='isaac_ros_image_proc', + plugin='nvidia::isaac_ros::image_proc::ResizeNode', + parameters=[{ + 'input_width': HAWK_IMAGE_WIDTH, + 'input_height': HAWK_IMAGE_HEIGHT, + 'output_width': RT_DETR_MODEL_INPUT_WIDTH, + 'keep_aspect_ratio': True, + 'encoding_desired': 'rgb8', + 'disable_padding': True + }], + remappings=[ + ('image', 'left/image_rect'), + ('camera_info', 'left/camera_info_rect') + ] + ) + # Pad the image from HAWK_IMAGE_WIDTH/HAWK_TO_RT_DETR_RATIO x + # HAWK_IMAGE_HEIGHT/HAWK_TO_RT_DETR_RATIO + # to RT_DETR_MODEL_INPUT_WIDTH x RT_DETR_MODEL_INPUT_HEIGHT + pad_node = ComposableNode( + name='pad_node', + package='isaac_ros_image_proc', + plugin='nvidia::isaac_ros::image_proc::PadNode', + parameters=[{ + 'output_image_width': RT_DETR_MODEL_INPUT_WIDTH, + 'output_image_height': RT_DETR_MODEL_INPUT_HEIGHT, + 'padding_type': 'BOTTOM_RIGHT' + }], + remappings=[( + 'image', 'resize/image' + )] + ) + + # Convert image to tensor and reshape + image_to_tensor_node = ComposableNode( + name='image_to_tensor_node', + package='isaac_ros_tensor_proc', + plugin='nvidia::isaac_ros::dnn_inference::ImageToTensorNode', + parameters=[{ + 'scale': False, + 'tensor_name': 'image', + }], + remappings=[ + ('image', 'padded_image'), + ('tensor', 'normalized_tensor'), + ] + ) + interleave_to_planar_node = ComposableNode( + name='interleaved_to_planar_node', + package='isaac_ros_tensor_proc', + plugin='nvidia::isaac_ros::dnn_inference::InterleavedToPlanarNode', + parameters=[{ + 'input_tensor_shape': [RT_DETR_MODEL_INPUT_WIDTH, + RT_DETR_MODEL_INPUT_HEIGHT, + RT_DETR_MODEL_NUM_CHANNELS] + }], + remappings=[ + ('interleaved_tensor', 'normalized_tensor') + ] + ) + reshape_node = ComposableNode( + name='reshape_node', + package='isaac_ros_tensor_proc', + plugin='nvidia::isaac_ros::dnn_inference::ReshapeNode', + parameters=[{ + 'output_tensor_name': 'input_tensor', + 'input_tensor_shape': [RT_DETR_MODEL_NUM_CHANNELS, + RT_DETR_MODEL_INPUT_WIDTH, + RT_DETR_MODEL_INPUT_HEIGHT], + 'output_tensor_shape': [1, RT_DETR_MODEL_NUM_CHANNELS, + RT_DETR_MODEL_INPUT_WIDTH, + RT_DETR_MODEL_INPUT_HEIGHT] + }], + remappings=[ + ('tensor', 'planar_tensor') + ], + ) + rtdetr_preprocessor_node = ComposableNode( + name='rtdetr_preprocessor', + package='isaac_ros_rtdetr', + plugin='nvidia::isaac_ros::rtdetr::RtDetrPreprocessorNode', + remappings=[ + ('encoded_tensor', 'reshaped_tensor') + ] + ) + + # RT-DETR objection detection pipeline + tensor_rt_node = ComposableNode( + name='tensor_rt', + package='isaac_ros_tensor_rt', + plugin='nvidia::isaac_ros::dnn_inference::TensorRTNode', + parameters=[{ + 'model_file_path': rt_detr_model_file_path, + 'engine_file_path': rt_detr_engine_file_path, + 'output_binding_names': ['labels', 'boxes', 'scores'], + 'output_tensor_names': ['labels', 'boxes', 'scores'], + 'input_tensor_names': ['images', 'orig_target_sizes'], + 'input_binding_names': ['images', 'orig_target_sizes'], + 'force_engine_update': False + }] + ) + rtdetr_decoder_node = ComposableNode( + name='rtdetr_decoder', + package='isaac_ros_rtdetr', + plugin='nvidia::isaac_ros::rtdetr::RtDetrDecoderNode', + ) + + # Create a binary segmentation mask from a Detection2DArray published by RT-DETR. + # The segmentation mask is of size int(HAWK_IMAGE_WIDTH/HAWK_TO_RT_DETR_RATIO) x + # int(HAWK_IMAGE_HEIGHT/HAWK_TO_RT_DETR_RATIO) + detection2_d_to_mask_node = ComposableNode( + name='detection2_d_to_mask', + package='isaac_ros_foundationpose', + plugin='nvidia::isaac_ros::foundationpose::Detection2DToMask', + parameters=[{ + 'mask_width': int(HAWK_IMAGE_WIDTH/HAWK_TO_RT_DETR_RATIO), + 'mask_height': int(HAWK_IMAGE_HEIGHT/HAWK_TO_RT_DETR_RATIO)}], + remappings=[('detection2_d_array', 'detections_output'), + ('segmentation', 'rt_detr_segmentation')]) + + # Resize segmentation mask to ESS model image size so it can be used by FoundationPose + # FoundationPose requires depth, rgb image and segmentation mask to be of the same size + # Resize from int(HAWK_IMAGE_WIDTH/HAWK_TO_RT_DETR_RATIO) x + # int(HAWK_IMAGE_HEIGHT/HAWK_TO_RT_DETR_RATIO) to + # ESS_MODEL_IMAGE_WIDTH x ESS_MODEL_IMAGE_HEIGHT + # output height constraint is used since keep_aspect_ratio is False + # and the image is padded + resize_mask_node = ComposableNode( + name='resize_mask_node', + package='isaac_ros_image_proc', + plugin='nvidia::isaac_ros::image_proc::ResizeNode', + parameters=[{ + 'input_width': int(HAWK_IMAGE_WIDTH/HAWK_TO_RT_DETR_RATIO), + 'input_height': int(HAWK_IMAGE_HEIGHT/HAWK_TO_RT_DETR_RATIO), + 'output_width': ESS_MODEL_IMAGE_WIDTH, + 'output_height': ESS_MODEL_IMAGE_HEIGHT, + 'keep_aspect_ratio': False, + 'disable_padding': False + }], + remappings=[ + ('image', 'rt_detr_segmentation'), + ('camera_info', 'resize/camera_info'), + ('resize/image', 'segmentation'), + ('resize/camera_info', 'camera_info_segmentation') + ] + ) + + # Resize hawk images to ESS model image size so it can be used by FoundationPose + # FoundationPose requires depth, rgb image and segmentation mask to be of the same size + resize_left_ess_size = ComposableNode( + name='resize_left_ess_size', + package='isaac_ros_image_proc', + plugin='nvidia::isaac_ros::image_proc::ResizeNode', + parameters=[{ + 'input_width': HAWK_IMAGE_WIDTH, + 'input_height': HAWK_IMAGE_HEIGHT, + 'output_width': ESS_MODEL_IMAGE_WIDTH, + 'output_height': ESS_MODEL_IMAGE_HEIGHT, + 'keep_aspect_ratio': False, + 'encoding_desired': 'rgb8', + 'disable_padding': False + }], + remappings=[ + ('image', 'left/image_rect'), + ('camera_info', 'left/camera_info_rect'), + ('resize/image', 'rgb/image_rect_color'), + ('resize/camera_info', 'rgb/camera_info') + ] + ) + + # Launch ESS(depth estimation) + ess_node = ComposableNode( + name='ess_node', + package='isaac_ros_ess', + plugin='nvidia::isaac_ros::dnn_stereo_depth::ESSDisparityNode', + parameters=[{'engine_file_path': ess_depth_engine_file_path, + 'threshold': ess_depth_threshold + }], + remappings=[ + ('left/image_rect', 'left/image_rect'), + ('right/image_rect', 'right/image_rect'), + ('left/camera_info', 'left/camera_info_rect'), + ('right/camera_info', 'right/camera_info_rect') + ] + ) + disparity_to_depth_node = ComposableNode( + name='disparity_to_depth', + package='isaac_ros_stereo_image_proc', + plugin='nvidia::isaac_ros::stereo_image_proc::DisparityToDepthNode', + remappings=[( + 'depth', 'depth_image' + )] + ) + + # Launch isaac_ros_foundationpose + selector_node = ComposableNode( + name='selector_node', + package='isaac_ros_foundationpose', + plugin='nvidia::isaac_ros::foundationpose::Selector', + parameters=[{ + 'reset_period': 10000 + }], + remappings=[ + ('depth_image', 'depth_image'), + ('image', 'rgb/image_rect_color'), + ('camera_info', 'rgb/camera_info'), + ('segmentation', 'segmentation')]) + + foundationpose_node = ComposableNode( + name='foundationpose_node', + package='isaac_ros_foundationpose', + plugin='nvidia::isaac_ros::foundationpose::FoundationPoseNode', + parameters=[{ + 'mesh_file_path': mesh_file_path, + 'texture_path': texture_path, + + 'refine_model_file_path': refine_model_file_path, + 'refine_engine_file_path': refine_engine_file_path, + 'refine_input_tensor_names': ['input_tensor1', 'input_tensor2'], + 'refine_input_binding_names': ['input1', 'input2'], + 'refine_output_tensor_names': ['output_tensor1', 'output_tensor2'], + 'refine_output_binding_names': ['output1', 'output2'], + + 'score_model_file_path': score_model_file_path, + 'score_engine_file_path': score_engine_file_path, + 'score_input_tensor_names': ['input_tensor1', 'input_tensor2'], + 'score_input_binding_names': ['input1', 'input2'], + 'score_output_tensor_names': ['output_tensor'], + 'score_output_binding_names': ['output1'], + }]) + + foundationpose_tracking_node = ComposableNode( + name='foundationpose_tracking_node', + package='isaac_ros_foundationpose', + plugin='nvidia::isaac_ros::foundationpose::FoundationPoseTrackingNode', + parameters=[{ + 'mesh_file_path': mesh_file_path, + 'texture_path': texture_path, + + 'refine_model_file_path': refine_model_file_path, + 'refine_engine_file_path': refine_engine_file_path, + 'refine_input_tensor_names': ['input_tensor1', 'input_tensor2'], + 'refine_input_binding_names': ['input1', 'input2'], + 'refine_output_tensor_names': ['output_tensor1', 'output_tensor2'], + 'refine_output_binding_names': ['output1', 'output2'], + }]) + + resize_left_viz = ComposableNode( + name='resize_left_viz', + package='isaac_ros_image_proc', + plugin='nvidia::isaac_ros::image_proc::ResizeNode', + parameters=[{ + 'input_width': HAWK_IMAGE_WIDTH, + 'input_height': HAWK_IMAGE_HEIGHT, + 'output_width': int(HAWK_IMAGE_WIDTH/VISUALIZATION_DOWNSCALING_FACTOR), + 'output_height': int(HAWK_IMAGE_HEIGHT/VISUALIZATION_DOWNSCALING_FACTOR), + 'keep_aspect_ratio': False, + 'encoding_desired': 'rgb8', + 'disable_padding': False + }], + remappings=[ + ('image', 'rgb/image_rect_color'), + ('camera_info', 'rgb/camera_info'), + ('resize/image', 'rgb/image_rect_color_viz'), + ('resize/camera_info', 'rgb/camera_info_viz') + ] + ) + + rviz_node = Node( + package='rviz2', + executable='rviz2', + name='rviz2', + arguments=['-d', rviz_config_path], + condition=IfCondition(launch_rviz)) + + foundationpose_container = ComposableNodeContainer( + name=container_name, + namespace='', + package='rclcpp_components', + executable='component_container_mt', + composable_node_descriptions=[ + correlated_timestamp_driver_node, + hawk_node, + drop_node, + left_rectify_node, + right_rectify_node, + resize_left_rt_detr_node, + pad_node, + image_to_tensor_node, + interleave_to_planar_node, + reshape_node, + rtdetr_preprocessor_node, + tensor_rt_node, + rtdetr_decoder_node, + detection2_d_to_mask_node, + resize_mask_node, + resize_left_ess_size, + ess_node, + disparity_to_depth_node, + selector_node, + foundationpose_tracking_node, + foundationpose_node, + resize_left_viz + ], + output='screen' + ) + + return launch.LaunchDescription(launch_args + [foundationpose_container, + rviz_node]) diff --git a/isaac_ros_foundationpose/launch/isaac_ros_foundationpose_isaac_sim.launch.py b/isaac_ros_foundationpose/launch/isaac_ros_foundationpose_isaac_sim.launch.py new file mode 100644 index 0000000..85bb743 --- /dev/null +++ b/isaac_ros_foundationpose/launch/isaac_ros_foundationpose_isaac_sim.launch.py @@ -0,0 +1,393 @@ +# SPDX-FileCopyrightText: NVIDIA CORPORATION & AFFILIATES +# Copyright (c) 2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +# SPDX-License-Identifier: Apache-2.0 + +import os + +from ament_index_python.packages import get_package_share_directory +import launch +from launch.actions import DeclareLaunchArgument +from launch.conditions import IfCondition +from launch.substitutions import LaunchConfiguration +from launch_ros.actions import ComposableNodeContainer, Node +from launch_ros.descriptions import ComposableNode + + +RT_DETR_MODEL_INPUT_SIZE = 640 # RT-DETR models expect 640x640 encoded image size +RT_DETR_MODEL_NUM_CHANNELS = 3 # RT-DETR models expect 3 image channels + +ESS_MODEL_IMAGE_WIDTH = 480 +ESS_MODEL_IMAGE_HEIGHT = 288 + +SIM_IMAGE_WIDTH = 1920 +SIM_IMAGE_HEIGHT = 1200 + +SIM_TO_RT_DETR_RATIO = SIM_IMAGE_WIDTH / RT_DETR_MODEL_INPUT_SIZE + +ISAAC_ROS_ASSETS_PATH = os.path.join(os.environ['ISAAC_ROS_WS'], 'isaac_ros_assets') +ISAAC_ROS_MODELS_PATH = os.path.join(ISAAC_ROS_ASSETS_PATH, 'models') +ISAAC_ROS_FP_MESHES_PATH = os.path.join(ISAAC_ROS_ASSETS_PATH, + 'isaac_ros_foundationpose') +STEREO_DISPARITY_MODELS_PATH = os.path.join(ISAAC_ROS_MODELS_PATH, + 'dnn_stereo_disparity', 'dnn_stereo_disparity_v4.0.0') +SYNTHETICA_DETR_MODELS_PATH = os.path.join(ISAAC_ROS_MODELS_PATH, 'synthetica_detr') +FOUDNATIONPOSE_MODELS_PATH = os.path.join(ISAAC_ROS_MODELS_PATH, 'foundationpose') +REFINE_ENGINE_PATH = os.path.join(FOUDNATIONPOSE_MODELS_PATH, 'refine_trt_engine.plan') +SCORE_ENGINE_PATH = os.path.join(FOUDNATIONPOSE_MODELS_PATH, 'score_trt_engine.plan') +ESS_ENGINE_PATH = os.path.join(STEREO_DISPARITY_MODELS_PATH, 'light_ess.engine') +RTDETR_ENGINE_PATH = os.path.join(SYNTHETICA_DETR_MODELS_PATH, 'sdetr_grasp.plan') +MESH_OBJ_PATH = os.path.join(ISAAC_ROS_FP_MESHES_PATH, + 'Mac_and_cheese_0_1', 'Mac_and_cheese_0_1.obj') +MESH_TEX_PATH = os.path.join(ISAAC_ROS_FP_MESHES_PATH, + 'Mac_and_cheese_0_1', 'materials', 'textures', 'baked_mesh_tex0.png') + + +def generate_launch_description(): + """Generate launch description for testing relevant nodes.""" + rviz_config_path = os.path.join( + get_package_share_directory('isaac_ros_foundationpose'), + 'rviz', 'foundationpose_isaac_sim.rviz') + + launch_args = [ + DeclareLaunchArgument( + 'mesh_file_path', + default_value=MESH_OBJ_PATH, + description='The absolute file path to the mesh file'), + + DeclareLaunchArgument( + 'texture_path', + default_value=MESH_TEX_PATH, + description='The absolute file path to the texture map'), + + DeclareLaunchArgument( + 'refine_engine_file_path', + default_value=REFINE_ENGINE_PATH, + description='The absolute file path to the refine trt engine'), + + DeclareLaunchArgument( + 'score_engine_file_path', + default_value=SCORE_ENGINE_PATH, + description='The absolute file path to the score trt engine'), + + DeclareLaunchArgument( + 'rt_detr_engine_file_path', + default_value=RTDETR_ENGINE_PATH, + description='The absolute file path to the RT-DETR TensorRT engine file'), + + DeclareLaunchArgument( + 'ess_depth_engine_file_path', + default_value=ESS_ENGINE_PATH, + description='The absolute path to the ESS engine plan.'), + + DeclareLaunchArgument( + 'ess_depth_threshold', + default_value='0.35', + description='Threshold value ranges between 0.0 and 1.0 ' + 'for filtering disparity with confidence.'), + + DeclareLaunchArgument( + 'launch_rviz', + default_value='True', + description='Flag to enable Rviz2 launch'), + + DeclareLaunchArgument( + 'container_name', + default_value='foundationpose_container', + description='Name for ComposableNodeContainer'), + ] + + mesh_file_path = LaunchConfiguration('mesh_file_path') + texture_path = LaunchConfiguration('texture_path') + refine_engine_file_path = LaunchConfiguration('refine_engine_file_path') + score_engine_file_path = LaunchConfiguration('score_engine_file_path') + rt_detr_engine_file_path = LaunchConfiguration('rt_detr_engine_file_path') + ess_depth_engine_file_path = LaunchConfiguration('ess_depth_engine_file_path') + ess_depth_threshold = LaunchConfiguration('ess_depth_threshold') + launch_rviz = LaunchConfiguration('launch_rviz') + container_name = LaunchConfiguration('container_name') + + # Resize and pad Isaac Sim images to RT-DETR model input image size + resize_left_rt_detr_node = ComposableNode( + name='resize_left_rt_detr_node', + package='isaac_ros_image_proc', + plugin='nvidia::isaac_ros::image_proc::ResizeNode', + parameters=[{ + 'input_width': SIM_IMAGE_WIDTH, + 'input_height': SIM_IMAGE_HEIGHT, + 'output_width': RT_DETR_MODEL_INPUT_SIZE, + 'output_height': RT_DETR_MODEL_INPUT_SIZE, + 'keep_aspect_ratio': True, + 'encoding_desired': 'rgb8', + 'disable_padding': True + }], + remappings=[ + ('image', 'front_stereo_camera/left_rgb/image_raw'), + ('camera_info', 'front_stereo_camera/left_rgb/camerainfo') + ] + ) + pad_node = ComposableNode( + name='pad_node', + package='isaac_ros_image_proc', + plugin='nvidia::isaac_ros::image_proc::PadNode', + parameters=[{ + 'output_image_width': RT_DETR_MODEL_INPUT_SIZE, + 'output_image_height': RT_DETR_MODEL_INPUT_SIZE, + 'padding_type': 'BOTTOM_RIGHT' + }], + remappings=[( + 'image', 'resize/image' + )] + ) + + # Convert image to tensor and reshape + image_to_tensor_node = ComposableNode( + name='image_to_tensor_node', + package='isaac_ros_tensor_proc', + plugin='nvidia::isaac_ros::dnn_inference::ImageToTensorNode', + parameters=[{ + 'scale': False, + 'tensor_name': 'image', + }], + remappings=[ + ('image', 'padded_image'), + ('tensor', 'normalized_tensor'), + ] + ) + interleave_to_planar_node = ComposableNode( + name='interleaved_to_planar_node', + package='isaac_ros_tensor_proc', + plugin='nvidia::isaac_ros::dnn_inference::InterleavedToPlanarNode', + parameters=[{ + 'input_tensor_shape': [RT_DETR_MODEL_INPUT_SIZE, + RT_DETR_MODEL_INPUT_SIZE, + RT_DETR_MODEL_NUM_CHANNELS] + }], + remappings=[ + ('interleaved_tensor', 'normalized_tensor') + ] + ) + reshape_node = ComposableNode( + name='reshape_node', + package='isaac_ros_tensor_proc', + plugin='nvidia::isaac_ros::dnn_inference::ReshapeNode', + parameters=[{ + 'output_tensor_name': 'input_tensor', + 'input_tensor_shape': [RT_DETR_MODEL_NUM_CHANNELS, + RT_DETR_MODEL_INPUT_SIZE, + RT_DETR_MODEL_INPUT_SIZE], + 'output_tensor_shape': [1, RT_DETR_MODEL_NUM_CHANNELS, + RT_DETR_MODEL_INPUT_SIZE, + RT_DETR_MODEL_INPUT_SIZE] + }], + remappings=[ + ('tensor', 'planar_tensor') + ], + ) + rtdetr_preprocessor_node = ComposableNode( + name='rtdetr_preprocessor', + package='isaac_ros_rtdetr', + plugin='nvidia::isaac_ros::rtdetr::RtDetrPreprocessorNode', + remappings=[ + ('encoded_tensor', 'reshaped_tensor') + ] + ) + + # RT-DETR objection detection pipeline + tensor_rt_node = ComposableNode( + name='tensor_rt', + package='isaac_ros_tensor_rt', + plugin='nvidia::isaac_ros::dnn_inference::TensorRTNode', + parameters=[{ + 'engine_file_path': rt_detr_engine_file_path, + 'output_binding_names': ['labels', 'boxes', 'scores'], + 'output_tensor_names': ['labels', 'boxes', 'scores'], + 'input_tensor_names': ['images', 'orig_target_sizes'], + 'input_binding_names': ['images', 'orig_target_sizes'], + 'force_engine_update': False + }] + ) + rtdetr_decoder_node = ComposableNode( + name='rtdetr_decoder', + package='isaac_ros_rtdetr', + plugin='nvidia::isaac_ros::rtdetr::RtDetrDecoderNode', + ) + + # Convert Detection2DArray from RT-DETR to a binary segmentation mask + detection2_d_to_mask_node = ComposableNode( + name='detection2_d_to_mask', + package='isaac_ros_foundationpose', + plugin='nvidia::isaac_ros::foundationpose::Detection2DToMask', + parameters=[{ + 'mask_width': int(SIM_IMAGE_WIDTH/SIM_TO_RT_DETR_RATIO), + 'mask_height': int(SIM_IMAGE_HEIGHT/SIM_TO_RT_DETR_RATIO), + 'sub_detection2_d_array': True}], + remappings=[('detection2_d_array', 'detections_output'), + ('segmentation', 'rt_detr_segmentation')]) + + # Resize segmentation mask to ESS model image size so it can be used by FoundationPose + # FoundationPose requires depth, rgb image and segmentation mask to be of the same size + resize_mask_node = ComposableNode( + name='resize_mask_node', + package='isaac_ros_image_proc', + plugin='nvidia::isaac_ros::image_proc::ResizeNode', + parameters=[{ + 'input_width': int(SIM_IMAGE_WIDTH/SIM_TO_RT_DETR_RATIO), + 'input_height': int(SIM_IMAGE_HEIGHT/SIM_TO_RT_DETR_RATIO), + 'output_width': ESS_MODEL_IMAGE_WIDTH, + 'output_height': ESS_MODEL_IMAGE_HEIGHT, + 'keep_aspect_ratio': False, + 'disable_padding': False + }], + remappings=[ + ('image', 'rt_detr_segmentation'), + ('camera_info', 'resize/camera_info'), + ('resize/image', 'segmentation'), + ('resize/camera_info', 'camera_info_segmentation') + ] + ) + + # Resize Isaac Sim images to ESS model image size so it can be used by FoundationPose + # FoundationPose requires depth, rgb image and segmentation mask to be of the same size + # Note: The resized rgb camera model is named "camera_info" because that is the + # name expected by the Rviz2 + resize_left_ess_size = ComposableNode( + name='resize_left_ess_size', + package='isaac_ros_image_proc', + plugin='nvidia::isaac_ros::image_proc::ResizeNode', + parameters=[{ + 'input_width': SIM_IMAGE_WIDTH, + 'input_height': SIM_IMAGE_HEIGHT, + 'output_width': ESS_MODEL_IMAGE_WIDTH, + 'output_height': ESS_MODEL_IMAGE_HEIGHT, + 'keep_aspect_ratio': False, + 'encoding_desired': 'rgb8', + 'disable_padding': False + }], + remappings=[ + ('image', 'front_stereo_camera/left_rgb/image_raw'), + ('camera_info', 'front_stereo_camera/left_rgb/camerainfo'), + ('resize/image', 'front_stereo_camera/left_rgb/image_raw_resized'), + ('resize/camera_info', 'front_stereo_camera/left_rgb/camera_info') + ] + ) + resize_right_ess_size = ComposableNode( + name='resize_right_ess_size', + package='isaac_ros_image_proc', + plugin='nvidia::isaac_ros::image_proc::ResizeNode', + parameters=[{ + 'input_width': SIM_IMAGE_WIDTH, + 'input_height': SIM_IMAGE_HEIGHT, + 'output_width': ESS_MODEL_IMAGE_WIDTH, + 'output_height': ESS_MODEL_IMAGE_HEIGHT, + 'keep_aspect_ratio': False, + 'encoding_desired': 'rgb8', + 'disable_padding': False + }], + remappings=[ + ('image', 'front_stereo_camera/right_rgb/image_raw'), + ('camera_info', 'front_stereo_camera/right_rgb/camerainfo'), + ('resize/image', 'front_stereo_camera/right_rgb/image_raw_resized'), + ('resize/camera_info', 'front_stereo_camera/right_rgb/camera_info') + ] + ) + + # Launch ESS(depth estimation) + ess_node = ComposableNode( + name='ess_node', + package='isaac_ros_ess', + plugin='nvidia::isaac_ros::dnn_stereo_depth::ESSDisparityNode', + parameters=[{'engine_file_path': ess_depth_engine_file_path, + 'threshold': ess_depth_threshold + }], + remappings=[ + ('left/image_rect', 'front_stereo_camera/left_rgb/image_raw_resized'), + ('right/image_rect', 'front_stereo_camera/right_rgb/image_raw_resized'), + ('left/camera_info', 'front_stereo_camera/left_rgb/camera_info'), + ('right/camera_info', 'front_stereo_camera/right_rgb/camera_info') + ] + ) + disparity_to_depth_node = ComposableNode( + name='disparity_to_depth', + package='isaac_ros_stereo_image_proc', + plugin='nvidia::isaac_ros::stereo_image_proc::DisparityToDepthNode', + remappings=[( + 'depth', 'depth_image' + )] + ) + + # Launch isaac_ros_foundationpose + foundationpose_node = ComposableNode( + name='foundationpose_node', + package='isaac_ros_foundationpose', + plugin='nvidia::isaac_ros::foundationpose::FoundationPoseNode', + parameters=[{ + 'mesh_file_path': mesh_file_path, + 'texture_path': texture_path, + + 'refine_engine_file_path': refine_engine_file_path, + 'refine_input_tensor_names': ['input_tensor1', 'input_tensor2'], + 'refine_input_binding_names': ['input1', 'input2'], + 'refine_output_tensor_names': ['output_tensor1', 'output_tensor2'], + 'refine_output_binding_names': ['output1', 'output2'], + + 'score_engine_file_path': score_engine_file_path, + 'score_input_tensor_names': ['input_tensor1', 'input_tensor2'], + 'score_input_binding_names': ['input1', 'input2'], + 'score_output_tensor_names': ['output_tensor'], + 'score_output_binding_names': ['output1'], + }], + remappings=[ + ('pose_estimation/depth_image', 'depth_image'), + ('pose_estimation/image', 'front_stereo_camera/left_rgb/image_raw_resized'), + ('pose_estimation/camera_info', 'front_stereo_camera/left_rgb/camera_info'), + ('pose_estimation/segmentation', 'segmentation'), + ('pose_estimation/output', 'output')] + ) + + rviz_node = Node( + package='rviz2', + executable='rviz2', + name='rviz2', + arguments=['-d', rviz_config_path], + condition=IfCondition(launch_rviz)) + + foundationpose_container = ComposableNodeContainer( + name=container_name, + namespace='', + package='rclcpp_components', + executable='component_container_mt', + composable_node_descriptions=[ + resize_left_rt_detr_node, + pad_node, + image_to_tensor_node, + interleave_to_planar_node, + reshape_node, + rtdetr_preprocessor_node, + tensor_rt_node, + rtdetr_decoder_node, + detection2_d_to_mask_node, + resize_mask_node, + resize_left_ess_size, + resize_right_ess_size, + ess_node, + disparity_to_depth_node, + foundationpose_node + ], + output='screen' + ) + + return launch.LaunchDescription(launch_args + [foundationpose_container, + rviz_node]) diff --git a/isaac_ros_foundationpose/launch/isaac_ros_foundationpose_realsense.launch.py b/isaac_ros_foundationpose/launch/isaac_ros_foundationpose_realsense.launch.py new file mode 100644 index 0000000..b390a1d --- /dev/null +++ b/isaac_ros_foundationpose/launch/isaac_ros_foundationpose_realsense.launch.py @@ -0,0 +1,412 @@ +# SPDX-FileCopyrightText: NVIDIA CORPORATION & AFFILIATES +# Copyright (c) 2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +# SPDX-License-Identifier: Apache-2.0 + +import os + +from ament_index_python.packages import get_package_share_directory +import launch +from launch.actions import DeclareLaunchArgument +from launch.conditions import IfCondition +from launch.substitutions import LaunchConfiguration +from launch_ros.actions import ComposableNodeContainer, Node +from launch_ros.descriptions import ComposableNode + +# Number of Realsense messages to be dropped in 1 second +HAWK_EXPECT_FREQ = 28 +# Expected number of Realsense messages in 1 second +INPUT_IMAGES_DROP_FREQ = 30 + +RT_DETR_MODEL_INPUT_SIZE = 640 # RT-DETR models expect 640x640 encoded image size +RT_DETR_MODEL_NUM_CHANNELS = 3 # RT-DETR models expect 3 image channels + +REALSENSE_IMAGE_WIDTH = 1280 +REALSENSE_IMAGE_HEIGHT = 720 + +VISUALIZATION_DOWNSCALING_FACTOR = 10 + +REALSENSE_TO_RT_DETR_RATIO = REALSENSE_IMAGE_WIDTH / RT_DETR_MODEL_INPUT_SIZE + +REFINE_MODEL_PATH = '/tmp/refine_model.onnx' +REFINE_ENGINE_PATH = '/tmp/refine_trt_engine.plan' +SCORE_MODEL_PATH = '/tmp/score_model.onnx' +SCORE_ENGINE_PATH = '/tmp/score_trt_engine.plan' + + +def generate_launch_description(): + """Generate launch description for testing relevant nodes.""" + rviz_config_path = os.path.join( + get_package_share_directory('isaac_ros_foundationpose'), + 'rviz', 'foundationpose_realsense.rviz') + + launch_args = [ + DeclareLaunchArgument( + 'hawk_expect_freq', + default_value=str(HAWK_EXPECT_FREQ), + description='Number of Realsense messages to be dropped in 1 second'), + + DeclareLaunchArgument( + 'input_images_drop_freq', + default_value=str(INPUT_IMAGES_DROP_FREQ), + description='Expected number of Realsense messages in 1 second'), + + DeclareLaunchArgument( + 'mesh_file_path', + default_value='', + description='The absolute file path to the mesh file'), + + DeclareLaunchArgument( + 'texture_path', + default_value='', + description='The absolute file path to the texture map'), + + DeclareLaunchArgument( + 'refine_model_file_path', + default_value=REFINE_MODEL_PATH, + description='The absolute file path to the refine model'), + + DeclareLaunchArgument( + 'refine_engine_file_path', + default_value=REFINE_ENGINE_PATH, + description='The absolute file path to the refine trt engine'), + + DeclareLaunchArgument( + 'score_model_file_path', + default_value=SCORE_MODEL_PATH, + description='The absolute file path to the score model'), + + DeclareLaunchArgument( + 'score_engine_file_path', + default_value=SCORE_ENGINE_PATH, + description='The absolute file path to the score trt engine'), + + DeclareLaunchArgument( + 'rt_detr_model_file_path', + default_value='', + description='The absolute file path to the RT-DETR ONNX file'), + + DeclareLaunchArgument( + 'rt_detr_engine_file_path', + default_value='', + description='The absolute file path to the RT-DETR TensorRT engine file'), + + DeclareLaunchArgument( + 'launch_rviz', + default_value='False', + description='Flag to enable Rviz2 launch'), + + DeclareLaunchArgument( + 'container_name', + default_value='foundationpose_container', + description='Name for ComposableNodeContainer'), + ] + + hawk_expect_freq = LaunchConfiguration('hawk_expect_freq') + input_images_drop_freq = LaunchConfiguration('input_images_drop_freq') + mesh_file_path = LaunchConfiguration('mesh_file_path') + texture_path = LaunchConfiguration('texture_path') + refine_model_file_path = LaunchConfiguration('refine_model_file_path') + refine_engine_file_path = LaunchConfiguration('refine_engine_file_path') + score_model_file_path = LaunchConfiguration('score_model_file_path') + score_engine_file_path = LaunchConfiguration('score_engine_file_path') + rt_detr_model_file_path = LaunchConfiguration('rt_detr_model_file_path') + rt_detr_engine_file_path = LaunchConfiguration('rt_detr_engine_file_path') + launch_rviz = LaunchConfiguration('launch_rviz') + container_name = LaunchConfiguration('container_name') + + # RealSense + realsense_config_file_path = os.path.join( + get_package_share_directory('isaac_ros_foundationpose'), + 'config', 'realsense.yaml' + ) + + realsense_node = ComposableNode( + package='realsense2_camera', + plugin='realsense2_camera::RealSenseNodeFactory', + parameters=[realsense_config_file_path] + ) + + # Drops hawk_expect_freq out of input_images_drop_freq RealSense messages + drop_node = ComposableNode( + name='drop_node', + package='isaac_ros_nitros_topic_tools', + plugin='nvidia::isaac_ros::nitros::NitrosCameraDropNode', + parameters=[{ + 'X': hawk_expect_freq, + 'Y': input_images_drop_freq, + 'mode': 'mono+depth', + 'depth_format_string': 'nitros_image_mono16' + }], + remappings=[ + ('image_1', '/color/image_raw'), + ('camera_info_1', '/color/camera_info'), + ('depth_1', '/aligned_depth_to_color/image_raw'), + ('image_1_drop', 'rgb/image_rect_color'), + ('camera_info_1_drop', 'rgb/camera_info'), + ('depth_1_drop', 'depth_uint16'), + ] + ) + + # Realsense depth is in uint16 and millimeters. Convert to float32 and meters + convert_metric_node = ComposableNode( + package='isaac_ros_depth_image_proc', + plugin='nvidia::isaac_ros::depth_image_proc::ConvertMetricNode', + remappings=[ + ('image_raw', 'depth_uint16'), + ('image', 'depth_image') + ] + ) + + # Resize and pad RealSense images to RT-DETR model input image size + # Resize from REALSENSE_IMAGE_WIDTH x REALSENSE_IMAGE_HEIGHT to + # REALSENSE_IMAGE_WIDTH/REALSENSE_TO_RT_DETR_RATIO x + # REALSENSE_IMAGE_HEIGHT/REALSENSE_TO_RT_DETR_RATIO + # output height constraint is not used since keep_aspect_ratio is True + resize_left_rt_detr_node = ComposableNode( + name='resize_left_rt_detr_node', + package='isaac_ros_image_proc', + plugin='nvidia::isaac_ros::image_proc::ResizeNode', + parameters=[{ + 'input_width': REALSENSE_IMAGE_WIDTH, + 'input_height': REALSENSE_IMAGE_HEIGHT, + 'output_width': RT_DETR_MODEL_INPUT_SIZE, + 'output_height': RT_DETR_MODEL_INPUT_SIZE, + 'keep_aspect_ratio': True, + 'encoding_desired': 'rgb8', + 'disable_padding': True + }], + remappings=[ + ('image', 'rgb/image_rect_color'), + ('camera_info', 'rgb/camera_info'), + ('resize/image', 'color_image_resized'), + ('resize/camera_info', 'camera_info_resized') + ] + ) + # Pad the image from REALSENSE_IMAGE_WIDTH/REALSENSE_TO_RT_DETR_RATIO x + # REALSENSE_IMAGE_HEIGHT/REALSENSE_TO_RT_DETR_RATIO + # to RT_DETR_MODEL_INPUT_WIDTH x RT_DETR_MODEL_INPUT_HEIGHT + pad_node = ComposableNode( + name='pad_node', + package='isaac_ros_image_proc', + plugin='nvidia::isaac_ros::image_proc::PadNode', + parameters=[{ + 'output_image_width': RT_DETR_MODEL_INPUT_SIZE, + 'output_image_height': RT_DETR_MODEL_INPUT_SIZE, + 'padding_type': 'BOTTOM_RIGHT' + }], + remappings=[( + 'image', 'color_image_resized' + )] + ) + + # Convert image to tensor and reshape + image_to_tensor_node = ComposableNode( + name='image_to_tensor_node', + package='isaac_ros_tensor_proc', + plugin='nvidia::isaac_ros::dnn_inference::ImageToTensorNode', + parameters=[{ + 'scale': False, + 'tensor_name': 'image', + }], + remappings=[ + ('image', 'padded_image'), + ('tensor', 'normalized_tensor'), + ] + ) + interleave_to_planar_node = ComposableNode( + name='interleaved_to_planar_node', + package='isaac_ros_tensor_proc', + plugin='nvidia::isaac_ros::dnn_inference::InterleavedToPlanarNode', + parameters=[{ + 'input_tensor_shape': [RT_DETR_MODEL_INPUT_SIZE, + RT_DETR_MODEL_INPUT_SIZE, + RT_DETR_MODEL_NUM_CHANNELS] + }], + remappings=[ + ('interleaved_tensor', 'normalized_tensor') + ] + ) + reshape_node = ComposableNode( + name='reshape_node', + package='isaac_ros_tensor_proc', + plugin='nvidia::isaac_ros::dnn_inference::ReshapeNode', + parameters=[{ + 'output_tensor_name': 'input_tensor', + 'input_tensor_shape': [RT_DETR_MODEL_NUM_CHANNELS, + RT_DETR_MODEL_INPUT_SIZE, + RT_DETR_MODEL_INPUT_SIZE], + 'output_tensor_shape': [1, RT_DETR_MODEL_NUM_CHANNELS, + RT_DETR_MODEL_INPUT_SIZE, + RT_DETR_MODEL_INPUT_SIZE] + }], + remappings=[ + ('tensor', 'planar_tensor') + ], + ) + rtdetr_preprocessor_node = ComposableNode( + name='rtdetr_preprocessor', + package='isaac_ros_rtdetr', + plugin='nvidia::isaac_ros::rtdetr::RtDetrPreprocessorNode', + remappings=[ + ('encoded_tensor', 'reshaped_tensor') + ] + ) + + # RT-DETR objection detection pipeline + tensor_rt_node = ComposableNode( + name='tensor_rt', + package='isaac_ros_tensor_rt', + plugin='nvidia::isaac_ros::dnn_inference::TensorRTNode', + parameters=[{ + 'model_file_path': rt_detr_model_file_path, + 'engine_file_path': rt_detr_engine_file_path, + 'output_binding_names': ['labels', 'boxes', 'scores'], + 'output_tensor_names': ['labels', 'boxes', 'scores'], + 'input_tensor_names': ['images', 'orig_target_sizes'], + 'input_binding_names': ['images', 'orig_target_sizes'], + 'force_engine_update': False + }] + ) + rtdetr_decoder_node = ComposableNode( + name='rtdetr_decoder', + package='isaac_ros_rtdetr', + plugin='nvidia::isaac_ros::rtdetr::RtDetrDecoderNode', + ) + + # Create a binary segmentation mask from a Detection2DArray published by RT-DETR. + # The segmentation mask is of size int(REALSENSE_IMAGE_WIDTH/REALSENSE_TO_RT_DETR_RATIO) x + # int(REALSENSE_IMAGE_HEIGHT/REALSENSE_TO_RT_DETR_RATIO) + detection2_d_to_mask_node = ComposableNode( + name='detection2_d_to_mask', + package='isaac_ros_foundationpose', + plugin='nvidia::isaac_ros::foundationpose::Detection2DToMask', + parameters=[{ + 'mask_width': int(REALSENSE_IMAGE_WIDTH/REALSENSE_TO_RT_DETR_RATIO), + 'mask_height': int(REALSENSE_IMAGE_HEIGHT/REALSENSE_TO_RT_DETR_RATIO)}], + remappings=[('detection2_d_array', 'detections_output'), + ('segmentation', 'rt_detr_segmentation')]) + + # Resize segmentation mask to ESS model image size so it can be used by FoundationPose + # FoundationPose requires depth, rgb image and segmentation mask to be of the same size + # Resize from int(REALSENSE_IMAGE_WIDTH/REALSENSE_TO_RT_DETR_RATIO) x + # int(REALSENSE_IMAGE_HEIGHT/REALSENSE_TO_RT_DETR_RATIO) to + # ESS_MODEL_IMAGE_WIDTH x ESS_MODEL_IMAGE_HEIGHT + # output height constraint is used since keep_aspect_ratio is False + # and the image is padded + resize_mask_node = ComposableNode( + name='resize_mask_node', + package='isaac_ros_image_proc', + plugin='nvidia::isaac_ros::image_proc::ResizeNode', + parameters=[{ + 'input_width': int(REALSENSE_IMAGE_WIDTH/REALSENSE_TO_RT_DETR_RATIO), + 'input_height': int(REALSENSE_IMAGE_HEIGHT/REALSENSE_TO_RT_DETR_RATIO), + 'output_width': REALSENSE_IMAGE_WIDTH, + 'output_height': REALSENSE_IMAGE_HEIGHT, + 'keep_aspect_ratio': False, + 'disable_padding': False + }], + remappings=[ + ('image', 'rt_detr_segmentation'), + ('camera_info', 'camera_info_resized'), + ('resize/image', 'segmentation'), + ('resize/camera_info', 'camera_info_segmentation') + ] + ) + + resize_left_viz = ComposableNode( + name='resize_left_viz', + package='isaac_ros_image_proc', + plugin='nvidia::isaac_ros::image_proc::ResizeNode', + parameters=[{ + 'input_width': REALSENSE_IMAGE_WIDTH, + 'input_height': REALSENSE_IMAGE_HEIGHT, + 'output_width': int(REALSENSE_IMAGE_WIDTH/VISUALIZATION_DOWNSCALING_FACTOR), + 'output_height': int(REALSENSE_IMAGE_HEIGHT/VISUALIZATION_DOWNSCALING_FACTOR), + 'keep_aspect_ratio': False, + 'encoding_desired': 'rgb8', + 'disable_padding': False + }], + remappings=[ + ('image', 'rgb/image_rect_color'), + ('camera_info', 'rgb/camera_info'), + ('resize/image', 'rgb/image_rect_color_viz'), + ('resize/camera_info', 'rgb/camera_info_viz') + ] + ) + + foundationpose_node = ComposableNode( + name='foundationpose_node', + package='isaac_ros_foundationpose', + plugin='nvidia::isaac_ros::foundationpose::FoundationPoseNode', + parameters=[{ + 'mesh_file_path': mesh_file_path, + 'texture_path': texture_path, + + 'refine_model_file_path': refine_model_file_path, + 'refine_engine_file_path': refine_engine_file_path, + 'refine_input_tensor_names': ['input_tensor1', 'input_tensor2'], + 'refine_input_binding_names': ['input1', 'input2'], + 'refine_output_tensor_names': ['output_tensor1', 'output_tensor2'], + 'refine_output_binding_names': ['output1', 'output2'], + + 'score_model_file_path': score_model_file_path, + 'score_engine_file_path': score_engine_file_path, + 'score_input_tensor_names': ['input_tensor1', 'input_tensor2'], + 'score_input_binding_names': ['input1', 'input2'], + 'score_output_tensor_names': ['output_tensor'], + 'score_output_binding_names': ['output1'], + }], + remappings=[ + ('pose_estimation/depth_image', 'depth_image'), + ('pose_estimation/image', 'rgb/image_rect_color'), + ('pose_estimation/camera_info', 'rgb/camera_info'), + ('pose_estimation/segmentation', 'segmentation'), + ('pose_estimation/output', 'output')]) + + rviz_node = Node( + package='rviz2', + executable='rviz2', + name='rviz2', + arguments=['-d', rviz_config_path], + condition=IfCondition(launch_rviz)) + + foundationpose_container = ComposableNodeContainer( + name=container_name, + namespace='', + package='rclcpp_components', + executable='component_container_mt', + composable_node_descriptions=[ + realsense_node, + drop_node, + convert_metric_node, + resize_left_rt_detr_node, + pad_node, + image_to_tensor_node, + interleave_to_planar_node, + reshape_node, + rtdetr_preprocessor_node, + tensor_rt_node, + rtdetr_decoder_node, + detection2_d_to_mask_node, + resize_mask_node, + foundationpose_node, + resize_left_viz + ], + output='screen' + ) + + return launch.LaunchDescription(launch_args + [foundationpose_container, + rviz_node]) diff --git a/isaac_ros_foundationpose/launch/isaac_ros_foundationpose_realsense_tracking.launch.py b/isaac_ros_foundationpose/launch/isaac_ros_foundationpose_realsense_tracking.launch.py new file mode 100644 index 0000000..6ce4a0b --- /dev/null +++ b/isaac_ros_foundationpose/launch/isaac_ros_foundationpose_realsense_tracking.launch.py @@ -0,0 +1,438 @@ +# SPDX-FileCopyrightText: NVIDIA CORPORATION & AFFILIATES +# Copyright (c) 2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +# SPDX-License-Identifier: Apache-2.0 + +import os + +from ament_index_python.packages import get_package_share_directory +import launch +from launch.actions import DeclareLaunchArgument +from launch.conditions import IfCondition +from launch.substitutions import LaunchConfiguration +from launch_ros.actions import ComposableNodeContainer, Node +from launch_ros.descriptions import ComposableNode + +# Number of Realsense messages to be dropped in 1 second +HAWK_EXPECT_FREQ = 6 +# Expected number of Realsense messages in 1 second +INPUT_IMAGES_DROP_FREQ = 30 + +RT_DETR_MODEL_INPUT_SIZE = 640 # RT-DETR models expect 640x640 encoded image size +RT_DETR_MODEL_NUM_CHANNELS = 3 # RT-DETR models expect 3 image channels + +REALSENSE_IMAGE_WIDTH = 1280 +REALSENSE_IMAGE_HEIGHT = 720 + +VISUALIZATION_DOWNSCALING_FACTOR = 10 + +REALSENSE_TO_RT_DETR_RATIO = REALSENSE_IMAGE_WIDTH / RT_DETR_MODEL_INPUT_SIZE + +REFINE_MODEL_PATH = '/tmp/refine_model.onnx' +REFINE_ENGINE_PATH = '/tmp/refine_trt_engine.plan' +SCORE_MODEL_PATH = '/tmp/score_model.onnx' +SCORE_ENGINE_PATH = '/tmp/score_trt_engine.plan' + + +def generate_launch_description(): + """Generate launch description for testing relevant nodes.""" + rviz_config_path = os.path.join( + get_package_share_directory('isaac_ros_foundationpose'), + 'rviz', 'foundationpose_realsense.rviz') + + launch_args = [ + DeclareLaunchArgument( + 'hawk_expect_freq', + default_value=str(HAWK_EXPECT_FREQ), + description='Number of Realsense messages to be dropped in 1 second'), + + DeclareLaunchArgument( + 'input_images_drop_freq', + default_value=str(INPUT_IMAGES_DROP_FREQ), + description='Expected number of Realsense messages in 1 second'), + + DeclareLaunchArgument( + 'mesh_file_path', + default_value='', + description='The absolute file path to the mesh file'), + + DeclareLaunchArgument( + 'texture_path', + default_value='', + description='The absolute file path to the texture map'), + + DeclareLaunchArgument( + 'refine_model_file_path', + default_value=REFINE_MODEL_PATH, + description='The absolute file path to the refine model'), + + DeclareLaunchArgument( + 'refine_engine_file_path', + default_value=REFINE_ENGINE_PATH, + description='The absolute file path to the refine trt engine'), + + DeclareLaunchArgument( + 'score_model_file_path', + default_value=SCORE_MODEL_PATH, + description='The absolute file path to the score model'), + + DeclareLaunchArgument( + 'score_engine_file_path', + default_value=SCORE_ENGINE_PATH, + description='The absolute file path to the score trt engine'), + + DeclareLaunchArgument( + 'rt_detr_model_file_path', + default_value='', + description='The absolute file path to the RT-DETR ONNX file'), + + DeclareLaunchArgument( + 'rt_detr_engine_file_path', + default_value='', + description='The absolute file path to the RT-DETR TensorRT engine file'), + + DeclareLaunchArgument( + 'launch_rviz', + default_value='False', + description='Flag to enable Rviz2 launch'), + + DeclareLaunchArgument( + 'container_name', + default_value='foundationpose_container', + description='Name for ComposableNodeContainer'), + ] + + hawk_expect_freq = LaunchConfiguration('hawk_expect_freq') + input_images_drop_freq = LaunchConfiguration('input_images_drop_freq') + mesh_file_path = LaunchConfiguration('mesh_file_path') + texture_path = LaunchConfiguration('texture_path') + refine_model_file_path = LaunchConfiguration('refine_model_file_path') + refine_engine_file_path = LaunchConfiguration('refine_engine_file_path') + score_model_file_path = LaunchConfiguration('score_model_file_path') + score_engine_file_path = LaunchConfiguration('score_engine_file_path') + rt_detr_model_file_path = LaunchConfiguration('rt_detr_model_file_path') + rt_detr_engine_file_path = LaunchConfiguration('rt_detr_engine_file_path') + launch_rviz = LaunchConfiguration('launch_rviz') + container_name = LaunchConfiguration('container_name') + + # RealSense + realsense_config_file_path = os.path.join( + get_package_share_directory('isaac_ros_foundationpose'), + 'config', 'realsense.yaml' + ) + + realsense_node = ComposableNode( + package='realsense2_camera', + plugin='realsense2_camera::RealSenseNodeFactory', + parameters=[realsense_config_file_path] + ) + + # Drops hawk_expect_freq out of input_images_drop_freq RealSense messages + drop_node = ComposableNode( + name='drop_node', + package='isaac_ros_nitros_topic_tools', + plugin='nvidia::isaac_ros::nitros::NitrosCameraDropNode', + parameters=[{ + 'X': hawk_expect_freq, + 'Y': input_images_drop_freq, + 'mode': 'mono+depth', + 'depth_format_string': 'nitros_image_mono16' + }], + remappings=[ + ('image_1', '/color/image_raw'), + ('camera_info_1', '/color/camera_info'), + ('depth_1', '/aligned_depth_to_color/image_raw'), + ('image_1_drop', 'rgb/image_rect_color'), + ('camera_info_1_drop', 'rgb/camera_info'), + ('depth_1_drop', 'depth_uint16'), + ] + ) + + # Realsense depth is in uint16 and millimeters. Convert to float32 and meters + convert_metric_node = ComposableNode( + package='isaac_ros_depth_image_proc', + plugin='nvidia::isaac_ros::depth_image_proc::ConvertMetricNode', + remappings=[ + ('image_raw', 'depth_uint16'), + ('image', 'depth_image') + ] + ) + + # Resize and pad RealSense images to RT-DETR model input image size + # Resize from REALSENSE_IMAGE_WIDTH x REALSENSE_IMAGE_HEIGHT to + # REALSENSE_IMAGE_WIDTH/REALSENSE_TO_RT_DETR_RATIO x + # REALSENSE_IMAGE_HEIGHT/REALSENSE_TO_RT_DETR_RATIO + # output height constraint is not used since keep_aspect_ratio is True + resize_left_rt_detr_node = ComposableNode( + name='resize_left_rt_detr_node', + package='isaac_ros_image_proc', + plugin='nvidia::isaac_ros::image_proc::ResizeNode', + parameters=[{ + 'input_width': REALSENSE_IMAGE_WIDTH, + 'input_height': REALSENSE_IMAGE_HEIGHT, + 'output_width': RT_DETR_MODEL_INPUT_SIZE, + 'output_height': RT_DETR_MODEL_INPUT_SIZE, + 'keep_aspect_ratio': True, + 'encoding_desired': 'rgb8', + 'disable_padding': True + }], + remappings=[ + ('image', 'rgb/image_rect_color'), + ('camera_info', 'rgb/camera_info'), + ('resize/image', 'color_image_resized'), + ('resize/camera_info', 'camera_info_resized') + ] + ) + # Pad the image from REALSENSE_IMAGE_WIDTH/REALSENSE_TO_RT_DETR_RATIO x + # REALSENSE_IMAGE_HEIGHT/REALSENSE_TO_RT_DETR_RATIO + # to RT_DETR_MODEL_INPUT_WIDTH x RT_DETR_MODEL_INPUT_HEIGHT + pad_node = ComposableNode( + name='pad_node', + package='isaac_ros_image_proc', + plugin='nvidia::isaac_ros::image_proc::PadNode', + parameters=[{ + 'output_image_width': RT_DETR_MODEL_INPUT_SIZE, + 'output_image_height': RT_DETR_MODEL_INPUT_SIZE, + 'padding_type': 'BOTTOM_RIGHT' + }], + remappings=[( + 'image', 'color_image_resized' + )] + ) + + # Convert image to tensor and reshape + image_to_tensor_node = ComposableNode( + name='image_to_tensor_node', + package='isaac_ros_tensor_proc', + plugin='nvidia::isaac_ros::dnn_inference::ImageToTensorNode', + parameters=[{ + 'scale': False, + 'tensor_name': 'image', + }], + remappings=[ + ('image', 'padded_image'), + ('tensor', 'normalized_tensor'), + ] + ) + interleave_to_planar_node = ComposableNode( + name='interleaved_to_planar_node', + package='isaac_ros_tensor_proc', + plugin='nvidia::isaac_ros::dnn_inference::InterleavedToPlanarNode', + parameters=[{ + 'input_tensor_shape': [RT_DETR_MODEL_INPUT_SIZE, + RT_DETR_MODEL_INPUT_SIZE, + RT_DETR_MODEL_NUM_CHANNELS] + }], + remappings=[ + ('interleaved_tensor', 'normalized_tensor') + ] + ) + reshape_node = ComposableNode( + name='reshape_node', + package='isaac_ros_tensor_proc', + plugin='nvidia::isaac_ros::dnn_inference::ReshapeNode', + parameters=[{ + 'output_tensor_name': 'input_tensor', + 'input_tensor_shape': [RT_DETR_MODEL_NUM_CHANNELS, + RT_DETR_MODEL_INPUT_SIZE, + RT_DETR_MODEL_INPUT_SIZE], + 'output_tensor_shape': [1, RT_DETR_MODEL_NUM_CHANNELS, + RT_DETR_MODEL_INPUT_SIZE, + RT_DETR_MODEL_INPUT_SIZE] + }], + remappings=[ + ('tensor', 'planar_tensor') + ], + ) + rtdetr_preprocessor_node = ComposableNode( + name='rtdetr_preprocessor', + package='isaac_ros_rtdetr', + plugin='nvidia::isaac_ros::rtdetr::RtDetrPreprocessorNode', + remappings=[ + ('encoded_tensor', 'reshaped_tensor') + ] + ) + + # RT-DETR objection detection pipeline + tensor_rt_node = ComposableNode( + name='tensor_rt', + package='isaac_ros_tensor_rt', + plugin='nvidia::isaac_ros::dnn_inference::TensorRTNode', + parameters=[{ + 'model_file_path': rt_detr_model_file_path, + 'engine_file_path': rt_detr_engine_file_path, + 'output_binding_names': ['labels', 'boxes', 'scores'], + 'output_tensor_names': ['labels', 'boxes', 'scores'], + 'input_tensor_names': ['images', 'orig_target_sizes'], + 'input_binding_names': ['images', 'orig_target_sizes'], + 'force_engine_update': False + }] + ) + rtdetr_decoder_node = ComposableNode( + name='rtdetr_decoder', + package='isaac_ros_rtdetr', + plugin='nvidia::isaac_ros::rtdetr::RtDetrDecoderNode', + ) + + # Create a binary segmentation mask from a Detection2DArray published by RT-DETR. + # The segmentation mask is of size int(REALSENSE_IMAGE_WIDTH/REALSENSE_TO_RT_DETR_RATIO) x + # int(REALSENSE_IMAGE_HEIGHT/REALSENSE_TO_RT_DETR_RATIO) + detection2_d_to_mask_node = ComposableNode( + name='detection2_d_to_mask', + package='isaac_ros_foundationpose', + plugin='nvidia::isaac_ros::foundationpose::Detection2DToMask', + parameters=[{ + 'mask_width': int(REALSENSE_IMAGE_WIDTH/REALSENSE_TO_RT_DETR_RATIO), + 'mask_height': int(REALSENSE_IMAGE_HEIGHT/REALSENSE_TO_RT_DETR_RATIO)}], + remappings=[('detection2_d_array', 'detections_output'), + ('segmentation', 'rt_detr_segmentation')]) + + # Resize segmentation mask to ESS model image size so it can be used by FoundationPose + # FoundationPose requires depth, rgb image and segmentation mask to be of the same size + # Resize from int(REALSENSE_IMAGE_WIDTH/REALSENSE_TO_RT_DETR_RATIO) x + # int(REALSENSE_IMAGE_HEIGHT/REALSENSE_TO_RT_DETR_RATIO) to + # ESS_MODEL_IMAGE_WIDTH x ESS_MODEL_IMAGE_HEIGHT + # output height constraint is used since keep_aspect_ratio is False + # and the image is padded + resize_mask_node = ComposableNode( + name='resize_mask_node', + package='isaac_ros_image_proc', + plugin='nvidia::isaac_ros::image_proc::ResizeNode', + parameters=[{ + 'input_width': int(REALSENSE_IMAGE_WIDTH/REALSENSE_TO_RT_DETR_RATIO), + 'input_height': int(REALSENSE_IMAGE_HEIGHT/REALSENSE_TO_RT_DETR_RATIO), + 'output_width': REALSENSE_IMAGE_WIDTH, + 'output_height': REALSENSE_IMAGE_HEIGHT, + 'keep_aspect_ratio': False, + 'disable_padding': False + }], + remappings=[ + ('image', 'rt_detr_segmentation'), + ('camera_info', 'camera_info_resized'), + ('resize/image', 'segmentation'), + ('resize/camera_info', 'camera_info_segmentation') + ] + ) + + resize_left_viz = ComposableNode( + name='resize_left_viz', + package='isaac_ros_image_proc', + plugin='nvidia::isaac_ros::image_proc::ResizeNode', + parameters=[{ + 'input_width': REALSENSE_IMAGE_WIDTH, + 'input_height': REALSENSE_IMAGE_HEIGHT, + 'output_width': int(REALSENSE_IMAGE_WIDTH/VISUALIZATION_DOWNSCALING_FACTOR), + 'output_height': int(REALSENSE_IMAGE_HEIGHT/VISUALIZATION_DOWNSCALING_FACTOR), + 'keep_aspect_ratio': False, + 'encoding_desired': 'rgb8', + 'disable_padding': False + }], + remappings=[ + ('image', 'rgb/image_rect_color'), + ('camera_info', 'rgb/camera_info'), + ('resize/image', 'rgb/image_rect_color_viz'), + ('resize/camera_info', 'rgb/camera_info_viz') + ] + ) + + # Launch isaac_ros_foundationpose + selector_node = ComposableNode( + name='selector_node', + package='isaac_ros_foundationpose', + plugin='nvidia::isaac_ros::foundationpose::Selector', + parameters=[{ + 'reset_period': 10000 + }], + remappings=[ + ('depth_image', 'depth_image'), + ('image', 'rgb/image_rect_color'), + ('camera_info', 'rgb/camera_info'), + ('segmentation', 'segmentation')]) + + foundationpose_node = ComposableNode( + name='foundationpose_node', + package='isaac_ros_foundationpose', + plugin='nvidia::isaac_ros::foundationpose::FoundationPoseNode', + parameters=[{ + 'mesh_file_path': mesh_file_path, + 'texture_path': texture_path, + + 'refine_model_file_path': refine_model_file_path, + 'refine_engine_file_path': refine_engine_file_path, + 'refine_input_tensor_names': ['input_tensor1', 'input_tensor2'], + 'refine_input_binding_names': ['input1', 'input2'], + 'refine_output_tensor_names': ['output_tensor1', 'output_tensor2'], + 'refine_output_binding_names': ['output1', 'output2'], + + 'score_model_file_path': score_model_file_path, + 'score_engine_file_path': score_engine_file_path, + 'score_input_tensor_names': ['input_tensor1', 'input_tensor2'], + 'score_input_binding_names': ['input1', 'input2'], + 'score_output_tensor_names': ['output_tensor'], + 'score_output_binding_names': ['output1'], + }]) + + foundationpose_tracking_node = ComposableNode( + name='foundationpose_tracking_node', + package='isaac_ros_foundationpose', + plugin='nvidia::isaac_ros::foundationpose::FoundationPoseTrackingNode', + parameters=[{ + 'mesh_file_path': mesh_file_path, + 'texture_path': texture_path, + + 'refine_model_file_path': refine_model_file_path, + 'refine_engine_file_path': refine_engine_file_path, + 'refine_input_tensor_names': ['input_tensor1', 'input_tensor2'], + 'refine_input_binding_names': ['input1', 'input2'], + 'refine_output_tensor_names': ['output_tensor1', 'output_tensor2'], + 'refine_output_binding_names': ['output1', 'output2'], + }]) + + rviz_node = Node( + package='rviz2', + executable='rviz2', + name='rviz2', + arguments=['-d', rviz_config_path], + condition=IfCondition(launch_rviz)) + + foundationpose_container = ComposableNodeContainer( + name=container_name, + namespace='', + package='rclcpp_components', + executable='component_container_mt', + composable_node_descriptions=[ + realsense_node, + drop_node, + convert_metric_node, + resize_left_rt_detr_node, + pad_node, + image_to_tensor_node, + interleave_to_planar_node, + reshape_node, + rtdetr_preprocessor_node, + tensor_rt_node, + rtdetr_decoder_node, + detection2_d_to_mask_node, + resize_mask_node, + selector_node, + foundationpose_node, + foundationpose_tracking_node, + resize_left_viz + ], + output='screen' + ) + + return launch.LaunchDescription(launch_args + [foundationpose_container, + rviz_node]) diff --git a/isaac_ros_foundationpose/launch/isaac_ros_foundationpose_tracking.launch.py b/isaac_ros_foundationpose/launch/isaac_ros_foundationpose_tracking.launch.py new file mode 100644 index 0000000..33098a6 --- /dev/null +++ b/isaac_ros_foundationpose/launch/isaac_ros_foundationpose_tracking.launch.py @@ -0,0 +1,160 @@ +# SPDX-FileCopyrightText: NVIDIA CORPORATION & AFFILIATES +# Copyright (c) 2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +# SPDX-License-Identifier: Apache-2.0 + +import os + +from ament_index_python.packages import get_package_share_directory +import launch +from launch.actions import DeclareLaunchArgument +from launch.conditions import IfCondition +from launch.substitutions import LaunchConfiguration +from launch_ros.actions import ComposableNodeContainer, Node +from launch_ros.descriptions import ComposableNode + +MESH_FILE_NAME = '/tmp/textured_simple.obj' +TEXTURE_MAP_NAME = '/tmp/texture_map.png' +REFINE_MODEL_NAME = '/tmp/refine_model.onnx' +REFINE_ENGINE_NAME = '/tmp/refine_trt_engine.plan' + +SCORE_MODEL_NAME = '/tmp/score_model.onnx' +SCORE_ENGINE_NAME = '/tmp/score_trt_engine.plan' + + +def generate_launch_description(): + """Generate launch description for testing relevant nodes.""" + rviz_config_path = os.path.join( + get_package_share_directory('isaac_ros_foundationpose'), + 'rviz', 'foundationpose_tracking.rviz') + + launch_args = [ + DeclareLaunchArgument( + 'mesh_file_path', + default_value=MESH_FILE_NAME, + description='The absolute file path to the mesh file'), + + DeclareLaunchArgument( + 'texture_path', + default_value=TEXTURE_MAP_NAME, + description='The absolute file path to the texture map'), + + DeclareLaunchArgument( + 'refine_model_file_path', + default_value=REFINE_MODEL_NAME, + description='The absolute file path to the refine model'), + + DeclareLaunchArgument( + 'refine_engine_file_path', + default_value=REFINE_ENGINE_NAME, + description='The absolute file path to the refine trt engine'), + + DeclareLaunchArgument( + 'score_model_file_path', + default_value=SCORE_MODEL_NAME, + description='The absolute file path to the score model'), + + DeclareLaunchArgument( + 'score_engine_file_path', + default_value=SCORE_ENGINE_NAME, + description='The absolute file path to the score trt engine'), + + DeclareLaunchArgument( + 'launch_rviz', + default_value='False', + description='Flag to enable Rviz2 launch'), + + DeclareLaunchArgument( + 'container_name', + default_value='foundationpose_container', + description='Name for ComposableNodeContainer'), + ] + + mesh_file_path = LaunchConfiguration('mesh_file_path') + texture_path = LaunchConfiguration('texture_path') + refine_model_file_path = LaunchConfiguration('refine_model_file_path') + refine_engine_file_path = LaunchConfiguration('refine_engine_file_path') + score_model_file_path = LaunchConfiguration('score_model_file_path') + score_engine_file_path = LaunchConfiguration('score_engine_file_path') + launch_rviz = LaunchConfiguration('launch_rviz') + container_name = LaunchConfiguration('container_name') + + selector_node = ComposableNode( + name='selector_node', + package='isaac_ros_foundationpose', + plugin='nvidia::isaac_ros::foundationpose::Selector', + parameters=[{ + # Expect to reset after the rosbag play complete + 'reset_period': 65000 + }]) + + foundationpose_node = ComposableNode( + name='foundationpose_node', + package='isaac_ros_foundationpose', + plugin='nvidia::isaac_ros::foundationpose::FoundationPoseNode', + parameters=[{ + 'mesh_file_path': mesh_file_path, + 'texture_path': texture_path, + + 'refine_model_file_path': refine_model_file_path, + 'refine_engine_file_path': refine_engine_file_path, + 'refine_input_tensor_names': ['input_tensor1', 'input_tensor2'], + 'refine_input_binding_names': ['input1', 'input2'], + 'refine_output_tensor_names': ['output_tensor1', 'output_tensor2'], + 'refine_output_binding_names': ['output1', 'output2'], + + 'score_model_file_path': score_model_file_path, + 'score_engine_file_path': score_engine_file_path, + 'score_input_tensor_names': ['input_tensor1', 'input_tensor2'], + 'score_input_binding_names': ['input1', 'input2'], + 'score_output_tensor_names': ['output_tensor'], + 'score_output_binding_names': ['output1'], + }]) + + foundationpose_tracking_node = ComposableNode( + name='foundationpose_tracking_node', + package='isaac_ros_foundationpose', + plugin='nvidia::isaac_ros::foundationpose::FoundationPoseTrackingNode', + parameters=[{ + 'mesh_file_path': mesh_file_path, + 'texture_path': texture_path, + + 'refine_model_file_path': REFINE_MODEL_NAME, + 'refine_engine_file_path': REFINE_ENGINE_NAME, + 'refine_input_tensor_names': ['input_tensor1', 'input_tensor2'], + 'refine_input_binding_names': ['input1', 'input2'], + 'refine_output_tensor_names': ['output_tensor1', 'output_tensor2'], + 'refine_output_binding_names': ['output1', 'output2'], + }]) + + rviz_node = Node( + package='rviz2', + executable='rviz2', + name='rviz2', + arguments=['-d', rviz_config_path], + condition=IfCondition(launch_rviz)) + + foundationpose_container = ComposableNodeContainer( + name=container_name, + namespace='', + package='rclcpp_components', + executable='component_container_mt', + composable_node_descriptions=[selector_node, foundationpose_node, + foundationpose_tracking_node], + output='screen' + ) + + return launch.LaunchDescription(launch_args + [foundationpose_container, + rviz_node]) diff --git a/isaac_ros_foundationpose/launch/isaac_ros_foundationpose_tracking_core.launch.py b/isaac_ros_foundationpose/launch/isaac_ros_foundationpose_tracking_core.launch.py new file mode 100644 index 0000000..4ac9547 --- /dev/null +++ b/isaac_ros_foundationpose/launch/isaac_ros_foundationpose_tracking_core.launch.py @@ -0,0 +1,336 @@ +# SPDX-FileCopyrightText: NVIDIA CORPORATION & AFFILIATES +# Copyright (c) 2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +# SPDX-License-Identifier: Apache-2.0 + +from typing import Any, Dict + +from isaac_ros_examples import IsaacROSLaunchFragment +import launch +from launch.actions import DeclareLaunchArgument +from launch.substitutions import LaunchConfiguration +from launch_ros.actions import ComposableNodeContainer +from launch_ros.descriptions import ComposableNode + + +# RT-DETR models expect 640x640 encoded image size +RT_DETR_MODEL_INPUT_SIZE = 640 +# RT-DETR models expect 3 image channels +RT_DETR_MODEL_NUM_CHANNELS = 3 + +REFINE_MODEL_PATH = '/tmp/refine_model.onnx' +REFINE_ENGINE_PATH = '/tmp/refine_trt_engine.plan' +SCORE_MODEL_PATH = '/tmp/score_model.onnx' +SCORE_ENGINE_PATH = '/tmp/score_trt_engine.plan' + + +class IsaacROSFoundationPoseTrackingLaunchFragment(IsaacROSLaunchFragment): + + @staticmethod + def get_composable_nodes(interface_specs: Dict[str, Any]) -> Dict[str, ComposableNode]: + + # FoundationPose parameters + mesh_file_path = LaunchConfiguration('mesh_file_path') + texture_path = LaunchConfiguration('texture_path') + refine_model_file_path = LaunchConfiguration('refine_model_file_path') + refine_engine_file_path = LaunchConfiguration('refine_engine_file_path') + score_model_file_path = LaunchConfiguration('score_model_file_path') + score_engine_file_path = LaunchConfiguration('score_engine_file_path') + # RT-DETR parameters + rt_detr_model_file_path = LaunchConfiguration('rt_detr_model_file_path') + rt_detr_engine_file_path = LaunchConfiguration('rt_detr_engine_file_path') + input_width = interface_specs['camera_resolution']['width'] + input_height = interface_specs['camera_resolution']['height'] + input_to_RT_DETR_ratio = input_width / RT_DETR_MODEL_INPUT_SIZE + return { + # Resize and pad input images to RT-DETR model input image size + # Resize from IMAGE_WIDTH x IMAGE_HEIGHT to + # IMAGE_WIDTH/input_TO_RT_DETR_RATIO x IMAGE_HEIGHT/input_TO_RT_DETR_RATIO + # output height constraint is not used since keep_aspect_ratio is True + 'resize_left_rt_detr_node': ComposableNode( + name='resize_left_rt_detr_node', + package='isaac_ros_image_proc', + plugin='nvidia::isaac_ros::image_proc::ResizeNode', + parameters=[{ + 'input_width': input_width, + 'input_height': input_height, + 'output_width': RT_DETR_MODEL_INPUT_SIZE, + 'output_height': RT_DETR_MODEL_INPUT_SIZE, + 'keep_aspect_ratio': True, + 'encoding_desired': 'rgb8', + 'disable_padding': True + }], + remappings=[ + ('image', 'image_rect'), + ('camera_info', 'camera_info_rect'), + ('resize/image', 'color_image_resized'), + ('resize/camera_info', 'camera_info_resized') + ] + ), + # Pad the image from IMAGE_WIDTH/input_TO_RT_DETR_RATIO x + # IMAGE_HEIGHT/input_TO_RT_DETR_RATIO + # to RT_DETR_MODEL_INPUT_WIDTH x RT_DETR_MODEL_INPUT_HEIGHT + 'pad_node': ComposableNode( + name='pad_node', + package='isaac_ros_image_proc', + plugin='nvidia::isaac_ros::image_proc::PadNode', + parameters=[{ + 'output_image_width': RT_DETR_MODEL_INPUT_SIZE, + 'output_image_height': RT_DETR_MODEL_INPUT_SIZE, + 'padding_type': 'BOTTOM_RIGHT' + }], + remappings=[( + 'image', 'color_image_resized' + )] + ), + + # Convert image to tensor and reshape + 'image_to_tensor_node': ComposableNode( + name='image_to_tensor_node', + package='isaac_ros_tensor_proc', + plugin='nvidia::isaac_ros::dnn_inference::ImageToTensorNode', + parameters=[{ + 'scale': False, + 'tensor_name': 'image', + }], + remappings=[ + ('image', 'padded_image'), + ('tensor', 'normalized_tensor'), + ] + ), + + 'interleave_to_planar_node': ComposableNode( + name='interleaved_to_planar_node', + package='isaac_ros_tensor_proc', + plugin='nvidia::isaac_ros::dnn_inference::InterleavedToPlanarNode', + parameters=[{ + 'input_tensor_shape': [RT_DETR_MODEL_INPUT_SIZE, + RT_DETR_MODEL_INPUT_SIZE, + RT_DETR_MODEL_NUM_CHANNELS] + }], + remappings=[ + ('interleaved_tensor', 'normalized_tensor') + ] + ), + + 'reshape_node': ComposableNode( + name='reshape_node', + package='isaac_ros_tensor_proc', + plugin='nvidia::isaac_ros::dnn_inference::ReshapeNode', + parameters=[{ + 'output_tensor_name': 'input_tensor', + 'input_tensor_shape': [RT_DETR_MODEL_NUM_CHANNELS, + RT_DETR_MODEL_INPUT_SIZE, + RT_DETR_MODEL_INPUT_SIZE], + 'output_tensor_shape': [1, RT_DETR_MODEL_NUM_CHANNELS, + RT_DETR_MODEL_INPUT_SIZE, + RT_DETR_MODEL_INPUT_SIZE] + }], + remappings=[ + ('tensor', 'planar_tensor') + ], + ), + + 'rtdetr_preprocessor_node': ComposableNode( + name='rtdetr_preprocessor', + package='isaac_ros_rtdetr', + plugin='nvidia::isaac_ros::rtdetr::RtDetrPreprocessorNode', + remappings=[ + ('encoded_tensor', 'reshaped_tensor') + ] + ), + + # RT-DETR objection detection pipeline + 'tensor_rt_node': ComposableNode( + name='tensor_rt', + package='isaac_ros_tensor_rt', + plugin='nvidia::isaac_ros::dnn_inference::TensorRTNode', + parameters=[{ + 'model_file_path': rt_detr_model_file_path, + 'engine_file_path': rt_detr_engine_file_path, + 'output_binding_names': ['labels', 'boxes', 'scores'], + 'output_tensor_names': ['labels', 'boxes', 'scores'], + 'input_tensor_names': ['images', 'orig_target_sizes'], + 'input_binding_names': ['images', 'orig_target_sizes'], + 'force_engine_update': False + }] + ), + 'rtdetr_decoder_node': ComposableNode( + name='rtdetr_decoder', + package='isaac_ros_rtdetr', + plugin='nvidia::isaac_ros::rtdetr::RtDetrDecoderNode', + parameters=[{ + 'confidence_threshold': 0.5, + }], + ), + + # Create a binary segmentation mask from a Detection2DArray published by RT-DETR. + # The segmentation mask is of size + # int(IMAGE_WIDTH/input_to_RT_DETR_ratio) x int(IMAGE_HEIGHT/input_to_RT_DETR_ratio) + 'detection2_d_to_mask_node': ComposableNode( + name='detection2_d_to_mask', + package='isaac_ros_foundationpose', + plugin='nvidia::isaac_ros::foundationpose::Detection2DToMask', + parameters=[{ + 'mask_width': int(input_width/input_to_RT_DETR_ratio), + 'mask_height': int(input_height/input_to_RT_DETR_ratio)}], + remappings=[('detection2_d_array', 'detections_output'), + ('segmentation', 'rt_detr_segmentation')]), + + # Resize segmentation mask to ESS model image size so it can be used by FoundationPose + # FoundationPose requires depth, rgb image and segmentation mask to be of the same size + # Resize from int(IMAGE_WIDTH/input_to_RT_DETR_ratio) x + # int(IMAGE_HEIGHT/input_to_RT_DETR_ratio) + # to ESS_MODEL_IMAGE_WIDTH x ESS_MODEL_IMAGE_HEIGHT + # output height constraint is used since keep_aspect_ratio is False + # and the image is padded + 'resize_mask_node': ComposableNode( + name='resize_mask_node', + package='isaac_ros_image_proc', + plugin='nvidia::isaac_ros::image_proc::ResizeNode', + parameters=[{ + 'input_width': int(input_width/input_to_RT_DETR_ratio), + 'input_height': int(input_height/input_to_RT_DETR_ratio), + 'output_width': input_width, + 'output_height': input_height, + 'keep_aspect_ratio': False, + 'disable_padding': False + }], + remappings=[ + ('image', 'rt_detr_segmentation'), + ('camera_info', 'camera_info_resized'), + ('resize/image', 'segmentation'), + ('resize/camera_info', 'camera_info_segmentation') + ] + ), + + 'selector_node': ComposableNode( + name='selector_node', + package='isaac_ros_foundationpose', + plugin='nvidia::isaac_ros::foundationpose::Selector', + parameters=[{ + # Expect to reset after the rosbag play complete + 'reset_period': 65000 + }], + remappings=[ + ('image', 'image_rect'), + ('camera_info', 'camera_info_rect'), + ('depth_image', 'depth'), + ] + ), + + 'foundationpose_node': ComposableNode( + name='foundationpose_node', + package='isaac_ros_foundationpose', + plugin='nvidia::isaac_ros::foundationpose::FoundationPoseNode', + parameters=[{ + 'mesh_file_path': mesh_file_path, + 'texture_path': texture_path, + + 'refine_model_file_path': refine_model_file_path, + 'refine_engine_file_path': refine_engine_file_path, + 'refine_input_tensor_names': ['input_tensor1', 'input_tensor2'], + 'refine_input_binding_names': ['input1', 'input2'], + 'refine_output_tensor_names': ['output_tensor1', 'output_tensor2'], + 'refine_output_binding_names': ['output1', 'output2'], + + 'score_model_file_path': score_model_file_path, + 'score_engine_file_path': score_engine_file_path, + 'score_input_tensor_names': ['input_tensor1', 'input_tensor2'], + 'score_input_binding_names': ['input1', 'input2'], + 'score_output_tensor_names': ['output_tensor'], + 'score_output_binding_names': ['output1'], + }] + ), + + 'foundationpose_tracking_node': ComposableNode( + name='foundationpose_tracking_node', + package='isaac_ros_foundationpose', + plugin='nvidia::isaac_ros::foundationpose::FoundationPoseTrackingNode', + parameters=[{ + 'mesh_file_path': mesh_file_path, + 'texture_path': texture_path, + + 'refine_model_file_path': refine_model_file_path, + 'refine_engine_file_path': refine_engine_file_path, + 'refine_input_tensor_names': ['input_tensor1', 'input_tensor2'], + 'refine_input_binding_names': ['input1', 'input2'], + 'refine_output_tensor_names': ['output_tensor1', 'output_tensor2'], + 'refine_output_binding_names': ['output1', 'output2'], + }]) + + } + + @staticmethod + def get_launch_actions(interface_specs: Dict[str, Any]) -> \ + Dict[str, launch.actions.OpaqueFunction]: + + return { + 'mesh_file_path': DeclareLaunchArgument( + 'mesh_file_path', + default_value='', + description='The absolute file path to the mesh file'), + + 'texture_path': DeclareLaunchArgument( + 'texture_path', + default_value='', + description='The absolute file path to the texture map'), + + 'refine_model_file_path': DeclareLaunchArgument( + 'refine_model_file_path', + default_value=REFINE_MODEL_PATH, + description='The absolute file path to the refine model'), + + 'refine_engine_file_path': DeclareLaunchArgument( + 'refine_engine_file_path', + default_value=REFINE_ENGINE_PATH, + description='The absolute file path to the refine trt engine'), + + 'score_model_file_path': DeclareLaunchArgument( + 'score_model_file_path', + default_value=SCORE_MODEL_PATH, + description='The absolute file path to the score model'), + + 'score_engine_file_path': DeclareLaunchArgument( + 'score_engine_file_path', + default_value=SCORE_ENGINE_PATH, + description='The absolute file path to the score trt engine'), + + 'rt_detr_model_file_path': DeclareLaunchArgument( + 'rt_detr_model_file_path', + default_value='', + description='The absolute file path to the RT-DETR ONNX file'), + + 'rt_detr_engine_file_path': DeclareLaunchArgument( + 'rt_detr_engine_file_path', + default_value='', + description='The absolute file path to the RT-DETR TensorRT engine file'), + } + + +def generate_launch_description(): + foundationpose_tracking_container = ComposableNodeContainer( + package='rclcpp_components', + name='foundationpose_tracking_container', + namespace='', + executable='component_container_mt', + composable_node_descriptions=IsaacROSFoundationPoseTrackingLaunchFragment + .get_composable_nodes().values(), + output='screen' + ) + + return launch.LaunchDescription( + [foundationpose_tracking_container] + + IsaacROSFoundationPoseTrackingLaunchFragment.get_launch_actions().values()) diff --git a/isaac_ros_foundationpose/package.xml b/isaac_ros_foundationpose/package.xml new file mode 100644 index 0000000..a7ec046 --- /dev/null +++ b/isaac_ros_foundationpose/package.xml @@ -0,0 +1,76 @@ + + + + + + + isaac_ros_foundationpose + 3.0.0 + FoundationPose 6D pose estimation + + Isaac ROS Maintainers + Apache-2.0 + https://developer.nvidia.com/isaac-ros-gems/ + Yuankun Zhu + + ament_cmake + + eigen + + cv_bridge + + isaac_ros_dnn_image_encoder + isaac_ros_nitros + isaac_ros_nitros_camera_info_type + isaac_ros_nitros_detection3_d_array_type + isaac_ros_tensor_list_interfaces + isaac_ros_nitros_point_cloud_type + + message_filters + rclcpp + sensor_msgs + vision_msgs + assimp + tf2_eigen + + gxf_isaac_gems + isaac_ros_common + isaac_ros_gxf + image_transport + + gxf_isaac_foundationpose + isaac_ros_stereo_image_proc + isaac_ros_depth_image_proc + isaac_ros_managed_nitros + isaac_ros_tensor_rt + isaac_ros_nitros_topic_tools + isaac_ros_image_proc + isaac_ros_tensor_proc + isaac_ros_rtdetr + rviz2 + + ament_lint_auto + ament_lint_common + isaac_ros_test + sensor_msgs_py + + + ament_cmake + + diff --git a/isaac_ros_foundationpose/rviz/foundationpose.rviz b/isaac_ros_foundationpose/rviz/foundationpose.rviz new file mode 100644 index 0000000..3602cf4 --- /dev/null +++ b/isaac_ros_foundationpose/rviz/foundationpose.rviz @@ -0,0 +1,170 @@ +Panels: + - Class: rviz_common/Displays + Help Height: 78 + Name: Displays + Property Tree Widget: + Expanded: + - /Global Options1 + - /Status1 + Splitter Ratio: 0.5 + Tree Height: 519 + - Class: rviz_common/Selection + Name: Selection + - Class: rviz_common/Tool Properties + Expanded: + - /2D Goal Pose1 + - /Publish Point1 + Name: Tool Properties + Splitter Ratio: 0.5886790156364441 + - Class: rviz_common/Views + Expanded: + - /Current View1 + Name: Views + Splitter Ratio: 0.5 + - Class: rviz_common/Time + Experimental: false + Name: Time + SyncMode: 0 + SyncSource: "" +Visualization Manager: + Class: "" + Displays: + - Alpha: 0.5 + Cell Size: 1 + Class: rviz_default_plugins/Grid + Color: 160; 160; 164 + Enabled: true + Line Style: + Line Width: 0.029999999329447746 + Value: Lines + Name: Grid + Normal Cell Count: 0 + Offset: + X: 0 + Y: 0 + Z: 0 + Plane: XY + Plane Cell Count: 10 + Reference Frame: + Value: true + - Alpha: 1 + Class: vision_msgs_rviz_plugins/Detection3DArray + ConfigPath: "" + Enabled: true + Line Width: 0.05000000074505806 + Name: Detection3DArray + Namespaces: + bounding_box: true + Only Edge: false + Show Score: false + Topic: + Depth: 5 + Durability Policy: Volatile + History Policy: Keep Last + Reliability Policy: Reliable + Value: output + Value: true + - Class: rviz_default_plugins/Camera + Enabled: true + Far Plane Distance: 100 + Image Rendering: background and overlay + Name: Camera + Overlay Alpha: 0.5 + Topic: + Depth: 5 + Durability Policy: Volatile + History Policy: Keep Last + Reliability Policy: Reliable + Value: /rgb/image_rect_color + Value: true + Visibility: + Detection3DArray: true + Grid: true + Value: true + Zoom Factor: 1 + Enabled: true + Global Options: + Background Color: 48; 48; 48 + Fixed Frame: tf_camera + Frame Rate: 30 + Name: root + Tools: + - Class: rviz_default_plugins/Interact + Hide Inactive Objects: true + - Class: rviz_default_plugins/MoveCamera + - Class: rviz_default_plugins/Select + - Class: rviz_default_plugins/FocusCamera + - Class: rviz_default_plugins/Measure + Line color: 128; 128; 0 + - Class: rviz_default_plugins/SetInitialPose + Covariance x: 0.25 + Covariance y: 0.25 + Covariance yaw: 0.06853891909122467 + Topic: + Depth: 5 + Durability Policy: Volatile + History Policy: Keep Last + Reliability Policy: Reliable + Value: /initialpose + - Class: rviz_default_plugins/SetGoal + Topic: + Depth: 5 + Durability Policy: Volatile + History Policy: Keep Last + Reliability Policy: Reliable + Value: /goal_pose + - Class: rviz_default_plugins/PublishPoint + Single click: true + Topic: + Depth: 5 + Durability Policy: Volatile + History Policy: Keep Last + Reliability Policy: Reliable + Value: /clicked_point + Transformation: + Current: + Class: rviz_default_plugins/TF + Value: true + Views: + Current: + Class: rviz_default_plugins/Orbit + Distance: 10 + Enable Stereo Rendering: + Stereo Eye Separation: 0.05999999865889549 + Stereo Focal Distance: 1 + Swap Stereo Eyes: false + Value: false + Focal Point: + X: 0 + Y: 0 + Z: 0 + Focal Shape Fixed Size: true + Focal Shape Size: 0.05000000074505806 + Invert Z Axis: false + Name: Current View + Near Clip Distance: 0.009999999776482582 + Pitch: 0.785398006439209 + Target Frame: + Value: Orbit (rviz) + Yaw: 0.785398006439209 + Saved: ~ +Window Geometry: + Camera: + collapsed: false + Displays: + collapsed: false + Height: 1016 + Hide Left Dock: false + Hide Right Dock: false + QMainWindow State: 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+ Selection: + collapsed: false + Time: + collapsed: false + Tool Properties: + collapsed: false + Views: + collapsed: false + Width: 1850 + X: 70 + Y: 27 diff --git a/isaac_ros_foundationpose/rviz/foundationpose_hawk.rviz b/isaac_ros_foundationpose/rviz/foundationpose_hawk.rviz new file mode 100644 index 0000000..06df3f7 --- /dev/null +++ b/isaac_ros_foundationpose/rviz/foundationpose_hawk.rviz @@ -0,0 +1,201 @@ +Panels: + - Class: rviz_common/Displays + Help Height: 138 + Name: Displays + Property Tree Widget: + Expanded: + - /Global Options1 + - /Status1 + Splitter Ratio: 0.5001519322395325 + Tree Height: 624 + - Class: rviz_common/Selection + Name: Selection + - Class: rviz_common/Tool Properties + Expanded: + - /2D Goal Pose1 + - /Publish Point1 + Name: Tool Properties + Splitter Ratio: 0.5886790156364441 + - Class: rviz_common/Views + Expanded: + - /Current View1 + Name: Views + Splitter Ratio: 0.5 + - Class: rviz_common/Time + Experimental: false + Name: Time + SyncMode: 0 + SyncSource: "" +Visualization Manager: + Class: "" + Displays: + - Alpha: 0.5 + Cell Size: 1 + Class: rviz_default_plugins/Grid + Color: 160; 160; 164 + Enabled: true + Line Style: + Line Width: 0.029999999329447746 + Value: Lines + Name: Grid + Normal Cell Count: 0 + Offset: + X: 0 + Y: 0 + Z: 0 + Plane: XY + Plane Cell Count: 10 + Reference Frame: + Value: true + - Class: rviz_default_plugins/Camera + Enabled: true + Far Plane Distance: 100 + Image Rendering: background and overlay + Name: Camera + Overlay Alpha: 0.5 + Topic: + Depth: 5 + Durability Policy: Volatile + History Policy: Keep Last + Reliability Policy: Reliable + Value: /rgb/image_rect_color_viz + Value: true + Visibility: + Detection3DArray: true + Grid: true + Image: true + Value: true + Zoom Factor: 1 + - Alpha: 1 + Class: vision_msgs_rviz_plugins/Detection3DArray + ConfigPath: "" + Enabled: true + Line Width: 0.05000000074505806 + Name: Detection3DArray + Namespaces: + bounding_box: true + Only Edge: false + Show Score: false + Topic: + Depth: 1 + Durability Policy: Volatile + History Policy: Keep Last + Reliability Policy: Reliable + Value: /output + Value: true + - Class: rviz_default_plugins/Image + Enabled: false + Max Value: 1 + Median window: 5 + Min Value: 0 + Name: Image + Normalize Range: true + Topic: + Depth: 5 + Durability Policy: Volatile + History Policy: Keep Last + Reliability Policy: Reliable + Value: /rt_detr_segmentation + Value: false + - Class: rviz_default_plugins/Image + Enabled: false + Max Value: 1 + Median window: 5 + Min Value: 0 + Name: Image + Normalize Range: true + Topic: + Depth: 5 + Durability Policy: Volatile + History Policy: Keep Last + Reliability Policy: Reliable + Value: /segmentation + Value: false + Enabled: true + Global Options: + Background Color: 48; 48; 48 + Fixed Frame: left_cam + Frame Rate: 30 + Name: root + Tools: + - Class: rviz_default_plugins/Interact + Hide Inactive Objects: true + - Class: rviz_default_plugins/MoveCamera + - Class: rviz_default_plugins/Select + - Class: rviz_default_plugins/FocusCamera + - Class: rviz_default_plugins/Measure + Line color: 128; 128; 0 + - Class: rviz_default_plugins/SetInitialPose + Covariance x: 0.25 + Covariance y: 0.25 + Covariance yaw: 0.06853891909122467 + Topic: + Depth: 5 + Durability Policy: Volatile + History Policy: Keep Last + Reliability Policy: Reliable + Value: /initialpose + - Class: rviz_default_plugins/SetGoal + Topic: + Depth: 5 + Durability Policy: Volatile + History Policy: Keep Last + Reliability Policy: Reliable + Value: /goal_pose + - Class: rviz_default_plugins/PublishPoint + Single click: true + Topic: + Depth: 5 + Durability Policy: Volatile + History Policy: Keep Last + Reliability Policy: Reliable + Value: /clicked_point + Transformation: + Current: + Class: rviz_default_plugins/TF + Value: true + Views: + Current: + Class: rviz_default_plugins/Orbit + Distance: 10 + Enable Stereo Rendering: + Stereo Eye Separation: 0.05999999865889549 + Stereo Focal Distance: 1 + Swap Stereo Eyes: false + Value: false + Focal Point: + X: 0 + Y: 0 + Z: 0 + Focal Shape Fixed Size: true + Focal Shape Size: 0.05000000074505806 + Invert Z Axis: false + Name: Current View + Near Clip Distance: 0.009999999776482582 + Pitch: -0.9897971153259277 + Target Frame: + Value: Orbit (rviz) + Yaw: 0.7003983855247498 + Saved: ~ +Window Geometry: + Camera: + collapsed: false + Displays: + collapsed: false + Height: 2272 + Hide Left Dock: false + Hide Right Dock: false + Image: + collapsed: false + QMainWindow State: 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+ Selection: + collapsed: false + Time: + collapsed: false + Tool Properties: + collapsed: false + Views: + collapsed: false + Width: 3700 + X: 140 + Y: 54 diff --git a/isaac_ros_foundationpose/rviz/foundationpose_hawk_tracking.rviz b/isaac_ros_foundationpose/rviz/foundationpose_hawk_tracking.rviz new file mode 100644 index 0000000..5ce51d5 --- /dev/null +++ b/isaac_ros_foundationpose/rviz/foundationpose_hawk_tracking.rviz @@ -0,0 +1,170 @@ +Panels: + - Class: rviz_common/Displays + Help Height: 138 + Name: Displays + Property Tree Widget: + Expanded: + - /Global Options1 + - /Status1 + Splitter Ratio: 0.5001519322395325 + Tree Height: 624 + - Class: rviz_common/Selection + Name: Selection + - Class: rviz_common/Tool Properties + Expanded: + - /2D Goal Pose1 + - /Publish Point1 + Name: Tool Properties + Splitter Ratio: 0.5886790156364441 + - Class: rviz_common/Views + Expanded: + - /Current View1 + Name: Views + Splitter Ratio: 0.5 + - Class: rviz_common/Time + Experimental: false + Name: Time + SyncMode: 0 + SyncSource: "" +Visualization Manager: + Class: "" + Displays: + - Alpha: 0.5 + Cell Size: 1 + Class: rviz_default_plugins/Grid + Color: 160; 160; 164 + Enabled: true + Line Style: + Line Width: 0.029999999329447746 + Value: Lines + Name: Grid + Normal Cell Count: 0 + Offset: + X: 0 + Y: 0 + Z: 0 + Plane: XY + Plane Cell Count: 10 + Reference Frame: + Value: true + - Class: rviz_default_plugins/Camera + Enabled: true + Far Plane Distance: 100 + Image Rendering: background and overlay + Name: Camera + Overlay Alpha: 0.5 + Topic: + Depth: 5 + Durability Policy: Volatile + History Policy: Keep Last + Reliability Policy: Reliable + Value: /tracking/image + Value: true + Visibility: + Detection3DArray: true + Grid: true + Value: true + Zoom Factor: 1 + - Alpha: 1 + Class: vision_msgs_rviz_plugins/Detection3DArray + ConfigPath: "" + Enabled: true + Line Width: 0.05000000074505806 + Name: Detection3DArray + Namespaces: + bounding_box: true + Only Edge: false + Show Score: false + Topic: + Depth: 1 + Durability Policy: Volatile + History Policy: Keep Last + Reliability Policy: Reliable + Value: /tracking/output + Value: true + Enabled: true + Global Options: + Background Color: 48; 48; 48 + Fixed Frame: left_cam + Frame Rate: 30 + Name: root + Tools: + - Class: rviz_default_plugins/Interact + Hide Inactive Objects: true + - Class: rviz_default_plugins/MoveCamera + - Class: rviz_default_plugins/Select + - Class: rviz_default_plugins/FocusCamera + - Class: rviz_default_plugins/Measure + Line color: 128; 128; 0 + - Class: rviz_default_plugins/SetInitialPose + Covariance x: 0.25 + Covariance y: 0.25 + Covariance yaw: 0.06853891909122467 + Topic: + Depth: 5 + Durability Policy: Volatile + History Policy: Keep Last + Reliability Policy: Reliable + Value: /initialpose + - Class: rviz_default_plugins/SetGoal + Topic: + Depth: 5 + Durability Policy: Volatile + History Policy: Keep Last + Reliability Policy: Reliable + Value: /goal_pose + - Class: rviz_default_plugins/PublishPoint + Single click: true + Topic: + Depth: 5 + Durability Policy: Volatile + History Policy: Keep Last + Reliability Policy: Reliable + Value: /clicked_point + Transformation: + Current: + Class: rviz_default_plugins/TF + Value: true + Views: + Current: + Class: rviz_default_plugins/Orbit + Distance: 10 + Enable Stereo Rendering: + Stereo Eye Separation: 0.05999999865889549 + Stereo Focal Distance: 1 + Swap Stereo Eyes: false + Value: false + Focal Point: + X: 0 + Y: 0 + Z: 0 + Focal Shape Fixed Size: true + Focal Shape Size: 0.05000000074505806 + Invert Z Axis: false + Name: Current View + Near Clip Distance: 0.009999999776482582 + Pitch: -0.9897971153259277 + Target Frame: + Value: Orbit (rviz) + Yaw: 0.7003983855247498 + Saved: ~ +Window Geometry: + Camera: + collapsed: false + Displays: + collapsed: false + Height: 2272 + Hide Left Dock: false + Hide Right Dock: false + QMainWindow State: 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+ Selection: + collapsed: false + Time: + collapsed: false + Tool Properties: + collapsed: false + Views: + collapsed: false + Width: 3700 + X: 140 + Y: 54 diff --git a/isaac_ros_foundationpose/rviz/foundationpose_isaac_sim.rviz b/isaac_ros_foundationpose/rviz/foundationpose_isaac_sim.rviz new file mode 100644 index 0000000..c0334a5 --- /dev/null +++ b/isaac_ros_foundationpose/rviz/foundationpose_isaac_sim.rviz @@ -0,0 +1,171 @@ +Panels: + - Class: rviz_common/Displays + Help Height: 70 + Name: Displays + Property Tree Widget: + Expanded: + - /Global Options1 + - /Status1 + - /Camera1 + Splitter Ratio: 0.5001519322395325 + Tree Height: 70 + - Class: rviz_common/Selection + Name: Selection + - Class: rviz_common/Tool Properties + Expanded: + - /2D Goal Pose1 + - /Publish Point1 + Name: Tool Properties + Splitter Ratio: 0.5886790156364441 + - Class: rviz_common/Views + Expanded: + - /Current View1 + Name: Views + Splitter Ratio: 0.5 + - Class: rviz_common/Time + Experimental: false + Name: Time + SyncMode: 0 + SyncSource: "" +Visualization Manager: + Class: "" + Displays: + - Alpha: 0.5 + Cell Size: 1 + Class: rviz_default_plugins/Grid + Color: 160; 160; 164 + Enabled: true + Line Style: + Line Width: 0.029999999329447746 + Value: Lines + Name: Grid + Normal Cell Count: 0 + Offset: + X: 0 + Y: 0 + Z: 0 + Plane: XY + Plane Cell Count: 10 + Reference Frame: + Value: true + - Class: rviz_default_plugins/Camera + Enabled: true + Far Plane Distance: 100 + Image Rendering: background and overlay + Name: Camera + Overlay Alpha: 0.5 + Topic: + Depth: 5 + Durability Policy: Volatile + History Policy: Keep Last + Reliability Policy: Reliable + Value: /front_stereo_camera/left_rgb/image_raw_resized + Value: true + Visibility: + Detection3DArray: true + Grid: true + Value: true + Zoom Factor: 1 + - Alpha: 1 + Class: vision_msgs_rviz_plugins/Detection3DArray + ConfigPath: "" + Enabled: true + Line Width: 0.05000000074505806 + Name: Detection3DArray + Namespaces: + bounding_box: true + Only Edge: false + Show Score: false + Topic: + Depth: 5 + Durability Policy: Volatile + History Policy: Keep Last + Reliability Policy: Reliable + Value: /output + Value: true + Enabled: true + Global Options: + Background Color: 48; 48; 48 + Fixed Frame: front_stereo_camera:left_rgb + Frame Rate: 30 + Name: root + Tools: + - Class: rviz_default_plugins/Interact + Hide Inactive Objects: true + - Class: rviz_default_plugins/MoveCamera + - Class: rviz_default_plugins/Select + - Class: rviz_default_plugins/FocusCamera + - Class: rviz_default_plugins/Measure + Line color: 128; 128; 0 + - Class: rviz_default_plugins/SetInitialPose + Covariance x: 0.25 + Covariance y: 0.25 + Covariance yaw: 0.06853891909122467 + Topic: + Depth: 5 + Durability Policy: Volatile + History Policy: Keep Last + Reliability Policy: Reliable + Value: /initialpose + - Class: rviz_default_plugins/SetGoal + Topic: + Depth: 5 + Durability Policy: Volatile + History Policy: Keep Last + Reliability Policy: Reliable + Value: /goal_pose + - Class: rviz_default_plugins/PublishPoint + Single click: true + Topic: + Depth: 5 + Durability Policy: Volatile + History Policy: Keep Last + Reliability Policy: Reliable + Value: /clicked_point + Transformation: + Current: + Class: rviz_default_plugins/TF + Value: true + Views: + Current: + Class: rviz_default_plugins/Orbit + Distance: 10 + Enable Stereo Rendering: + Stereo Eye Separation: 0.05999999865889549 + Stereo Focal Distance: 1 + Swap Stereo Eyes: false + Value: false + Focal Point: + X: 0 + Y: 0 + Z: 0 + Focal Shape Fixed Size: true + Focal Shape Size: 0.05000000074505806 + Invert Z Axis: false + Name: Current View + Near Clip Distance: 0.009999999776482582 + Pitch: -0.9897971153259277 + Target Frame: + Value: Orbit (rviz) + Yaw: 0.7003983855247498 + Saved: ~ +Window Geometry: + Camera: + collapsed: false + Displays: + collapsed: false + Height: 1072 + Hide Left Dock: false + Hide Right Dock: false + QMainWindow State: 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+ Selection: + collapsed: false + Time: + collapsed: false + Tool Properties: + collapsed: false + Views: + collapsed: false + Width: 960 + X: 0 + Y: 27 diff --git a/isaac_ros_foundationpose/rviz/foundationpose_realsense.rviz b/isaac_ros_foundationpose/rviz/foundationpose_realsense.rviz new file mode 100644 index 0000000..58f59ce --- /dev/null +++ b/isaac_ros_foundationpose/rviz/foundationpose_realsense.rviz @@ -0,0 +1,170 @@ +Panels: + - Class: rviz_common/Displays + Help Height: 138 + Name: Displays + Property Tree Widget: + Expanded: + - /Global Options1 + - /Status1 + Splitter Ratio: 0.5001519322395325 + Tree Height: 625 + - Class: rviz_common/Selection + Name: Selection + - Class: rviz_common/Tool Properties + Expanded: + - /2D Goal Pose1 + - /Publish Point1 + Name: Tool Properties + Splitter Ratio: 0.5886790156364441 + - Class: rviz_common/Views + Expanded: + - /Current View1 + Name: Views + Splitter Ratio: 0.5 + - Class: rviz_common/Time + Experimental: false + Name: Time + SyncMode: 0 + SyncSource: "" +Visualization Manager: + Class: "" + Displays: + - Alpha: 0.5 + Cell Size: 1 + Class: rviz_default_plugins/Grid + Color: 160; 160; 164 + Enabled: true + Line Style: + Line Width: 0.029999999329447746 + Value: Lines + Name: Grid + Normal Cell Count: 0 + Offset: + X: 0 + Y: 0 + Z: 0 + Plane: XY + Plane Cell Count: 10 + Reference Frame: + Value: true + - Class: rviz_default_plugins/Camera + Enabled: true + Far Plane Distance: 100 + Image Rendering: background and overlay + Name: Camera + Overlay Alpha: 0.5 + Topic: + Depth: 5 + Durability Policy: Volatile + History Policy: Keep Last + Reliability Policy: Reliable + Value: /rgb/image_rect_color_viz + Value: true + Visibility: + Detection3DArray: true + Grid: true + Value: true + Zoom Factor: 1 + - Alpha: 1 + Class: vision_msgs_rviz_plugins/Detection3DArray + ConfigPath: "" + Enabled: true + Line Width: 0.05000000074505806 + Name: Detection3DArray + Namespaces: + bounding_box: true + Only Edge: false + Show Score: false + Topic: + Depth: 5 + Durability Policy: Volatile + History Policy: Keep Last + Reliability Policy: Reliable + Value: /output + Value: true + Enabled: true + Global Options: + Background Color: 48; 48; 48 + Fixed Frame: camera_color_optical_frame + Frame Rate: 30 + Name: root + Tools: + - Class: rviz_default_plugins/Interact + Hide Inactive Objects: true + - Class: rviz_default_plugins/MoveCamera + - Class: rviz_default_plugins/Select + - Class: rviz_default_plugins/FocusCamera + - Class: rviz_default_plugins/Measure + Line color: 128; 128; 0 + - Class: rviz_default_plugins/SetInitialPose + Covariance x: 0.25 + Covariance y: 0.25 + Covariance yaw: 0.06853891909122467 + Topic: + Depth: 5 + Durability Policy: Volatile + History Policy: Keep Last + Reliability Policy: Reliable + Value: /initialpose + - Class: rviz_default_plugins/SetGoal + Topic: + Depth: 5 + Durability Policy: Volatile + History Policy: Keep Last + Reliability Policy: Reliable + Value: /goal_pose + - Class: rviz_default_plugins/PublishPoint + Single click: true + Topic: + Depth: 5 + Durability Policy: Volatile + History Policy: Keep Last + Reliability Policy: Reliable + Value: /clicked_point + Transformation: + Current: + Class: rviz_default_plugins/TF + Value: true + Views: + Current: + Class: rviz_default_plugins/Orbit + Distance: 10 + Enable Stereo Rendering: + Stereo Eye Separation: 0.05999999865889549 + Stereo Focal Distance: 1 + Swap Stereo Eyes: false + Value: false + Focal Point: + X: 0 + Y: 0 + Z: 0 + Focal Shape Fixed Size: true + Focal Shape Size: 0.05000000074505806 + Invert Z Axis: false + Name: Current View + Near Clip Distance: 0.009999999776482582 + Pitch: -0.7196017503738403 + Target Frame: + Value: Orbit (rviz) + Yaw: 1.4653973579406738 + Saved: ~ +Window Geometry: + Camera: + collapsed: false + Displays: + collapsed: false + Height: 2272 + Hide Left Dock: false + Hide Right Dock: false + QMainWindow State: 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+ Selection: + collapsed: false + Time: + collapsed: false + Tool Properties: + collapsed: false + Views: + collapsed: false + Width: 3700 + X: 140 + Y: 54 diff --git a/isaac_ros_foundationpose/rviz/foundationpose_realsense_tracking.rviz b/isaac_ros_foundationpose/rviz/foundationpose_realsense_tracking.rviz new file mode 100644 index 0000000..09c2d08 --- /dev/null +++ b/isaac_ros_foundationpose/rviz/foundationpose_realsense_tracking.rviz @@ -0,0 +1,170 @@ +Panels: + - Class: rviz_common/Displays + Help Height: 138 + Name: Displays + Property Tree Widget: + Expanded: + - /Global Options1 + - /Status1 + Splitter Ratio: 0.5001519322395325 + Tree Height: 625 + - Class: rviz_common/Selection + Name: Selection + - Class: rviz_common/Tool Properties + Expanded: + - /2D Goal Pose1 + - /Publish Point1 + Name: Tool Properties + Splitter Ratio: 0.5886790156364441 + - Class: rviz_common/Views + Expanded: + - /Current View1 + Name: Views + Splitter Ratio: 0.5 + - Class: rviz_common/Time + Experimental: false + Name: Time + SyncMode: 0 + SyncSource: "" +Visualization Manager: + Class: "" + Displays: + - Alpha: 0.5 + Cell Size: 1 + Class: rviz_default_plugins/Grid + Color: 160; 160; 164 + Enabled: true + Line Style: + Line Width: 0.029999999329447746 + Value: Lines + Name: Grid + Normal Cell Count: 0 + Offset: + X: 0 + Y: 0 + Z: 0 + Plane: XY + Plane Cell Count: 10 + Reference Frame: + Value: true + - Class: rviz_default_plugins/Camera + Enabled: true + Far Plane Distance: 100 + Image Rendering: background and overlay + Name: Camera + Overlay Alpha: 0.5 + Topic: + Depth: 5 + Durability Policy: Volatile + History Policy: Keep Last + Reliability Policy: Reliable + Value: /tracking/image + Value: true + Visibility: + Detection3DArray: true + Grid: true + Value: true + Zoom Factor: 1 + - Alpha: 1 + Class: vision_msgs_rviz_plugins/Detection3DArray + ConfigPath: "" + Enabled: true + Line Width: 0.05000000074505806 + Name: Detection3DArray + Namespaces: + bounding_box: true + Only Edge: false + Show Score: false + Topic: + Depth: 5 + Durability Policy: Volatile + History Policy: Keep Last + Reliability Policy: Reliable + Value: /tracking/output + Value: true + Enabled: true + Global Options: + Background Color: 48; 48; 48 + Fixed Frame: camera_color_optical_frame + Frame Rate: 30 + Name: root + Tools: + - Class: rviz_default_plugins/Interact + Hide Inactive Objects: true + - Class: rviz_default_plugins/MoveCamera + - Class: rviz_default_plugins/Select + - Class: rviz_default_plugins/FocusCamera + - Class: rviz_default_plugins/Measure + Line color: 128; 128; 0 + - Class: rviz_default_plugins/SetInitialPose + Covariance x: 0.25 + Covariance y: 0.25 + Covariance yaw: 0.06853891909122467 + Topic: + Depth: 5 + Durability Policy: Volatile + History Policy: Keep Last + Reliability Policy: Reliable + Value: /initialpose + - Class: rviz_default_plugins/SetGoal + Topic: + Depth: 5 + Durability Policy: Volatile + History Policy: Keep Last + Reliability Policy: Reliable + Value: /goal_pose + - Class: rviz_default_plugins/PublishPoint + Single click: true + Topic: + Depth: 5 + Durability Policy: Volatile + History Policy: Keep Last + Reliability Policy: Reliable + Value: /clicked_point + Transformation: + Current: + Class: rviz_default_plugins/TF + Value: true + Views: + Current: + Class: rviz_default_plugins/Orbit + Distance: 10 + Enable Stereo Rendering: + Stereo Eye Separation: 0.05999999865889549 + Stereo Focal Distance: 1 + Swap Stereo Eyes: false + Value: false + Focal Point: + X: 0 + Y: 0 + Z: 0 + Focal Shape Fixed Size: true + Focal Shape Size: 0.05000000074505806 + Invert Z Axis: false + Name: Current View + Near Clip Distance: 0.009999999776482582 + Pitch: -0.7196017503738403 + Target Frame: + Value: Orbit (rviz) + Yaw: 1.4653973579406738 + Saved: ~ +Window Geometry: + Camera: + collapsed: false + Displays: + collapsed: false + Height: 2272 + Hide Left Dock: false + Hide Right Dock: false + QMainWindow State: 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+ Selection: + collapsed: false + Time: + collapsed: false + Tool Properties: + collapsed: false + Views: + collapsed: false + Width: 3700 + X: 140 + Y: 54 diff --git a/isaac_ros_foundationpose/rviz/foundationpose_tracking.rviz b/isaac_ros_foundationpose/rviz/foundationpose_tracking.rviz new file mode 100644 index 0000000..0a23911 --- /dev/null +++ b/isaac_ros_foundationpose/rviz/foundationpose_tracking.rviz @@ -0,0 +1,171 @@ +Panels: + - Class: rviz_common/Displays + Help Height: 78 + Name: Displays + Property Tree Widget: + Expanded: + - /Global Options1 + - /Status1 + - /Detection3DArray1 + Splitter Ratio: 0.5 + Tree Height: 433 + - Class: rviz_common/Selection + Name: Selection + - Class: rviz_common/Tool Properties + Expanded: + - /2D Goal Pose1 + - /Publish Point1 + Name: Tool Properties + Splitter Ratio: 0.5886790156364441 + - Class: rviz_common/Views + Expanded: + - /Current View1 + Name: Views + Splitter Ratio: 0.5 + - Class: rviz_common/Time + Experimental: false + Name: Time + SyncMode: 0 + SyncSource: "" +Visualization Manager: + Class: "" + Displays: + - Alpha: 0.5 + Cell Size: 1 + Class: rviz_default_plugins/Grid + Color: 160; 160; 164 + Enabled: true + Line Style: + Line Width: 0.029999999329447746 + Value: Lines + Name: Grid + Normal Cell Count: 0 + Offset: + X: 0 + Y: 0 + Z: 0 + Plane: XY + Plane Cell Count: 10 + Reference Frame: + Value: true + - Alpha: 1 + Class: vision_msgs_rviz_plugins/Detection3DArray + ConfigPath: "" + Enabled: true + Line Width: 0.05000000074505806 + Name: Detection3DArray + Namespaces: + bounding_box: true + Only Edge: false + Show Score: false + Topic: + Depth: 5 + Durability Policy: Volatile + History Policy: Keep Last + Reliability Policy: Reliable + Value: tracking/output + Value: true + - Class: rviz_default_plugins/Camera + Enabled: true + Far Plane Distance: 100 + Image Rendering: background and overlay + Name: Camera + Overlay Alpha: 0.5 + Topic: + Depth: 5 + Durability Policy: Volatile + History Policy: Keep Last + Reliability Policy: Reliable + Value: /tracking/image + Value: true + Visibility: + Detection3DArray: true + Grid: true + Value: true + Zoom Factor: 1 + Enabled: true + Global Options: + Background Color: 48; 48; 48 + Fixed Frame: tf_camera + Frame Rate: 30 + Name: root + Tools: + - Class: rviz_default_plugins/Interact + Hide Inactive Objects: true + - Class: rviz_default_plugins/MoveCamera + - Class: rviz_default_plugins/Select + - Class: rviz_default_plugins/FocusCamera + - Class: rviz_default_plugins/Measure + Line color: 128; 128; 0 + - Class: rviz_default_plugins/SetInitialPose + Covariance x: 0.25 + Covariance y: 0.25 + Covariance yaw: 0.06853891909122467 + Topic: + Depth: 5 + Durability Policy: Volatile + History Policy: Keep Last + Reliability Policy: Reliable + Value: /initialpose + - Class: rviz_default_plugins/SetGoal + Topic: + Depth: 5 + Durability Policy: Volatile + History Policy: Keep Last + Reliability Policy: Reliable + Value: /goal_pose + - Class: rviz_default_plugins/PublishPoint + Single click: true + Topic: + Depth: 5 + Durability Policy: Volatile + History Policy: Keep Last + Reliability Policy: Reliable + Value: /clicked_point + Transformation: + Current: + Class: rviz_default_plugins/TF + Value: true + Views: + Current: + Class: rviz_default_plugins/Orbit + Distance: 10 + Enable Stereo Rendering: + Stereo Eye Separation: 0.05999999865889549 + Stereo Focal Distance: 1 + Swap Stereo Eyes: false + Value: false + Focal Point: + X: 0 + Y: 0 + Z: 0 + Focal Shape Fixed Size: true + Focal Shape Size: 0.05000000074505806 + Invert Z Axis: false + Name: Current View + Near Clip Distance: 0.009999999776482582 + Pitch: 0.785398006439209 + Target Frame: + Value: Orbit (rviz) + Yaw: 0.785398006439209 + Saved: ~ +Window Geometry: + Camera: + collapsed: false + Displays: + collapsed: false + Height: 1016 + Hide Left Dock: false + Hide Right Dock: false + QMainWindow State: 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+ Selection: + collapsed: false + Time: + collapsed: false + Tool Properties: + collapsed: false + Views: + collapsed: false + Width: 1850 + X: 251 + Y: 213 diff --git a/isaac_ros_foundationpose/src/detection2_d_to_mask.cpp b/isaac_ros_foundationpose/src/detection2_d_to_mask.cpp new file mode 100644 index 0000000..2488864 --- /dev/null +++ b/isaac_ros_foundationpose/src/detection2_d_to_mask.cpp @@ -0,0 +1,143 @@ +// SPDX-FileCopyrightText: NVIDIA CORPORATION & AFFILIATES +// Copyright (c) 2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. +// +// SPDX-License-Identifier: Apache-2.0 + +#include +#include +#include +#include +#include +#include +#include + +namespace nvidia +{ +namespace isaac_ros +{ +namespace foundationpose +{ + +/* +ROS 2 node that generates a binary segmentation mask image from +1. a vision_msgs::msg::Detection2D or vision_msgs::msg::Detection2DArray +2. image_height and image_width read from ROS parameters +*/ +class Detection2DToMask : public rclcpp::Node +{ +public: + explicit Detection2DToMask(const rclcpp::NodeOptions & options) + : Node("detection2_d_to_mask", options) + { + // Create a publisher for the mono8 image + image_pub_ = this->create_publisher("segmentation", 10); + + this->declare_parameter("mask_width", 640); + this->declare_parameter("mask_height", 480); + this->get_parameter("mask_width", mask_width_); + this->get_parameter("mask_height", mask_height_); + + detection2_d_array_sub_ = this->create_subscription( + "detection2_d_array", 10, + std::bind(&Detection2DToMask::boundingBoxArrayCallback, this, std::placeholders::_1)); + // Create subscriber for Detection2D + detection2_d_sub_ = this->create_subscription( + "detection2_d", 10, + std::bind(&Detection2DToMask::boundingBoxCallback, this, std::placeholders::_1)); + + RCLCPP_INFO(this->get_logger(), "Mask Height: %d, Mask Width: %d", mask_height_, mask_width_); + } + + void boundingBoxCallback(const vision_msgs::msg::Detection2D::SharedPtr msg) + { + // Convert Detection2D to a binary mono8 image + cv::Mat image = cv::Mat::zeros(mask_height_, mask_width_, CV_8UC1); + // Draws a rectangle filled with 255 + cv::rectangle( + image, + cv::Point( + msg->bbox.center.position.x - msg->bbox.size_x / 2, + msg->bbox.center.position.y - msg->bbox.size_y / 2), + cv::Point( + msg->bbox.center.position.x + msg->bbox.size_x / 2, + msg->bbox.center.position.y + msg->bbox.size_y / 2), + cv::Scalar(255), -1); + + // Convert the OpenCV image to a ROS sensor_msgs::msg::Image and publish it + std_msgs::msg::Header header(msg->header); + cv_bridge::CvImage cv_image(header, "mono8", image); + sensor_msgs::msg::Image image_msg; + cv_image.toImageMsg(image_msg); + image_pub_->publish(image_msg); + } + + void boundingBoxArrayCallback(const vision_msgs::msg::Detection2DArray::SharedPtr msg) + { + // Find the detection bounding box with the highest confidence + float max_confidence = 0; + vision_msgs::msg::Detection2D max_confidence_detection; + // Iterate through the detections and find the one with the highest confidence + for (auto detection : msg->detections) { + // Iterate through all the hypotheses for this detection + // and find the one with the highest confidence + for (auto result : detection.results) { + if (result.hypothesis.score > max_confidence) { + max_confidence = result.hypothesis.score; + max_confidence_detection = detection; + } + } + } + + // If no detection was found, return error + if (max_confidence == 0) { + RCLCPP_INFO(this->get_logger(), "No detection found with non-zero confidence"); + return; + } + + // Convert Detection2D to a binary mono8 image + cv::Mat image = cv::Mat::zeros(mask_height_, mask_width_, CV_8UC1); + // Draws a rectangle filled with 255 + cv::rectangle( + image, + cv::Point( + max_confidence_detection.bbox.center.position.x - max_confidence_detection.bbox.size_x / 2, + max_confidence_detection.bbox.center.position.y - max_confidence_detection.bbox.size_y / 2), + cv::Point( + max_confidence_detection.bbox.center.position.x + max_confidence_detection.bbox.size_x / 2, + max_confidence_detection.bbox.center.position.y + max_confidence_detection.bbox.size_y / 2), + cv::Scalar(255), -1); + + // Convert the OpenCV image to a ROS sensor_msgs::msg::Image and publish it + std_msgs::msg::Header header(msg->header); + cv_bridge::CvImage cv_image(header, "mono8", image); + sensor_msgs::msg::Image image_msg; + cv_image.toImageMsg(image_msg); + image_pub_->publish(image_msg); + } + +private: + rclcpp::Publisher::SharedPtr image_pub_; + rclcpp::Subscription::SharedPtr detection2_d_sub_; + rclcpp::Subscription::SharedPtr detection2_d_array_sub_; + int mask_height_; + int mask_width_; +}; + +} // namespace foundationpose +} // namespace isaac_ros +} // namespace nvidia + +// Register the component with the ROS system to create a shared library +RCLCPP_COMPONENTS_REGISTER_NODE(nvidia::isaac_ros::foundationpose::Detection2DToMask) diff --git a/isaac_ros_foundationpose/src/foundationpose_node.cpp b/isaac_ros_foundationpose/src/foundationpose_node.cpp new file mode 100644 index 0000000..5c580eb --- /dev/null +++ b/isaac_ros_foundationpose/src/foundationpose_node.cpp @@ -0,0 +1,453 @@ +// SPDX-FileCopyrightText: NVIDIA CORPORATION & AFFILIATES +// Copyright (c) 2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. +// +// SPDX-License-Identifier: Apache-2.0 + +#include + +#include "ament_index_cpp/get_package_share_directory.hpp" +#include "detection3_d_array_message/detection3_d_array_message.hpp" +#include "geometry_msgs/msg/transform_stamped.hpp" +#include "rclcpp/rclcpp.hpp" +#include "tf2_ros/transform_broadcaster.h" + +#include "isaac_ros_nitros_camera_info_type/nitros_camera_info.hpp" +#include "isaac_ros_nitros_detection3_d_array_type/nitros_detection3_d_array.hpp" +#include "isaac_ros_nitros_image_type/nitros_image.hpp" +#include "isaac_ros_nitros_tensor_list_type/nitros_tensor_list.hpp" + +#include "isaac_ros_foundationpose/foundationpose_node.hpp" + +namespace nvidia +{ +namespace isaac_ros +{ +namespace foundationpose +{ + +using nvidia::gxf::optimizer::GraphIOGroupSupportedDataTypesInfoList; + +constexpr char INPUT_DEPTH_COMPONENT_KEY[] = "sync/depth_receiver"; +constexpr char INPUT_DEPTH_TENSOR_FORMAT[] = "nitros_image_32FC1"; +constexpr char INPUT_DEPTH_TOPIC_NAME[] = "pose_estimation/depth_image"; + +constexpr char INPUT_RGB_IMAGE_COMPONENT_KEY[] = "sync/rgb_image_receiver"; +constexpr char INPUT_RGB_IMAGE_TENSOR_FORMAT[] = "nitros_image_rgb8"; +constexpr char INPUT_RGB_IMAGE_TOPIC_NAME[] = "pose_estimation/image"; + +constexpr char INPUT_CAMERA_INFO_COMPONENT_KEY[] = "sync/camera_model_receiver"; +constexpr char INPUT_CAMERA_INFO_FORMAT[] = "nitros_camera_info"; +constexpr char INPUT_CAMERA_INFO_TOPIC_NAME[] = "pose_estimation/camera_info"; + +constexpr char INPUT_SEGMENTATION_COMPONENT_KEY[] = "sync/mask_receiver"; +constexpr char INPUT_SEGMENTATION_FORMAT[] = "nitros_image_mono8"; +constexpr char INPUT_SEGMENTATION_TOPIC_NAME[] = "pose_estimation/segmentation"; + +constexpr char OUTPUT_MATRIX_COMPONENT_KEY[] = "pose_matrix_sink/sink"; +constexpr char OUTPUT_MATRIX_FORMAT[] = "nitros_tensor_list_nchw"; +constexpr char OUTPUT_MATRIX_TOPIC_NAME[] = "pose_estimation/pose_matrix_output"; + +constexpr char OUTPUT_POSE_COMPONENT_KEY[] = "pose_sink/sink"; +constexpr char OUTPUT_POSE_FORMAT[] = "nitros_detection3_d_array"; +constexpr char OUTPUT_POSE_TOPIC_NAME[] = "pose_estimation/output"; + +constexpr char APP_YAML_FILENAME[] = "config/nitros_foundationpose_node.yaml"; +constexpr char PACKAGE_NAME[] = "isaac_ros_foundationpose"; + +const std::vector> EXTENSIONS = { + {"isaac_ros_gxf", "gxf/lib/std/libgxf_std.so"}, + {"isaac_ros_gxf", "gxf/lib/multimedia/libgxf_multimedia.so"}, + {"isaac_ros_gxf", "gxf/lib/serialization/libgxf_serialization.so"}, + {"isaac_ros_gxf", "gxf/lib/cuda/libgxf_cuda.so"}, + {"gxf_isaac_depth_image_proc", "gxf/lib/libgxf_isaac_depth_image_proc.so"}, + {"gxf_isaac_sgm", "gxf/lib/libgxf_isaac_sgm.so"}, + {"gxf_isaac_messages", "gxf/lib/libgxf_isaac_messages.so"}, + {"gxf_isaac_ros_messages", "gxf/lib/libgxf_isaac_ros_messages.so"}, + {"gxf_isaac_tensor_rt", "gxf/lib/libgxf_isaac_tensor_rt.so"}, + {"gxf_isaac_foundationpose", "gxf/lib/libgxf_isaac_foundationpose.so"}, +}; + +const std::vector PRESET_EXTENSION_SPEC_NAMES = { + "isaac_ros_foundationpose", +}; +const std::vector EXTENSION_SPEC_FILENAMES = {}; +const std::vector GENERATOR_RULE_FILENAMES = {"config/namespace_injector_rule.yaml"}; +#pragma GCC diagnostic push +#pragma GCC diagnostic ignored "-Wpedantic" +const nitros::NitrosPublisherSubscriberConfigMap CONFIG_MAP = { + {INPUT_DEPTH_COMPONENT_KEY, + { + .type = nitros::NitrosPublisherSubscriberType::NEGOTIATED, + .qos = rclcpp::QoS(10), + .compatible_data_format = INPUT_DEPTH_TENSOR_FORMAT, + .topic_name = INPUT_DEPTH_TOPIC_NAME, + }}, + {INPUT_RGB_IMAGE_COMPONENT_KEY, + { + .type = nitros::NitrosPublisherSubscriberType::NEGOTIATED, + .qos = rclcpp::QoS(10), + .compatible_data_format = INPUT_RGB_IMAGE_TENSOR_FORMAT, + .topic_name = INPUT_RGB_IMAGE_TOPIC_NAME, + }}, + {INPUT_CAMERA_INFO_COMPONENT_KEY, + { + .type = nitros::NitrosPublisherSubscriberType::NEGOTIATED, + .qos = rclcpp::QoS(10), + .compatible_data_format = INPUT_CAMERA_INFO_FORMAT, + .topic_name = INPUT_CAMERA_INFO_TOPIC_NAME, + }}, + {INPUT_SEGMENTATION_COMPONENT_KEY, + { + .type = nitros::NitrosPublisherSubscriberType::NEGOTIATED, + .qos = rclcpp::QoS(10), + .compatible_data_format = INPUT_SEGMENTATION_FORMAT, + .topic_name = INPUT_SEGMENTATION_TOPIC_NAME, + }}, + {OUTPUT_POSE_COMPONENT_KEY, + { + .type = nitros::NitrosPublisherSubscriberType::NEGOTIATED, + .qos = rclcpp::QoS(10), + .compatible_data_format = OUTPUT_POSE_FORMAT, + .topic_name = OUTPUT_POSE_TOPIC_NAME, + .frame_id_source_key = INPUT_RGB_IMAGE_COMPONENT_KEY, + }}, + {OUTPUT_MATRIX_COMPONENT_KEY, + { + .type = nitros::NitrosPublisherSubscriberType::NEGOTIATED, + .qos = rclcpp::QoS(10), + .compatible_data_format = OUTPUT_MATRIX_FORMAT, + .topic_name = OUTPUT_MATRIX_TOPIC_NAME, + .frame_id_source_key = INPUT_RGB_IMAGE_COMPONENT_KEY, + }}, +}; +#pragma GCC diagnostic pop + +FoundationPoseNode::FoundationPoseNode(rclcpp::NodeOptions options) +: nitros::NitrosNode( + options, APP_YAML_FILENAME, CONFIG_MAP, PRESET_EXTENSION_SPEC_NAMES, EXTENSION_SPEC_FILENAMES, + GENERATOR_RULE_FILENAMES, EXTENSIONS, PACKAGE_NAME), + configuration_file_( + declare_parameter("configuration_file", "foundationpose_model_config.yaml")), + mesh_file_path_(declare_parameter("mesh_file_path", "textured_simple.obj")), + texture_path_(declare_parameter("texture_path", "texture_map.png")), + min_depth_(declare_parameter("min_depth", 0.1)), + max_depth_(declare_parameter("max_depth", 4.0)), + refine_iterations_(declare_parameter("refine_iterations", 1)), + refine_model_file_path_( + declare_parameter("refine_model_file_path", "/tmp/refine_model.onnx")), + refine_engine_file_path_( + declare_parameter("refine_engine_file_path", "/tmp/refine_trt_engine.plan")), + score_model_file_path_( + declare_parameter("score_model_file_path", "/tmp/score_model.onnx")), + score_engine_file_path_( + declare_parameter("score_engine_file_path", "/tmp/score_trt_engine.plan")), + + refine_input_tensor_names_( + declare_parameter("refine_input_tensor_names", StringList())), + refine_input_binding_names_( + declare_parameter("refine_input_binding_names", StringList())), + score_input_tensor_names_( + declare_parameter("score_input_tensor_names", StringList())), + score_input_binding_names_( + declare_parameter("score_input_binding_names", StringList())), + + refine_output_tensor_names_( + declare_parameter("refine_output_tensor_names", StringList())), + refine_output_binding_names_( + declare_parameter("refine_output_binding_names", StringList())), + score_output_tensor_names_( + declare_parameter("score_output_tensor_names", StringList())), + score_output_binding_names_( + declare_parameter("score_output_binding_names", StringList())), + + tf_frame_name_(declare_parameter("tf_frame_name", "fp_object")) +{ + RCLCPP_DEBUG(get_logger(), "[FoundationPoseNode] Constructor"); + + // Add callback function for FoundationPose Detection3D array to broadcast to ROS TF tree + config_map_[OUTPUT_POSE_COMPONENT_KEY].callback = std::bind( + &FoundationPoseNode::FoundationPoseDetectionCallback, this, std::placeholders::_1, + std::placeholders::_2); + + // Open configuration YAML file + const std::string package_directory = ament_index_cpp::get_package_share_directory( + "isaac_ros_foundationpose"); + std::filesystem::path yaml_path = + package_directory / std::filesystem::path("config") / + std::filesystem::path(configuration_file_); + if (!std::filesystem::exists(yaml_path)) { + RCLCPP_ERROR(this->get_logger(), "%s could not be found. Exiting.", yaml_path.string().c_str()); + throw std::runtime_error("Parameter parsing failure."); + } + + // Parse parameters + rcl_params_t * foundationpose_params = rcl_yaml_node_struct_init(rcutils_get_default_allocator()); + rcl_parse_yaml_file(yaml_path.c_str(), foundationpose_params); + + rcl_variant_t * max_hypothesis = rcl_yaml_node_struct_get( + "foundationpose", "max_hypothesis", foundationpose_params); + if (!max_hypothesis->integer_value) { + RCLCPP_ERROR(this->get_logger(), "No max_hypothesis parameter found"); + throw std::runtime_error("Parameter parsing failure."); + } + max_hypothesis_ = static_cast(*max_hypothesis->integer_value); + + rcl_variant_t * resized_image_width = rcl_yaml_node_struct_get( + "foundationpose", "resized_image_width", foundationpose_params); + if (!resized_image_width->integer_value) { + RCLCPP_ERROR(this->get_logger(), "No resized_image_width parameter found"); + throw std::runtime_error("Parameter parsing failure."); + } + resized_image_width_ = static_cast(*resized_image_width->integer_value); + + rcl_variant_t * resized_image_height = rcl_yaml_node_struct_get( + "foundationpose", "resized_image_height", foundationpose_params); + if (!resized_image_width->integer_value) { + RCLCPP_ERROR(this->get_logger(), "No resized_image_height parameter found"); + throw std::runtime_error("Parameter parsing failure."); + } + resized_image_height_ = static_cast(*resized_image_height->integer_value); + + rcl_variant_t * refine_crop_ratio = rcl_yaml_node_struct_get( + "foundationpose", "refine_crop_ratio", foundationpose_params); + if (!refine_crop_ratio->double_value) { + RCLCPP_ERROR(this->get_logger(), "No refine_crop_ratio parameter found"); + throw std::runtime_error("Parameter parsing failure."); + } + refine_crop_ratio_ = static_cast(*refine_crop_ratio->double_value); + + rcl_variant_t * score_crop_ratio = rcl_yaml_node_struct_get( + "foundationpose", "score_crop_ratio", foundationpose_params); + if (!score_crop_ratio->double_value) { + RCLCPP_ERROR(this->get_logger(), "No score_crop_ratio parameter found"); + throw std::runtime_error("Parameter parsing failure."); + } + score_crop_ratio_ = static_cast(*score_crop_ratio->double_value); + + rcl_variant_t * rot_normalizer = rcl_yaml_node_struct_get( + "foundationpose", "rot_normalizer", foundationpose_params); + if (!rot_normalizer->double_value) { + RCLCPP_ERROR(this->get_logger(), "No rot_normalizer parameter found"); + throw std::runtime_error("Parameter parsing failure."); + } + rot_normalizer_ = static_cast(*rot_normalizer->double_value); + + rcl_yaml_node_struct_fini(foundationpose_params); + + registerSupportedType(); + registerSupportedType(); + registerSupportedType(); + registerSupportedType(); + + startNitrosNode(); +} + +FoundationPoseNode::~FoundationPoseNode() = default; + +void FoundationPoseNode::postLoadGraphCallback() +{ + RCLCPP_DEBUG(get_logger(), "[FoundationPoseNode] postLoadGraphCallback()."); + + // Set the model configuration file path from parameter + getNitrosContext().setParameterUInt32( + "sampling", "nvidia::isaac_ros::FoundationposeSampling", "max_hypothesis", + max_hypothesis_); + + getNitrosContext().setParameterUInt32( + "render", "nvidia::isaac_ros::FoundationposeRender", "resized_image_width", + resized_image_width_); + + getNitrosContext().setParameterUInt32( + "render_score", "nvidia::isaac_ros::FoundationposeRender", "resized_image_width", + resized_image_width_); + + getNitrosContext().setParameterUInt32( + "render", "nvidia::isaac_ros::FoundationposeRender", "resized_image_height", + resized_image_height_); + + getNitrosContext().setParameterUInt32( + "render_score", "nvidia::isaac_ros::FoundationposeRender", "resized_image_height", + resized_image_height_); + + getNitrosContext().setParameterFloat32( + "render", "nvidia::isaac_ros::FoundationposeRender", "crop_ratio", refine_crop_ratio_); + + getNitrosContext().setParameterFloat32( + "render_score", "nvidia::isaac_ros::FoundationposeRender", "crop_ratio", score_crop_ratio_); + + getNitrosContext().setParameterFloat32( + "transform", "nvidia::isaac_ros::FoundationposeTransformation", "rot_normalizer", + rot_normalizer_); + + // Set the mesh path from parameter + getNitrosContext().setParameterStr( + "render", "nvidia::isaac_ros::FoundationposeRender", "mesh_file_path", mesh_file_path_); + + getNitrosContext().setParameterStr( + "render_score", "nvidia::isaac_ros::FoundationposeRender", "mesh_file_path", mesh_file_path_); + + getNitrosContext().setParameterStr( + "render", "nvidia::isaac_ros::FoundationposeRender", "texture_path", texture_path_); + + getNitrosContext().setParameterStr( + "render_score", "nvidia::isaac_ros::FoundationposeRender", "texture_path", texture_path_); + + getNitrosContext().setParameterStr( + "decoder", "nvidia::isaac_ros::FoundationposeDecoder", "mesh_file_path", mesh_file_path_); + + getNitrosContext().setParameterStr( + "decoder", "nvidia::isaac_ros::FoundationposeDecoder", "mesh_file_path", mesh_file_path_); + + getNitrosContext().setParameterStr( + "transform", "nvidia::isaac_ros::FoundationposeTransformation", "mesh_file_path", + mesh_file_path_); + + // Set the depth threshold + getNitrosContext().setParameterFloat32( + "render", "nvidia::isaac_ros::FoundationposeRender", "min_depth", min_depth_); + + getNitrosContext().setParameterFloat32( + "render_score", "nvidia::isaac_ros::FoundationposeRender", "min_depth", min_depth_); + + getNitrosContext().setParameterFloat32( + "render", "nvidia::isaac_ros::FoundationposeRender", "max_depth", max_depth_); + + getNitrosContext().setParameterFloat32( + "render_score", "nvidia::isaac_ros::FoundationposeRender", "max_depth", max_depth_); + + getNitrosContext().setParameterFloat32( + "sampling", "nvidia::isaac_ros::FoundationposeSampling", "min_depth", min_depth_); + + // Set the refine iterations + getNitrosContext().setParameterInt32( + "render", "nvidia::isaac_ros::FoundationposeRender", "refine_iterations", + refine_iterations_); + getNitrosContext().setParameterInt32( + "transform", "nvidia::isaac_ros::FoundationposeTransformation", "refine_iterations", + refine_iterations_); + + // Set the refine network TensorRT configs from parameter + getNitrosContext().setParameterStr( + "refine_inference", "nvidia::gxf::TensorRtInference", "model_file_path", + refine_model_file_path_); + + getNitrosContext().setParameterStr( + "refine_inference", "nvidia::gxf::TensorRtInference", "engine_file_path", + refine_engine_file_path_); + + getNitrosContext().setParameter1DStrVector( + "refine_inference", "nvidia::gxf::TensorRtInference", "input_tensor_names", + refine_input_tensor_names_); + + getNitrosContext().setParameter1DStrVector( + "refine_inference", "nvidia::gxf::TensorRtInference", "input_binding_names", + refine_input_binding_names_); + + getNitrosContext().setParameter1DStrVector( + "refine_inference", "nvidia::gxf::TensorRtInference", "output_tensor_names", + refine_output_tensor_names_); + + getNitrosContext().setParameter1DStrVector( + "refine_inference", "nvidia::gxf::TensorRtInference", "output_binding_names", + refine_output_binding_names_); + + // Set the score network TensorRT configs from parameter + getNitrosContext().setParameterStr( + "score_inference", "nvidia::gxf::TensorRtInference", "model_file_path", + score_model_file_path_); + + getNitrosContext().setParameterStr( + "score_inference", "nvidia::gxf::TensorRtInference", "engine_file_path", + score_engine_file_path_); + + getNitrosContext().setParameter1DStrVector( + "score_inference", "nvidia::gxf::TensorRtInference", "input_tensor_names", + score_input_tensor_names_); + + getNitrosContext().setParameter1DStrVector( + "score_inference", "nvidia::gxf::TensorRtInference", "input_binding_names", + score_input_binding_names_); + + getNitrosContext().setParameter1DStrVector( + "score_inference", "nvidia::gxf::TensorRtInference", "output_tensor_names", + score_output_tensor_names_); + + getNitrosContext().setParameter1DStrVector( + "score_inference", "nvidia::gxf::TensorRtInference", "output_binding_names", + score_output_binding_names_); +} + +void FoundationPoseNode::FoundationPoseDetectionCallback( + const gxf_context_t context, nitros::NitrosTypeBase & msg) +{ + std::unique_ptr tf_broadcaster_ = + std::make_unique(*this); + geometry_msgs::msg::TransformStamped transform_stamped; + + auto msg_entity = nvidia::gxf::Entity::Shared(context, msg.handle); + + // Populate timestamp information back into the header + auto maybe_input_timestamp = msg_entity->get(); + if (!maybe_input_timestamp) { // Fallback to label 'timestamp' + maybe_input_timestamp = msg_entity->get("timestamp"); + } + if (maybe_input_timestamp) { + transform_stamped.header.stamp.sec = static_cast( + maybe_input_timestamp.value()->acqtime / static_cast(1e9)); + transform_stamped.header.stamp.nanosec = static_cast( + maybe_input_timestamp.value()->acqtime % static_cast(1e9)); + } else { + RCLCPP_WARN( + get_logger(), + "[FoundationPoseNode] Failed to get timestamp"); + } + + // Extract foundation pose list to a struct type defined in detection3_d_array_message.hpp + auto foundation_pose_detections_array_expected = nvidia::isaac::GetDetection3DListMessage( + msg_entity.value()); + if (!foundation_pose_detections_array_expected) { + RCLCPP_ERROR( + get_logger(), "[FoundationPoseNode] Failed to get detections data from message entity"); + return; + } + auto foundation_pose_detections_array = foundation_pose_detections_array_expected.value(); + + // Extract number of tags detected + size_t tags_count = foundation_pose_detections_array.count; + + if (tags_count > 0) { + // struct is defined in fiducial_message.hpp + auto pose = foundation_pose_detections_array.poses.at(0).value(); + + transform_stamped.header.frame_id = msg.frame_id; + transform_stamped.child_frame_id = tf_frame_name_; + transform_stamped.transform.translation.x = pose->translation.x(); + transform_stamped.transform.translation.y = pose->translation.y(); + transform_stamped.transform.translation.z = pose->translation.z(); + transform_stamped.transform.rotation.x = pose->rotation.quaternion().x(); + transform_stamped.transform.rotation.y = pose->rotation.quaternion().y(); + transform_stamped.transform.rotation.z = pose->rotation.quaternion().z(); + transform_stamped.transform.rotation.w = pose->rotation.quaternion().w(); + + tf_broadcaster_->sendTransform(transform_stamped); + } +} + +} // namespace foundationpose +} // namespace isaac_ros +} // namespace nvidia + +#include "rclcpp_components/register_node_macro.hpp" +RCLCPP_COMPONENTS_REGISTER_NODE(nvidia::isaac_ros::foundationpose::FoundationPoseNode) diff --git a/isaac_ros_foundationpose/src/foundationpose_selector_node.cpp b/isaac_ros_foundationpose/src/foundationpose_selector_node.cpp new file mode 100644 index 0000000..2081cd9 --- /dev/null +++ b/isaac_ros_foundationpose/src/foundationpose_selector_node.cpp @@ -0,0 +1,232 @@ +// SPDX-FileCopyrightText: NVIDIA CORPORATION & AFFILIATES +// Copyright (c) 2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. +// +// SPDX-License-Identifier: Apache-2.0 + +#include +#include +#include +#include +#include +#include +#include + +#include "message_filters/subscriber.h" +#include "message_filters/synchronizer.h" +#include "message_filters/sync_policies/exact_time.h" + +#include "isaac_ros_tensor_list_interfaces/msg/tensor_list.hpp" + +#include "isaac_ros_managed_nitros/managed_nitros_publisher.hpp" +#include "isaac_ros_managed_nitros/managed_nitros_subscriber.hpp" +#include "isaac_ros_managed_nitros/managed_nitros_message_filters_subscriber.hpp" + +#include "isaac_ros_nitros_image_type/nitros_image.hpp" +#include "isaac_ros_nitros_image_type/nitros_image_view.hpp" +#include "isaac_ros_nitros_tensor_list_type/nitros_tensor_list.hpp" +#include "isaac_ros_nitros_tensor_list_type/nitros_tensor_list_view.hpp" + +namespace nvidia +{ +namespace isaac_ros +{ +namespace foundationpose +{ + +/* +ROS 2 node that select the next action - tracking or pose estimation. +State flow: kPoseEstimatino -> kWaitingRest -> kTracking +*/ + +class Selector : public rclcpp::Node +{ +public: + explicit Selector(const rclcpp::NodeOptions & options) + : Node("selector", options) + { + // Create publishers for pose estimation + pose_estimation_image_pub_ = std::make_shared>( + this, "pose_estimation/image", + nvidia::isaac_ros::nitros::nitros_image_rgb8_t::supported_type_name); + pose_estimation_depth_pub_ = std::make_shared>( + this, "pose_estimation/depth_image", + nvidia::isaac_ros::nitros::nitros_image_32FC1_t::supported_type_name); + pose_estimation_segmenation_pub_ = std::make_shared< + nvidia::isaac_ros::nitros::ManagedNitrosPublisher>( + this, "pose_estimation/segmentation", + nvidia::isaac_ros::nitros::nitros_image_mono8_t::supported_type_name); + pose_estimation_camera_pub_ = this->create_publisher< + sensor_msgs::msg::CameraInfo>("pose_estimation/camera_info", 1); + + // Create publishers for tracking + tracking_image_pub_ = std::make_shared>( + this, "tracking/image", + nvidia::isaac_ros::nitros::nitros_image_rgb8_t::supported_type_name); + tracking_depth_pub_ = std::make_shared>( + this, "tracking/depth_image", + nvidia::isaac_ros::nitros::nitros_image_32FC1_t::supported_type_name); + tracking_pose_pub_ = this->create_publisher< + isaac_ros_tensor_list_interfaces::msg::TensorList>("tracking/pose_input", 1); + tracking_camera_pub_ = this->create_publisher< + sensor_msgs::msg::CameraInfo>("tracking/camera_info", 1); + + // Exact Sync + using namespace std::placeholders; + exact_sync_ = std::make_shared( + ExactPolicy(20), rgb_image_sub_, depth_image_sub_, segmentation_sub_, + camera_info_sub_); + exact_sync_->registerCallback( + std::bind(&Selector::selectionCallback, this, _1, _2, _3, _4)); + + segmentation_sub_.subscribe(this, "segmentation"); + rgb_image_sub_.subscribe(this, "image"); + depth_image_sub_.subscribe(this, "depth_image"); + camera_info_sub_.subscribe(this, "camera_info"); + + // Create subscriber for pose input + tracking_output_sub_ = + this->create_subscription( + "tracking/pose_matrix_output", 1, std::bind(&Selector::poseForwardCallback, this, _1)); + pose_estimation_output_sub_ = + this->create_subscription( + "pose_estimation/pose_matrix_output", 1, std::bind(&Selector::poseResetCallback, this, _1)); + + // reset period in ms after which pose estimation will be triggered + this->declare_parameter("reset_period", 20000); + this->get_parameter("reset_period", reset_period_); + + timer_ = this->create_wall_timer( + std::chrono::milliseconds(reset_period_), + std::bind(&Selector::timerCallback, this)); + } + + void selectionCallback( + const nvidia::isaac_ros::nitros::NitrosImage::ConstSharedPtr & image_msg, + const nvidia::isaac_ros::nitros::NitrosImage::ConstSharedPtr & depth_msg, + const nvidia::isaac_ros::nitros::NitrosImage::ConstSharedPtr & segmentaion_msg, + const sensor_msgs::msg::CameraInfo::ConstSharedPtr & camera_info_msg) + { + std::unique_lock lock(mutex_); + // Trigger next action + if (state_ == State::kPoseEstimation) { + // Publish all other messages except pose matrix to pose estimation + pose_estimation_image_pub_->publish(*image_msg); + pose_estimation_camera_pub_->publish(*camera_info_msg); + pose_estimation_depth_pub_->publish(*depth_msg); + pose_estimation_segmenation_pub_->publish(*segmentaion_msg); + state_ = State::kWaitingReset; + } else if (state_ == State::kTracking) { + // Publish all messages except segmentation to tracking + tracking_image_pub_->publish(*image_msg); + tracking_camera_pub_->publish(*camera_info_msg); + tracking_depth_pub_->publish(*depth_msg); + tracking_pose_pub_->publish(*tracking_pose_msg_); + } + } + + void poseForwardCallback( + const isaac_ros_tensor_list_interfaces::msg::TensorList::ConstSharedPtr & tracking_output_msg) + { + std::unique_lock lock(pose_mutex_); + // Discard the stale pose messages from tracking to avoid drift + if (state_ == State::kTracking) { + tracking_pose_msg_ = tracking_output_msg; + } + } + + void poseResetCallback( + const isaac_ros_tensor_list_interfaces::msg::TensorList::ConstSharedPtr + & pose_estimation_output_msg) + { + std::unique_lock lock(pose_mutex_); + tracking_pose_msg_ = pose_estimation_output_msg; + state_ = kTracking; + } + + void timerCallback() + { + std::unique_lock lock(mutex_); + state_ = State::kPoseEstimation; + } + +private: + // Publishers for pose estimation + std::shared_ptr> pose_estimation_image_pub_; + std::shared_ptr> pose_estimation_depth_pub_; + std::shared_ptr> pose_estimation_segmenation_pub_; + rclcpp::Publisher::SharedPtr pose_estimation_camera_pub_; + + // Publishers for tracking + std::shared_ptr> tracking_image_pub_; + std::shared_ptr> tracking_depth_pub_; + rclcpp::Publisher::SharedPtr tracking_camera_pub_; + rclcpp::Publisher::SharedPtr + tracking_pose_pub_; + + // Subscribers + nvidia::isaac_ros::nitros::message_filters::Subscriber< + nvidia::isaac_ros::nitros::NitrosImageView> rgb_image_sub_; + nvidia::isaac_ros::nitros::message_filters::Subscriber< + nvidia::isaac_ros::nitros::NitrosImageView> depth_image_sub_; + nvidia::isaac_ros::nitros::message_filters::Subscriber< + nvidia::isaac_ros::nitros::NitrosImageView> segmentation_sub_; + message_filters::Subscriber camera_info_sub_; + + rclcpp::Subscription::SharedPtr + tracking_output_sub_; + rclcpp::Subscription::SharedPtr + pose_estimation_output_sub_; + + enum State + { + kTracking, + kPoseEstimation, + kWaitingReset + }; + // State + State state_ = State::kPoseEstimation; + + // Exact message sync policy + using ExactPolicy = message_filters::sync_policies::ExactTime< + nvidia::isaac_ros::nitros::NitrosImage, + nvidia::isaac_ros::nitros::NitrosImage, + nvidia::isaac_ros::nitros::NitrosImage, + sensor_msgs::msg::CameraInfo>; + using ExactSync = message_filters::Synchronizer; + std::shared_ptr exact_sync_; + + rclcpp::TimerBase::SharedPtr timer_; + std::mutex mutex_; + std::mutex pose_mutex_; + isaac_ros_tensor_list_interfaces::msg::TensorList::ConstSharedPtr tracking_pose_msg_; + + int reset_period_; +}; + +} // namespace foundationpose +} // namespace isaac_ros +} // namespace nvidia + +// Register the component with the ROS system to create a shared library +RCLCPP_COMPONENTS_REGISTER_NODE(nvidia::isaac_ros::foundationpose::Selector) diff --git a/isaac_ros_foundationpose/src/foundationpose_tracking_node.cpp b/isaac_ros_foundationpose/src/foundationpose_tracking_node.cpp new file mode 100644 index 0000000..7e1d4d0 --- /dev/null +++ b/isaac_ros_foundationpose/src/foundationpose_tracking_node.cpp @@ -0,0 +1,362 @@ +// SPDX-FileCopyrightText: NVIDIA CORPORATION & AFFILIATES +// Copyright (c) 2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. +// +// SPDX-License-Identifier: Apache-2.0 + +#include + +#include "ament_index_cpp/get_package_share_directory.hpp" +#include "detection3_d_array_message/detection3_d_array_message.hpp" +#include "geometry_msgs/msg/transform_stamped.hpp" +#include "rclcpp/rclcpp.hpp" +#include "tf2_ros/transform_broadcaster.h" + +#include "isaac_ros_nitros_camera_info_type/nitros_camera_info.hpp" +#include "isaac_ros_nitros_detection3_d_array_type/nitros_detection3_d_array.hpp" +#include "isaac_ros_nitros_image_type/nitros_image.hpp" +#include "isaac_ros_nitros_tensor_list_type/nitros_tensor_list.hpp" + +#include "isaac_ros_foundationpose/foundationpose_tracking_node.hpp" + +namespace nvidia +{ +namespace isaac_ros +{ +namespace foundationpose +{ + +using nvidia::gxf::optimizer::GraphIOGroupSupportedDataTypesInfoList; + +constexpr char INPUT_DEPTH_COMPONENT_KEY[] = "sync/depth_receiver"; +constexpr char INPUT_DEPTH_TENSOR_FORMAT[] = "nitros_image_32FC1"; +constexpr char INPUT_DEPTH_TOPIC_NAME[] = "tracking/depth_image"; + +constexpr char INPUT_RGB_IMAGE_COMPONENT_KEY[] = "sync/rgb_image_receiver"; +constexpr char INPUT_RGB_IMAGE_TENSOR_FORMAT[] = "nitros_image_rgb8"; +constexpr char INPUT_RGB_IMAGE_TOPIC_NAME[] = "tracking/image"; + +constexpr char INPUT_CAMERA_INFO_COMPONENT_KEY[] = "sync/camera_model_receiver"; +constexpr char INPUT_CAMERA_INFO_FORMAT[] = "nitros_camera_info"; +constexpr char INPUT_CAMERA_INFO_TOPIC_NAME[] = "tracking/camera_info"; + +constexpr char INPUT_POSE_COMPONENT_KEY[] = "pose_broadcaster/poses_input"; +constexpr char INPUT_POSE_FORMAT[] = "nitros_tensor_list_nchw"; +constexpr char INPUT_POSE_TOPIC_NAME[] = "tracking/pose_input"; + +constexpr char OUTPUT_MATRIX_COMPONENT_KEY[] = "pose_matrix_sink/sink"; +constexpr char OUTPUT_MATRIX_FORMAT[] = "nitros_tensor_list_nchw"; +constexpr char OUTPUT_MATRIX_TOPIC_NAME[] = "tracking/pose_matrix_output"; + +constexpr char OUTPUT_POSE_COMPONENT_KEY[] = "pose_sink/sink"; +constexpr char OUTPUT_POSE_FORMAT[] = "nitros_detection3_d_array"; +constexpr char OUTPUT_POSE_TOPIC_NAME[] = "tracking/output"; + + +constexpr char APP_YAML_FILENAME[] = "config/nitros_foundationpose_tracking_node.yaml"; +constexpr char PACKAGE_NAME[] = "isaac_ros_foundationpose"; + +const std::vector> EXTENSIONS = { + {"isaac_ros_gxf", "gxf/lib/std/libgxf_std.so"}, + {"isaac_ros_gxf", "gxf/lib/multimedia/libgxf_multimedia.so"}, + {"isaac_ros_gxf", "gxf/lib/serialization/libgxf_serialization.so"}, + {"isaac_ros_gxf", "gxf/lib/cuda/libgxf_cuda.so"}, + {"gxf_isaac_depth_image_proc", "gxf/lib/libgxf_isaac_depth_image_proc.so"}, + {"gxf_isaac_sgm", "gxf/lib/libgxf_isaac_sgm.so"}, + {"gxf_isaac_messages", "gxf/lib/libgxf_isaac_messages.so"}, + {"gxf_isaac_ros_messages", "gxf/lib/libgxf_isaac_ros_messages.so"}, + {"gxf_isaac_tensor_rt", "gxf/lib/libgxf_isaac_tensor_rt.so"}, + {"gxf_isaac_foundationpose", "gxf/lib/libgxf_isaac_foundationpose.so"}, +}; + +const std::vector PRESET_EXTENSION_SPEC_NAMES = { + "isaac_ros_foundationpose", +}; +const std::vector EXTENSION_SPEC_FILENAMES = {}; +const std::vector GENERATOR_RULE_FILENAMES = {"config/namespace_injector_rule.yaml"}; +#pragma GCC diagnostic push +#pragma GCC diagnostic ignored "-Wpedantic" +const nitros::NitrosPublisherSubscriberConfigMap CONFIG_MAP = { + {INPUT_DEPTH_COMPONENT_KEY, + { + .type = nitros::NitrosPublisherSubscriberType::NEGOTIATED, + .qos = rclcpp::QoS(1), + .compatible_data_format = INPUT_DEPTH_TENSOR_FORMAT, + .topic_name = INPUT_DEPTH_TOPIC_NAME, + }}, + {INPUT_RGB_IMAGE_COMPONENT_KEY, + { + .type = nitros::NitrosPublisherSubscriberType::NEGOTIATED, + .qos = rclcpp::QoS(1), + .compatible_data_format = INPUT_RGB_IMAGE_TENSOR_FORMAT, + .topic_name = INPUT_RGB_IMAGE_TOPIC_NAME, + }}, + {INPUT_CAMERA_INFO_COMPONENT_KEY, + { + .type = nitros::NitrosPublisherSubscriberType::NEGOTIATED, + .qos = rclcpp::QoS(1), + .compatible_data_format = INPUT_CAMERA_INFO_FORMAT, + .topic_name = INPUT_CAMERA_INFO_TOPIC_NAME, + }}, + {INPUT_POSE_COMPONENT_KEY, + { + .type = nitros::NitrosPublisherSubscriberType::NEGOTIATED, + .qos = rclcpp::QoS(1), + .compatible_data_format = INPUT_POSE_FORMAT, + .topic_name = INPUT_POSE_TOPIC_NAME, + }}, + {OUTPUT_POSE_COMPONENT_KEY, + { + .type = nitros::NitrosPublisherSubscriberType::NEGOTIATED, + .qos = rclcpp::QoS(1), + .compatible_data_format = OUTPUT_POSE_FORMAT, + .topic_name = OUTPUT_POSE_TOPIC_NAME, + .frame_id_source_key = INPUT_RGB_IMAGE_COMPONENT_KEY, + }}, + {OUTPUT_MATRIX_COMPONENT_KEY, + { + .type = nitros::NitrosPublisherSubscriberType::NEGOTIATED, + .qos = rclcpp::QoS(1), + .compatible_data_format = OUTPUT_MATRIX_FORMAT, + .topic_name = OUTPUT_MATRIX_TOPIC_NAME, + .frame_id_source_key = INPUT_RGB_IMAGE_COMPONENT_KEY, + }}, +}; +#pragma GCC diagnostic pop + +FoundationPoseTrackingNode::FoundationPoseTrackingNode(rclcpp::NodeOptions options) +: nitros::NitrosNode( + options, APP_YAML_FILENAME, CONFIG_MAP, PRESET_EXTENSION_SPEC_NAMES, EXTENSION_SPEC_FILENAMES, + GENERATOR_RULE_FILENAMES, EXTENSIONS, PACKAGE_NAME), + configuration_file_( + declare_parameter("configuration_file", "foundationpose_model_config.yaml")), + mesh_file_path_(declare_parameter("mesh_file_path", "textured_simple.obj")), + texture_path_(declare_parameter("texture_path", "texture_map.png")), + min_depth_(declare_parameter("min_depth", 0.1)), + max_depth_(declare_parameter("max_depth", 4.0)), + refine_model_file_path_( + declare_parameter("refine_model_file_path", "/tmp/refine_model.onnx")), + refine_engine_file_path_( + declare_parameter("refine_engine_file_path", "/tmp/refine_trt_engine.plan")), + + refine_input_tensor_names_( + declare_parameter("refine_input_tensor_names", StringList())), + refine_input_binding_names_( + declare_parameter("refine_input_binding_names", StringList())), + refine_output_tensor_names_( + declare_parameter("refine_output_tensor_names", StringList())), + refine_output_binding_names_( + declare_parameter("refine_output_binding_names", StringList())), + + tf_frame_name_(declare_parameter("tf_frame_name", "fp_object")) +{ + RCLCPP_DEBUG(get_logger(), "[FoundationPoseTrackingNode] Constructor"); + + // Add callback function for Fundation Pose Detection3D array to broadcast to ros tf tree + config_map_[OUTPUT_POSE_COMPONENT_KEY].callback = std::bind( + &FoundationPoseTrackingNode::FoundationPoseTrackingCallback, this, + std::placeholders::_1, std::placeholders::_2); + + RCLCPP_DEBUG(get_logger(), "[FoundationPoseTrackingNode] Constructor"); + + // Open configuration YAML file + const std::string package_directory = ament_index_cpp::get_package_share_directory( + "isaac_ros_foundationpose"); + std::filesystem::path yaml_path = + package_directory / std::filesystem::path("config") / + std::filesystem::path(configuration_file_); + if (!std::filesystem::exists(yaml_path)) { + RCLCPP_ERROR(this->get_logger(), "%s could not be found. Exiting.", yaml_path.string().c_str()); + throw std::runtime_error("Parameter parsing failure."); + } + + // Parse parameters + rcl_params_t * foundationpose_params = rcl_yaml_node_struct_init(rcutils_get_default_allocator()); + rcl_parse_yaml_file(yaml_path.c_str(), foundationpose_params); + + rcl_variant_t * resized_image_width = rcl_yaml_node_struct_get( + "foundationpose", "resized_image_width", foundationpose_params); + if (!resized_image_width->integer_value) { + RCLCPP_ERROR(this->get_logger(), "No resized_image_width parameter found"); + throw std::runtime_error("Parameter parsing failure."); + } + resized_image_width_ = static_cast(*resized_image_width->integer_value); + + rcl_variant_t * resized_image_height = rcl_yaml_node_struct_get( + "foundationpose", "resized_image_height", foundationpose_params); + if (!resized_image_width->integer_value) { + RCLCPP_ERROR(this->get_logger(), "No resized_image_height parameter found"); + throw std::runtime_error("Parameter parsing failure."); + } + resized_image_height_ = static_cast(*resized_image_height->integer_value); + + rcl_variant_t * refine_crop_ratio = rcl_yaml_node_struct_get( + "foundationpose", "refine_crop_ratio", foundationpose_params); + if (!refine_crop_ratio->double_value) { + RCLCPP_ERROR(this->get_logger(), "No refine_crop_ratio parameter found"); + throw std::runtime_error("Parameter parsing failure."); + } + refine_crop_ratio_ = static_cast(*refine_crop_ratio->double_value); + + rcl_variant_t * rot_normalizer = rcl_yaml_node_struct_get( + "foundationpose", "rot_normalizer", foundationpose_params); + if (!rot_normalizer->double_value) { + RCLCPP_ERROR(this->get_logger(), "No rot_normalizer parameter found"); + throw std::runtime_error("Parameter parsing failure."); + } + rot_normalizer_ = static_cast(*rot_normalizer->double_value); + + rcl_yaml_node_struct_fini(foundationpose_params); + + registerSupportedType(); + registerSupportedType(); + registerSupportedType(); + registerSupportedType(); + + startNitrosNode(); +} + +FoundationPoseTrackingNode::~FoundationPoseTrackingNode() = default; + +void FoundationPoseTrackingNode::postLoadGraphCallback() +{ + RCLCPP_DEBUG(get_logger(), "[FoundationPoseTrackingNode] postLoadGraphCallback()."); + + getNitrosContext().setParameterUInt32( + "render", "nvidia::isaac_ros::FoundationposeRender", "resized_image_width", + resized_image_width_); + + getNitrosContext().setParameterUInt32( + "render", "nvidia::isaac_ros::FoundationposeRender", "resized_image_height", + resized_image_height_); + + getNitrosContext().setParameterFloat32( + "render", "nvidia::isaac_ros::FoundationposeRender", "crop_ratio", refine_crop_ratio_); + + getNitrosContext().setParameterFloat32( + "transform", "nvidia::isaac_ros::FoundationposeTransformation", "rot_normalizer", + rot_normalizer_); + + // Set the mesh path from parameter + getNitrosContext().setParameterStr( + "render", "nvidia::isaac_ros::FoundationposeRender", "mesh_file_path", mesh_file_path_); + + getNitrosContext().setParameterStr( + "render", "nvidia::isaac_ros::FoundationposeRender", "texture_path", texture_path_); + + getNitrosContext().setParameterStr( + "transform", "nvidia::isaac_ros::FoundationposeTransformation", "mesh_file_path", + mesh_file_path_); + + getNitrosContext().setParameterStr( + "decoder", "nvidia::isaac_ros::FoundationposeDecoder", "mesh_file_path", mesh_file_path_); + + getNitrosContext().setParameterStr( + "decoder", "nvidia::isaac_ros::FoundationposeDecoder", "mesh_file_path", mesh_file_path_); + + // Set the refine network TensorRT configs from parameter + getNitrosContext().setParameterStr( + "refine_inference", "nvidia::gxf::TensorRtInference", "model_file_path", + refine_model_file_path_); + + getNitrosContext().setParameterStr( + "refine_inference", "nvidia::gxf::TensorRtInference", "engine_file_path", + refine_engine_file_path_); + + getNitrosContext().setParameter1DStrVector( + "refine_inference", "nvidia::gxf::TensorRtInference", "input_tensor_names", + refine_input_tensor_names_); + + getNitrosContext().setParameter1DStrVector( + "refine_inference", "nvidia::gxf::TensorRtInference", "input_binding_names", + refine_input_binding_names_); + + getNitrosContext().setParameter1DStrVector( + "refine_inference", "nvidia::gxf::TensorRtInference", "output_tensor_names", + refine_output_tensor_names_); + + getNitrosContext().setParameter1DStrVector( + "refine_inference", "nvidia::gxf::TensorRtInference", "output_binding_names", + refine_output_binding_names_); + + getNitrosContext().setParameterFloat32( + "render", "nvidia::isaac_ros::FoundationposeRender", "min_depth", min_depth_); + + getNitrosContext().setParameterFloat32( + "render", "nvidia::isaac_ros::FoundationposeRender", "max_depth", max_depth_); +} + +void FoundationPoseTrackingNode::FoundationPoseTrackingCallback( + const gxf_context_t context, nitros::NitrosTypeBase & msg) +{ + std::unique_ptr tf_broadcaster_ = + std::make_unique(*this); + geometry_msgs::msg::TransformStamped transform_stamped; + + auto msg_entity = nvidia::gxf::Entity::Shared(context, msg.handle); + + // Populate timestamp information back into the header + auto maybe_input_timestamp = msg_entity->get(); + if (!maybe_input_timestamp) { // Fallback to label 'timestamp' + maybe_input_timestamp = msg_entity->get("timestamp"); + } + if (maybe_input_timestamp) { + transform_stamped.header.stamp.sec = static_cast( + maybe_input_timestamp.value()->acqtime / static_cast(1e9)); + transform_stamped.header.stamp.nanosec = static_cast( + maybe_input_timestamp.value()->acqtime % static_cast(1e9)); + } else { + RCLCPP_WARN( + get_logger(), + "[FoundationPoseTrackingNode] Failed to get timestamp"); + } + + // Extract foundation pose list to a struct type defined in detection3_d_array_message.hpp + auto foundation_pose_detections_array_expected = nvidia::isaac::GetDetection3DListMessage( + msg_entity.value()); + if (!foundation_pose_detections_array_expected) { + RCLCPP_ERROR( + get_logger(), + "[FoundationPoseTrackingNode] Failed to get detections data from message entity"); + return; + } + auto foundation_pose_detections_array = foundation_pose_detections_array_expected.value(); + + // Extract number of tags detected + size_t tags_count = foundation_pose_detections_array.count; + + if (tags_count > 0) { + // struct is defined in fiducial_message.hpp + auto pose = foundation_pose_detections_array.poses.at(0).value(); + + transform_stamped.header.frame_id = msg.frame_id; + transform_stamped.child_frame_id = tf_frame_name_; + transform_stamped.transform.translation.x = pose->translation.x(); + transform_stamped.transform.translation.y = pose->translation.y(); + transform_stamped.transform.translation.z = pose->translation.z(); + transform_stamped.transform.rotation.x = pose->rotation.quaternion().x(); + transform_stamped.transform.rotation.y = pose->rotation.quaternion().y(); + transform_stamped.transform.rotation.z = pose->rotation.quaternion().z(); + transform_stamped.transform.rotation.w = pose->rotation.quaternion().w(); + + tf_broadcaster_->sendTransform(transform_stamped); + } +} + +} // namespace foundationpose +} // namespace isaac_ros +} // namespace nvidia + +#include "rclcpp_components/register_node_macro.hpp" +RCLCPP_COMPONENTS_REGISTER_NODE(nvidia::isaac_ros::foundationpose::FoundationPoseTrackingNode) diff --git a/isaac_ros_foundationpose/test/isaac_ros_foundationpose_bbox_pol.py b/isaac_ros_foundationpose/test/isaac_ros_foundationpose_bbox_pol.py new file mode 100644 index 0000000..35411dc --- /dev/null +++ b/isaac_ros_foundationpose/test/isaac_ros_foundationpose_bbox_pol.py @@ -0,0 +1,225 @@ +# SPDX-FileCopyrightText: NVIDIA CORPORATION & AFFILIATES +# Copyright (c) 2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +# SPDX-License-Identifier: Apache-2.0 + +""" +Basic Proof-Of-Life test for the Isaac ROS FoundationPose 6D Pose Estimation Node. + +This node takes RGBD image and segmentation mask into a tensor, run through +TensorRT using a FoundationPose models, and the inference decoded into a series of poses. +""" +import os +import pathlib +import time + +import cv2 +from cv_bridge import CvBridge +from isaac_ros_test import IsaacROSBaseTest, JSONConversion +from launch_ros.actions import ComposableNodeContainer +from launch_ros.descriptions import ComposableNode + +import numpy as np +import pytest +import rclpy + +from sensor_msgs.msg import CameraInfo, Image +from vision_msgs.msg import Detection2D, Detection3DArray + + +MESH_FILE_NAME = 'textured_simple.obj' +TEXTURE_MAP_NAME = 'texture_map.png' + +REFINE_MODEL_NAME = 'dummy_refine_model.onnx' +REFINE_ENGINE_NAME = 'dummy_refine_trt_engine.plan' +SCORE_MODEL_NAME = 'dummy_score_model.onnx' +SCORE_ENGINE_NAME = 'dummy_score_trt_engine.plan' + +REFINE_ENGINE_PATH = '/tmp/' + REFINE_ENGINE_NAME +SCORE_ENGINE_PATH = '/tmp/' + SCORE_ENGINE_NAME + +MODEL_GENERATION_TIMEOUT_SEC = 900 +INIT_WAIT_SEC = 10 +DELTA = 0.05 + + +@pytest.mark.rostest +def generate_test_description(): + + foundationpose_node = ComposableNode( + name='foundationpose', + package='isaac_ros_foundationpose', + plugin='nvidia::isaac_ros::foundationpose::FoundationPoseNode', + namespace=IsaacROSFoundationPosePOLTest.generate_namespace(), + parameters=[{ + 'mesh_file_path': os.path.dirname(__file__) + + '/test_cases/foundationpose/' + MESH_FILE_NAME, + 'texture_path': os.path.dirname(__file__) + + '/test_cases/foundationpose/' + TEXTURE_MAP_NAME, + + 'refine_model_file_path': os.path.dirname(__file__) + + '/../../test/models/' + REFINE_MODEL_NAME, + 'refine_engine_file_path': REFINE_ENGINE_PATH, + 'refine_input_tensor_names': ['input_tensor1', 'input_tensor2'], + 'refine_input_binding_names': ['input1', 'input2'], + 'refine_output_tensor_names': ['output_tensor1', 'output_tensor2'], + 'refine_output_binding_names': ['output1', 'output2'], + + 'score_model_file_path': os.path.dirname(__file__) + + '/../../test/models/' + SCORE_MODEL_NAME, + 'score_engine_file_path': SCORE_ENGINE_PATH, + 'score_input_tensor_names': ['input_tensor1', 'input_tensor2'], + 'score_input_binding_names': ['input1', 'input2'], + 'score_output_tensor_names': ['output_tensor'], + 'score_output_binding_names': ['output1'], + }]) + + detection2_d_to_mask_node = ComposableNode( + name='detection2_d_to_mask', + package='isaac_ros_foundationpose', + namespace=IsaacROSFoundationPosePOLTest.generate_namespace(), + plugin='nvidia::isaac_ros::foundationpose::Detection2DToMask', + parameters=[{ + 'mask_width': 640, + 'mask_height': 480 + }], + remappings=[ + ('segmentation', 'pose_estimation/segmentation')]) + + container = ComposableNodeContainer( + name='foundationpose_container', + namespace='', + package='rclcpp_components', + executable='component_container_mt', + composable_node_descriptions=[ + detection2_d_to_mask_node, + foundationpose_node + ], + output='screen', + ) + + return IsaacROSFoundationPosePOLTest.generate_test_description([container]) + + +class IsaacROSFoundationPosePOLTest(IsaacROSBaseTest): + filepath = pathlib.Path(os.path.dirname(__file__)) + + @IsaacROSBaseTest.for_each_test_case() + def test_pol(self, test_folder): + self.node._logger.info( + f'Generating model (timeout={MODEL_GENERATION_TIMEOUT_SEC}s)') + start_time = time.time() + wait_cycles = 1 + while not os.path.isfile(SCORE_ENGINE_PATH) or not os.path.isfile(REFINE_ENGINE_PATH): + time_diff = time.time() - start_time + if time_diff > MODEL_GENERATION_TIMEOUT_SEC: + self.fail('Model generation timed out') + if time_diff > wait_cycles*10: + self.node._logger.info( + f'Waiting for model generation to finish... ({int(time_diff)}s passed)') + wait_cycles += 1 + time.sleep(1) + + # Wait for TensorRT engine + time.sleep(INIT_WAIT_SEC) + + self.node._logger.info( + f'Model generation was finished (took {(time.time() - start_time)}s)') + + received_messages = {} + + self.generate_namespace_lookup(['detection2_d', + 'pose_estimation/depth_image', + 'pose_estimation/image', + 'pose_estimation/camera_info', + 'pose_estimation/segmentation', + 'pose_estimation/output']) + + subs = self.create_logging_subscribers( + [('pose_estimation/output', Detection3DArray)], received_messages) + + depth_pub = self.node.create_publisher( + Image, self.namespaces['pose_estimation/depth_image'], self.DEFAULT_QOS + ) + rgb_image_pub = self.node.create_publisher( + Image, self.namespaces['pose_estimation/image'], self.DEFAULT_QOS + ) + detection2_d_pub = self.node.create_publisher( + Detection2D, self.namespaces['detection2_d'], self.DEFAULT_QOS + ) + cam_info_pub = self.node.create_publisher( + CameraInfo, self.namespaces['pose_estimation/camera_info'], self.DEFAULT_QOS + ) + + try: + + time_now_msg = self.node.get_clock().now().to_msg() + depth_path = str(test_folder) + '/mustard_depth.png' + mask = cv2.imread(depth_path, cv2.IMREAD_UNCHANGED) + + depth_array = np.array(mask, dtype=np.float32) + # Apply the same principle with the demo + depth_array = depth_array / 1e3 + depth_image = CvBridge().cv2_to_imgmsg(depth_array, '32FC1') + + bgr_path = str(test_folder) + '/mustard_rgb.png' + bgr = cv2.imread( + bgr_path) + rgb = cv2.cvtColor(bgr, cv2.COLOR_BGR2RGB) + rgb_image = CvBridge().cv2_to_imgmsg(rgb, 'rgb8') + + bbox_json = JSONConversion.load_from_json( + test_folder / 'detection2_d.json') + detection2_d = Detection2D() + detection2_d.bbox.center.position.x = bbox_json['center']['position']['x'] + detection2_d.bbox.center.position.y = bbox_json['center']['position']['y'] + detection2_d.bbox.center.theta = bbox_json['center']['theta'] + detection2_d.bbox.size_x = bbox_json['size_x'] + detection2_d.bbox.size_y = bbox_json['size_y'] + + camera_info = JSONConversion.load_camera_info_from_json( + test_folder / 'camera_info.json') + + TIMEOUT = 60 + end_time = time.time() + TIMEOUT + done = False + + depth_image.header.stamp = time_now_msg + rgb_image.header.stamp = time_now_msg + detection2_d.header.stamp = time_now_msg + camera_info.header.stamp = time_now_msg + rgb_image.header.frame_id = 'tf_camera' + detection2_d.header.frame_id = 'tf_camera' + camera_info.header.frame_id = 'tf_camera' + + while time.time() < end_time: + rgb_image_pub.publish(rgb_image) + depth_pub.publish(depth_image) + detection2_d_pub.publish(detection2_d) + cam_info_pub.publish(camera_info) + rclpy.spin_once(self.node, timeout_sec=(10)) + if 'pose_estimation/output' in received_messages: + done = True + break + + self.assertTrue(done, 'Timeout. Appropriate output not received') + self.node._logger.info('A message was successfully received') + + finally: + self.node.destroy_subscription(subs) + self.node.destroy_publisher(rgb_image_pub) + self.node.destroy_publisher(depth_pub) + self.node.destroy_publisher(cam_info_pub) + self.node.destroy_publisher(detection2_d_pub) diff --git a/isaac_ros_foundationpose/test/isaac_ros_foundationpose_iterative_refine_pol.py b/isaac_ros_foundationpose/test/isaac_ros_foundationpose_iterative_refine_pol.py new file mode 100644 index 0000000..ebf8126 --- /dev/null +++ b/isaac_ros_foundationpose/test/isaac_ros_foundationpose_iterative_refine_pol.py @@ -0,0 +1,202 @@ +# SPDX-FileCopyrightText: NVIDIA CORPORATION & AFFILIATES +# Copyright (c) 2021-2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +# SPDX-License-Identifier: Apache-2.0 + +""" +Basic Proof-Of-Life test for the Isaac ROS FoundationPose 6D Pose Estimation Node. + +This node takes RGBD image and segmentation mask into a tensor, run through +TensorRT using a FoundationPose models, and the inference decoded into a series of poses. +""" +import os +import pathlib +import time + +import cv2 +from cv_bridge import CvBridge +from isaac_ros_test import IsaacROSBaseTest, JSONConversion +from launch_ros.actions import ComposableNodeContainer +from launch_ros.descriptions import ComposableNode + +import numpy as np +import pytest +import rclpy + +from sensor_msgs.msg import CameraInfo, Image +from vision_msgs.msg import Detection3DArray + + +MESH_FILE_NAME = 'textured_simple.obj' +TEXTURE_MAP_NAME = 'texture_map.png' + +REFINE_MODEL_NAME = 'dummy_refine_model.onnx' +REFINE_ENGINE_NAME = 'dummy_refine_trt_engine.plan' +SCORE_MODEL_NAME = 'dummy_score_model.onnx' +SCORE_ENGINE_NAME = 'dummy_score_trt_engine.plan' + +REFINE_ENGINE_PATH = '/tmp/' + REFINE_ENGINE_NAME +SCORE_ENGINE_PATH = '/tmp/' + SCORE_ENGINE_NAME + +MODEL_GENERATION_TIMEOUT_SEC = 900 +INIT_WAIT_SEC = 10 +DELTA = 0.05 + + +@pytest.mark.rostest +def generate_test_description(): + + foundationpose_node = ComposableNode( + name='foundationpose', + package='isaac_ros_foundationpose', + plugin='nvidia::isaac_ros::foundationpose::FoundationPoseNode', + namespace=IsaacROSFoundationPosePOLTest.generate_namespace(), + parameters=[{ + 'mesh_file_path': os.path.dirname(__file__) + + '/test_cases/foundationpose/' + MESH_FILE_NAME, + 'texture_path': os.path.dirname(__file__) + + '/test_cases/foundationpose/' + TEXTURE_MAP_NAME, + 'refine_iterations': 5, + + 'refine_model_file_path': os.path.dirname(__file__) + + '/../../test/models/' + REFINE_MODEL_NAME, + 'refine_engine_file_path': REFINE_ENGINE_PATH, + 'refine_input_tensor_names': ['input_tensor1', 'input_tensor2'], + 'refine_input_binding_names': ['input1', 'input2'], + 'refine_output_tensor_names': ['output_tensor1', 'output_tensor2'], + 'refine_output_binding_names': ['output1', 'output2'], + + 'score_model_file_path': os.path.dirname(__file__) + + '/../../test/models/' + SCORE_MODEL_NAME, + 'score_engine_file_path': SCORE_ENGINE_PATH, + 'score_input_tensor_names': ['input_tensor1', 'input_tensor2'], + 'score_input_binding_names': ['input1', 'input2'], + 'score_output_tensor_names': ['output_tensor'], + 'score_output_binding_names': ['output1'], + }]) + + container = ComposableNodeContainer( + name='foundationpose_container', + namespace='', + package='rclcpp_components', + executable='component_container_mt', + composable_node_descriptions=[foundationpose_node], + output='screen', + ) + + return IsaacROSFoundationPosePOLTest.generate_test_description([container]) + + +class IsaacROSFoundationPosePOLTest(IsaacROSBaseTest): + filepath = pathlib.Path(os.path.dirname(__file__)) + + @IsaacROSBaseTest.for_each_test_case() + def test_pol(self, test_folder): + self.node._logger.info( + f'Generating model (timeout={MODEL_GENERATION_TIMEOUT_SEC}s)') + start_time = time.time() + wait_cycles = 1 + while not os.path.isfile(SCORE_ENGINE_PATH) or not os.path.isfile(REFINE_ENGINE_PATH): + time_diff = time.time() - start_time + if time_diff > MODEL_GENERATION_TIMEOUT_SEC: + self.fail('Model generation timed out') + if time_diff > wait_cycles*10: + self.node._logger.info( + f'Waiting for model generation to finish... ({int(time_diff)}s passed)') + wait_cycles += 1 + time.sleep(1) + + # Wait for TensorRT engine + time.sleep(INIT_WAIT_SEC) + + received_messages = {} + + self.generate_namespace_lookup(['pose_estimation/depth_image', + 'pose_estimation/image', + 'pose_estimation/camera_info', + 'pose_estimation/segmentation', + 'pose_estimation/output']) + + subs = self.create_logging_subscribers( + [('pose_estimation/output', Detection3DArray)], received_messages) + + depth_pub = self.node.create_publisher( + Image, self.namespaces['pose_estimation/depth_image'], self.DEFAULT_QOS + ) + rgb_image_pub = self.node.create_publisher( + Image, self.namespaces['pose_estimation/image'], self.DEFAULT_QOS + ) + mask_pub = self.node.create_publisher( + Image, self.namespaces['pose_estimation/segmentation'], self.DEFAULT_QOS + ) + cam_info_pub = self.node.create_publisher( + CameraInfo, self.namespaces['pose_estimation/camera_info'], self.DEFAULT_QOS + ) + + try: + + time_now_msg = self.node.get_clock().now().to_msg() + depth_path = str(test_folder) + '/mustard_depth.png' + mask = cv2.imread(depth_path, cv2.IMREAD_UNCHANGED) + + depth_array = np.array(mask, dtype=np.float32) + # Apply the same principle with the demo + depth_array = depth_array / 1e3 + depth_image = CvBridge().cv2_to_imgmsg(depth_array, '32FC1') + + bgr_path = str(test_folder) + '/mustard_rgb.png' + bgr = cv2.imread( + bgr_path) + rgb = cv2.cvtColor(bgr, cv2.COLOR_BGR2RGB) + rgb_image = CvBridge().cv2_to_imgmsg(rgb, 'rgb8') + + segmentation_path = str(test_folder) + '/mustard_mask.png' + raw_segmentation = cv2.imread( + segmentation_path, cv2.IMREAD_GRAYSCALE) + mask = CvBridge().cv2_to_imgmsg(raw_segmentation, 'mono8') + + camera_info = JSONConversion.load_camera_info_from_json( + test_folder / 'camera_info.json') + + TIMEOUT = 60 + end_time = time.time() + TIMEOUT + done = False + + depth_image.header.stamp = time_now_msg + rgb_image.header.stamp = time_now_msg + mask.header.stamp = time_now_msg + camera_info.header.stamp = time_now_msg + rgb_image.header.frame_id = 'tf_camera' + camera_info.header.frame_id = 'tf_camera' + + while time.time() < end_time: + rgb_image_pub.publish(rgb_image) + depth_pub.publish(depth_image) + mask_pub.publish(mask) + cam_info_pub.publish(camera_info) + rclpy.spin_once(self.node, timeout_sec=(10)) + if 'pose_estimation/output' in received_messages: + done = True + break + + self.assertTrue(done, 'Timeout. Appropriate output not received') + self.node._logger.info('A message was successfully received') + + finally: + self.node.destroy_subscription(subs) + self.node.destroy_publisher(rgb_image_pub) + self.node.destroy_publisher(depth_pub) + self.node.destroy_publisher(cam_info_pub) + self.node.destroy_publisher(mask_pub) diff --git a/isaac_ros_foundationpose/test/isaac_ros_foundationpose_pol.py b/isaac_ros_foundationpose/test/isaac_ros_foundationpose_pol.py new file mode 100644 index 0000000..4a0621f --- /dev/null +++ b/isaac_ros_foundationpose/test/isaac_ros_foundationpose_pol.py @@ -0,0 +1,202 @@ +# SPDX-FileCopyrightText: NVIDIA CORPORATION & AFFILIATES +# Copyright (c) 2021-2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +# SPDX-License-Identifier: Apache-2.0 + +""" +Basic Proof-Of-Life test for the Isaac ROS FoundationPose 6D Pose Estimation Node. + +This node takes RGBD image and segmentation mask into a tensor, run through +TensorRT using a FoundationPose models, and the inference decoded into a series of poses. +""" +import os +import pathlib +import time + +import cv2 +from cv_bridge import CvBridge +from isaac_ros_test import IsaacROSBaseTest, JSONConversion +from launch_ros.actions import ComposableNodeContainer +from launch_ros.descriptions import ComposableNode + +import numpy as np +import pytest +import rclpy + +from sensor_msgs.msg import CameraInfo, Image +from vision_msgs.msg import Detection3DArray + + +MESH_FILE_NAME = 'textured_simple.obj' +TEXTURE_MAP_NAME = 'texture_map.png' + +REFINE_MODEL_NAME = 'dummy_refine_model.onnx' +REFINE_ENGINE_NAME = 'dummy_refine_trt_engine.plan' +SCORE_MODEL_NAME = 'dummy_score_model.onnx' +SCORE_ENGINE_NAME = 'dummy_score_trt_engine.plan' + +REFINE_ENGINE_PATH = '/tmp/' + REFINE_ENGINE_NAME +SCORE_ENGINE_PATH = '/tmp/' + SCORE_ENGINE_NAME + +MODEL_GENERATION_TIMEOUT_SEC = 900 +INIT_WAIT_SEC = 10 +DELTA = 0.05 + + +@pytest.mark.rostest +def generate_test_description(): + + foundationpose_node = ComposableNode( + name='foundationpose', + package='isaac_ros_foundationpose', + plugin='nvidia::isaac_ros::foundationpose::FoundationPoseNode', + namespace=IsaacROSFoundationPosePOLTest.generate_namespace(), + parameters=[{ + 'mesh_file_path': os.path.dirname(__file__) + + '/test_cases/foundationpose/' + MESH_FILE_NAME, + 'texture_path': os.path.dirname(__file__) + + '/test_cases/foundationpose/' + TEXTURE_MAP_NAME, + 'refine_iterations': 1, + + 'refine_model_file_path': os.path.dirname(__file__) + + '/../../test/models/' + REFINE_MODEL_NAME, + 'refine_engine_file_path': REFINE_ENGINE_PATH, + 'refine_input_tensor_names': ['input_tensor1', 'input_tensor2'], + 'refine_input_binding_names': ['input1', 'input2'], + 'refine_output_tensor_names': ['output_tensor1', 'output_tensor2'], + 'refine_output_binding_names': ['output1', 'output2'], + + 'score_model_file_path': os.path.dirname(__file__) + + '/../../test/models/' + SCORE_MODEL_NAME, + 'score_engine_file_path': SCORE_ENGINE_PATH, + 'score_input_tensor_names': ['input_tensor1', 'input_tensor2'], + 'score_input_binding_names': ['input1', 'input2'], + 'score_output_tensor_names': ['output_tensor'], + 'score_output_binding_names': ['output1'], + }]) + + container = ComposableNodeContainer( + name='foundationpose_container', + namespace='', + package='rclcpp_components', + executable='component_container_mt', + composable_node_descriptions=[foundationpose_node], + output='screen', + ) + + return IsaacROSFoundationPosePOLTest.generate_test_description([container]) + + +class IsaacROSFoundationPosePOLTest(IsaacROSBaseTest): + filepath = pathlib.Path(os.path.dirname(__file__)) + + @IsaacROSBaseTest.for_each_test_case() + def test_pol(self, test_folder): + self.node._logger.info( + f'Generating model (timeout={MODEL_GENERATION_TIMEOUT_SEC}s)') + start_time = time.time() + wait_cycles = 1 + while not os.path.isfile(SCORE_ENGINE_PATH) or not os.path.isfile(REFINE_ENGINE_PATH): + time_diff = time.time() - start_time + if time_diff > MODEL_GENERATION_TIMEOUT_SEC: + self.fail('Model generation timed out') + if time_diff > wait_cycles*10: + self.node._logger.info( + f'Waiting for model generation to finish... ({int(time_diff)}s passed)') + wait_cycles += 1 + time.sleep(1) + + # Wait for TensorRT engine + time.sleep(INIT_WAIT_SEC) + + received_messages = {} + + self.generate_namespace_lookup(['pose_estimation/depth_image', + 'pose_estimation/image', + 'pose_estimation/camera_info', + 'pose_estimation/segmentation', + 'pose_estimation/output']) + + subs = self.create_logging_subscribers( + [('pose_estimation/output', Detection3DArray)], received_messages) + + depth_pub = self.node.create_publisher( + Image, self.namespaces['pose_estimation/depth_image'], self.DEFAULT_QOS + ) + rgb_image_pub = self.node.create_publisher( + Image, self.namespaces['pose_estimation/image'], self.DEFAULT_QOS + ) + mask_pub = self.node.create_publisher( + Image, self.namespaces['pose_estimation/segmentation'], self.DEFAULT_QOS + ) + cam_info_pub = self.node.create_publisher( + CameraInfo, self.namespaces['pose_estimation/camera_info'], self.DEFAULT_QOS + ) + + try: + + time_now_msg = self.node.get_clock().now().to_msg() + depth_path = str(test_folder) + '/mustard_depth.png' + mask = cv2.imread(depth_path, cv2.IMREAD_UNCHANGED) + + depth_array = np.array(mask, dtype=np.float32) + # Apply the same principle with the demo + depth_array = depth_array / 1e3 + depth_image = CvBridge().cv2_to_imgmsg(depth_array, '32FC1') + + bgr_path = str(test_folder) + '/mustard_rgb.png' + bgr = cv2.imread( + bgr_path) + rgb = cv2.cvtColor(bgr, cv2.COLOR_BGR2RGB) + rgb_image = CvBridge().cv2_to_imgmsg(rgb, 'rgb8') + + segmentation_path = str(test_folder) + '/mustard_mask.png' + raw_segmentation = cv2.imread( + segmentation_path, cv2.IMREAD_GRAYSCALE) + mask = CvBridge().cv2_to_imgmsg(raw_segmentation, 'mono8') + + camera_info = JSONConversion.load_camera_info_from_json( + test_folder / 'camera_info.json') + + TIMEOUT = 60 + end_time = time.time() + TIMEOUT + done = False + + depth_image.header.stamp = time_now_msg + rgb_image.header.stamp = time_now_msg + mask.header.stamp = time_now_msg + camera_info.header.stamp = time_now_msg + rgb_image.header.frame_id = 'tf_camera' + camera_info.header.frame_id = 'tf_camera' + + while time.time() < end_time: + rgb_image_pub.publish(rgb_image) + depth_pub.publish(depth_image) + mask_pub.publish(mask) + cam_info_pub.publish(camera_info) + rclpy.spin_once(self.node, timeout_sec=(10)) + if 'pose_estimation/output' in received_messages: + done = True + break + + self.assertTrue(done, 'Timeout. Appropriate output not received') + self.node._logger.info('A message was successfully received') + + finally: + self.node.destroy_subscription(subs) + self.node.destroy_publisher(rgb_image_pub) + self.node.destroy_publisher(depth_pub) + self.node.destroy_publisher(cam_info_pub) + self.node.destroy_publisher(mask_pub) diff --git a/isaac_ros_foundationpose/test/isaac_ros_foundationpose_tracking_pol.py b/isaac_ros_foundationpose/test/isaac_ros_foundationpose_tracking_pol.py new file mode 100644 index 0000000..b72d8e9 --- /dev/null +++ b/isaac_ros_foundationpose/test/isaac_ros_foundationpose_tracking_pol.py @@ -0,0 +1,232 @@ +# SPDX-FileCopyrightText: NVIDIA CORPORATION & AFFILIATES +# Copyright (c) 2021-2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +# SPDX-License-Identifier: Apache-2.0 + +""" +Basic Proof-Of-Life test for the Isaac ROS FoundationPose 6D Pose Estimation Node. + +This node takes RGBD image and segmentation mask into a tensor, run through +TensorRT using a FoundationPose models, and the inference decoded into a series of poses. +""" +import os +import pathlib +import time + +import cv2 +from cv_bridge import CvBridge +from isaac_ros_test import IsaacROSBaseTest, JSONConversion +from launch_ros.actions import ComposableNodeContainer +from launch_ros.descriptions import ComposableNode + +import numpy as np +import pytest +import rclpy + +from sensor_msgs.msg import CameraInfo, Image +from vision_msgs.msg import Detection3DArray + + +MESH_FILE_NAME = 'textured_simple.obj' +TEXTURE_MAP_NAME = 'texture_map.png' + +REFINE_MODEL_NAME = 'dummy_refine_model.onnx' +REFINE_ENGINE_NAME = 'dummy_refine_trt_engine.plan' +SCORE_MODEL_NAME = 'dummy_score_model.onnx' +SCORE_ENGINE_NAME = 'dummy_score_trt_engine.plan' + +REFINE_ENGINE_PATH = '/tmp/' + REFINE_ENGINE_NAME +SCORE_ENGINE_PATH = '/tmp/' + SCORE_ENGINE_NAME + +MODEL_GENERATION_TIMEOUT_SEC = 900 +INIT_WAIT_SEC = 10 +DELTA = 0.05 + + +@pytest.mark.rostest +def generate_test_description(): + + selector_node = ComposableNode( + name='foundationpose', + package='isaac_ros_foundationpose', + plugin='nvidia::isaac_ros::foundationpose::Selector', + namespace=IsaacROSFoundationPosePOLTest.generate_namespace(), + parameters=[{ + 'reset_period': 5000 + }]) + + foundationpose_node = ComposableNode( + name='foundationpose', + package='isaac_ros_foundationpose', + plugin='nvidia::isaac_ros::foundationpose::FoundationPoseNode', + namespace=IsaacROSFoundationPosePOLTest.generate_namespace(), + parameters=[{ + 'mesh_file_path': os.path.dirname(__file__) + + '/test_cases/foundationpose/' + MESH_FILE_NAME, + 'texture_path': os.path.dirname(__file__) + + '/test_cases/foundationpose/' + TEXTURE_MAP_NAME, + + 'refine_model_file_path': os.path.dirname(__file__) + + '/../../test/models/' + REFINE_MODEL_NAME, + 'refine_engine_file_path': REFINE_ENGINE_PATH, + 'refine_input_tensor_names': ['input_tensor1', 'input_tensor2'], + 'refine_input_binding_names': ['input1', 'input2'], + 'refine_output_tensor_names': ['output_tensor1', 'output_tensor2'], + 'refine_output_binding_names': ['output1', 'output2'], + + 'score_model_file_path': os.path.dirname(__file__) + + '/../../test/models/' + SCORE_MODEL_NAME, + 'score_engine_file_path': SCORE_ENGINE_PATH, + 'score_input_tensor_names': ['input_tensor1', 'input_tensor2'], + 'score_input_binding_names': ['input1', 'input2'], + 'score_output_tensor_names': ['output_tensor'], + 'score_output_binding_names': ['output1'], + }]) + + foundationpose_tracking_node = ComposableNode( + name='foundationpose', + package='isaac_ros_foundationpose', + plugin='nvidia::isaac_ros::foundationpose::FoundationPoseTrackingNode', + namespace=IsaacROSFoundationPosePOLTest.generate_namespace(), + parameters=[{ + 'mesh_file_path': os.path.dirname(__file__) + + '/test_cases/foundationpose/' + MESH_FILE_NAME, + 'texture_path': os.path.dirname(__file__) + + '/test_cases/foundationpose/' + TEXTURE_MAP_NAME, + + 'refine_model_file_path': os.path.dirname(__file__) + + '/../../test/models/' + REFINE_MODEL_NAME, + 'refine_engine_file_path': '/tmp/' + REFINE_ENGINE_NAME, + 'refine_input_tensor_names': ['input_tensor1', 'input_tensor2'], + 'refine_input_binding_names': ['input1', 'input2'], + 'refine_output_tensor_names': ['output_tensor1', 'output_tensor2'], + 'refine_output_binding_names': ['output1', 'output2'], + }]) + + container = ComposableNodeContainer( + name='foundationpose_container', + namespace='', + package='rclcpp_components', + executable='component_container_mt', + composable_node_descriptions=[selector_node, foundationpose_node, + foundationpose_tracking_node], + output='screen', + ) + + return IsaacROSFoundationPosePOLTest.generate_test_description([container]) + + +class IsaacROSFoundationPosePOLTest(IsaacROSBaseTest): + filepath = pathlib.Path(os.path.dirname(__file__)) + + @IsaacROSBaseTest.for_each_test_case() + def test_pol(self, test_folder): + self.node._logger.info( + f'Generating model (timeout={MODEL_GENERATION_TIMEOUT_SEC}s)') + start_time = time.time() + wait_cycles = 1 + while ( + not os.path.isfile(SCORE_ENGINE_PATH) or + not os.path.isfile(REFINE_ENGINE_PATH) + ): + time_diff = time.time() - start_time + if time_diff > MODEL_GENERATION_TIMEOUT_SEC: + self.fail('Model generation timed out') + if time_diff > wait_cycles*10: + self.node._logger.info( + f'Waiting for model generation to finish... ({int(time_diff)}s passed)') + wait_cycles += 1 + time.sleep(1) + + # Wait for TensorRT engine + time.sleep(INIT_WAIT_SEC) + + received_messages = {} + + self.generate_namespace_lookup(['depth_image', 'camera_info', + 'image', 'segmentation', + 'tracking/output']) + + subs = self.create_logging_subscribers( + [('tracking/output', Detection3DArray)], received_messages) + + depth_pub = self.node.create_publisher( + Image, self.namespaces['depth_image'], self.DEFAULT_QOS + ) + rgb_image_pub = self.node.create_publisher( + Image, self.namespaces['image'], self.DEFAULT_QOS + ) + mask_pub = self.node.create_publisher( + Image, self.namespaces['segmentation'], self.DEFAULT_QOS + ) + cam_info_pub = self.node.create_publisher( + CameraInfo, self.namespaces['camera_info'], self.DEFAULT_QOS + ) + + try: + + time_now_msg = self.node.get_clock().now().to_msg() + depth_path = str(test_folder) + '/mustard_depth.png' + mask = cv2.imread(depth_path, cv2.IMREAD_UNCHANGED) + + depth_array = np.array(mask, dtype=np.float32) + # Apply the same principle with the demo + depth_array = depth_array / 1e3 + depth_image = CvBridge().cv2_to_imgmsg(depth_array, '32FC1') + + bgr_path = str(test_folder) + '/mustard_rgb.png' + bgr = cv2.imread( + bgr_path) + rgb = cv2.cvtColor(bgr, cv2.COLOR_BGR2RGB) + rgb_image = CvBridge().cv2_to_imgmsg(rgb, 'rgb8') + + segmentation_path = str(test_folder) + '/mustard_mask.png' + raw_segmentation = cv2.imread( + segmentation_path, cv2.IMREAD_GRAYSCALE) + mask = CvBridge().cv2_to_imgmsg(raw_segmentation, 'mono8') + + camera_info = JSONConversion.load_camera_info_from_json( + test_folder / 'camera_info.json') + + TIMEOUT = 60 + end_time = time.time() + TIMEOUT + done = False + + depth_image.header.stamp = time_now_msg + rgb_image.header.stamp = time_now_msg + mask.header.stamp = time_now_msg + camera_info.header.stamp = time_now_msg + rgb_image.header.frame_id = 'tf_camera' + camera_info.header.frame_id = 'tf_camera' + + while time.time() < end_time: + rgb_image_pub.publish(rgb_image) + depth_pub.publish(depth_image) + mask_pub.publish(mask) + cam_info_pub.publish(camera_info) + rclpy.spin_once(self.node, timeout_sec=(1)) + if 'tracking/output' in received_messages: + done = True + break + + self.assertTrue(done, 'Timeout. Appropriate output not received') + self.node._logger.info('A message was successfully received') + + finally: + self.node.destroy_subscription(subs) + self.node.destroy_publisher(rgb_image_pub) + self.node.destroy_publisher(depth_pub) + self.node.destroy_publisher(cam_info_pub) + self.node.destroy_publisher(mask_pub) diff --git a/isaac_ros_foundationpose/test/test_cases/foundationpose/camera_info.json b/isaac_ros_foundationpose/test/test_cases/foundationpose/camera_info.json new file mode 100644 index 0000000..7755f90 --- /dev/null +++ b/isaac_ros_foundationpose/test/test_cases/foundationpose/camera_info.json @@ -0,0 +1,51 @@ +{ + "header": { + "frame_id": "tf_camera" + }, + "width": 640, + "height": 480, + "distortion_model": "plumb_bob", + "D": [ + 0.0, + 0.0, + 0.0, + 0.0, + 0.0 + ], + "K": [ + 319.582000732421875, + 0.000000, + 320.2149847676955687, + 0.0, + 417.1186828613281250, + 244.3486680871046701, + 0.0, + 0.0, + 1.0 + ], + "R": [ + 1.0, + 0.0, + 0.0, + 0.0, + 1.0, + 0.0, + 0.0, + 0.0, + 1.0 + ], + "P": [ + 319.582000732421875, + 0.0, + 320.2149847676955687, + 0.0, + 0.0, + 417.1186828613281250, + 244.3486680871046701, + 0.0, + 0.0, + 0.0, + 1.0, + 0.0 + ] +} \ No newline at end of file diff --git a/isaac_ros_foundationpose/test/test_cases/foundationpose/detection2_d.json b/isaac_ros_foundationpose/test/test_cases/foundationpose/detection2_d.json new file mode 100644 index 0000000..8da7d32 --- /dev/null +++ b/isaac_ros_foundationpose/test/test_cases/foundationpose/detection2_d.json @@ -0,0 +1,11 @@ +{ + "center": { + "position": { + "x": 138.0, + "y": 308.0 + }, + "theta": 0.0 + }, + "size_x": 75.0, + "size_y": 85.0 +} \ No newline at end of file diff --git a/isaac_ros_foundationpose/test/test_cases/foundationpose/mustard_depth.png b/isaac_ros_foundationpose/test/test_cases/foundationpose/mustard_depth.png new file mode 100755 index 0000000..0ba7553 --- /dev/null +++ b/isaac_ros_foundationpose/test/test_cases/foundationpose/mustard_depth.png @@ -0,0 +1,3 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7840/10010/7840 7832/10955/7832 7838/10920/7838 +f 7839/8669/7839 7755/10806/7755 6355/8635/6355 +f 7839/8669/7839 6355/8635/6355 6374/8658/6374 +f 7791/8595/7791 7839/8669/7839 6383/8596/6383 +f 7841/8690/7841 7865/7569/7865 6389/8599/6389 +f 7838/10920/7838 7238/9849/7238 7840/10010/7840 +vt 0.402247 0.096789 +f 7843/10958/7843 7835/9722/7835 7842/10948/7842 +f 7840/10010/7840 7843/10958/7843 7842/10948/7842 +f 7835/9722/7835 7843/10958/7843 7844/9438/7844 +vt 0.446108 0.934440 +f 7860/10959/7860 7845/7386/7845 5486/7385/5486 +vt 0.398655 0.094445 +f 7261/10960/7261 7843/10958/7843 7268/10009/7268 +vt 0.442488 0.938866 +f 7860/10959/7860 7852/10203/7852 7850/10961/7850 +f 7268/10009/7268 7843/10958/7843 7840/10010/7840 +vt 0.401893 0.092515 +f 7265/10962/7265 7844/9438/7844 7843/10958/7843 +vt 0.403700 0.091067 +f 7844/9438/7844 7265/10962/7265 7114/10963/7114 +f 7843/10958/7843 7261/10960/7261 7265/10962/7265 +vt 0.454628 0.933375 +f 7864/10964/7864 7860/10959/7860 5486/7385/5486 +vt 0.453153 0.938744 +f 7864/10964/7864 7863/10965/7863 7860/10959/7860 +f 7860/10959/7860 7863/10965/7863 7852/10203/7852 +f 6842/9225/6842 7846/9353/7846 7784/9352/7784 +f 7846/9353/7846 6842/9225/6842 6834/9206/6834 +f 7557/10576/7557 7846/9353/7846 7848/9220/7848 +f 7852/10203/7852 7535/10191/7535 7847/10467/7847 +f 7847/10467/7847 7851/10882/7851 7852/10203/7852 +f 7850/10961/7850 7851/10882/7851 7855/10884/7855 +f 7848/9220/7848 6855/9208/6855 7849/9316/7849 +f 6834/9206/6834 7848/9220/7848 7846/9353/7846 +f 7852/10203/7852 7851/10882/7851 7850/10961/7850 +f 7331/10206/7331 7863/10965/7863 7442/10384/7442 +f 7863/10965/7863 7331/10206/7331 7852/10203/7852 +vt 0.437731 0.927814 +vt 0.437334 0.934040 +vt 0.433253 0.929483 +f 5442/10966/5442 7854/10967/7854 7853/10968/7853 +f 7845/7386/7845 7854/10967/7854 5442/10966/5442 +f 7857/10894/7857 7853/10968/7853 7854/10967/7854 +f 7854/10967/7854 7850/10961/7850 7855/10884/7855 +f 7857/10894/7857 7854/10967/7854 7855/10884/7855 +vt 0.431627 0.928937 +f 7853/10968/7853 7857/10894/7857 7858/10969/7858 +vt 0.430700 0.928600 +vt 0.429654 0.929050 +f 7858/10970/7858 7857/10894/7857 7856/10971/7856 +vt 0.428473 0.929200 +f 7857/10894/7857 6389/10896/6389 7856/10972/7856 +vt 0.919977 0.296516 +vt 0.920348 0.300787 +vt 0.917476 0.300164 +f 7866/10973/7866 7858/10974/7858 7856/10975/7856 +vt 0.921848 0.300657 +f 7866/10973/7866 7853/10976/7853 7858/10974/7858 +vt 0.434200 0.928100 +f 7859/10977/7859 5442/10966/5442 7853/10968/7853 +f 7866/10973/7866 7859/7389/7859 7853/10976/7853 +f 7845/7386/7845 7860/10959/7860 7854/10967/7854 +f 5445/7396/5445 7845/7386/7845 5442/10966/5442 +f 7854/10967/7854 7860/10959/7860 7850/10961/7850 +vt 0.455921 0.930888 +f 7864/10964/7864 5486/7385/5486 7861/10978/7861 +vt 0.344073 0.074527 +vt 0.344522 0.072088 +f 7861/10979/7861 5491/10980/5491 7403/10329/7403 +f 5491/10980/5491 5479/10360/5479 7403/10329/7403 +vt 0.344318 0.078463 +f 7861/10979/7861 7862/10380/7862 7864/10981/7864 +vt 0.454423 0.934667 +vt 0.456697 0.937557 +f 7864/10982/7864 7862/10983/7862 7863/10965/7863 +f 7861/10979/7861 7403/10329/7403 7862/10380/7862 +f 5486/7385/5486 5491/7464/5491 7861/10978/7861 +f 7862/10983/7862 7442/10384/7442 7863/10965/7863 +f 7865/7569/7865 7856/10975/7856 6389/8599/6389 +f 7866/10973/7866 7856/10975/7856 7865/7569/7865 +f 5565/7571/5565 7859/7389/7859 7866/10973/7866 +f 5565/7571/5565 7866/10973/7866 7865/7569/7865 +f 5565/7571/5565 5453/7390/5453 7859/7389/7859 +# 15728 faces, 10983 coords texture + +# End of File \ No newline at end of file diff --git a/isaac_ros_foundationpose/test/test_cases/foundationpose/textured_simple.obj.mtl b/isaac_ros_foundationpose/test/test_cases/foundationpose/textured_simple.obj.mtl new file mode 100755 index 0000000..8dfc1f6 --- /dev/null +++ b/isaac_ros_foundationpose/test/test_cases/foundationpose/textured_simple.obj.mtl @@ -0,0 +1,14 @@ +# +# Wavefront material file +# Converted by Meshlab Group +# + +newmtl material_0 +Ka 0.200000 0.200000 0.200000 +Kd 0.639216 0.639216 0.639216 +Ks 1.000000 1.000000 1.000000 +Tr 1.000000 +illum 2 +Ns 0.000000 +map_Kd texture_map.png + diff --git a/isaac_ros_gxf_extensions/gxf_isaac_centerpose/CMakeLists.txt b/isaac_ros_gxf_extensions/gxf_isaac_centerpose/CMakeLists.txt new file mode 100644 index 0000000..0624f2a --- /dev/null +++ b/isaac_ros_gxf_extensions/gxf_isaac_centerpose/CMakeLists.txt @@ -0,0 +1,78 @@ +# SPDX-FileCopyrightText: NVIDIA CORPORATION & AFFILIATES +# Copyright (c) 2023-2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +# SPDX-License-Identifier: Apache-2.0 + +cmake_minimum_required(VERSION 3.22.1) +project(gxf_isaac_centerpose LANGUAGES C CXX) + +if(CMAKE_COMPILER_IS_GNUCXX OR CMAKE_CXX_COMPILER_ID MATCHES "Clang") + add_compile_options(-fPIC -w) +endif() + +find_package(ament_cmake_auto REQUIRED) +ament_auto_find_build_dependencies() + +# Dependencies +find_package(CUDAToolkit REQUIRED) +find_package (Eigen3 3.3 REQUIRED) +find_package(OpenCV REQUIRED) +find_package(yaml-cpp) +find_package(isaac_ros_nitros_detection3_d_array_type REQUIRED) + +# Centerpose extension +ament_auto_add_library(${PROJECT_NAME} SHARED + gxf/extensions/centerpose/components/centerpose_postprocessor.cpp + gxf/extensions/centerpose/components/centerpose_postprocessor.hpp + gxf/extensions/centerpose/components/cuboid3d.hpp + gxf/extensions/centerpose/components/cuboid3d.cpp + gxf/extensions/centerpose/components/cuboid_pnp_solver.cpp + gxf/extensions/centerpose/components/cuboid_pnp_solver.hpp + gxf/extensions/centerpose/components/centerpose_detection.hpp + gxf/extensions/centerpose/components/centerpose_detection_to_isaac.hpp + gxf/extensions/centerpose/components/centerpose_detection_to_isaac.cpp + gxf/extensions/centerpose/components/soft_nms_nvidia.cpp + gxf/extensions/centerpose/components/soft_nms_nvidia.hpp + gxf/extensions/centerpose/components/centerpose_visualizer.cpp + gxf/extensions/centerpose/components/centerpose_visualizer.hpp + gxf/extensions/centerpose/components/video_buffer_utils.hpp + gxf/extensions/centerpose/centerpose_ext.cpp +) + +target_include_directories(${PROJECT_NAME} PUBLIC + ${CMAKE_CURRENT_SOURCE_DIR}/gxf + ${CMAKE_CURRENT_SOURCE_DIR}/gxf/extensions/centerpose/components + ${OpenCV_INCLUDE_DIRS} + ${isaac_ros_nitros_detection3_d_array_type_INCLUDE_DIRS} +) + +target_link_libraries(${PROJECT_NAME} + CUDA::cudart + Eigen3::Eigen + ${OpenCV_LIBS} + yaml-cpp + ${isaac_ros_nitros_detection3_d_array_type_LIBRARIES} +) + +set_target_properties(${PROJECT_NAME} PROPERTIES + BUILD_WITH_INSTALL_RPATH TRUE + BUILD_RPATH_USE_ORIGIN TRUE + INSTALL_RPATH_USE_LINK_PATH TRUE +) + +# Install the binary file +install(TARGETS ${PROJECT_NAME} DESTINATION share/${PROJECT_NAME}/gxf/lib) + +ament_auto_package(INSTALL_TO_SHARE) diff --git a/isaac_ros_centerpose/gxf/centerpose/centerpose_ext.cpp b/isaac_ros_gxf_extensions/gxf_isaac_centerpose/gxf/extensions/centerpose/centerpose_ext.cpp similarity index 86% rename from isaac_ros_centerpose/gxf/centerpose/centerpose_ext.cpp rename to isaac_ros_gxf_extensions/gxf_isaac_centerpose/gxf/extensions/centerpose/centerpose_ext.cpp index 12e24d2..1deed04 100644 --- a/isaac_ros_centerpose/gxf/centerpose/centerpose_ext.cpp +++ b/isaac_ros_gxf_extensions/gxf_isaac_centerpose/gxf/extensions/centerpose/centerpose_ext.cpp @@ -1,5 +1,5 @@ // SPDX-FileCopyrightText: NVIDIA CORPORATION & AFFILIATES -// Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +// Copyright (c) 2023-2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved. // // Licensed under the Apache License, Version 2.0 (the "License"); // you may not use this file except in compliance with the License. @@ -14,9 +14,9 @@ // limitations under the License. // // SPDX-License-Identifier: Apache-2.0 -#include "components/centerpose_detection_to_isaac.hpp" -#include "components/centerpose_postprocessor.hpp" -#include "components/centerpose_visualizer.hpp" +#include "extensions/centerpose/components/centerpose_detection_to_isaac.hpp" +#include "extensions/centerpose/components/centerpose_postprocessor.hpp" +#include "extensions/centerpose/components/centerpose_visualizer.hpp" #include "detection3_d_array_message.hpp" #include "gxf/std/extension_factory_helper.hpp" diff --git a/isaac_ros_centerpose/gxf/centerpose/components/centerpose_detection.hpp b/isaac_ros_gxf_extensions/gxf_isaac_centerpose/gxf/extensions/centerpose/components/centerpose_detection.hpp similarity index 95% rename from isaac_ros_centerpose/gxf/centerpose/components/centerpose_detection.hpp rename to isaac_ros_gxf_extensions/gxf_isaac_centerpose/gxf/extensions/centerpose/components/centerpose_detection.hpp index 939446d..60c13bc 100644 --- a/isaac_ros_centerpose/gxf/centerpose/components/centerpose_detection.hpp +++ b/isaac_ros_gxf_extensions/gxf_isaac_centerpose/gxf/extensions/centerpose/components/centerpose_detection.hpp @@ -1,5 +1,5 @@ // SPDX-FileCopyrightText: NVIDIA CORPORATION & AFFILIATES -// Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +// Copyright (c) 2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved. // // Licensed under the Apache License, Version 2.0 (the "License"); // you may not use this file except in compliance with the License. diff --git a/isaac_ros_centerpose/gxf/centerpose/components/centerpose_detection_to_isaac.cpp b/isaac_ros_gxf_extensions/gxf_isaac_centerpose/gxf/extensions/centerpose/components/centerpose_detection_to_isaac.cpp similarity index 91% rename from isaac_ros_centerpose/gxf/centerpose/components/centerpose_detection_to_isaac.cpp rename to isaac_ros_gxf_extensions/gxf_isaac_centerpose/gxf/extensions/centerpose/components/centerpose_detection_to_isaac.cpp index 800a38a..9678d86 100644 --- a/isaac_ros_centerpose/gxf/centerpose/components/centerpose_detection_to_isaac.cpp +++ b/isaac_ros_gxf_extensions/gxf_isaac_centerpose/gxf/extensions/centerpose/components/centerpose_detection_to_isaac.cpp @@ -1,5 +1,5 @@ // SPDX-FileCopyrightText: NVIDIA CORPORATION & AFFILIATES -// Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +// Copyright (c) 2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved. // // Licensed under the Apache License, Version 2.0 (the "License"); // you may not use this file except in compliance with the License. @@ -14,7 +14,10 @@ // limitations under the License. // // SPDX-License-Identifier: Apache-2.0 -#include "centerpose_detection_to_isaac.hpp" +#include "extensions/centerpose/components/centerpose_detection_to_isaac.hpp" + +#include +#include #include "detection3_d_array_message.hpp" #include "gxf/std/tensor.hpp" @@ -27,9 +30,9 @@ namespace { template struct PointerView { PointerView() : data{}, size{} {} - PointerView(const std::vector& vec) : data{vec.data()}, size{vec.size()} {} + explicit PointerView(const std::vector& vec) : data{vec.data()}, size{vec.size()} {} - PointerView(gxf::Handle tensor) + explicit PointerView(gxf::Handle tensor) : data{tensor->data().value()}, size{tensor->size() / sizeof(T)} {} const T* data{}; @@ -129,7 +132,6 @@ gxf::Expected ToDetection3DList( quaternions_view = PointerView{quaternions}; scores_view = PointerView{scores}; bbox_sizes_view = PointerView{bbox_sizes}; - } break; case gxf::MemoryStorageType::kHost: case gxf::MemoryStorageType::kSystem: { @@ -197,16 +199,17 @@ gxf_result_t CenterPoseDetectionToIsaac::tick() { return gxf::ToResultCode(maybe_detection3_d_list); } - auto maybe_result = - ToDetection3DList( - maybe_detection3_d_list.value(), class_id_tensor, position_tensor, quaternion_tensor, - score_tensor, bbox_size_tensor, object_name_.get()) - .and_then([&]() { *maybe_detection3_d_list.value().timestamp = *input_timestamp; }); - if (!maybe_result) { - GXF_LOG_ERROR("Failed to transfer data into Isaac!"); - return gxf::ToResultCode(maybe_result); + if (class_id_tensor->shape().dimension(0) != 0) { + auto maybe_result = ToDetection3DList( + maybe_detection3_d_list.value(), class_id_tensor, position_tensor, quaternion_tensor, + score_tensor, bbox_size_tensor, object_name_.get()); + if (!maybe_result) { + GXF_LOG_ERROR("Failed to transfer data into Isaac!"); + return gxf::ToResultCode(maybe_result); + } } + *maybe_detection3_d_list.value().timestamp = *input_timestamp; return gxf::ToResultCode(output_->publish(maybe_detection3_d_list.value().entity)); } diff --git a/isaac_ros_centerpose/gxf/centerpose/components/centerpose_detection_to_isaac.hpp b/isaac_ros_gxf_extensions/gxf_isaac_centerpose/gxf/extensions/centerpose/components/centerpose_detection_to_isaac.hpp similarity index 87% rename from isaac_ros_centerpose/gxf/centerpose/components/centerpose_detection_to_isaac.hpp rename to isaac_ros_gxf_extensions/gxf_isaac_centerpose/gxf/extensions/centerpose/components/centerpose_detection_to_isaac.hpp index 3278e55..e279aa5 100644 --- a/isaac_ros_centerpose/gxf/centerpose/components/centerpose_detection_to_isaac.hpp +++ b/isaac_ros_gxf_extensions/gxf_isaac_centerpose/gxf/extensions/centerpose/components/centerpose_detection_to_isaac.hpp @@ -1,5 +1,5 @@ // SPDX-FileCopyrightText: NVIDIA CORPORATION & AFFILIATES -// Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +// Copyright (c) 2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved. // // Licensed under the Apache License, Version 2.0 (the "License"); // you may not use this file except in compliance with the License. @@ -16,12 +16,12 @@ // SPDX-License-Identifier: Apache-2.0 #pragma once -#include "centerpose_detection.hpp" -#include "cuboid3d.hpp" -#include "cuboid_pnp_solver.hpp" +#include + +#include "extensions/centerpose/components/centerpose_detection.hpp" +#include "gxf/core/parameter_parser_std.hpp" #include "gxf/std/allocator.hpp" #include "gxf/std/codelet.hpp" -#include "gxf/std/parameter_parser_std.hpp" #include "gxf/std/receiver.hpp" #include "gxf/std/transmitter.hpp" diff --git a/isaac_ros_centerpose/gxf/centerpose/components/centerpose_postprocessor.cpp b/isaac_ros_gxf_extensions/gxf_isaac_centerpose/gxf/extensions/centerpose/components/centerpose_postprocessor.cpp similarity index 97% rename from isaac_ros_centerpose/gxf/centerpose/components/centerpose_postprocessor.cpp rename to isaac_ros_gxf_extensions/gxf_isaac_centerpose/gxf/extensions/centerpose/components/centerpose_postprocessor.cpp index 4f4b908..ee12726 100644 --- a/isaac_ros_centerpose/gxf/centerpose/components/centerpose_postprocessor.cpp +++ b/isaac_ros_gxf_extensions/gxf_isaac_centerpose/gxf/extensions/centerpose/components/centerpose_postprocessor.cpp @@ -1,5 +1,5 @@ // SPDX-FileCopyrightText: NVIDIA CORPORATION & AFFILIATES -// Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +// Copyright (c) 2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved. // // Licensed under the Apache License, Version 2.0 (the "License"); // you may not use this file except in compliance with the License. @@ -14,28 +14,28 @@ // limitations under the License. // // SPDX-License-Identifier: Apache-2.0 -#include "centerpose_postprocessor.hpp" -#include +#include "extensions/centerpose/components/centerpose_postprocessor.hpp" #include #include #include +#include #include #include #include -#include "centerpose_detection.hpp" -#include "cuboid3d.hpp" -#include "cuboid_pnp_solver.hpp" -#include "cuda.h" -#include "cuda_runtime.h" +#include "cuda.h" // NOLINT +#include "cuda_runtime.h" // NOLINT #include "Eigen/Dense" +#include "extensions/centerpose/components/centerpose_detection.hpp" +#include "extensions/centerpose/components/cuboid3d.hpp" +#include "extensions/centerpose/components/cuboid_pnp_solver.hpp" +#include "extensions/centerpose/components/soft_nms_nvidia.hpp" #include "gxf/multimedia/camera.hpp" #include "gxf/std/tensor.hpp" #include "gxf/std/timestamp.hpp" #include "opencv2/core/eigen.hpp" #include "opencv2/opencv.hpp" -#include "soft_nms_nvidia.hpp" namespace nvidia { namespace isaac { @@ -106,7 +106,7 @@ Eigen::Matrix3fRM ComputeAffineTransform( // Compute the corresponding points of interest in the output_field_size image plane std::vector dst_points; dst_points.push_back({dst_w * 0.5f, dst_h * 0.5f}); - dst_points.push_back(cv::Point2f{dst_w * 0.5f, dst_h * 0.5f} + dst_direction); + dst_points.push_back(cv::Point2f(dst_w * 0.5f, dst_h * 0.5f) + dst_direction); dst_points.push_back(calculate_third_point(dst_points[0], dst_points[1])); // Use the computed src and dst points to find the affine transform (mapping @@ -126,7 +126,7 @@ gxf::Expected ToTensor( const gxf::MemoryStorageType storage_type, const cudaMemcpyKind operation) { // Handle special case when input is empty if (eigen_type.size() == 0) { - return tensor->reshape(gxf::Shape{0, 0, 0}, storage_type, allocator); + return gxf::Success; } return tensor ->reshape( @@ -154,6 +154,10 @@ gxf::Expected ToTensor( const std::vector& vec, gxf::Handle tensor, gxf::Handle allocator, const gxf::MemoryStorageType storage_type, const cudaMemcpyKind operation) { + // handle when input is empty + if (vec.size() == 0) { + return gxf::Success; + } return tensor->reshape(gxf::Shape{static_cast(vec.size()), 1}, storage_type, allocator) .and_then([&]() -> gxf::Expected { const cudaError_t error = diff --git a/isaac_ros_centerpose/gxf/centerpose/components/centerpose_postprocessor.hpp b/isaac_ros_gxf_extensions/gxf_isaac_centerpose/gxf/extensions/centerpose/components/centerpose_postprocessor.hpp similarity index 84% rename from isaac_ros_centerpose/gxf/centerpose/components/centerpose_postprocessor.hpp rename to isaac_ros_gxf_extensions/gxf_isaac_centerpose/gxf/extensions/centerpose/components/centerpose_postprocessor.hpp index 4b8f96f..8ce5454 100644 --- a/isaac_ros_centerpose/gxf/centerpose/components/centerpose_postprocessor.hpp +++ b/isaac_ros_gxf_extensions/gxf_isaac_centerpose/gxf/extensions/centerpose/components/centerpose_postprocessor.hpp @@ -1,5 +1,5 @@ // SPDX-FileCopyrightText: NVIDIA CORPORATION & AFFILIATES -// Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +// Copyright (c) 2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved. // // Licensed under the Apache License, Version 2.0 (the "License"); // you may not use this file except in compliance with the License. @@ -16,15 +16,17 @@ // SPDX-License-Identifier: Apache-2.0 #pragma once -#include "centerpose_detection.hpp" -#include "centerpose_types.hpp" -#include "cuboid3d.hpp" -#include "cuboid_pnp_solver.hpp" +#include + #include "Eigen/Dense" +#include "extensions/centerpose/components/centerpose_detection.hpp" +#include "extensions/centerpose/components/centerpose_types.hpp" +#include "extensions/centerpose/components/cuboid3d.hpp" +#include "extensions/centerpose/components/cuboid_pnp_solver.hpp" +#include "gxf/core/parameter_parser_std.hpp" #include "gxf/multimedia/camera.hpp" #include "gxf/std/allocator.hpp" #include "gxf/std/codelet.hpp" -#include "gxf/std/parameter_parser_std.hpp" #include "gxf/std/receiver.hpp" #include "gxf/std/timestamp.hpp" #include "gxf/std/transmitter.hpp" diff --git a/isaac_ros_centerpose/gxf/centerpose/components/centerpose_types.hpp b/isaac_ros_gxf_extensions/gxf_isaac_centerpose/gxf/extensions/centerpose/components/centerpose_types.hpp similarity index 93% rename from isaac_ros_centerpose/gxf/centerpose/components/centerpose_types.hpp rename to isaac_ros_gxf_extensions/gxf_isaac_centerpose/gxf/extensions/centerpose/components/centerpose_types.hpp index f34c98d..2b38f8e 100644 --- a/isaac_ros_centerpose/gxf/centerpose/components/centerpose_types.hpp +++ b/isaac_ros_gxf_extensions/gxf_isaac_centerpose/gxf/extensions/centerpose/components/centerpose_types.hpp @@ -1,5 +1,5 @@ // SPDX-FileCopyrightText: NVIDIA CORPORATION & AFFILIATES -// Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +// Copyright (c) 2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved. // // Licensed under the Apache License, Version 2.0 (the "License"); // you may not use this file except in compliance with the License. diff --git a/isaac_ros_centerpose/gxf/centerpose/components/centerpose_visualizer.cpp b/isaac_ros_gxf_extensions/gxf_isaac_centerpose/gxf/extensions/centerpose/components/centerpose_visualizer.cpp similarity index 96% rename from isaac_ros_centerpose/gxf/centerpose/components/centerpose_visualizer.cpp rename to isaac_ros_gxf_extensions/gxf_isaac_centerpose/gxf/extensions/centerpose/components/centerpose_visualizer.cpp index 14a6641..9665222 100644 --- a/isaac_ros_centerpose/gxf/centerpose/components/centerpose_visualizer.cpp +++ b/isaac_ros_gxf_extensions/gxf_isaac_centerpose/gxf/extensions/centerpose/components/centerpose_visualizer.cpp @@ -1,5 +1,5 @@ // SPDX-FileCopyrightText: NVIDIA CORPORATION & AFFILIATES -// Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +// Copyright (c) 2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved. // // Licensed under the Apache License, Version 2.0 (the "License"); // you may not use this file except in compliance with the License. @@ -14,19 +14,21 @@ // limitations under the License. // // SPDX-License-Identifier: Apache-2.0 -#include "centerpose_visualizer.hpp" +#include "extensions/centerpose/components/centerpose_visualizer.hpp" -#include "centerpose_types.hpp" -#include "cuboid3d.hpp" -#include "cuda.h" -#include "cuda_runtime.h" -#include "detection3_d_array_message.hpp" +#include + +#include "cuda.h" // NOLINT +#include "cuda_runtime.h" // NOLINT #include "Eigen/Dense" +#include "extensions/centerpose/components/centerpose_types.hpp" +#include "extensions/centerpose/components/cuboid3d.hpp" +#include "extensions/centerpose/components/video_buffer_utils.hpp" +#include "detection3_d_array_message.hpp" #include "gxf/multimedia/camera.hpp" #include "gxf/multimedia/video.hpp" #include "opencv2/core/eigen.hpp" #include "opencv2/opencv.hpp" -#include "video_buffer_utils.hpp" namespace nvidia { namespace isaac { @@ -94,7 +96,7 @@ void DarkenROI(const std::vector& reprojected_points, cv::Mat& img) void DrawBoundingBox( const std::vector& reprojected_points, const int32_t color_int, cv::Mat& img) { - const std::vector edges = {cv::Point2i{2, 4}, cv::Point2i{2, 6}, cv::Point2i{6, 8}, + const std::vector edges = {cv::Point2i{2, 4}, cv::Point2i{2, 6}, cv::Point2i{6, 8}, cv::Point2i{4, 8}, cv::Point2i{1, 2}, cv::Point2i{3, 4}, cv::Point2i{5, 6}, cv::Point2i{7, 8}, cv::Point2i{1, 3}, cv::Point2i{1, 5}, cv::Point2i{3, 7}, cv::Point2i{5, 7}}; diff --git a/isaac_ros_centerpose/gxf/centerpose/components/centerpose_visualizer.hpp b/isaac_ros_gxf_extensions/gxf_isaac_centerpose/gxf/extensions/centerpose/components/centerpose_visualizer.hpp similarity index 93% rename from isaac_ros_centerpose/gxf/centerpose/components/centerpose_visualizer.hpp rename to isaac_ros_gxf_extensions/gxf_isaac_centerpose/gxf/extensions/centerpose/components/centerpose_visualizer.hpp index 5d7b50d..40bee17 100644 --- a/isaac_ros_centerpose/gxf/centerpose/components/centerpose_visualizer.hpp +++ b/isaac_ros_gxf_extensions/gxf_isaac_centerpose/gxf/extensions/centerpose/components/centerpose_visualizer.hpp @@ -1,5 +1,5 @@ // SPDX-FileCopyrightText: NVIDIA CORPORATION & AFFILIATES -// Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +// Copyright (c) 2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved. // // Licensed under the Apache License, Version 2.0 (the "License"); // you may not use this file except in compliance with the License. @@ -17,9 +17,9 @@ #pragma once #include "Eigen/Dense" +#include "gxf/core/parameter_parser_std.hpp" #include "gxf/std/allocator.hpp" #include "gxf/std/codelet.hpp" -#include "gxf/std/parameter_parser_std.hpp" #include "gxf/std/receiver.hpp" #include "gxf/std/timestamp.hpp" #include "gxf/std/transmitter.hpp" diff --git a/isaac_ros_centerpose/gxf/centerpose/components/cuboid3d.cpp b/isaac_ros_gxf_extensions/gxf_isaac_centerpose/gxf/extensions/centerpose/components/cuboid3d.cpp similarity index 92% rename from isaac_ros_centerpose/gxf/centerpose/components/cuboid3d.cpp rename to isaac_ros_gxf_extensions/gxf_isaac_centerpose/gxf/extensions/centerpose/components/cuboid3d.cpp index e9d25bd..4621594 100644 --- a/isaac_ros_centerpose/gxf/centerpose/components/cuboid3d.cpp +++ b/isaac_ros_gxf_extensions/gxf_isaac_centerpose/gxf/extensions/centerpose/components/cuboid3d.cpp @@ -1,5 +1,5 @@ // SPDX-FileCopyrightText: NVIDIA CORPORATION & AFFILIATES -// Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +// Copyright (c) 2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved. // // Licensed under the Apache License, Version 2.0 (the "License"); // you may not use this file except in compliance with the License. @@ -15,8 +15,9 @@ // // SPDX-License-Identifier: Apache-2.0 -#include "cuboid3d.hpp" -#include "centerpose_types.hpp" +#include "extensions/centerpose/components/cuboid3d.hpp" + +#include "extensions/centerpose/components/centerpose_types.hpp" namespace nvidia { namespace isaac { diff --git a/isaac_ros_centerpose/gxf/centerpose/components/cuboid3d.hpp b/isaac_ros_gxf_extensions/gxf_isaac_centerpose/gxf/extensions/centerpose/components/cuboid3d.hpp similarity index 96% rename from isaac_ros_centerpose/gxf/centerpose/components/cuboid3d.hpp rename to isaac_ros_gxf_extensions/gxf_isaac_centerpose/gxf/extensions/centerpose/components/cuboid3d.hpp index 3b59e16..08886d5 100644 --- a/isaac_ros_centerpose/gxf/centerpose/components/cuboid3d.hpp +++ b/isaac_ros_gxf_extensions/gxf_isaac_centerpose/gxf/extensions/centerpose/components/cuboid3d.hpp @@ -1,5 +1,5 @@ // SPDX-FileCopyrightText: NVIDIA CORPORATION & AFFILIATES -// Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +// Copyright (c) 2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved. // // Licensed under the Apache License, Version 2.0 (the "License"); // you may not use this file except in compliance with the License. diff --git a/isaac_ros_centerpose/gxf/centerpose/components/cuboid_pnp_solver.cpp b/isaac_ros_gxf_extensions/gxf_isaac_centerpose/gxf/extensions/centerpose/components/cuboid_pnp_solver.cpp similarity index 96% rename from isaac_ros_centerpose/gxf/centerpose/components/cuboid_pnp_solver.cpp rename to isaac_ros_gxf_extensions/gxf_isaac_centerpose/gxf/extensions/centerpose/components/cuboid_pnp_solver.cpp index 69823f0..5a46e17 100644 --- a/isaac_ros_centerpose/gxf/centerpose/components/cuboid_pnp_solver.cpp +++ b/isaac_ros_gxf_extensions/gxf_isaac_centerpose/gxf/extensions/centerpose/components/cuboid_pnp_solver.cpp @@ -1,5 +1,5 @@ // SPDX-FileCopyrightText: NVIDIA CORPORATION & AFFILIATES -// Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +// Copyright (c) 2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved. // // Licensed under the Apache License, Version 2.0 (the "License"); // you may not use this file except in compliance with the License. @@ -14,9 +14,10 @@ // limitations under the License. // // SPDX-License-Identifier: Apache-2.0 -#include "cuboid_pnp_solver.hpp" +#include "extensions/centerpose/components/cuboid_pnp_solver.hpp" #include +#include #include "opencv2/core/eigen.hpp" #include "opencv2/opencv.hpp" diff --git a/isaac_ros_centerpose/gxf/centerpose/components/cuboid_pnp_solver.hpp b/isaac_ros_gxf_extensions/gxf_isaac_centerpose/gxf/extensions/centerpose/components/cuboid_pnp_solver.hpp similarity index 94% rename from isaac_ros_centerpose/gxf/centerpose/components/cuboid_pnp_solver.hpp rename to isaac_ros_gxf_extensions/gxf_isaac_centerpose/gxf/extensions/centerpose/components/cuboid_pnp_solver.hpp index 5f114de..880ed7f 100644 --- a/isaac_ros_centerpose/gxf/centerpose/components/cuboid_pnp_solver.hpp +++ b/isaac_ros_gxf_extensions/gxf_isaac_centerpose/gxf/extensions/centerpose/components/cuboid_pnp_solver.hpp @@ -1,5 +1,5 @@ // SPDX-FileCopyrightText: NVIDIA CORPORATION & AFFILIATES -// Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +// Copyright (c) 2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved. // // Licensed under the Apache License, Version 2.0 (the "License"); // you may not use this file except in compliance with the License. @@ -16,12 +16,13 @@ // SPDX-License-Identifier: Apache-2.0 #pragma once -#include #include -#include "centerpose_types.hpp" -#include "cuboid3d.hpp" +#include + #include "Eigen/Dense" +#include "extensions/centerpose/components/centerpose_types.hpp" +#include "extensions/centerpose/components/cuboid3d.hpp" namespace nvidia { namespace isaac { diff --git a/isaac_ros_centerpose/gxf/centerpose/components/soft_nms_nvidia.cpp b/isaac_ros_gxf_extensions/gxf_isaac_centerpose/gxf/extensions/centerpose/components/soft_nms_nvidia.cpp similarity index 95% rename from isaac_ros_centerpose/gxf/centerpose/components/soft_nms_nvidia.cpp rename to isaac_ros_gxf_extensions/gxf_isaac_centerpose/gxf/extensions/centerpose/components/soft_nms_nvidia.cpp index 597091e..e3e5cc4 100644 --- a/isaac_ros_centerpose/gxf/centerpose/components/soft_nms_nvidia.cpp +++ b/isaac_ros_gxf_extensions/gxf_isaac_centerpose/gxf/extensions/centerpose/components/soft_nms_nvidia.cpp @@ -1,5 +1,5 @@ // SPDX-FileCopyrightText: NVIDIA CORPORATION & AFFILIATES -// Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +// Copyright (c) 2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved. // // Licensed under the Apache License, Version 2.0 (the "License"); // you may not use this file except in compliance with the License. @@ -14,7 +14,7 @@ // limitations under the License. // // SPDX-License-Identifier: Apache-2.0 -#include "soft_nms_nvidia.hpp" +#include "extensions/centerpose/components/soft_nms_nvidia.hpp" #include #include @@ -23,7 +23,7 @@ #include #include -#include "centerpose_detection.hpp" +#include "extensions/centerpose/components/centerpose_detection.hpp" namespace nvidia { namespace isaac { diff --git a/isaac_ros_centerpose/gxf/centerpose/components/soft_nms_nvidia.hpp b/isaac_ros_gxf_extensions/gxf_isaac_centerpose/gxf/extensions/centerpose/components/soft_nms_nvidia.hpp similarity index 89% rename from isaac_ros_centerpose/gxf/centerpose/components/soft_nms_nvidia.hpp rename to isaac_ros_gxf_extensions/gxf_isaac_centerpose/gxf/extensions/centerpose/components/soft_nms_nvidia.hpp index 514ca10..8463c19 100644 --- a/isaac_ros_centerpose/gxf/centerpose/components/soft_nms_nvidia.hpp +++ b/isaac_ros_gxf_extensions/gxf_isaac_centerpose/gxf/extensions/centerpose/components/soft_nms_nvidia.hpp @@ -1,5 +1,5 @@ // SPDX-FileCopyrightText: NVIDIA CORPORATION & AFFILIATES -// Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +// Copyright (c) 2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved. // // Licensed under the Apache License, Version 2.0 (the "License"); // you may not use this file except in compliance with the License. @@ -19,7 +19,7 @@ #include #include -#include "centerpose_detection.hpp" +#include "extensions/centerpose/components/centerpose_detection.hpp" namespace nvidia { namespace isaac { diff --git a/isaac_ros_centerpose/gxf/centerpose/components/video_buffer_utils.hpp b/isaac_ros_gxf_extensions/gxf_isaac_centerpose/gxf/extensions/centerpose/components/video_buffer_utils.hpp similarity index 97% rename from isaac_ros_centerpose/gxf/centerpose/components/video_buffer_utils.hpp rename to isaac_ros_gxf_extensions/gxf_isaac_centerpose/gxf/extensions/centerpose/components/video_buffer_utils.hpp index 5cef189..43bf805 100644 --- a/isaac_ros_centerpose/gxf/centerpose/components/video_buffer_utils.hpp +++ b/isaac_ros_gxf_extensions/gxf_isaac_centerpose/gxf/extensions/centerpose/components/video_buffer_utils.hpp @@ -1,5 +1,5 @@ // SPDX-FileCopyrightText: NVIDIA CORPORATION & AFFILIATES -// Copyright (c) 2022-2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +// Copyright (c) 2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved. // // Licensed under the Apache License, Version 2.0 (the "License"); // you may not use this file except in compliance with the License. diff --git a/isaac_ros_gxf_extensions/gxf_isaac_centerpose/package.xml b/isaac_ros_gxf_extensions/gxf_isaac_centerpose/package.xml new file mode 100644 index 0000000..d6daaff --- /dev/null +++ b/isaac_ros_gxf_extensions/gxf_isaac_centerpose/package.xml @@ -0,0 +1,46 @@ + + + + + + + gxf_isaac_centerpose + 3.0.0 + GXF extension containing CenterPose pose estimation related components. + + Isaac ROS Maintainers + Apache-2.0 + https://developer.nvidia.com/isaac-ros-gems/ + CY Chen + + ament_cmake_auto + + isaac_ros_common + isaac_ros_gxf + isaac_ros_nitros_detection3_d_array_type + + gxf_isaac_gems + + ament_lint_auto + ament_lint_common + + + ament_cmake + + diff --git a/isaac_ros_gxf_extensions/gxf_isaac_dope/CMakeLists.txt b/isaac_ros_gxf_extensions/gxf_isaac_dope/CMakeLists.txt new file mode 100644 index 0000000..ed536ab --- /dev/null +++ b/isaac_ros_gxf_extensions/gxf_isaac_dope/CMakeLists.txt @@ -0,0 +1,62 @@ +# SPDX-FileCopyrightText: NVIDIA CORPORATION & AFFILIATES +# Copyright (c) 2023-2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +# SPDX-License-Identifier: Apache-2.0 + +cmake_minimum_required(VERSION 3.22.1) +project(gxf_isaac_dope LANGUAGES C CXX) + +if(CMAKE_COMPILER_IS_GNUCXX OR CMAKE_CXX_COMPILER_ID MATCHES "Clang") + add_compile_options(-fPIC -w) +endif() + +find_package(ament_cmake_auto REQUIRED) +ament_auto_find_build_dependencies() + +# Dependencies +find_package(CUDAToolkit REQUIRED) +find_package(Eigen3 3.3 REQUIRED NO_MODULE) +find_package(OpenCV REQUIRED) +find_package(yaml-cpp) + +# DOPE extension +ament_auto_add_library(${PROJECT_NAME} SHARED + gxf/dope/dope_decoder.cpp + gxf/dope/dope_decoder.hpp + gxf/dope/dope_ext.cpp +) + +target_include_directories(${PROJECT_NAME} PUBLIC + ${CMAKE_CURRENT_SOURCE_DIR}/gxf + ${OpenCV_INCLUDE_DIRS} +) + +target_link_libraries(${PROJECT_NAME} + CUDA::cudart + Eigen3::Eigen + ${OpenCV_LIBS} + yaml-cpp +) + +set_target_properties(${PROJECT_NAME} PROPERTIES + BUILD_WITH_INSTALL_RPATH TRUE + BUILD_RPATH_USE_ORIGIN TRUE + INSTALL_RPATH_USE_LINK_PATH TRUE +) + +# Install the binary file +install(TARGETS ${PROJECT_NAME} DESTINATION share/${PROJECT_NAME}/gxf/lib) + +ament_auto_package(INSTALL_TO_SHARE) diff --git a/isaac_ros_dope/gxf/dope/dope_decoder.cpp b/isaac_ros_gxf_extensions/gxf_isaac_dope/gxf/dope/dope_decoder.cpp similarity index 96% rename from isaac_ros_dope/gxf/dope/dope_decoder.cpp rename to isaac_ros_gxf_extensions/gxf_isaac_dope/gxf/dope/dope_decoder.cpp index d0b838f..66f635b 100644 --- a/isaac_ros_dope/gxf/dope/dope_decoder.cpp +++ b/isaac_ros_gxf_extensions/gxf_isaac_dope/gxf/dope/dope_decoder.cpp @@ -1,5 +1,5 @@ // SPDX-FileCopyrightText: NVIDIA CORPORATION & AFFILIATES -// Copyright (c) 2022-2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +// Copyright (c) 2022-2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved. // // Licensed under the Apache License, Version 2.0 (the "License"); // you may not use this file except in compliance with the License. @@ -30,7 +30,7 @@ #include "gxf/multimedia/camera.hpp" #include "gxf/multimedia/video.hpp" -#include "gxf/std/parameter_parser_std.hpp" +#include "gxf/core/parameter_parser_std.hpp" #include "gxf/std/tensor.hpp" #include "gxf/std/timestamp.hpp" @@ -182,24 +182,24 @@ FindObjects(const std::array &maps) { for (size_t pp = 0; pp < peaks.size(); ++pp) { const auto peak = peaks[pp]; - // Compute the weighted average for localizing the peak, using an 11x11 + // Compute the weighted average for localizing the peak, using a 5x5 // window Vector2f peak_sum(0, 0); float weight_sum = 0.0f; - const int WINDOW_SIZE = 11; - for (int ii = -(WINDOW_SIZE - 1) / 2; ii <= (WINDOW_SIZE - 1) / 2; ++ii) { - for (int jj = -(WINDOW_SIZE - 1) / 2; jj <= (WINDOW_SIZE - 1) / 2; ++jj) { - const int col = peak[0] + ii; - const int row = peak[1] + jj; + for (int ii = -2; ii <= 2; ++ii) { + for (int jj = -2; jj <= 2; ++jj) { + const int row = peak[0] + ii; + const int col = peak[1] + jj; - if (col < 0 || col >= image.cols || row < 0 || row >= image.rows) { + if (col < 0 || col >= image.size[1] || row < 0 || + row >= image.size[0]) { continue; } const float weight = image.at(row, col); weight_sum += weight; - peak_sum[0] += col * weight; - peak_sum[1] += row * weight; + peak_sum[0] += row * weight; + peak_sum[1] += col * weight; } } @@ -322,7 +322,7 @@ ExtractPose(const DopeObjectKeypoints &object, cv::Mat cv_keypoints_2d; cv::eigen2cv(object.second, cv_keypoints_2d); if (!cv::solvePnP(cv_keypoints_3d.t(), cv_keypoints_2d.t(), camera_matrix, - dist_coeffs, rvec, tvec, false, cv::SOLVEPNP_EPNP)) { + dist_coeffs, rvec, tvec)) { GXF_LOG_ERROR("cv::solvePnP failed"); return nvidia::gxf::Unexpected{GXF_FAILURE}; } @@ -465,13 +465,6 @@ gxf_result_t DopeDecoder::tick() noexcept { } auto posearray_message = maybe_posearray_message.value(); - auto maybe_added_timestamp = AddInputTimestampToOutput( - posearray_message, maybe_beliefmaps_message.value()); - if (!maybe_added_timestamp) { - GXF_LOG_ERROR("Failed to add timestamp"); - return gxf::ToResultCode(maybe_added_timestamp); - } - // Copy tensor data over to a more portable form std::array maps; const int input_map_row{belief_maps->shape().dimension(2)}; @@ -495,6 +488,13 @@ gxf_result_t DopeDecoder::tick() noexcept { // Analyze the belief map to find vertex locations in image space const std::vector dope_objects = FindObjects(maps); + + // Add timestamp to output msg + auto maybe_added_timestamp = AddInputTimestampToOutput( + posearray_message, maybe_beliefmaps_message.value()); + if (!maybe_added_timestamp) { + return gxf::ToResultCode(maybe_added_timestamp); + } if (dope_objects.empty()) { GXF_LOG_INFO("No objects detected."); diff --git a/isaac_ros_dope/gxf/dope/dope_decoder.hpp b/isaac_ros_gxf_extensions/gxf_isaac_dope/gxf/dope/dope_decoder.hpp similarity index 95% rename from isaac_ros_dope/gxf/dope/dope_decoder.hpp rename to isaac_ros_gxf_extensions/gxf_isaac_dope/gxf/dope/dope_decoder.hpp index 1d53512..871b52d 100644 --- a/isaac_ros_dope/gxf/dope/dope_decoder.hpp +++ b/isaac_ros_gxf_extensions/gxf_isaac_dope/gxf/dope/dope_decoder.hpp @@ -1,5 +1,5 @@ // SPDX-FileCopyrightText: NVIDIA CORPORATION & AFFILIATES -// Copyright (c) 2022-2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +// Copyright (c) 2022-2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved. // // Licensed under the Apache License, Version 2.0 (the "License"); // you may not use this file except in compliance with the License. @@ -26,7 +26,7 @@ #include "gxf/core/parameter.hpp" #include "gxf/std/codelet.hpp" #include "gxf/std/memory_buffer.hpp" -#include "gxf/std/parameter_parser_std.hpp" +#include "gxf/core/parameter_parser_std.hpp" #include "gxf/std/receiver.hpp" #include "gxf/std/transmitter.hpp" diff --git a/isaac_ros_dope/gxf/dope/dope_ext.cpp b/isaac_ros_gxf_extensions/gxf_isaac_dope/gxf/dope/dope_ext.cpp similarity index 95% rename from isaac_ros_dope/gxf/dope/dope_ext.cpp rename to isaac_ros_gxf_extensions/gxf_isaac_dope/gxf/dope/dope_ext.cpp index 3da22c9..7cab5df 100644 --- a/isaac_ros_dope/gxf/dope/dope_ext.cpp +++ b/isaac_ros_gxf_extensions/gxf_isaac_dope/gxf/dope/dope_ext.cpp @@ -1,5 +1,5 @@ // SPDX-FileCopyrightText: NVIDIA CORPORATION & AFFILIATES -// Copyright (c) 2022-2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +// Copyright (c) 2022-2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved. // // Licensed under the Apache License, Version 2.0 (the "License"); // you may not use this file except in compliance with the License. diff --git a/isaac_ros_gxf_extensions/gxf_isaac_dope/package.xml b/isaac_ros_gxf_extensions/gxf_isaac_dope/package.xml new file mode 100644 index 0000000..a5acda8 --- /dev/null +++ b/isaac_ros_gxf_extensions/gxf_isaac_dope/package.xml @@ -0,0 +1,45 @@ + + + + + + + gxf_isaac_dope + 3.0.0 + GXF extension containing CenterPose pose estimation related components. + + Isaac ROS Maintainers + Apache-2.0 + https://developer.nvidia.com/isaac-ros-gems/ + CY Chen + + ament_cmake_auto + + isaac_ros_common + isaac_ros_gxf + + gxf_isaac_gems + + ament_lint_auto + ament_lint_common + + + ament_cmake + + diff --git a/isaac_ros_gxf_extensions/gxf_isaac_foundationpose/CMakeLists.txt b/isaac_ros_gxf_extensions/gxf_isaac_foundationpose/CMakeLists.txt new file mode 100644 index 0000000..c0195fc --- /dev/null +++ b/isaac_ros_gxf_extensions/gxf_isaac_foundationpose/CMakeLists.txt @@ -0,0 +1,90 @@ +# SPDX-FileCopyrightText: NVIDIA CORPORATION & AFFILIATES +# Copyright (c) 2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +# SPDX-License-Identifier: Apache-2.0 + +cmake_minimum_required(VERSION 3.22.1) +project(gxf_isaac_foundationpose LANGUAGES C CXX) + +if(CMAKE_COMPILER_IS_GNUCXX OR CMAKE_CXX_COMPILER_ID MATCHES "Clang") + add_compile_options(-fPIC -w) +endif() + +enable_language(CUDA) +find_package(ament_cmake_auto REQUIRED) +ament_auto_find_build_dependencies() + +# Dependencies +find_package(CUDAToolkit REQUIRED) +find_package(Eigen3 3.3 REQUIRED NO_MODULE) +find_package(nvcv_types REQUIRED) +find_package(cvcuda REQUIRED) +find_package(assimp REQUIRED) +find_package(isaac_ros_nitros_detection3_d_array_type REQUIRED) +find_package(yaml-cpp) +find_package(OpenCV REQUIRED) + +# FoundationPose extension +ament_auto_add_library(${PROJECT_NAME} SHARED + gxf/foundationpose/nvdiffrast/common/cudaraster/impl/Buffer.cpp + gxf/foundationpose/nvdiffrast/common/cudaraster/impl/CudaRaster.cpp + gxf/foundationpose/nvdiffrast/common/cudaraster/impl/RasterImpl.cpp + gxf/foundationpose/nvdiffrast/common/cudaraster/impl/RasterImpl.cu + gxf/foundationpose/nvdiffrast/common/common.cpp + gxf/foundationpose/nvdiffrast/common/interpolate.cu + gxf/foundationpose/nvdiffrast/common/rasterize.cu + gxf/foundationpose/nvdiffrast/common/texture.cu + + gxf/foundationpose/foundationpose_sampling.cpp + gxf/foundationpose/foundationpose_sampling.cu + gxf/foundationpose/foundationpose_render.cpp + gxf/foundationpose/foundationpose_render.cu + gxf/foundationpose/foundationpose_decoder.cpp + gxf/foundationpose/foundationpose_decoder.cu + gxf/foundationpose/foundationpose_transformation.cpp + gxf/foundationpose/foundationpose_utils.cpp + gxf/foundationpose/render_receptive_scheduling_term.cpp + gxf/foundationpose/foundationpose_ext.cpp +) + +target_include_directories(${PROJECT_NAME} + PUBLIC + ${isaac_ros_nitros_detection3_d_array_type_INCLUDE_DIRS} + ${OpenCV_INCLUDE_DIRS} +) + +target_compile_options(${PROJECT_NAME} PUBLIC -fPIC) + +target_link_libraries(${PROJECT_NAME} + CUDA::cudart + Eigen3::Eigen + yaml-cpp + cvcuda + assimp + nvcv_types + ${OpenCV_LIBS} + ${isaac_ros_nitros_detection3_d_array_type_LIBRARIES} +) + +set_target_properties(${PROJECT_NAME} PROPERTIES + BUILD_WITH_INSTALL_RPATH TRUE + BUILD_RPATH_USE_ORIGIN TRUE + INSTALL_RPATH_USE_LINK_PATH TRUE +) + +# Install the binary file +install(TARGETS ${PROJECT_NAME} DESTINATION share/${PROJECT_NAME}/gxf/lib) + +ament_auto_package(INSTALL_TO_SHARE) \ No newline at end of file diff --git a/isaac_ros_centerpose/gxf/AMENT_IGNORE b/isaac_ros_gxf_extensions/gxf_isaac_foundationpose/gxf/AMENT_IGNORE similarity index 100% rename from isaac_ros_centerpose/gxf/AMENT_IGNORE rename to isaac_ros_gxf_extensions/gxf_isaac_foundationpose/gxf/AMENT_IGNORE diff --git a/isaac_ros_gxf_extensions/gxf_isaac_foundationpose/gxf/foundationpose/foundationpose_decoder.cpp b/isaac_ros_gxf_extensions/gxf_isaac_foundationpose/gxf/foundationpose/foundationpose_decoder.cpp new file mode 100644 index 0000000..f0f1c86 --- /dev/null +++ b/isaac_ros_gxf_extensions/gxf_isaac_foundationpose/gxf/foundationpose/foundationpose_decoder.cpp @@ -0,0 +1,271 @@ +// SPDX-FileCopyrightText: NVIDIA CORPORATION & AFFILIATES +// Copyright (c) 2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. +// +// SPDX-License-Identifier: Apache-2.0 + +#include "foundationpose_decoder.hpp" +#include "foundationpose_decoder.cu.hpp" +#include "foundationpose_utils.hpp" + +#include "detection3_d_array_message.hpp" +#include "gxf/core/parameter_parser_std.hpp" +#include "gxf/std/tensor.hpp" +#include "gxf/std/timestamp.hpp" + +namespace nvidia { +namespace isaac_ros { + +namespace { + +constexpr char kNamePoses[] = "poses"; +constexpr size_t kPoseMatrixLength = 4; + +} // namepsace + +gxf_result_t FoundationposeDecoder::ExtractBoundingBoxDimensions() { + // Load the obj mesh file to extrac bbox dimensions + Assimp::Importer importer; + const aiScene* scene = importer.ReadFile( + mesh_file_path_.get(), + aiProcess_Triangulate | aiProcess_JoinIdenticalVertices | aiProcess_SortByPType); + + if (!scene || scene->mFlags & AI_SCENE_FLAGS_INCOMPLETE || !scene->mRootNode) { + GXF_LOG_ERROR("[FoundationPoseDecoder] Error while loading mesh file ERROR::ASSIMP::"); + GXF_LOG_ERROR(importer.GetErrorString()); + return GXF_FAILURE; + } + + if (scene->mNumMeshes == 0) { + GXF_LOG_ERROR("[FoundationPoseDecoder] No mesh was found in the mesh file"); + return GXF_FAILURE; + } + + const aiMesh* mesh = scene->mMeshes[0]; + + auto min_max_vertex = FindMinMaxVertex(mesh); + mesh_model_center_ = (min_max_vertex.second + min_max_vertex.first) / 2.0; + Eigen::Vector3f min_vertex = min_max_vertex.first; + Eigen::Vector3f max_vertex = min_max_vertex.second; + + // Now you have the bounding box defined by the min and max coordinates + bbox_size_x_ = abs(max_vertex[0] - min_vertex[0]); + bbox_size_y_ = abs(max_vertex[1] - min_vertex[1]); + bbox_size_z_ = abs(max_vertex[2] - min_vertex[2]); + + GXF_LOG_INFO( + "[FoundationposeDecoder] bbox_size_x_: %f \n bbox_size_y_: %f \n bbox_size_z_: %f)", + bbox_size_x_, bbox_size_y_, bbox_size_z_); + return GXF_SUCCESS; +} + +gxf_result_t FoundationposeDecoder::registerInterface(gxf::Registrar* registrar) noexcept { + gxf::Expected result; + + result &= registrar->parameter( + pose_array_receiver_, "pose_array_input", "Pose Array Input", + "The pose detections array as a tensor"); + + result &= registrar->parameter( + pose_scores_receiver_, "pose_scores_input", "Pose Scores Input", + "The pose detection scores as a tensor", + gxf::Registrar::NoDefaultParameter(), GXF_PARAMETER_FLAGS_OPTIONAL); + + result &= registrar->parameter( + pose_matrix_transmitter_, "pose_matrix_output", "Pose matrix output need for tracking", + "The ouput pose matrix as a tensor list"); + + result &= registrar->parameter( + detection3_d_list_transmitter_, "output", "Detection 3D list output", + "The ouput poses as a Detection 3D list"); + + result &= registrar->parameter( + mesh_file_path_, "mesh_file_path", "obj mesh file path", "Path to your object mesh file"); + + result &= registrar->parameter( + mode_, "mode", "Decoder Mode", "Tracking or Pose Estimation"); + + result &= registrar->parameter( + allocator_, "allocator", "Allocator", "Output Allocator"); + + result &= registrar->parameter( + cuda_stream_pool_, "cuda_stream_pool", "Cuda Stream Pool", + "Instance of gxf::CudaStreamPool to allocate CUDA stream."); + return gxf::ToResultCode(result); +} + +gxf_result_t FoundationposeDecoder::start() noexcept { + // Get cuda stream from stream pool + auto maybe_stream = cuda_stream_pool_.get()->allocateStream(); + if (!maybe_stream) { return gxf::ToResultCode(maybe_stream); } + + cuda_stream_handle_ = std::move(maybe_stream.value()); + if (!cuda_stream_handle_->stream()) { + GXF_LOG_ERROR("[FoundationposeDecoder] Allocated stream is not initialized!"); + return GXF_FAILURE; + } + if (!cuda_stream_handle_.is_null()) { + cuda_stream_ = cuda_stream_handle_->stream().value(); + } + + return ExtractBoundingBoxDimensions(); +} + +gxf_result_t FoundationposeDecoder::tick() noexcept { + GXF_LOG_DEBUG("[FoundationposeDecoder] Tick"); + + // Receive pose array data and score tensors + const auto maybe_pose_array_message = pose_array_receiver_->receive(); + if (!maybe_pose_array_message) { + GXF_LOG_ERROR("[FoundationposeDecoder] Failed to get pose array message from receiver."); + return gxf::ToResultCode(maybe_pose_array_message); + } + auto maybe_pose_array_tensor = maybe_pose_array_message.value().get(); + if (!maybe_pose_array_tensor) { + GXF_LOG_ERROR("[FoundationposeDecoder] Failed to get pose array tensor from the message."); + return gxf::ToResultCode(maybe_pose_array_tensor); + } + auto pose_array_tensor = maybe_pose_array_tensor.value(); + + auto n_detections = pose_array_tensor->shape().dimension(0); + if (n_detections == 0) { + GXF_LOG_ERROR("[FoundationposeDecoder] Zero sample received from sampling node"); + return GXF_FAILURE; + } + GXF_LOG_DEBUG("[FoundationposeDecoder] Number of poses recevied: %d", n_detections); + + int best_score_index = 0; + if (mode_.get()!= "tracking") { + auto pose_scores_receiver = pose_scores_receiver_.try_get(); + if (!pose_scores_receiver) { + GXF_LOG_ERROR("[FoundationposeDecoder] Failed to get pose score receiver."); + return gxf::ToResultCode(pose_scores_receiver); + } + const auto maybe_pose_scores_message = pose_scores_receiver.value()->receive(); + if (!maybe_pose_scores_message) { + GXF_LOG_ERROR("[FoundationposeDecoder] Failed to get pose scores message from receiver."); + return gxf::ToResultCode(maybe_pose_scores_message); + } + auto maybe_pose_scores_tensor = maybe_pose_scores_message.value().get(); + if (!maybe_pose_scores_tensor) { + GXF_LOG_ERROR("[FoundationposeDecoder] Failed to get pose scores tensor from the message."); + return gxf::ToResultCode(maybe_pose_scores_tensor); + } + auto pose_scores_tensor = maybe_pose_scores_tensor.value(); + + // Ensure number of poses in pose array and pose scores are equal + if (pose_array_tensor->shape().dimension(0) != pose_scores_tensor->shape().dimension(1)) { + GXF_LOG_ERROR( + "[FoundationposeDecoder] Number of poses in pose array(%d) and pose scores(%d) are not " + "equal", + pose_array_tensor->shape().dimension(0), pose_scores_tensor->shape().dimension(1)); + return GXF_FAILURE; + } + + // Find the index of the highest score if there are multiple detections + best_score_index = + getMaxScoreIndex(cuda_stream_, reinterpret_cast(pose_scores_tensor->pointer()), n_detections); + CHECK_CUDA_ERRORS(cudaGetLastError()); + GXF_LOG_DEBUG("[FoundationposeDecoder] Selected index from the score model: %d", best_score_index); + } + + // Extract the pose matrix with highest score + Eigen::Matrix4f pose_matrix; + int bytes_per_element = 4 * 4 * sizeof(float); + CHECK_CUDA_ERRORS(cudaMemcpyAsync( + pose_matrix.data(), pose_array_tensor->pointer() + best_score_index * bytes_per_element, + bytes_per_element, cudaMemcpyDeviceToHost, cuda_stream_)); + CHECK_CUDA_ERRORS(cudaStreamSynchronize(cuda_stream_)); + + // Add the distance from edge to the center because + Eigen::Matrix4f tf_to_center = Eigen::Matrix4f::Identity(); + tf_to_center.block<3, 1>(0, 3) = mesh_model_center_; + pose_matrix = pose_matrix * tf_to_center; + + + // Publish the pose matrix as the input of next frame for tracking mode + auto maybe_pose_matrix_message = gxf::Entity::New(context()); + if (!maybe_pose_matrix_message) { + GXF_LOG_ERROR("[FoundationposeDecoder] Failed to allocate PoseArray Message"); + return gxf::ToResultCode(maybe_pose_matrix_message); + } + auto pose_matrix_message = maybe_pose_matrix_message.value(); + + // Pose Array is column-major + auto maybe_pose_arrays = pose_matrix_message.add(kNamePoses); + if (!maybe_pose_arrays) { + GXF_LOG_ERROR("[FoundationposeDecoder] Failed to allocate output Tensor "); + return gxf::ToResultCode(maybe_pose_arrays); + } + auto pose_arrays = maybe_pose_arrays.value(); + + auto maybe_added_timestamp_pose_matrix = + AddInputTimestampToOutput(pose_matrix_message, maybe_pose_array_message.value()); + if (!maybe_added_timestamp_pose_matrix) { + GXF_LOG_ERROR("[FoundationposeDecoder] Failed to add timestamp"); + return gxf::ToResultCode(maybe_added_timestamp_pose_matrix); + } + + std::array pose_arrays_shape{ + 1, kPoseMatrixLength, kPoseMatrixLength}; + // Initializing GXF tensor + auto result = pose_arrays->reshape( + nvidia::gxf::Shape{pose_arrays_shape, 3}, nvidia::gxf::MemoryStorageType::kDevice, + allocator_); + if (!result) { + GXF_LOG_ERROR("[FoundationposeDecoder] Failed to reshape pose array tensor"); + return gxf::ToResultCode(result); + } + CHECK_CUDA_ERRORS(cudaMemcpyAsync( + pose_arrays->pointer(), pose_matrix.data(), pose_arrays->size(), cudaMemcpyHostToDevice, cuda_stream_)); + CHECK_CUDA_ERRORS(cudaStreamSynchronize(cuda_stream_)); + pose_matrix_transmitter_->publish(std::move(pose_matrix_message)); + + + // Convert pose matrix into translation and rotation + Eigen::Matrix4d pose_matrixd = pose_matrix.cast(); + + Eigen::Vector3d translation = pose_matrixd.col(3).head(3); + Eigen::Matrix3d rotation_matrix = pose_matrixd.block<3, 3>(0, 0); + Eigen::Quaterniond rotation(rotation_matrix); + + // Prepare outputs and publish + auto maybe_detection3_d_list = nvidia::isaac::CreateDetection3DListMessage(context(), 1); + if (!maybe_detection3_d_list) { + GXF_LOG_ERROR("[FoundationposeDecoder] Failed to create detection3d list"); + return gxf::ToResultCode(maybe_detection3_d_list); + } + + auto maybe_added_timestamp = + AddInputTimestampToOutput(maybe_detection3_d_list->entity, maybe_pose_array_message.value()); + if (!maybe_added_timestamp) { + GXF_LOG_ERROR("[FoundationposeDecoder] Failed to add timestamp"); + return gxf::ToResultCode(maybe_added_timestamp); + } + auto detection3_d_list = maybe_detection3_d_list.value(); + + nvidia::isaac::Pose3d pose3d{ + nvidia::isaac::SO3d::FromQuaternion({rotation.w(), rotation.x(), rotation.y(), rotation.z()}), + nvidia::isaac::Vector3d(translation[0], translation[1], translation[2])}; + + **detection3_d_list.poses[0] = pose3d; + **detection3_d_list.bbox_sizes[0] = + nvidia::isaac::Vector3f(bbox_size_x_, bbox_size_y_, bbox_size_z_); + + return gxf::ToResultCode( + detection3_d_list_transmitter_->publish(std::move(detection3_d_list.entity))); +} + +} // namespace isaac_ros +} // namespace nvidia diff --git a/isaac_ros_gxf_extensions/gxf_isaac_foundationpose/gxf/foundationpose/foundationpose_decoder.cu b/isaac_ros_gxf_extensions/gxf_isaac_foundationpose/gxf/foundationpose/foundationpose_decoder.cu new file mode 100644 index 0000000..2e09cc2 --- /dev/null +++ b/isaac_ros_gxf_extensions/gxf_isaac_foundationpose/gxf/foundationpose/foundationpose_decoder.cu @@ -0,0 +1,40 @@ +// SPDX-FileCopyrightText: NVIDIA CORPORATION & AFFILIATES +// Copyright (c) 2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. +// +// SPDX-License-Identifier: Apache-2.0 + +#include "foundationpose_decoder.cu.hpp" + +#include +#include + +namespace nvidia { +namespace isaac_ros { + +// This function will find the index with the maximum score +int getMaxScoreIndex(cudaStream_t cuda_stream, float* scores, int N) { + // Wrap raw pointers with device pointers + thrust::device_ptr dev_scores(scores); + // Find the maximum score + thrust::device_ptr max_score_ptr = + thrust::max_element(thrust::cuda::par.on(cuda_stream), dev_scores, dev_scores + N); + + // Calculate the index of the maximum score + int max_index = max_score_ptr - dev_scores; + return max_index; +} + +} // namespace isaac_ros +} // namespace nvidia \ No newline at end of file diff --git a/isaac_ros_gxf_extensions/gxf_isaac_foundationpose/gxf/foundationpose/foundationpose_decoder.cu.hpp b/isaac_ros_gxf_extensions/gxf_isaac_foundationpose/gxf/foundationpose/foundationpose_decoder.cu.hpp new file mode 100644 index 0000000..4241eb8 --- /dev/null +++ b/isaac_ros_gxf_extensions/gxf_isaac_foundationpose/gxf/foundationpose/foundationpose_decoder.cu.hpp @@ -0,0 +1,36 @@ +// SPDX-FileCopyrightText: NVIDIA CORPORATION & AFFILIATES +// Copyright (c) 2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. +// +// SPDX-License-Identifier: Apache-2.0 +#pragma once + +#ifndef NVIDIA_ISAAC_ROS_EXTENSIONS_FOUNDATIONPOSE_DECODER_CUDA_HPP_ +#define NVIDIA_ISAAC_ROS_EXTENSIONS_FOUNDATIONPOSE_DECODER_CUDA_HPP_ + +#include +#include + +#include "cuda.h" +#include "cuda_runtime.h" + +namespace nvidia { +namespace isaac_ros { + +int getMaxScoreIndex(cudaStream_t cuda_stream, float* scores, int N); + +} // namespace isaac_ros +} // namespace nvidia + +#endif // NVIDIA_ISAAC_ROS_EXTENSIONS_FOUNDATIONPOSE_DECODER_CUDA_HPP_ \ No newline at end of file diff --git a/isaac_ros_gxf_extensions/gxf_isaac_foundationpose/gxf/foundationpose/foundationpose_decoder.hpp b/isaac_ros_gxf_extensions/gxf_isaac_foundationpose/gxf/foundationpose/foundationpose_decoder.hpp new file mode 100644 index 0000000..7d12b02 --- /dev/null +++ b/isaac_ros_gxf_extensions/gxf_isaac_foundationpose/gxf/foundationpose/foundationpose_decoder.hpp @@ -0,0 +1,71 @@ +// SPDX-FileCopyrightText: NVIDIA CORPORATION & AFFILIATES +// Copyright (c) 2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. +// +// SPDX-License-Identifier: Apache-2.0 + +#ifndef NVIDIA_ISAAC_ROS_EXTENSIONS_FOUNDATIONPOSE_DECODER_HPP_ +#define NVIDIA_ISAAC_ROS_EXTENSIONS_FOUNDATIONPOSE_DECODER_HPP_ + +#include +#include + +#include "gxf/cuda/cuda_stream.hpp" +#include "gxf/cuda/cuda_stream_pool.hpp" +#include "gxf/core/entity.hpp" +#include "gxf/core/gxf.h" +#include "gxf/core/parameter.hpp" +#include "gxf/std/allocator.hpp" +#include "gxf/std/codelet.hpp" +#include "gxf/std/receiver.hpp" +#include "gxf/std/transmitter.hpp" + +namespace nvidia { +namespace isaac_ros { + +// GXF codelet that selects the pose with the highest score +// and outputs its as a Detection3DListMessage +class FoundationposeDecoder : public gxf::Codelet { + public: + gxf_result_t start() noexcept override; + gxf_result_t tick() noexcept override; + gxf_result_t stop() noexcept override { return GXF_SUCCESS; } + gxf_result_t registerInterface(gxf::Registrar* registrar) noexcept override; + + private: + gxf::Parameter> pose_array_receiver_; + gxf::Parameter> pose_scores_receiver_; + gxf::Parameter> detection3_d_list_transmitter_; + gxf::Parameter> pose_matrix_transmitter_; + gxf::Parameter> allocator_; + gxf::Parameter> cuda_stream_pool_; + gxf::Parameter mesh_file_path_; + gxf::Parameter mode_; + gxf::Handle cuda_stream_handle_; + + // Bounding box size + float bbox_size_x_; + float bbox_size_y_; + float bbox_size_z_; + + Eigen::Vector3f mesh_model_center_; + cudaStream_t cuda_stream_ = 0; + + gxf_result_t ExtractBoundingBoxDimensions(); +}; + +} // namespace isaac_ros +} // namespace nvidia + +#endif // NVIDIA_ISAAC_ROS_EXTENSIONS_FOUNDATIONPOSE_DECODER_HPP_ diff --git a/isaac_ros_gxf_extensions/gxf_isaac_foundationpose/gxf/foundationpose/foundationpose_ext.cpp b/isaac_ros_gxf_extensions/gxf_isaac_foundationpose/gxf/foundationpose/foundationpose_ext.cpp new file mode 100644 index 0000000..de8d0c1 --- /dev/null +++ b/isaac_ros_gxf_extensions/gxf_isaac_foundationpose/gxf/foundationpose/foundationpose_ext.cpp @@ -0,0 +1,66 @@ +// SPDX-FileCopyrightText: NVIDIA CORPORATION & AFFILIATES +// Copyright (c) 2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. +// +// SPDX-License-Identifier: Apache-2.0 +#include + +#include "gxf/core/gxf.h" +#include "gxf/std/extension_factory_helper.hpp" + +#include "detection3_d_array_message.hpp" +#include "foundationpose_decoder.hpp" +#include "foundationpose_render.hpp" +#include "foundationpose_sampling.hpp" +#include "foundationpose_transformation.hpp" +#include "render_receptive_scheduling_term.hpp" + +extern "C" { + +GXF_EXT_FACTORY_BEGIN() + +GXF_EXT_FACTORY_SET_INFO( + 0x5b942ff659bc4502, 0xa0b000b36b53f74f, "FoundationPoseExtension", + "FoundationPose GXF extension", "NVIDIA", "1.0.0", "LICENSE"); + +GXF_EXT_FACTORY_ADD( + 0x52138759e5824999, 0x8d26bd90e602d335, nvidia::isaac_ros::FoundationposeSampling, + nvidia::gxf::Codelet, "Codelet to generate pose hypothesis."); + +GXF_EXT_FACTORY_ADD( + 0xb2d0e3737b4f583f, 0xa13437c086f114e3, nvidia::isaac_ros::FoundationposeDecoder, + nvidia::gxf::Codelet, "Codelet to decode, sort and select the final pose."); + +GXF_EXT_FACTORY_ADD( + 0x97104bb7e89f2b2a, 0x9de6f7fd399a201d, nvidia::isaac_ros::FoundationposeTransformation, + nvidia::gxf::Codelet, "Codelet to trasnform poses for foundation pose."); + +GXF_EXT_FACTORY_ADD( + 0x63138759e5624879, 0x4a95bd90e602d324, nvidia::isaac_ros::FoundationposeRender, + nvidia::gxf::Codelet, "Codelet to generate pose hypothesis."); + +GXF_EXT_FACTORY_ADD( + 0x7b605fa9ec204371, 0x90bdab77b52927e8, nvidia::isaac_ros::RenderReceptiveSchedulingTerm, + nvidia::gxf::SchedulingTerm, "Codelet to generate pose hypothesis."); + +GXF_EXT_FACTORY_ADD_0( + 0x65d4476051a512ee, 0xb3f727d2ed955145, nvidia::isaac::ObjectHypothesis, + "List of scores and class ids"); + +GXF_EXT_FACTORY_ADD_0( + 0x782823c851dc11ea, 0xa5dc87b46496e7bd, nvidia::isaac::Vector3f, "3 Dimensional Vector"); + +GXF_EXT_FACTORY_END() + +} // extern "C" diff --git a/isaac_ros_gxf_extensions/gxf_isaac_foundationpose/gxf/foundationpose/foundationpose_render.cpp b/isaac_ros_gxf_extensions/gxf_isaac_foundationpose/gxf/foundationpose/foundationpose_render.cpp new file mode 100644 index 0000000..f1f1a46 --- /dev/null +++ b/isaac_ros_gxf_extensions/gxf_isaac_foundationpose/gxf/foundationpose/foundationpose_render.cpp @@ -0,0 +1,1156 @@ +// SPDX-FileCopyrightText: NVIDIA CORPORATION & AFFILIATES +// Copyright (c) 2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. +// +// SPDX-License-Identifier: Apache-2.0 + +#include "foundationpose_render.hpp" +#include "foundationpose_utils.hpp" + +#include +#include +#include +#include + +#include +#include +#include +#include + +#include "gxf/multimedia/camera.hpp" +#include "gxf/multimedia/video.hpp" +#include "gxf/std/tensor.hpp" +#include "gxf/std/timestamp.hpp" + +namespace nvidia { +namespace isaac_ros { + +namespace { + +constexpr char kNamePoints[] = "points"; +constexpr char kNamePoses[] = "poses"; + +constexpr char kRenderedTensor[] = "input_tensor1"; +constexpr char kOriginalTensor[] = "input_tensor2"; +constexpr char RAW_CAMERA_MODEL_GXF_NAME[] = "intrinsics"; + +constexpr size_t kVertexPoints = 3; +constexpr size_t kTriangleVertices = 3; +constexpr size_t kTexcoordsDim = 2; +constexpr size_t kPTMatrixDim = 3; +constexpr size_t kTransformationMatrixSize = 4; +constexpr size_t kPoseMatrixLength = 4; +constexpr size_t kNumChannels = 3; +constexpr size_t kOutputRank = 4; +// Number of slices on the input +constexpr int kNumBatches = 6; + +typedef Eigen::Matrix RowMajorMatrix; + +// From OpenCV camera (cvcam) coordinate system to the OpenGL camera (glcam) coordinate system +const Eigen::Matrix4f kGLCamInCVCam = + (Eigen::Matrix4f(4, 4) << 1, 0, 0, 0, 0, -1, 0, 0, 0, 0, -1, 0, 0, 0, 0, 1).finished(); + +RowMajorMatrix ComputeTF( + float left, float right, float top, float bottom, Eigen::Vector2i out_size) { + left = std::round(left); + right = std::round(right); + top = std::round(top); + bottom = std::round(bottom); + + RowMajorMatrix tf = Eigen::MatrixXf::Identity(3, 3); + tf(0, 2) = -left; + tf(1, 2) = -top; + + RowMajorMatrix new_tf = Eigen::MatrixXf::Identity(3, 3); + new_tf(0, 0) = out_size(0) / (right - left); + new_tf(1, 1) = out_size(1) / (bottom - top); + + auto result = new_tf * tf; + return result; +} + +std::vector ComputeCropWindowTF( + const std::vector& poses, const Eigen::MatrixXf& K, Eigen::Vector2i out_size, + float crop_ratio, float mesh_diameter) { + // Compute the tf batch from the left, right, top, and bottom coordinates + int B = poses.size(); + float r = mesh_diameter * crop_ratio / 2; + Eigen::MatrixXf offsets(5, 3); + offsets << 0, 0, 0, r, 0, 0, -r, 0, 0, 0, r, 0, 0, -r, 0; + + std::vector tfs; + for (int i = 0; i < B; i++) { + auto block = poses[i].block<3, 1>(0, 3).transpose(); + Eigen::MatrixXf pts = block.replicate(offsets.rows(), 1).array() + offsets.array(); + Eigen::MatrixXf projected = (K * pts.transpose()).transpose(); + Eigen::MatrixXf uvs = + projected.leftCols(2).array() / projected.rightCols(1).replicate(1, 2).array(); + Eigen::MatrixXf center = uvs.row(0); + + float radius = std::abs((uvs - center.replicate(uvs.rows(), 1)).rightCols(1).maxCoeff()); + float left = center(0, 0) - radius; + float right = center(0, 0) + radius; + float top = center(0, 1) - radius; + float bottom = center(0, 1) + radius; + + tfs.push_back(ComputeTF(left, right, top, bottom, out_size)); + } + return tfs; +} + +// 2D tensor * 3D tensor +gxf_result_t TransformPts( + std::vector& output, const Eigen::MatrixXf& pts, const std::vector& tfs) { + // Get the dimensions of the inputs + int rows = pts.rows(); + int cols = pts.cols(); + int tfs_size = tfs.size(); + if (tfs_size == 0) { + GXF_LOG_ERROR("[FoundationposeRender] The transfomation matrix is empty! "); + return GXF_FAILURE; + } + + if (tfs[0].cols() != tfs[0].rows()) { + GXF_LOG_ERROR("[FoundationposeRender] The transfomation matrix has different rows and cols! "); + return GXF_FAILURE; + } + int dim = tfs[0].rows(); + + if (cols != dim - 1) { + GXF_LOG_ERROR("[FoundationposeRender] The dimension of pts and tf are not match! "); + return GXF_FAILURE; + } + + for (int i = 0; i < tfs_size; i++) { + RowMajorMatrix transformed_matrix; + transformed_matrix.resize(rows, dim - 1); + auto submatrix = tfs[i].block(0, 0, dim - 1, dim - 1); + auto last_col = tfs[i].block(0, dim - 1, dim - 1, 1); + + // Apply the transformation to the points + for (int j = 0; j < rows; j++) { + auto new_row = submatrix * pts.row(j).transpose() + last_col; + transformed_matrix.row(j) = new_row.transpose(); + } + output.push_back(transformed_matrix); + } + + // Return the result vector + return GXF_SUCCESS; +} + +// Adding one column of ones to the matrix +Eigen::MatrixXf ToHomo(const Eigen::MatrixXf& pts) { + int rows = pts.rows(); + int cols = pts.cols(); + + Eigen::MatrixXf ones = Eigen::MatrixXf::Ones(rows, 1); + + Eigen::MatrixXf homo(rows, cols + 1); + homo << pts, ones; + + return homo; +} + +void WrapImgPtrToNHWCTensor( + uint8_t* input_ptr, nvcv::Tensor& output_tensor, int N, int H, int W, int C) { + nvcv::TensorDataStridedCuda::Buffer output_buffer; + output_buffer.strides[3] = sizeof(uint8_t); + output_buffer.strides[2] = C * output_buffer.strides[3]; + output_buffer.strides[1] = W * output_buffer.strides[2]; + output_buffer.strides[0] = H * output_buffer.strides[1]; + output_buffer.basePtr = reinterpret_cast(input_ptr); + + nvcv::TensorShape::ShapeType shape{N, H, W, C}; + nvcv::TensorShape tensor_shape{shape, "NHWC"}; + nvcv::TensorDataStridedCuda output_data(tensor_shape, nvcv::TYPE_U8, output_buffer); + output_tensor = nvcv::TensorWrapData(output_data); +} + +void WrapFloatPtrToNHWCTensor( + float* input_ptr, nvcv::Tensor& output_tensor, int N, int H, int W, int C) { + nvcv::TensorDataStridedCuda::Buffer output_buffer; + output_buffer.strides[3] = sizeof(float); + output_buffer.strides[2] = C * output_buffer.strides[3]; + output_buffer.strides[1] = W * output_buffer.strides[2]; + output_buffer.strides[0] = H * output_buffer.strides[1]; + output_buffer.basePtr = reinterpret_cast(input_ptr); + + nvcv::TensorShape::ShapeType shape{N, H, W, C}; + nvcv::TensorShape tensor_shape{shape, "NHWC"}; + nvcv::TensorDataStridedCuda output_data(tensor_shape, nvcv::TYPE_F32, output_buffer); + output_tensor = nvcv::TensorWrapData(output_data); +} + +gxf_result_t ProjectMatrixFromIntrinsics( + Eigen::Matrix4f& proj_output, const Eigen::Matrix3f& K, int height, int width, float znear = 0.1, float zfar = 100.0, + std::string window_coords = "y_down") { + int x0 = 0; + int y0 = 0; + int w = width; + int h = height; + float nc = znear; + float fc = zfar; + + float depth = fc - nc; + float q = -(fc + nc) / depth; + float qn = -2 * (fc * nc) / depth; + + // Get the projection matrix from camera K matrix + if (window_coords == "y_up") { + proj_output << 2 * K(0, 0) / w, -2 * K(0, 1) / w, (-2 * K(0, 2) + w + 2 * x0) / w, 0, 0, + -2 * K(1, 1) / h, (-2 * K(1, 2) + h + 2 * y0) / h, 0, 0, 0, q, qn, 0, 0, -1, 0; + } else if (window_coords == "y_down") { + proj_output << 2 * K(0, 0) / w, -2 * K(0, 1) / w, (-2 * K(0, 2) + w + 2 * x0) / w, 0, 0, + 2 * K(1, 1) / h, (2 * K(1, 2) - h + 2 * y0) / h, 0, 0, 0, q, qn, 0, 0, -1, 0; + } else { + GXF_LOG_ERROR("[FoundationposeRender] The window coordinates should be y_up or y_down"); + return GXF_FAILURE; + } + + return GXF_SUCCESS; +} + +gxf_result_t GeneratePoseClip( + std::vector& pose_clip, const std::vector& poses, const Eigen::MatrixXf& bbox2d, const Eigen::MatrixXf& pose_homo, + const Eigen::Matrix4f& projection_mat, int rgb_H, int rgb_W) { + std::vector mtx; + for (const auto& pose : poses) { + mtx.push_back(projection_mat * (kGLCamInCVCam * pose)); + } + + if (mtx[0].cols() != pose_homo.cols()) { + GXF_LOG_ERROR("[FoundationposeRender] The col size of mtx is not the same as pos_home"); + return GXF_FAILURE; + } + + Eigen::VectorXf l = bbox2d.col(0).array(); + Eigen::VectorXf t = rgb_H - bbox2d.col(1).array(); + Eigen::VectorXf r = bbox2d.col(2).array(); + Eigen::VectorXf b = rgb_H - bbox2d.col(3).array(); + + int N = poses.size(); + pose_clip.reserve(N * pose_homo.rows() * pose_homo.cols()); + for (size_t i = 0; i < poses.size(); i++) { + auto m = mtx[i]; + // Make sure eigen matrix is row major if need to copy into GPU memory + Eigen::Matrix new_pos; + new_pos.resize(pose_homo.rows(), pose_homo.cols()); + for (int j = 0; j < pose_homo.rows(); j++) { + Eigen::VectorXf temp = m * pose_homo.row(j).transpose(); + new_pos.row(j) = temp.transpose(); + } + + Eigen::Matrix4f tf = Eigen::Matrix4f::Identity(); + tf(0, 0) = rgb_W / (r(i) - l(i)); + tf(1, 1) = rgb_H / (t(i) - b(i)); + tf(3, 0) = (rgb_W - r(i) - l(i)) / (r(i) - l(i)); + tf(3, 1) = (rgb_H - t(i) - b(i)) / (t(i) - b(i)); + new_pos = new_pos * tf; + // Vector of Eigen matrix is not continous in memory, flatten matrix and insert to vector + std::vector pose_data(new_pos.data(), new_pos.data() + new_pos.size()); + pose_clip.insert(pose_clip.end(), pose_data.begin(), pose_data.end()); + } + + return GXF_SUCCESS; +} + +gxf_result_t ConstructBBox2D( + Eigen::MatrixXf& bbox2d, const std::vector& tfs, int H, int W) { + Eigen::MatrixXf bbox2d_crop(2, 2); + bbox2d_crop << 0.0, 0.0, W - 1, H - 1; + + std::vector inversed_tfs; + // Inverse tfs before transform + for (size_t i = 0; i < tfs.size(); i++) { + inversed_tfs.push_back(tfs[i].inverse()); + } + + std::vector bbox2d_ori_vec; + auto gxf_result_t = TransformPts(bbox2d_ori_vec, bbox2d_crop, inversed_tfs); + if(gxf_result_t != GXF_SUCCESS) { + GXF_LOG_ERROR("[FoundationposeRender] Failed to transform the 2D bounding box"); + return gxf_result_t; + } + + for (size_t i = 0; i < bbox2d_ori_vec.size(); i++) { + bbox2d.row(i) = + Eigen::Map(bbox2d_ori_vec[i].data(), bbox2d_ori_vec[i].size()); + } + return GXF_SUCCESS; +} + +} // namespace + +gxf_result_t FoundationposeRender::registerInterface(gxf::Registrar* registrar) noexcept { + gxf::Expected result; + + result &= registrar->parameter( + pose_receiver_, "pose_array_input", "Pose Array Input", + "The pose estimation input as tensor "); + + result &= registrar->parameter( + point_cloud_receiver_, "point_cloud_input", "Point Cloud Input", + "The point cloud input as a tensor"); + + result &= registrar->parameter( + rgb_receiver_, "rgb_input", "RGB image Input", "The RGB image input as a videobuffer"); + + result &= registrar->parameter( + camera_model_receiver_, "camera_model_input", "Camera model input", + "The camera intrinsics and extrinsics wrapped in a gxf message"); + + result &= registrar->parameter( + pose_array_transmitter_, "output", "Pose Array Output", "The ouput poses as a pose array"); + + result &= registrar->parameter( + mesh_file_path_, "mesh_file_path", "obj mesh file path", "Path to your object mesh file"); + + result &= registrar->parameter( + texture_path_, "texture_path", "texture file path", "Path to your texture file"); + + result &= registrar->parameter( + mode_, "mode", "Render Mode", "render mode select from refine or score"); + + result &= registrar->parameter( + crop_ratio_, "crop_ratio", "crop ratio", "Input image crop ratio"); + + result &= registrar->parameter( + resized_image_height_, "resized_image_height", "desired height", "Desired height of the rendered object"); + + result &= registrar->parameter( + resized_image_width_, "resized_image_width", "desired width", "Desired width of the rendered object"); + + result &= registrar->parameter( + min_depth_, "min_depth", "Minimum Depth", "Minimum allowed Z-axis value of pointcloud"); + + result &= registrar->parameter( + max_depth_, "max_depth", "Maximum Depth", "Maximum allowed X,Y,Z-axis value of pointcloud to threshold"); + + result &= registrar->parameter( + refine_iterations_, "refine_iterations", "refine iterations", + "Number of iterations on the refine network (render->refine->transoformation)", 1); + + result &= registrar->parameter( + allocator_, "allocator", "Allocator", "Output Allocator"); + + result &= registrar->parameter( + cuda_stream_pool_, "cuda_stream_pool", "Cuda Stream Pool", + "Instance of gxf::CudaStreamPool to allocate CUDA stream."); + + // Only used when multiple refinement iterations are required + result &= registrar->parameter( + iterative_pose_receiver_, "iterative_pose_array_input", "Iterative Pose Array Input", + "The pose estimation input as tensor", + gxf::Registrar::NoDefaultParameter(), GXF_PARAMETER_FLAGS_OPTIONAL); + + result &= registrar->parameter( + iterative_point_cloud_receiver_, "iterative_point_cloud_input", "Iterative Point Cloud Input", + "The point cloud input as a tensor", + gxf::Registrar::NoDefaultParameter(), GXF_PARAMETER_FLAGS_OPTIONAL); + + result &= registrar->parameter( + iterative_rgb_receiver_, "iterative_rgb_input", "Iterative RGB image Input", + "The RGB image input as a videobuffer", + gxf::Registrar::NoDefaultParameter(), GXF_PARAMETER_FLAGS_OPTIONAL); + + result &= registrar->parameter( + iterative_camera_model_receiver_, "iterative_camera_model_input", "Iterative Camera model input", + "The camera intrinsics and extrinsics wrapped in a gxf message", + gxf::Registrar::NoDefaultParameter(), GXF_PARAMETER_FLAGS_OPTIONAL); + + result &= registrar->parameter( + iterative_point_cloud_transmitter_, "iterative_point_cloud_output", "Iterative Point Cloud Output", + "The ouput poses as a pose array", + gxf::Registrar::NoDefaultParameter(), GXF_PARAMETER_FLAGS_OPTIONAL); + + result &= registrar->parameter( + iterative_rgb_transmitter_, "iterative_rgb_output", "Iterative RGB Output", + "The ouput poses as a pose array", + gxf::Registrar::NoDefaultParameter(), GXF_PARAMETER_FLAGS_OPTIONAL); + + result &= registrar->parameter( + iterative_camera_model_transmitter_, "iterative_camera_model_output", "Iterative Camera Model Output", + "The ouput poses as a pose array", + gxf::Registrar::NoDefaultParameter(), GXF_PARAMETER_FLAGS_OPTIONAL); + + return gxf::ToResultCode(result); +} + +gxf_result_t FoundationposeRender::start() noexcept { + GXF_LOG_DEBUG("[FoundationposeRender] FoundationposeRender start"); + // Validate input parameters + if (refine_iterations_ < 1) { + GXF_LOG_ERROR("[FoundationposeRender] Refine iterations should be at least 1"); + return GXF_FAILURE; + } + + // Load the obj mesh file + // TODO: optimize the mesh loading by passing mesh data between codelets to avoid + // duplicate calculation. Could calcuate from sampling node and broadcast to the other nodes + Assimp::Importer importer; + const aiScene* scene = importer.ReadFile( + mesh_file_path_.get(), + aiProcess_Triangulate | aiProcess_JoinIdenticalVertices | aiProcess_SortByPType); + + if (!scene || scene->mFlags & AI_SCENE_FLAGS_INCOMPLETE || !scene->mRootNode) { + GXF_LOG_ERROR("[FoundationposeRender] Error while loading mesh file ERROR::ASSIMP::"); + GXF_LOG_ERROR(importer.GetErrorString()); + return GXF_FAILURE; + } + + if (scene->mNumMeshes == 0) { + GXF_LOG_ERROR("[FoundationposeRender] No mesh was found in the mesh file"); + return GXF_FAILURE; + } + + // Only take the first mesh + const aiMesh* mesh = scene->mMeshes[0]; + + mesh_diameter_ = CalcMeshDiameter(mesh); + auto min_max_vertex = FindMinMaxVertex(mesh); + auto mesh_model_center = (min_max_vertex.second + min_max_vertex.first) / 2.0; + + // Walk through each of the mesh's vertices + for (unsigned int v = 0; v < mesh->mNumVertices; v++) { + vertices_.push_back(mesh->mVertices[v].x - mesh_model_center[0]); + vertices_.push_back(mesh->mVertices[v].y - mesh_model_center[1]); + vertices_.push_back(mesh->mVertices[v].z - mesh_model_center[2]); + + // Check if the mesh has texture coordinates + if (mesh->mTextureCoords[0]) { + texcoords_.push_back(mesh->mTextureCoords[0][v].x); + texcoords_.push_back(1 - mesh->mTextureCoords[0][v].y); + } + } + + // Walk through each of the mesh's faces (a face is a mesh its triangle) + for (unsigned int f = 0; f < mesh->mNumFaces; f++) { + const aiFace& face = mesh->mFaces[f]; + + // We assume the face is a triangle due to aiProcess_Triangulate + if (face.mNumIndices == 3) { + for (unsigned int i = 0; i < face.mNumIndices; i++) { + mesh_faces_.push_back(face.mIndices[i]); + } + } else { + GXF_LOG_ERROR( + "Only triangle is supported, but the object face has %u vertices. ", face.mNumIndices); + return GXF_FAILURE; + } + } + + // Load the texture map + cv::Mat texture_map = cv::imread(texture_path_.get()); + cv::Mat rgb_texture_map; + cv::cvtColor(texture_map, rgb_texture_map, cv::COLOR_BGR2RGB); + + if (!rgb_texture_map.isContinuous()) { + GXF_LOG_ERROR("[FoundationposeRender] Texture map is not continuous"); + return GXF_FAILURE; + } + + if (rgb_texture_map.channels() != kNumChannels) { + GXF_LOG_ERROR( + "[FoundationposeRender] Recieved texture map has %d number of channels, but expected %lu.", + rgb_texture_map.channels(), kNumChannels); + return GXF_FAILURE; + } + texture_map_height_ = rgb_texture_map.rows; + texture_map_width_ = rgb_texture_map.cols; + GXF_LOG_DEBUG("[FoundationposeRender] Texture map height: %d, width: %d", texture_map_height_, texture_map_width_); + + // The number of vertices is the size of the vertices array divided by 3 (since it's x,y,z) + num_vertices_ = vertices_.size() / kVertexPoints; + // The number of texture coordinates is the size of the texcoords array divided by 2 (since it's u,v) + num_texcoords_ = texcoords_.size() / kTexcoordsDim; + // The number of faces is the size of the faces array divided by 3 (since each face has 3 edges) + num_faces_ = mesh_faces_.size() / kTriangleVertices; + + // Check if the mesh data is valid + if (num_vertices_ == 0 || num_texcoords_ == 0 || num_faces_ == 0) { + GXF_LOG_ERROR("[FoundationposeRender] Empty input from mesh file. "); + return GXF_FAILURE; + } + + if (sizeof(vertices_[0]) != sizeof(float) || + sizeof(texcoords_[0]) != sizeof(float) || + sizeof(mesh_faces_[0]) != sizeof(int32_t)) { + GXF_LOG_ERROR("[FoundationposeRender] Invalid data type from mesh file. "); + return GXF_FAILURE; + } + + // Assign vertices to the class memeber + mesh_vertices_ = + Eigen::Map>( + vertices_.data(), num_vertices_, 3); + + GXF_LOG_DEBUG("[FoundationposeRender] Number of mesh vertices: %d", num_vertices_); + GXF_LOG_DEBUG("[FoundationposeRender] Number of mesh faces: %d", num_faces_); + GXF_LOG_DEBUG("[FoundationposeRender] Number of mesh texcoords: %d", num_texcoords_); + + // Get cuda stream from stream pool + auto maybe_stream = cuda_stream_pool_.get()->allocateStream(); + if (!maybe_stream) { return gxf::ToResultCode(maybe_stream); } + + cuda_stream_handle_ = std::move(maybe_stream.value()); + if (!cuda_stream_handle_->stream()) { + GXF_LOG_ERROR("[FoundationposeRender] Allocated stream is not initialized!"); + return GXF_FAILURE; + } + if (!cuda_stream_handle_.is_null()) { + cuda_stream_ = cuda_stream_handle_->stream().value(); + } + + // Allocate device memory for mesh data + size_t faces_size = mesh_faces_.size() * sizeof(int32_t); + size_t texcoords_size = texcoords_.size() * sizeof(float); + + CHECK_CUDA_ERRORS(cudaMalloc(&mesh_faces_device_, faces_size)); + CHECK_CUDA_ERRORS(cudaMalloc(&texcoords_device_, texcoords_size)); + CHECK_CUDA_ERRORS(cudaMalloc(&texture_map_device_, rgb_texture_map.total() * kNumChannels)); + + CHECK_CUDA_ERRORS(cudaMemcpyAsync( + mesh_faces_device_, mesh_faces_.data(), faces_size, cudaMemcpyHostToDevice, cuda_stream_)); + CHECK_CUDA_ERRORS(cudaMemcpyAsync( + texcoords_device_, texcoords_.data(), texcoords_.size() * sizeof(float), cudaMemcpyHostToDevice, cuda_stream_)); + CHECK_CUDA_ERRORS(cudaMemcpyAsync( + texture_map_device_, reinterpret_cast(rgb_texture_map.data), + rgb_texture_map.total() * kNumChannels, cudaMemcpyHostToDevice, cuda_stream_)); + + // Preprocess mesh data + nvcv::Tensor texture_map_tensor; + WrapImgPtrToNHWCTensor(texture_map_device_, texture_map_tensor, 1, texture_map_height_, texture_map_width_, kNumChannels); + + nvcv::TensorShape::ShapeType shape{1, texture_map_height_, texture_map_width_, kNumChannels}; + nvcv::TensorShape tensor_shape{shape, "NHWC"}; + float_texture_map_tensor_ = nvcv::Tensor(tensor_shape, nvcv::TYPE_F32); + + const float scale_factor = 1.0f / 255.0f; + cvcuda::ConvertTo convert_op; + convert_op(cuda_stream_, texture_map_tensor, float_texture_map_tensor_, scale_factor, 0.0f); + CHECK_CUDA_ERRORS(cudaGetLastError()); + CHECK_CUDA_ERRORS(cudaStreamSynchronize(cuda_stream_)); + + return GXF_SUCCESS; +} + + +gxf_result_t FoundationposeRender::NvdiffrastRender( + cudaStream_t cuda_stream_, std::vector& poses, Eigen::Matrix3f& K, Eigen::MatrixXf& bbox2d, int rgb_H, + int rgb_W, int H, int W, nvcv::Tensor& flip_color_tensor, nvcv::Tensor& flip_xyz_map_tensor) { + size_t N = poses.size(); + + // Generate attributes for interpolate + std::vector pts_cam; + auto gxf_result = TransformPts(pts_cam, mesh_vertices_, poses); + if(gxf_result != GXF_SUCCESS) { + return gxf_result; + } + if (pts_cam.size() == 0 || pts_cam.size() != N) { + GXF_LOG_ERROR("[FoundationposeRender] The attribute size doesn't match pose size after transform"); + return GXF_FAILURE; + } + if (pts_cam[0].rows() != num_vertices_) { + GXF_LOG_ERROR("[FoundationposeRender] The attribute dimension doesn't match with vertices after transform"); + return GXF_FAILURE; + } + + // Vector of Eigen matrix is not continous in memory, flatten matrix and insert to vector + size_t num_attr = pts_cam[0].cols(); + if (!pts_cam[0].IsRowMajor) { + GXF_LOG_ERROR("[FoundationposeRender] Pts cam need to be row major in order to copy into device memory"); + return GXF_FAILURE; + } + + std::vector pts_cam_vector; + pts_cam_vector.reserve(N * num_vertices_ * num_attr); + for (auto& mat : pts_cam) { + std::vector mat_data(mat.data(), mat.data() + mat.size()); + pts_cam_vector.insert(pts_cam_vector.end(), mat_data.begin(), mat_data.end()); + } + + Eigen::Matrix4f projection_mat; + gxf_result = ProjectMatrixFromIntrinsics(projection_mat, K, rgb_H, rgb_W); + if(gxf_result != GXF_SUCCESS) { + return GXF_FAILURE; + } + + // Homogeneliaze the vertices to N * 4 + auto pose_homo = ToHomo(mesh_vertices_); + if (pose_homo.rows() != num_vertices_) { + GXF_LOG_ERROR("[FoundationposeRender] The number of vertice should not change after homogeneliaze "); + return GXF_FAILURE; + } + if (pose_homo.cols() != mesh_vertices_.cols() + 1) { + GXF_LOG_ERROR("[FoundationposeRender] Points per vertex should increase by one after homogeneliaze"); + return GXF_FAILURE; + } + + std::vector pose_clip; + gxf_result = GeneratePoseClip(pose_clip, poses, bbox2d, pose_homo, projection_mat, rgb_H, rgb_W); + if (gxf_result != GXF_SUCCESS) { + return GXF_FAILURE; + } + + if (pose_clip.size() == 0) { + GXF_LOG_ERROR("[FoundationposeRender] Pose clip should not be empty"); + return GXF_FAILURE; + } + + // Allocate device memory for the intermedia results on the first frame + size_t pose_clip_size = pose_clip.size() * sizeof(float); + size_t pts_cam_size = pts_cam_vector.size() * sizeof(float); + size_t rast_out_size = N * H * W * 4 * sizeof(float); + size_t color_size = N * H * W * kNumChannels * sizeof(float); + size_t xyz_map_size = N * H * W * kNumChannels * sizeof(float); + size_t texcoords_out_size = N * H * W * kTexcoordsDim * sizeof(float); + + if (render_data_cached_ == false) { + CHECK_CUDA_ERRORS(cudaMalloc(&pose_clip_device_, pose_clip_size)); + CHECK_CUDA_ERRORS(cudaMalloc(&rast_out_device_, rast_out_size)); + CHECK_CUDA_ERRORS(cudaMalloc(&pts_cam_device_, pts_cam_size)); + CHECK_CUDA_ERRORS(cudaMalloc(&texcoords_out_device_, texcoords_out_size)); + CHECK_CUDA_ERRORS(cudaMalloc(&color_device_, color_size)); + CHECK_CUDA_ERRORS(cudaMalloc(&xyz_map_device_, xyz_map_size)); + cr_ = new CR::CudaRaster(); + render_data_cached_ = true; + } + + // Copy other data to device memory + CHECK_CUDA_ERRORS(cudaMemcpyAsync( + pose_clip_device_, pose_clip.data(), pose_clip_size, cudaMemcpyHostToDevice, cuda_stream_)); + CHECK_CUDA_ERRORS(cudaMemcpyAsync( + pts_cam_device_, pts_cam_vector.data(), pts_cam_size, cudaMemcpyHostToDevice, cuda_stream_)); + + rasterize( + cuda_stream_, cr_, + pose_clip_device_, mesh_faces_device_, rast_out_device_, + num_vertices_, num_faces_, + H, W, N); + CHECK_CUDA_ERRORS(cudaGetLastError()); + + interpolate( + cuda_stream_, + pts_cam_device_, rast_out_device_, mesh_faces_device_, xyz_map_device_, + num_vertices_, num_faces_, kVertexPoints, + H, W, N); + CHECK_CUDA_ERRORS(cudaGetLastError()); + + interpolate( + cuda_stream_, + texcoords_device_, rast_out_device_, mesh_faces_device_, texcoords_out_device_, + num_vertices_, num_faces_, kTexcoordsDim, + H, W, N); + CHECK_CUDA_ERRORS(cudaGetLastError()); + + auto float_texture_map_data = float_texture_map_tensor_.exportData(); + texture( + cuda_stream_, + reinterpret_cast(float_texture_map_data->basePtr()), + texcoords_out_device_, + color_device_, + texture_map_height_, texture_map_width_, kNumChannels, + 1, H, W, N); + CHECK_CUDA_ERRORS(cudaGetLastError()); + + float min_value = 0.0; + float max_value = 1.0; + clamp(cuda_stream_, color_device_, min_value, max_value, N * H * W * kNumChannels); + CHECK_CUDA_ERRORS(cudaGetLastError()); + + nvcv::Tensor color_tensor, xyz_map_tensor; + WrapFloatPtrToNHWCTensor(color_device_, color_tensor, N, H, W, kNumChannels); + WrapFloatPtrToNHWCTensor(xyz_map_device_, xyz_map_tensor, N, H, W, kNumChannels); + + cvcuda::Flip flip_op; + flip_op(cuda_stream_, color_tensor, flip_color_tensor, 0); + CHECK_CUDA_ERRORS(cudaGetLastError()); + + flip_op(cuda_stream_, xyz_map_tensor, flip_xyz_map_tensor, 0); + CHECK_CUDA_ERRORS(cudaGetLastError()); + + return GXF_SUCCESS; +} + +gxf_result_t FoundationposeRender::tick() noexcept { + GXF_LOG_DEBUG("[FoundationposeRender] tick"); + + // Selective receive messages based on the states + gxf::Entity rgb_message; + gxf::Entity camera_model_message; + gxf::Entity pose_message; + gxf::Entity xyz_message; + + // Receive from upstream node on the first iteration + if (iteration_count_ == 0) { + auto maybe_rgb_message = rgb_receiver_->receive(); + if (!maybe_rgb_message) { + GXF_LOG_ERROR("[FoundationposeRender] Failed to receive image message"); + return maybe_rgb_message.error(); + } + rgb_message = maybe_rgb_message.value(); + + auto maybe_camera_model_message = camera_model_receiver_->receive(); + if (!maybe_camera_model_message) { + GXF_LOG_ERROR("[FoundationposeRender] Failed to receive camera model message"); + return maybe_camera_model_message.error(); + } + camera_model_message = maybe_camera_model_message.value(); + + auto maybe_pose_message = pose_receiver_->receive(); + if (!maybe_pose_message) { + GXF_LOG_ERROR("[FoundationposeRender] Failed to receive image message"); + return maybe_pose_message.error(); + } + pose_message = maybe_pose_message.value(); + + auto maybe_xyz_message = point_cloud_receiver_->receive(); + if (!maybe_xyz_message) { + GXF_LOG_ERROR("[FoundationposeRender] Failed to receive image message"); + return maybe_xyz_message.error(); + } + xyz_message = maybe_xyz_message.value();; + } else { + // Receive the inputs published by itself + auto iterative_rgb_receiver = iterative_rgb_receiver_.try_get(); + if (!iterative_rgb_receiver) { + GXF_LOG_ERROR("[FoundationposeRender] Failed to get iterative RGB receiver."); + return gxf::ToResultCode(iterative_rgb_receiver); + } + auto maybe_rgb_message = iterative_rgb_receiver.value()->receive(); + if (!maybe_rgb_message) { + GXF_LOG_ERROR("[FoundationposeRender] Failed to receive image message"); + return maybe_rgb_message.error(); + } + rgb_message = maybe_rgb_message.value(); + + auto iterative_camera_model_receiver = iterative_camera_model_receiver_.try_get(); + if (!iterative_camera_model_receiver) { + GXF_LOG_ERROR("[FoundationposeRender] Failed to get iterative camera model receiver."); + return gxf::ToResultCode(iterative_camera_model_receiver); + } + auto maybe_camera_model_message = iterative_camera_model_receiver.value()->receive(); + if (!maybe_camera_model_message) { + GXF_LOG_ERROR("[FoundationposeRender] Failed to receive camera model message"); + return maybe_camera_model_message.error(); + } + camera_model_message = maybe_camera_model_message.value(); + + auto iterative_point_cloud_receiver = iterative_point_cloud_receiver_.try_get(); + if (!iterative_point_cloud_receiver) { + GXF_LOG_ERROR("[FoundationposeRender] Failed to get iterative camera model receiver."); + return gxf::ToResultCode(iterative_point_cloud_receiver); + } + auto maybe_xyz_message = iterative_point_cloud_receiver.value()->receive(); + if (!maybe_xyz_message) { + GXF_LOG_ERROR("[FoundationposeRender] Failed to receive image message"); + return maybe_xyz_message.error(); + } + xyz_message = maybe_xyz_message.value(); + + auto iterative_pose_receiver = iterative_pose_receiver_.try_get(); + if (!iterative_pose_receiver) { + GXF_LOG_ERROR("[FoundationposeRender] Failed to get iterative pose receiver."); + return gxf::ToResultCode(iterative_pose_receiver); + } + auto maybe_pose_message = iterative_pose_receiver.value()->receive(); + if (!maybe_pose_message) { + GXF_LOG_ERROR("[FoundationposeRender] Failed to receive iterative pose message"); + return maybe_pose_message.error(); + } + pose_message = maybe_pose_message.value(); + } + + auto maybe_rgb_image = rgb_message.get(); + if (!maybe_rgb_image) { + GXF_LOG_ERROR("[FoundationposeRender] Failed to get rgb image from message"); + return maybe_rgb_image.error(); + } + + auto maybe_gxf_camera_model = camera_model_message.get( + RAW_CAMERA_MODEL_GXF_NAME); + if (!maybe_gxf_camera_model) { + GXF_LOG_ERROR("[FoundationposeRender] Failed to receive image message"); + return maybe_gxf_camera_model.error(); + } + auto gxf_camera_model = maybe_gxf_camera_model.value(); + + auto maybe_xyz_map = xyz_message.get(kNamePoints); + if (!maybe_xyz_map) { + GXF_LOG_ERROR("[FoundationposeRender] Failed to get xyz map from message"); + return maybe_xyz_map.error(); + } + + auto maybe_poses = pose_message.get(kNamePoses); + if (!maybe_poses) { + GXF_LOG_ERROR("[FoundationposeRender] Failed to get poses from message"); + return maybe_poses.error(); + } + + // Validate input + auto rgb_img_handle = maybe_rgb_image.value(); + auto rgb_img_info = rgb_img_handle->video_frame_info(); + + auto xyz_map_handle = maybe_xyz_map.value(); + auto poses_handle = maybe_poses.value(); + + const size_t pose_nums = poses_handle->shape().dimension(0); + const size_t pose_rows = poses_handle->shape().dimension(1); + const size_t pose_cols = poses_handle->shape().dimension(2); + + if (pose_nums == 0) { + GXF_LOG_ERROR("[FoundationposeRender] The received pose is empty"); + return GXF_FAILURE; + } + + // Each pose should be a 4*4 matrix + if (pose_rows != kPoseMatrixLength || pose_cols != kPoseMatrixLength) { + GXF_LOG_ERROR("[FoundationposeRender] The received pose has wrong dimension"); + return GXF_FAILURE; + } + + if (poses_handle->size() != pose_nums * pose_rows * pose_cols * sizeof(float)) { + GXF_LOG_ERROR("[FoundationposeRender] Unexpected size of the pose tensor"); + return GXF_FAILURE; + } + + const uint32_t rgb_H = rgb_img_info.height; + const uint32_t rgb_W = rgb_img_info.width; + + const uint32_t N = pose_nums; + const uint32_t W = resized_image_width_; + const uint32_t H = resized_image_height_; + const uint32_t C = kNumChannels; + + const uint32_t total_poses = N * kNumBatches; + + // Create Eigen matrix from the gxf camera model message + Eigen::Matrix3f K; + K << gxf_camera_model->focal_length.x, 0.0, gxf_camera_model->principal_point.x, + 0.0, gxf_camera_model->focal_length.y, gxf_camera_model->principal_point.y, + 0.0, 0.0, 1.0; + + // Malloc device memory for the output + if (!rgb_data_cached_) { + CHECK_CUDA_ERRORS(cudaMalloc(&transformed_xyz_map_device_, N * H * W * C * sizeof(float))); + CHECK_CUDA_ERRORS(cudaMalloc(&transformed_rgb_device_, N * H * W * C * sizeof(float))); + CHECK_CUDA_ERRORS(cudaMalloc(&score_rendered_output_device_, total_poses * 2 * H * W * C * sizeof(float))); + CHECK_CUDA_ERRORS(cudaMalloc(&score_original_output_device_, total_poses * 2 * H * W * C * sizeof(float))); + + nvcv::TensorShape::ShapeType shape{N, H, W, C}; + nvcv::TensorShape tensor_shape{shape, "NHWC"}; + render_rgb_tensor_ = nvcv::Tensor(tensor_shape, nvcv::TYPE_F32); + render_xyz_map_tensor_ = nvcv::Tensor(tensor_shape, nvcv::TYPE_F32); + + rgb_data_cached_ = true; + } + + std::vector pose_host(pose_nums * pose_rows * pose_cols); + CHECK_CUDA_ERRORS(cudaMemcpyAsync( + pose_host.data(), poses_handle->pointer(), poses_handle->size(), cudaMemcpyDeviceToHost, cuda_stream_)); + cudaStreamSynchronize(cuda_stream_); + + // Construct pose matrix from pointer + std::vector poses; + for (size_t i = 0; i < pose_nums; i++) { + Eigen::Map mat( + pose_host.data() + i * pose_rows * pose_cols, pose_rows, pose_cols); + poses.push_back(mat); + } + + Eigen::Vector2i out_size = {H, W}; + auto tfs = ComputeCropWindowTF(poses, K, out_size, crop_ratio_, mesh_diameter_); + if (tfs.size() == 0) { + GXF_LOG_ERROR("[FoundationposeRender] The transform matrix vector is empty"); + return GXF_FAILURE; + } + + // Convert the bbox2d from vector N of 2*2 matrix into a N*4 matrix + Eigen::MatrixXf bbox2d(tfs.size(), 4); + auto gxf_result = ConstructBBox2D(bbox2d, tfs, H, W); + if (gxf_result != GXF_SUCCESS) { + return GXF_FAILURE; + } + + // Render the object using give poses + gxf_result = NvdiffrastRender( + cuda_stream_, poses, K, bbox2d, rgb_H, rgb_W, H, W, render_rgb_tensor_, render_xyz_map_tensor_); + if (gxf_result != GXF_SUCCESS) { + return GXF_FAILURE; + } + + // WarpPerspective on each image and xyz map, and cache the output for the future iterations + if (mode_.get() == "score" || iteration_count_ == 0) { + nvcv::TensorShape::ShapeType rgb_shape{1, rgb_H, rgb_W, C}; + nvcv::TensorShape rgb_tensor_shape{rgb_shape, "NHWC"}; + + nvcv::Tensor rgb_tensor = nvcv::Tensor(rgb_tensor_shape, nvcv::TYPE_U8); + nvcv::Tensor xyz_map_tensor = nvcv::Tensor(rgb_tensor_shape, nvcv::TYPE_F32); + + WrapImgPtrToNHWCTensor(reinterpret_cast(rgb_img_handle->pointer()), rgb_tensor, 1, rgb_H, rgb_W, C); + WrapFloatPtrToNHWCTensor(reinterpret_cast(xyz_map_handle->pointer()), xyz_map_tensor, 1, rgb_H, rgb_W, C); + + const int rgb_flags = NVCV_INTERP_LINEAR; + const int xyz_flags = NVCV_INTERP_NEAREST; + const float4 border_value = {0,0,0,0}; + + const float scale_factor = 1.0f / 255.0f; + cvcuda::WarpPerspective warpPerspectiveOp(0); + cvcuda::ConvertTo convert_op; + for (size_t index = 0; index < N; index++) { + nvcv::TensorShape::ShapeType transformed_shape{1,H,W,C}; + nvcv::TensorShape transformed_tensor_shape{transformed_shape, "NHWC"}; + + nvcv::Tensor transformed_rgb_tensor = nvcv::Tensor(transformed_tensor_shape, nvcv::TYPE_U8); + nvcv::Tensor float_rgb_tensor = nvcv::Tensor(transformed_tensor_shape, nvcv::TYPE_F32); + nvcv::Tensor transformed_xyz_map_tensor = nvcv::Tensor(transformed_tensor_shape, nvcv::TYPE_F32); + + // get ptr offset from index + WrapFloatPtrToNHWCTensor(transformed_rgb_device_ + index * H * W * C, float_rgb_tensor, 1, H, W, C); + WrapFloatPtrToNHWCTensor(transformed_xyz_map_device_ + index * H * W * C, transformed_xyz_map_tensor, 1, H, W, C); + + NVCVPerspectiveTransform trans_matrix; + for (size_t i = 0; i < kPTMatrixDim; i++) { + for (size_t j = 0; j < kPTMatrixDim; j++) { + trans_matrix[i*kPTMatrixDim+j] = tfs[index](i,j); + } + } + + warpPerspectiveOp(cuda_stream_, rgb_tensor, transformed_rgb_tensor, trans_matrix, rgb_flags, NVCV_BORDER_CONSTANT, border_value); + CHECK_CUDA_ERRORS(cudaGetLastError()); + + convert_op(cuda_stream_, transformed_rgb_tensor, float_rgb_tensor, scale_factor, 0.0f); + CHECK_CUDA_ERRORS(cudaGetLastError()); + + warpPerspectiveOp(cuda_stream_, xyz_map_tensor, transformed_xyz_map_tensor, trans_matrix, xyz_flags, NVCV_BORDER_CONSTANT, border_value); + CHECK_CUDA_ERRORS(cudaGetLastError()); + } + + threshold_and_downscale_pointcloud( + cuda_stream_, + transformed_xyz_map_device_, + reinterpret_cast(poses_handle->pointer()), + N, W * H, mesh_diameter_ / 2, min_depth_, max_depth_); + CHECK_CUDA_ERRORS(cudaGetLastError()); + + transformed_xyz_map_cache_[refine_recevied_batches_] = transformed_xyz_map_device_; + transformed_rgb_cache_[refine_recevied_batches_] = transformed_rgb_device_; + } else { + if (transformed_xyz_map_cache_.find(refine_recevied_batches_) != transformed_xyz_map_cache_.end()) { + transformed_xyz_map_device_ = transformed_xyz_map_cache_[refine_recevied_batches_]; + } else { + GXF_LOG_ERROR("[FoundationposeRender] Unable to find index %d from transformed xyz map cache", refine_recevied_batches_); + return GXF_FAILURE; + } + if (transformed_rgb_cache_.find(refine_recevied_batches_) != transformed_rgb_cache_.end()) { + transformed_rgb_device_ = transformed_rgb_cache_[refine_recevied_batches_]; + } else { + GXF_LOG_ERROR("[FoundationposeRender] Unable to find index %d from transformed rgb cache", refine_recevied_batches_); + return GXF_FAILURE; + } + } + + auto render_rgb_data = render_rgb_tensor_.exportData(); + auto render_xyz_map_data = render_xyz_map_tensor_.exportData(); + + threshold_and_downscale_pointcloud( + cuda_stream_, + reinterpret_cast(render_xyz_map_data->basePtr()), + reinterpret_cast(poses_handle->pointer()), + N, W * H, mesh_diameter_ / 2, min_depth_, max_depth_); + CHECK_CUDA_ERRORS(cudaGetLastError()); + + // Score mode, accumulation stage + // Only accumulate the output tensors and return + if (mode_.get() == "score" && score_recevied_batches_ < kNumBatches - 1) { + auto score_output_offset = score_recevied_batches_*N*H*W*2*C; + concat( + cuda_stream_, + reinterpret_cast(render_rgb_data->basePtr()), + reinterpret_cast(render_xyz_map_data->basePtr()), + score_rendered_output_device_ + score_output_offset, + N, H, W, C, C); + CHECK_CUDA_ERRORS(cudaGetLastError()); + + concat( + cuda_stream_, + transformed_rgb_device_, + transformed_xyz_map_device_, + score_original_output_device_ + score_output_offset, + N, H, W, C, C); + CHECK_CUDA_ERRORS(cudaGetLastError()); + CHECK_CUDA_ERRORS(cudaStreamSynchronize(cuda_stream_)); + score_recevied_batches_ += 1; + return GXF_SUCCESS; + } + + // Refine mode, or all input required by score inference are received. + // Allocate output message + auto maybe_output_message = gxf::Entity::New(context()); + if (!maybe_output_message) { + GXF_LOG_ERROR("[FoundationposeRender] Failed to allocate PoseArray Message"); + return gxf::ToResultCode(maybe_output_message); + } + auto output_message = maybe_output_message.value(); + + auto maybe_rendered_tensor = output_message.add(kRenderedTensor); + if (!maybe_rendered_tensor) { + GXF_LOG_ERROR("[FoundationposeRender] Failed to allocate ouptut Tensor 1"); + return gxf::ToResultCode(maybe_rendered_tensor); + } + auto rendered_tensor = maybe_rendered_tensor.value(); + + auto maybe_original_tensor = output_message.add(kOriginalTensor); + if (!maybe_original_tensor) { + GXF_LOG_ERROR("[FoundationposeRender] Failed to allocate output Tensor 2"); + return gxf::ToResultCode(maybe_original_tensor); + } + auto original_tensor = maybe_original_tensor.value(); + + auto maybe_added_timestamp = AddInputTimestampToOutput(output_message, rgb_message); + if (!maybe_added_timestamp) { + GXF_LOG_ERROR("[FoundationposeRender] Failed to add timestamp"); + return gxf::ToResultCode(maybe_added_timestamp); + } + + uint32_t batch_size = mode_.get() == "refine" ? N : total_poses; + std::array output_shape{ + static_cast(batch_size), static_cast(H), static_cast(W), C + C}; + + // Initializing GXF tensor + auto result = rendered_tensor->reshape( + nvidia::gxf::Shape{output_shape, kOutputRank}, nvidia::gxf::MemoryStorageType::kDevice, + allocator_); + if (!result) { + GXF_LOG_ERROR("[FoundationposeRender] Failed to reshape rendered Tensor"); + return gxf::ToResultCode(result); + } + + result = original_tensor->reshape( + nvidia::gxf::Shape{output_shape, kOutputRank}, nvidia::gxf::MemoryStorageType::kDevice, + allocator_); + if (!result) { + GXF_LOG_ERROR("[FoundationposeRender] Failed to reshape original tensor"); + return gxf::ToResultCode(result); + } + + if (mode_.get() == "refine") { + // Refine mode, concat the output publish + concat( + cuda_stream_, + reinterpret_cast(render_rgb_data->basePtr()), + reinterpret_cast(render_xyz_map_data->basePtr()), + reinterpret_cast(rendered_tensor->pointer()), + N, H, W, C, C); + CHECK_CUDA_ERRORS(cudaGetLastError()); + + concat( + cuda_stream_, + transformed_rgb_device_, transformed_xyz_map_device_, + reinterpret_cast(original_tensor->pointer()), + N, H, W, C, C); + CHECK_CUDA_ERRORS(cudaGetLastError()); + + // Publish to itself during refinement iterations + if (iteration_count_ < refine_iterations_-1) { + auto iterative_rgb_transmitter = iterative_rgb_transmitter_.try_get(); + if (!iterative_rgb_transmitter) { + GXF_LOG_ERROR("[FoundationposeRender] Failed to get iterative pose array transmitter."); + return gxf::ToResultCode(iterative_rgb_transmitter); + } + + auto iterative_point_cloud_transmitter = iterative_point_cloud_transmitter_.try_get(); + if (!iterative_point_cloud_transmitter) { + GXF_LOG_ERROR("[FoundationposeRender] Failed to get iterative pose array transmitter."); + return gxf::ToResultCode(iterative_point_cloud_transmitter); + } + + auto iterative_camera_model_transmitter = iterative_camera_model_transmitter_.try_get(); + if (!iterative_camera_model_transmitter) { + GXF_LOG_ERROR("[FoundationposeRender] Failed to get iterative pose array transmitter."); + return gxf::ToResultCode(iterative_camera_model_transmitter); + } + iterative_rgb_transmitter.value()->publish(std::move(rgb_message)); + iterative_point_cloud_transmitter.value()->publish(std::move(xyz_message)); + iterative_camera_model_transmitter.value()->publish(std::move(camera_model_message)); + } + + // Update loop and batch counters + refine_recevied_batches_ += 1; + if (refine_recevied_batches_ == kNumBatches) { + refine_recevied_batches_ = 0; + iteration_count_ += 1; + } + if (iteration_count_ == refine_iterations_) { + iteration_count_ = 0; + } + } else { + // Score mode, and all messages are accumulated. + // Concat the last sliced output tensors and publish the results + auto score_output_offset = score_recevied_batches_*N*H*W*2*C; + concat( + cuda_stream_, + reinterpret_cast(render_rgb_data->basePtr()), + reinterpret_cast(render_xyz_map_data->basePtr()), + score_rendered_output_device_ + score_output_offset, + N, H, W, C, C); + CHECK_CUDA_ERRORS(cudaGetLastError()); + + concat( + cuda_stream_, + transformed_rgb_device_, + transformed_xyz_map_device_, + score_original_output_device_ + score_output_offset, + N, H, W, C, C); + CHECK_CUDA_ERRORS(cudaGetLastError()); + + CHECK_CUDA_ERRORS(cudaMemcpyAsync( + reinterpret_cast(rendered_tensor->pointer()), score_rendered_output_device_, rendered_tensor->size(), cudaMemcpyDeviceToDevice, cuda_stream_)); + CHECK_CUDA_ERRORS(cudaMemcpyAsync( + reinterpret_cast(original_tensor->pointer()), score_original_output_device_, original_tensor->size(), cudaMemcpyDeviceToDevice, cuda_stream_)); + score_recevied_batches_ = 0; + } + + CHECK_CUDA_ERRORS(cudaStreamSynchronize(cuda_stream_)); + return gxf::ToResultCode(pose_array_transmitter_->publish(std::move(output_message))); +} + +nvidia::gxf::Expected FoundationposeRender::isAcceptingRequest() { + return (iteration_count_ == 0 && refine_recevied_batches_ == 0); +} + +gxf_result_t FoundationposeRender::stop() noexcept { + delete cr_; + + CHECK_CUDA_ERRORS(cudaFree(pose_clip_device_)); + CHECK_CUDA_ERRORS(cudaFree(mesh_faces_device_)); + CHECK_CUDA_ERRORS(cudaFree(rast_out_device_)); + CHECK_CUDA_ERRORS(cudaFree(pts_cam_device_)); + CHECK_CUDA_ERRORS(cudaFree(texcoords_out_device_)); + CHECK_CUDA_ERRORS(cudaFree(color_device_)); + CHECK_CUDA_ERRORS(cudaFree(xyz_map_device_)); + CHECK_CUDA_ERRORS(cudaFree(texcoords_device_)); + CHECK_CUDA_ERRORS(cudaFree(texture_map_device_)); + + CHECK_CUDA_ERRORS(cudaFree(transformed_xyz_map_device_)); + CHECK_CUDA_ERRORS(cudaFree(transformed_rgb_device_)); + CHECK_CUDA_ERRORS(cudaFree(score_rendered_output_device_)); + CHECK_CUDA_ERRORS(cudaFree(score_original_output_device_)); + + return GXF_SUCCESS; +} +} // namespace isaac_ros +} // namespace nvidia \ No newline at end of file diff --git a/isaac_ros_gxf_extensions/gxf_isaac_foundationpose/gxf/foundationpose/foundationpose_render.cu b/isaac_ros_gxf_extensions/gxf_isaac_foundationpose/gxf/foundationpose/foundationpose_render.cu new file mode 100644 index 0000000..a8dfc6d --- /dev/null +++ b/isaac_ros_gxf_extensions/gxf_isaac_foundationpose/gxf/foundationpose/foundationpose_render.cu @@ -0,0 +1,268 @@ +// SPDX-FileCopyrightText: NVIDIA CORPORATION & AFFILIATES +// Copyright (c) 2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. +// +// SPDX-License-Identifier: Apache-2.0 + +#include +#include "foundationpose_render.cu.hpp" + +void RasterizeCudaFwdShaderKernel(const RasterizeCudaFwdShaderParams p); +void InterpolateFwdKernel(const InterpolateKernelParams p); +void TextureFwdKernelLinear1(const TextureKernelParams p); + +namespace nvidia { +namespace isaac_ros { + +__device__ float clamp_func(float f, float a, float b) { + return fmaxf(a, fminf(f, b)); +} + +__global__ void clamp_kernel(float* input, float min_value, float max_value, int N) { + int idx = threadIdx.x + blockIdx.x * blockDim.x; + // Check the boundaries + if (idx >= N) { + return; + } + input[idx] = clamp_func(input[idx], min_value, max_value); +} + + +/* +This kernel performs: + 1. thresholdingof the point cloud + 2. subtraction of the position of pose array from the pointcloud + 3. downscaling of the point cloud + + pose_array_input is of size N*16, where N is the number of poses. 16 = transformation_mat_size + pointcloud_input is of size N*n_points*3, where N is the number of poses + and n_points is the number of points in the point cloud. + + It subtracts the pose transformation from each point in the cloud, + 1. checks if the z-component of the point is below "min_depth" and sets it to zero if it is + 2. and applies a downscaling factor to reduce the number of points. + 3. Then it checks if the absolute value of any of the x, y, or z components of the point + is above "max_depth" and sets it to zero if it is. + + The result is stored back in the input array. +*/ +__global__ void threshold_and_downscale_pointcloud_kernel( + float* input, float* pose_array_input, int N, int n_points, float downscale_factor, + float min_depth, float max_depth) { + int idx = threadIdx.x + blockIdx.x * blockDim.x; + if (idx >= N * n_points) { + return; // Check the boundaries + } + + int pose_idx = idx / n_points; + + // 16 is the size of pose transformation matrix + float pose_x = pose_array_input[16 * pose_idx + 12]; + float pose_y = pose_array_input[16 * pose_idx + 13]; + float pose_z = pose_array_input[16 * pose_idx + 14]; + + // Calculate the index for the x, y, and z components of the point + int x_idx = idx * 3; + int y_idx = x_idx + 1; + int z_idx = x_idx + 2; + + bool invalid_flag = false; + // Any points with z below min_depth is set to 0 + if (input[z_idx] < min_depth) { + invalid_flag = true; + } + + input[x_idx] -= pose_x; + input[y_idx] -= pose_y; + input[z_idx] -= pose_z; + + // Divide all values by downscale_factor + input[x_idx] /= downscale_factor; + input[y_idx] /= downscale_factor; + input[z_idx] /= downscale_factor; + + // Any points with absolute value(x,y or z) above max_depth is set to 0 + if (fabs(input[x_idx]) > max_depth || invalid_flag) { + input[x_idx] = 0.0f; + } + if (fabs(input[y_idx]) > max_depth || invalid_flag) { + input[y_idx] = 0.0f; + } + + if (fabs(input[z_idx]) > max_depth || invalid_flag) { + input[z_idx] = 0.0f; + } + return; +} + + +// concat two NHWC array on the last dimension +__global__ void concat_kernel( + float* input_a, float* input_b, float* output, int N, int H, int W, int C1, int C2) { + int idx = threadIdx.x + blockIdx.x * blockDim.x; + // Check the boundaries + if (idx >= N * H * W) { + return; + } + + for (int i = 0; i < C1; i++) { + output[idx * (C1 + C2) + i] = input_a[idx * C1 + i]; + } + + for (int i = 0; i < C2; i++) { + output[idx * (C1 + C2) + C1 + i] = input_b[idx * C2 + i]; + } +} + + +void clamp(cudaStream_t stream, float* input, float min_value, float max_value, int N) { + int block_size = 256; + int grid_size = (N + block_size - 1) / block_size; + + clamp_kernel<<>>(input, min_value, max_value, N); +} + +void threshold_and_downscale_pointcloud( + cudaStream_t stream, float* pointcloud_input, float* pose_array_input, int N, int n_points, float downscale_factor, + float min_depth, float max_depth) { + // Launch n_points threads + int block_size = 256; + int grid_size = ((N * n_points) + block_size - 1) / block_size; + + threshold_and_downscale_pointcloud_kernel<<>>( + pointcloud_input, pose_array_input, N, n_points, downscale_factor, min_depth, max_depth); +} + +void concat(cudaStream_t stream, float* input_a, float* input_b, float* output, int N, int H, int W, int C1, int C2) { + // Launch N*H*W threads, each thread handle a vector of size C + int block_size = 256; + int grid_size = (N * H * W + block_size - 1) / block_size; + + concat_kernel<<>>(input_a, input_b, output, N, H, W, C1, C2); +} + +void rasterize( + cudaStream_t stream, CR::CudaRaster* cr, float* pos_ptr, int32_t* tri_ptr, float* out, int pos_count, int tri_count, + int H, int W, int C) { + const int32_t* range_ptr = 0; + + bool enablePeel = false; + cr->setViewportSize(W, H, C); + cr->setVertexBuffer((void*)pos_ptr, pos_count); + cr->setIndexBuffer((void*)tri_ptr, tri_count); + cr->setRenderModeFlags(0); + + cr->deferredClear(0u); + bool success = cr->drawTriangles(range_ptr, enablePeel, stream); + + // Populate pixel shader kernel parameters. + RasterizeCudaFwdShaderParams p; + p.pos = pos_ptr; + p.tri = tri_ptr; + p.in_idx = (const int*)cr->getColorBuffer(); + p.out = out; + p.numTriangles = tri_count; + p.numVertices = pos_count; + p.width = W; + p.height = H; + p.depth = C; + + p.instance_mode = 1; + p.xs = 2.f / (float)p.width; + p.xo = 1.f / (float)p.width - 1.f; + p.ys = 2.f / (float)p.height; + p.yo = 1.f / (float)p.height - 1.f; + + // Choose launch parameters. + dim3 blockSize = getLaunchBlockSize( + RAST_CUDA_FWD_SHADER_KERNEL_BLOCK_WIDTH, RAST_CUDA_FWD_SHADER_KERNEL_BLOCK_HEIGHT, p.width, + p.height); + dim3 gridSize = getLaunchGridSize(blockSize, p.width, p.height, p.depth); + + // Launch CUDA kernel. + void* args[] = {&p}; + CHECK_CUDA_ERRORS( + cudaLaunchKernel((void*)RasterizeCudaFwdShaderKernel, gridSize, blockSize, args, 0, stream)); +} + +void interpolate( + cudaStream_t stream, float* attr_ptr, float* rast_ptr, int32_t* tri_ptr, float* out, int num_vertices, + int num_triangles, int attr_dim, int H, int W, int C) { + int instance_mode = attr_dim > 2 ? 1 : 0; + + InterpolateKernelParams p = {}; // Initialize all fields to zero. + p.instance_mode = instance_mode; + p.numVertices = num_vertices; + p.numAttr = attr_dim; + p.numTriangles = num_triangles; + p.height = H; + p.width = W; + p.depth = C; + + // Get input pointers. + p.attr = attr_ptr; + p.rast = rast_ptr; + p.tri = tri_ptr; + p.attrBC = 0; + p.out = out; + + // Choose launch parameters. + dim3 blockSize = getLaunchBlockSize( + IP_FWD_MAX_KERNEL_BLOCK_WIDTH, IP_FWD_MAX_KERNEL_BLOCK_HEIGHT, p.width, p.height); + dim3 gridSize = getLaunchGridSize(blockSize, p.width, p.height, p.depth); + + // Launch CUDA kernel. + void* args[] = {&p}; + void* func = (void*)InterpolateFwdKernel; + CHECK_CUDA_ERRORS(cudaLaunchKernel(func, gridSize, blockSize, args, 0, stream)); +} + +void texture( + cudaStream_t stream, float* tex_ptr, float* uv_ptr, float* out, int tex_height, int tex_width, int tex_channel, + int tex_depth, int H, int W, int N) { + TextureKernelParams p = {}; // Initialize all fields to zero. + p.enableMip = false; + p.filterMode = TEX_MODE_LINEAR; + p.boundaryMode = TEX_BOUNDARY_MODE_WRAP; + + p.texDepth = tex_depth; + p.texHeight = tex_height; + p.texWidth = tex_width; + p.channels = tex_channel; + + p.n = N; + p.imgHeight = H; + p.imgWidth = W; + + // Get input pointers. + p.tex[0] = tex_ptr; + p.uv = uv_ptr; + p.mipLevelBias = NULL; + + p.out = out; + + // Choose kernel variants based on channel count. + void* args[] = {&p}; + + // Choose launch parameters for texture lookup kernel. + dim3 blockSize = getLaunchBlockSize( + TEX_FWD_MAX_KERNEL_BLOCK_WIDTH, TEX_FWD_MAX_KERNEL_BLOCK_HEIGHT, p.imgWidth, p.imgHeight); + dim3 gridSize = getLaunchGridSize(blockSize, p.imgWidth, p.imgHeight, p.n); + + void* func = (void*)TextureFwdKernelLinear1; + CHECK_CUDA_ERRORS(cudaLaunchKernel(func, gridSize, blockSize, args, 0, stream)); +} + +} // namespace isaac_ros +} // namespace nvidia diff --git a/isaac_ros_gxf_extensions/gxf_isaac_foundationpose/gxf/foundationpose/foundationpose_render.cu.hpp b/isaac_ros_gxf_extensions/gxf_isaac_foundationpose/gxf/foundationpose/foundationpose_render.cu.hpp new file mode 100644 index 0000000..92ec9dc --- /dev/null +++ b/isaac_ros_gxf_extensions/gxf_isaac_foundationpose/gxf/foundationpose/foundationpose_render.cu.hpp @@ -0,0 +1,55 @@ +// SPDX-FileCopyrightText: NVIDIA CORPORATION & AFFILIATES +// Copyright (c) 2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. +// +// SPDX-License-Identifier: Apache-2.0 + +#ifndef NVIDIA_ISAAC_ROS_EXTENSIONS_FOUNDATIONPOSE_RENDER_CUDA_HPP_ +#define NVIDIA_ISAAC_ROS_EXTENSIONS_FOUNDATIONPOSE_RENDER_CUDA_HPP_ + +#include +#include + +#include "cuda.h" +#include "cuda_runtime.h" +#include "foundationpose_utils.hpp" + +#include "nvdiffrast/common/common.h" +#include "nvdiffrast/common/cudaraster/CudaRaster.hpp" +#include "nvdiffrast/common/interpolate.h" +#include "nvdiffrast/common/rasterize.h" +#include "nvdiffrast/common/texture.h" + +namespace nvidia { +namespace isaac_ros { + +void clamp(cudaStream_t stream, float* input, float min_value, float max_value, int N); +void threshold_and_downscale_pointcloud( + cudaStream_t stream, float* pointcloud_input, float* pose_array_input, int N, int n_points, float downscale_factor, + float min_depth, float max_depth); +void concat(cudaStream_t stream, float* input_a, float* input_b, float* output, int N, int H, int W, int C1, int C2); + +void rasterize( + cudaStream_t stream, CR::CudaRaster* cr, float* pos_ptr, int32_t* tri_ptr, float* out, int pos_count, int tri_count, + int H, int W, int C); +void interpolate( + cudaStream_t stream, float* attr_ptr, float* rast_ptr, int32_t* tri_ptr, float* out, int num_vertices, + int num_triangles, int attr_dim, int H, int W, int C); +void texture( + cudaStream_t stream, float* tex_ptr, float* uv_ptr, float* out, int tex_height, int tex_width, int tex_channel, + int tex_depth, int H, int W, int N); + +} // namespace isaac_ros +} // namespace nvidia +#endif // NVIDIA_ISAAC_ROS_EXTENSIONS_FOUNDATIONPOSE_RENDER_CUDA_HPP_ \ No newline at end of file diff --git a/isaac_ros_gxf_extensions/gxf_isaac_foundationpose/gxf/foundationpose/foundationpose_render.hpp b/isaac_ros_gxf_extensions/gxf_isaac_foundationpose/gxf/foundationpose/foundationpose_render.hpp new file mode 100644 index 0000000..4839b6c --- /dev/null +++ b/isaac_ros_gxf_extensions/gxf_isaac_foundationpose/gxf/foundationpose/foundationpose_render.hpp @@ -0,0 +1,131 @@ +// SPDX-FileCopyrightText: NVIDIA CORPORATION & AFFILIATES +// Copyright (c) 2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. +// +// SPDX-License-Identifier: Apache-2.0 + +#ifndef NVIDIA_ISAAC_ROS_EXTENSIONS_FOUNDATIONPOSE_RENDER_HPP_ +#define NVIDIA_ISAAC_ROS_EXTENSIONS_FOUNDATIONPOSE_RENDER_HPP_ + +#include +#include + +#include +#include +#include + +#include "gxf/core/entity.hpp" +#include "gxf/core/gxf.h" +#include "gxf/core/parameter.hpp" +#include "gxf/core/parameter_parser_std.hpp" +#include "gxf/std/codelet.hpp" +#include "gxf/std/memory_buffer.hpp" +#include "gxf/std/receiver.hpp" +#include "gxf/std/transmitter.hpp" + +#include "foundationpose_render.cu.hpp" + +namespace nvidia { +namespace isaac_ros { + +// GXF codelet takes the pose estimations and render them into image and point clouds. +// The rendered results are then concatenated and sent to refine or score network for further processing +class FoundationposeRender : public gxf::Codelet { + public: + gxf_result_t start() noexcept override; + gxf_result_t tick() noexcept override; + gxf_result_t stop() noexcept override; + gxf_result_t registerInterface(gxf::Registrar* registrar) noexcept override; + gxf::Expected isAcceptingRequest(); + + private: + gxf_result_t NvdiffrastRender( + cudaStream_t cuda_stream, std::vector& poses, Eigen::Matrix3f& K, Eigen::MatrixXf& bbox2d, int rgb_H, + int rgb_W, int H, int W, nvcv::Tensor& flip_color_tensor, nvcv::Tensor& flip_xyz_map_tensor); + + gxf::Parameter> pose_receiver_; + gxf::Parameter> rgb_receiver_; + gxf::Parameter> camera_model_receiver_; + gxf::Parameter> point_cloud_receiver_; + gxf::Parameter> iterative_pose_receiver_; + gxf::Parameter> iterative_rgb_receiver_; + gxf::Parameter> iterative_camera_model_receiver_; + gxf::Parameter> iterative_point_cloud_receiver_; + gxf::Parameter> pose_array_transmitter_; + gxf::Parameter> iterative_rgb_transmitter_; + gxf::Parameter> iterative_camera_model_transmitter_; + gxf::Parameter> iterative_point_cloud_transmitter_; + + gxf::Parameter mesh_file_path_; + gxf::Parameter texture_path_; + gxf::Parameter mode_; + gxf::Parameter crop_ratio_; + gxf::Parameter min_depth_; + gxf::Parameter max_depth_; + gxf::Parameter resized_image_height_; + gxf::Parameter resized_image_width_; + gxf::Parameter refine_iterations_; + gxf::Parameter> allocator_; + gxf::Parameter> cuda_stream_pool_; + gxf::Handle cuda_stream_handle_; + + Eigen::Matrix mesh_vertices_; + std::vector mesh_faces_; + std::vector texcoords_; + std::vector vertices_; + + int num_vertices_; + int num_faces_; + int num_texcoords_; + int texture_map_height_; + int texture_map_width_; + float mesh_diameter_; + + // Input data (GPU) for rendering kernels + float* pose_clip_device_; + int32_t* mesh_faces_device_; + float* texcoords_device_; + float* pts_cam_device_; + uint8_t* texture_map_device_; + nvcv::Tensor float_texture_map_tensor_; + + // Output data (GPU) for rendering kernels + float* rast_out_device_; + float* texcoords_out_device_; + float* color_device_; + float* xyz_map_device_; + float* transformed_rgb_device_; + float* transformed_xyz_map_device_; + nvcv::Tensor render_rgb_tensor_; + nvcv::Tensor render_xyz_map_tensor_; + std::unordered_map transformed_xyz_map_cache_; + std::unordered_map transformed_rgb_cache_; + + float* score_rendered_output_device_; + float* score_original_output_device_; + bool render_data_cached_ = false; + bool rgb_data_cached_ = false; + CR::CudaRaster* cr_; + + int32_t score_recevied_batches_ = 0; + int32_t refine_recevied_batches_ = 0; + int32_t iteration_count_ = 0; + + cudaStream_t cuda_stream_ = 0; +}; + +} // namespace isaac_ros +} // namespace nvidia + +#endif // NVIDIA_ISAAC_ROS_EXTENSIONS_FOUNDATIONPOSE_RENDER_HPP_ diff --git a/isaac_ros_gxf_extensions/gxf_isaac_foundationpose/gxf/foundationpose/foundationpose_sampling.cpp b/isaac_ros_gxf_extensions/gxf_isaac_foundationpose/gxf/foundationpose/foundationpose_sampling.cpp new file mode 100644 index 0000000..77150cb --- /dev/null +++ b/isaac_ros_gxf_extensions/gxf_isaac_foundationpose/gxf/foundationpose/foundationpose_sampling.cpp @@ -0,0 +1,578 @@ +// SPDX-FileCopyrightText: NVIDIA CORPORATION & AFFILIATES +// Copyright (c) 2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. +// +// SPDX-License-Identifier: Apache-2.0 + +#include "foundationpose_sampling.hpp" +#include "foundationpose_utils.hpp" + +#include + +#include + +#include +#include +#include +#include + +#include "gxf/multimedia/camera.hpp" +#include "gxf/multimedia/video.hpp" +#include "gxf/std/tensor.hpp" +#include "gxf/std/timestamp.hpp" + +namespace nvidia { +namespace isaac_ros { + +namespace { + +constexpr char kNamePoses[] = "poses"; +constexpr char kNamePoints[] = "points"; +constexpr char RAW_CAMERA_MODEL_GXF_NAME[] = "intrinsics"; +constexpr size_t kPoseMatrixLength = 4; +constexpr int kNumBatches = 6; + +typedef Eigen::Matrix RowMajorMatrix8u; + +// A helper function to create a vertex from a point +int AddVertex(const Eigen::Vector3f& p, std::vector& vertices) { + vertices.push_back(p.normalized()); + return vertices.size() - 1; +} + +// A helper function to create a face from three indices +void AddFace(int i, int j, int k, std::vector& faces) { + faces.emplace_back(i, j, k); +} + +// A helper function to get the middle point of two vertices +int GetMiddlePoint( + int i, int j, std::vector& vertices, std::map& cache) { + // check if the edge (i, j) has been processed before + bool first_is_smaller = i < j; + int64_t smaller = first_is_smaller ? i : j; + int64_t greater = first_is_smaller ? j : i; + int64_t key = (smaller << 32) + greater; + + auto it = cache.find(key); + if (it != cache.end()) { + return it->second; + } + + // if not cached, create a new vertex + Eigen::Vector3f p1 = vertices[i]; + Eigen::Vector3f p2 = vertices[j]; + Eigen::Vector3f pm = (p1 + p2) / 2.0; + int index = AddVertex(pm, vertices); + cache[key] = index; + return index; +} + +// A function to generate an icosphere +// Initial triangle values could found from https://sinestesia.co/blog/tutorials/python-icospheres/ +std::vector GenerateIcosphere(unsigned int n_views) { + std::map cache; + std::vector vertices; + std::vector faces; + + // create 12 vertices + float t = (1.0 + std::sqrt(5.0)) / 2.0; // the golden ratio + AddVertex(Eigen::Vector3f(-1, t, 0), vertices); + AddVertex(Eigen::Vector3f(1, t, 0), vertices); + AddVertex(Eigen::Vector3f(-1, -t, 0), vertices); + AddVertex(Eigen::Vector3f(1, -t, 0), vertices); + AddVertex(Eigen::Vector3f(0, -1, t), vertices); + AddVertex(Eigen::Vector3f(0, 1, t), vertices); + AddVertex(Eigen::Vector3f(0, -1, -t), vertices); + AddVertex(Eigen::Vector3f(0, 1, -t), vertices); + AddVertex(Eigen::Vector3f(t, 0, -1), vertices); + AddVertex(Eigen::Vector3f(t, 0, 1), vertices); + AddVertex(Eigen::Vector3f(-t, 0, -1), vertices); + AddVertex(Eigen::Vector3f(-t, 0, 1), vertices); + + // create 20 faces + AddFace(0, 11, 5, faces); + AddFace(0, 5, 1, faces); + AddFace(0, 1, 7, faces); + AddFace(0, 7, 10, faces); + AddFace(0, 10, 11, faces); + AddFace(1, 5, 9, faces); + AddFace(5, 11, 4, faces); + AddFace(11, 10, 2, faces); + AddFace(10, 7, 6, faces); + AddFace(7, 1, 8, faces); + AddFace(3, 9, 4, faces); + AddFace(3, 4, 2, faces); + AddFace(3, 2, 6, faces); + AddFace(3, 6, 8, faces); + AddFace(3, 8, 9, faces); + AddFace(4, 9, 5, faces); + AddFace(2, 4, 11, faces); + AddFace(6, 2, 10, faces); + AddFace(8, 6, 7, faces); + AddFace(9, 8, 1, faces); + + // subdivide each face into four smaller faces + while (vertices.size() < n_views) { + std::vector new_faces; + for (const auto& face : faces) { + int a = face[0]; + int b = face[1]; + int c = face[2]; + + int ab = GetMiddlePoint(a, b, vertices, cache); + int bc = GetMiddlePoint(b, c, vertices, cache); + int ca = GetMiddlePoint(c, a, vertices, cache); + + AddFace(a, ab, ca, new_faces); + AddFace(b, bc, ab, new_faces); + AddFace(c, ca, bc, new_faces); + AddFace(ab, bc, ca, new_faces); + } + faces = new_faces; + } + GXF_LOG_DEBUG("[FoundationposeSampling] Generated vertice size: %lu", vertices.size()); + return std::move(vertices); +} + + +float RotationGeodesticDistance(const Eigen::Matrix3f& R1, const Eigen::Matrix3f& R2) { + float cos = ((R1 * R2.transpose()).trace() - 1) / 2.0; + cos = std::max(std::min(cos, 1.0f), -1.0f); + return std::acos(cos); +} + +std::vector ClusterPoses( + float angle_diff, float dist_diff, std::vector& poses_in, + std::vector& symmetry_tfs) { + std::vector poses_out; + poses_out.push_back(poses_in[0]); + const float radian_thres = angle_diff / 180.0 * M_PI; + + for (unsigned int i = 1; i < poses_in.size(); i++) { + bool is_new = true; + Eigen::Matrix4f cur_pose = poses_in[i]; + + for (const auto& cluster : poses_out) { + Eigen::Vector3f t0 = cluster.block(0, 3, 3, 1); + Eigen::Vector3f t1 = cur_pose.block(0, 3, 3, 1); + if ((t0 - t1).norm() >= dist_diff) { + continue; + } + // Remove symmetry + for (const auto& tf : symmetry_tfs) { + Eigen::Matrix4f cur_pose_tmp = cur_pose * tf; + float rot_diff = + RotationGeodesticDistance(cur_pose_tmp.block(0, 0, 3, 3), cluster.block(0, 0, 3, 3)); + if (rot_diff < radian_thres) { + is_new = false; + break; + } + } + if (!is_new) { + break; + } + } + + if (is_new) { + poses_out.push_back(poses_in[i]); + } + } + return std::move(poses_out); +} + +std::vector SampleViewsIcosphere(unsigned int n_views) { + auto vertices = GenerateIcosphere(n_views); + std::vector> cam_in_obs( + vertices.size(), Eigen::Matrix4f::Identity(4, 4)); + for (unsigned int i = 0; i < vertices.size(); i++) { + cam_in_obs[i].block<3, 1>(0, 3) = vertices[i]; + Eigen::Vector3f up(0, 0, 1); + Eigen::Vector3f z_axis = -cam_in_obs[i].block<3, 1>(0, 3); + z_axis.normalize(); + + Eigen::Vector3f x_axis = up.cross(z_axis); + if (x_axis.isZero()) { + x_axis << 1, 0, 0; + } + x_axis.normalize(); + Eigen::Vector3f y_axis = z_axis.cross(x_axis); + y_axis.normalize(); + cam_in_obs[i].block<3, 1>(0, 0) = x_axis; + cam_in_obs[i].block<3, 1>(0, 1) = y_axis; + cam_in_obs[i].block<3, 1>(0, 2) = z_axis; + } + return std::move(cam_in_obs); +} + +std::vector MakeRotationGrid(unsigned int n_views = 40, int inplane_step = 60) { + auto cam_in_obs = SampleViewsIcosphere(n_views); + + std::vector rot_grid; + for (unsigned int i = 0; i < cam_in_obs.size(); i++) { + for (double inplane_rot = 0; inplane_rot < 360; inplane_rot += inplane_step) { + Eigen::Matrix4f cam_in_ob = cam_in_obs[i]; + auto R_inplane = Eigen::Affine3f::Identity(); + R_inplane.rotate(Eigen::AngleAxisf(0, Eigen::Vector3f::UnitX())) + .rotate(Eigen::AngleAxisf(0, Eigen::Vector3f::UnitY())) + .rotate(Eigen::AngleAxisf(inplane_rot, Eigen::Vector3f::UnitZ())); + + cam_in_ob = cam_in_ob * R_inplane.matrix(); + Eigen::Matrix4f ob_in_cam = cam_in_ob.inverse(); + rot_grid.push_back(ob_in_cam); + } + } + + std::vector symmetry_tfs = std::vector(); + symmetry_tfs.push_back(Eigen::Matrix4f::Identity()); + ClusterPoses(30.0, 99999.0, rot_grid, symmetry_tfs); + return std::move(rot_grid); +} + +bool GuessTranslation( + const Eigen::MatrixXf& depth, const RowMajorMatrix8u& mask, const Eigen::Matrix3f& K, + float min_depth, Eigen::Vector3f& center) { + // Find the indices where mask is positive + std::vector vs, us; + for (int i = 0; i < mask.rows(); i++) { + for (int j = 0; j < mask.cols(); j++) { + if (mask(i, j) > 0) { + vs.push_back(i); + us.push_back(j); + } + } + } + if (us.empty()) { + GXF_LOG_ERROR("[FoundationposeSampling] Mask is all zero."); + return false; + } + + float uc = + (*std::min_element(us.begin(), us.end()) + *std::max_element(us.begin(), us.end())) / 2.0; + float vc = + (*std::min_element(vs.begin(), vs.end()) + *std::max_element(vs.begin(), vs.end())) / 2.0; + + Eigen::Matrix valid = + (mask.array() > 0) && (depth.array() >= min_depth); + if (!valid.any()) { + GXF_LOG_ERROR("[FoundationposeSampling] No valid value in mask."); + return false; + } + + std::vector valid_depth; + for (int i = 0; i < valid.rows(); i++) { + for (int j = 0; j < valid.cols(); j++) { + if (valid(i, j)) { + valid_depth.push_back(depth(i, j)); + } + } + } + std::sort(valid_depth.begin(), valid_depth.end()); + int n = valid_depth.size(); + float zc = + (n % 2 == 0) ? (valid_depth[n / 2 - 1] + valid_depth[n / 2]) / 2.0 : valid_depth[n / 2]; + + center = K.inverse() * Eigen::Vector3f(uc, vc, 1) * zc; + return true; +} + +} // namespace + +gxf_result_t FoundationposeSampling::registerInterface(gxf::Registrar* registrar) noexcept { + gxf::Expected result; + + result &= registrar->parameter( + point_cloud_receiver_, "point_cloud_input", "Point Cloud Input", + "The point cloud input as a tensor"); + + result &= registrar->parameter( + depth_receiver_, "depth_input", "Depth Image Input", + "The depth image input as a video buffer"); + + result &= registrar->parameter( + segmentation_receiver_, "segmentation_input", "Segmentation Image Input", + "The segmentation input as a video buffer"); + + result &= registrar->parameter( + rgb_receiver_, "rgb_input", "RGB image Input", "The RGB image input as a videobuffer"); + + result &= registrar->parameter( + posearray_transmitter_, "output", "PoseArray Output", "The ouput poses as a pose array"); + + result &= registrar->parameter( + camera_model_transmitter_, "camera_model_output", "Camera Model Output", "The camera model as a videobuffer"); + + result &= registrar->parameter( + point_cloud_transmitter_, "point_cloud_output", "Point Cloud Output", + "The output point cloud as a tensor"); + + result &= registrar->parameter( + rgb_transmitter_, "rgb_output", "RGB Output", + "The output RGB as a videobuffer"); + + result &= registrar->parameter( + allocator_, "allocator", "Allocator", "Output Allocator"); + + result &= registrar->parameter( + max_hypothesis_, "max_hypothesis", "Maximum number of hypothesis", + "Maximum number of pose hypothesis generated by sampling."); + + result &= registrar->parameter( + cuda_stream_pool_, "cuda_stream_pool", "Cuda Stream Pool", + "Instance of gxf::CudaStreamPool to allocate CUDA stream."); + + result &= registrar->parameter( + min_depth_, "min_depth", "Minimum Depth", + "Minimum depth value to consider for estimating object center in image space.", + 0.1f); + + return gxf::ToResultCode(result); +} + +gxf_result_t FoundationposeSampling::start() noexcept { + GXF_LOG_DEBUG("[FoundationposeSampling] Start FoundationPose FoundationposeSampling"); + + auto maybe_stream = cuda_stream_pool_.get()->allocateStream(); + if (!maybe_stream) { return gxf::ToResultCode(maybe_stream); } + + stream_ = std::move(maybe_stream.value()); + if (!stream_->stream()) { + GXF_LOG_ERROR("[FoundationposeSampling] allocated stream is not initialized!"); + return GXF_FAILURE; + } + + return GXF_SUCCESS; +} + +gxf_result_t FoundationposeSampling::tick() noexcept { + GXF_LOG_DEBUG("[FoundationposeSampling] Tick FoundationPose FoundationposeSampling"); + + auto maybe_xyz_message = point_cloud_receiver_->receive(); + if (!maybe_xyz_message) { + GXF_LOG_ERROR("[FoundationposeSampling] Failed to receive point cloud message"); + return maybe_xyz_message.error(); + } + + auto maybe_depth_message = depth_receiver_->receive(); + if (!maybe_depth_message) { + GXF_LOG_ERROR("[FoundationposeSampling] Failed to receive point cloud message"); + return maybe_depth_message.error(); + } + + auto maybe_segmentation_message = segmentation_receiver_->receive(); + if (!maybe_segmentation_message) { + GXF_LOG_ERROR("[FoundationposeSampling] Failed to receive point cloud message"); + return maybe_segmentation_message.error(); + } + + auto maybe_rgb_message = rgb_receiver_->receive(); + if (!maybe_rgb_message) { + GXF_LOG_ERROR("[FoundationposeSampling] Failed to receive point cloud message"); + return maybe_rgb_message.error(); + } + + auto maybe_depth_image = maybe_depth_message.value().get(); + if (!maybe_depth_image) { + GXF_LOG_ERROR("[FoundationposeSampling] Failed to get point cloud from message"); + return maybe_depth_image.error(); + } + + auto maybe_segmentation_image = maybe_segmentation_message.value().get(); + if (!maybe_segmentation_image) { + GXF_LOG_ERROR("[FoundationposeSampling] Failed to get point cloud from message"); + return maybe_segmentation_image.error(); + } + + cudaStream_t cuda_stream = 0; + if (!stream_.is_null()) { + cuda_stream = stream_->stream().value(); + } + + auto depth_handle = maybe_depth_image.value(); + auto depth_info = depth_handle->video_frame_info(); + + auto segmentation_handle = maybe_segmentation_image.value(); + auto segmentation_info = segmentation_handle->video_frame_info(); + + if (depth_info.width != segmentation_info.width || + depth_info.height != segmentation_info.height) { + GXF_LOG_ERROR("[FoundationposeSampling] Input depth image and segmentation image have different dimension"); + return GXF_FAILURE; + } + + if (depth_info.color_planes[0].stride != depth_info.width * sizeof(float)) { + GXF_LOG_ERROR("[FoundationposeSampling] Expected no padding for depth image"); + return GXF_FAILURE; + } + + if (segmentation_info.color_planes[0].stride != segmentation_info.width * sizeof(uint8_t)) { + GXF_LOG_ERROR("[FoundationposeSampling] Expected no padding for segmenation image"); + return GXF_FAILURE; + } + + // Get camera intrinsics from depth message + auto maybe_gxf_camera_model = maybe_depth_message.value().get( + RAW_CAMERA_MODEL_GXF_NAME); + if (!maybe_gxf_camera_model) { + GXF_LOG_ERROR("[FoundationposeSampling] Failed to receive image message"); + return maybe_gxf_camera_model.error(); + } + auto gxf_camera_model = maybe_gxf_camera_model.value(); + Eigen::Matrix3f K; + K << gxf_camera_model->focal_length.x, 0.0, gxf_camera_model->principal_point.x, + 0.0, gxf_camera_model->focal_length.y, gxf_camera_model->principal_point.y, + 0.0, 0.0, 1.0; + + const uint32_t height = depth_info.height; + const uint32_t width = depth_info.width; + + if (!cached_) { + int size = width * height * sizeof(float); + CHECK_CUDA_ERRORS(cudaMalloc(&erode_depth_device_, size)); + CHECK_CUDA_ERRORS(cudaMalloc(&bilateral_filter_depth_device_, size)); + cached_ = true; + } + + // Generate Pose Hypothesis + auto ob_in_cams = MakeRotationGrid(); + if (ob_in_cams.size() == 0 || ob_in_cams.size() > max_hypothesis_) { + GXF_LOG_ERROR("[FoundationposeSampling] The size of rotation grid is not valid."); + return GXF_FAILURE; + } + + if (ob_in_cams[0].rows() != kPoseMatrixLength || ob_in_cams[0].cols() != kPoseMatrixLength) { + GXF_LOG_ERROR("[FoundationposeSampling] The rotation grid dimension does not match pose matrix."); + return GXF_FAILURE; + } + + Eigen::Matrix bilateral_filter_depth_host; + bilateral_filter_depth_host.resize(height, width); + + erode_depth(cuda_stream, reinterpret_cast(depth_handle->pointer()), erode_depth_device_, height, width); + CHECK_CUDA_ERRORS(cudaGetLastError()); + + bilateral_filter_depth(cuda_stream, erode_depth_device_, bilateral_filter_depth_device_, height, width); + CHECK_CUDA_ERRORS(cudaGetLastError()); + + RowMajorMatrix8u mask; + mask.resize(height, width); + + CHECK_CUDA_ERRORS(cudaMemcpyAsync( + bilateral_filter_depth_host.data(), bilateral_filter_depth_device_, + height * width * sizeof(float), cudaMemcpyDeviceToHost, cuda_stream)); + + CHECK_CUDA_ERRORS(cudaMemcpyAsync( + mask.data(), segmentation_handle->pointer(), height * width * sizeof(uint8_t), + cudaMemcpyDeviceToHost, cuda_stream)); + cudaStreamSynchronize(cuda_stream); + + Eigen::Vector3f center; + auto success = GuessTranslation(bilateral_filter_depth_host, mask, K, min_depth_, center); + if (!success) { + GXF_LOG_ERROR("[FoundationposeSampling] Failed to guess translation. Not processing this image"); + return GXF_SUCCESS; + } + for (auto& m : ob_in_cams) { + m.block<3, 1>(0, 3) = center; + } + + // Flatten vector of eigen matrix into vector to make the memory continuous + std::vector ob_in_cams_vector; + ob_in_cams_vector.reserve(ob_in_cams.size() * ob_in_cams[0].size()); + for (auto& mat : ob_in_cams) { + std::vector mat_data(mat.data(), mat.data() + mat.size()); + ob_in_cams_vector.insert(ob_in_cams_vector.end(), mat_data.begin(), mat_data.end()); + } + + if (ob_in_cams.size() % kNumBatches !=0 ) { + GXF_LOG_WARNING( + "[FoundationposeSampling] The total pose size is not divisible by the iteration number, a few pose estimations might be dropped"); + } + + int batch_size = ob_in_cams.size() / kNumBatches; + for (int i = 0; i < kNumBatches; i++) { + // Allocate output message + auto maybe_output_message = gxf::Entity::New(context()); + if (!maybe_output_message) { + GXF_LOG_ERROR("[FoundationposeSampling] Failed to allocate PoseArray Message"); + return gxf::ToResultCode(maybe_output_message); + } + auto output_message = maybe_output_message.value(); + + // Pose Array is column-major + auto maybe_pose_arrays = output_message.add(kNamePoses); + if (!maybe_pose_arrays) { + GXF_LOG_ERROR("[FoundationposeSampling] Failed to add output Tensor"); + return gxf::ToResultCode(maybe_pose_arrays); + } + auto pose_arrays = maybe_pose_arrays.value(); + + auto maybe_added_timestamp = + AddInputTimestampToOutput(output_message, maybe_depth_message.value()); + if (!maybe_added_timestamp) { + GXF_LOG_ERROR("[FoundationposeSampling] Failed to add timestamp"); + return gxf::ToResultCode(maybe_added_timestamp); + } + + // Initialize output GXF tensor + std::array pose_arrays_shape{ + static_cast(batch_size), kPoseMatrixLength, kPoseMatrixLength}; + auto result = pose_arrays->reshape( + nvidia::gxf::Shape{pose_arrays_shape, 3}, nvidia::gxf::MemoryStorageType::kDevice, + allocator_); + if (!result) { + GXF_LOG_ERROR("[FoundationposeSampling] Failed to reshape pose array tensor"); + return gxf::ToResultCode(result); + } + + auto expected_output_size = batch_size * kPoseMatrixLength * kPoseMatrixLength * sizeof(float); + if (pose_arrays->size() != expected_output_size) { + GXF_LOG_ERROR( + "[FoundationposeSampling] Pose array output size %lu is not same as expected %lu", pose_arrays->size(), expected_output_size); + return gxf::ToResultCode(result); + } + + CHECK_CUDA_ERRORS(cudaMemcpyAsync( + pose_arrays->pointer(), &ob_in_cams_vector[i*batch_size*16], pose_arrays->size(), cudaMemcpyHostToDevice, cuda_stream)); + cudaStreamSynchronize(cuda_stream); + + // Create entity, and forward camera model + auto maybe_camera_model_out_message = gxf::Entity::New(context()); + if (!maybe_camera_model_out_message) { + GXF_LOG_ERROR("[FoundationposeSampling] Unable to create entity"); + return gxf::ToResultCode(maybe_camera_model_out_message); + } + // Pose Array is column-major + auto maybe_camera_model = maybe_camera_model_out_message.value().add(RAW_CAMERA_MODEL_GXF_NAME); + if (!maybe_camera_model) { + GXF_LOG_ERROR("[FoundationposeSampling] Failed to allocate output Tensor "); + return gxf::ToResultCode(maybe_camera_model); + } + *maybe_camera_model.value() = *gxf_camera_model; + posearray_transmitter_->publish(output_message); + point_cloud_transmitter_->publish(maybe_xyz_message.value()); + rgb_transmitter_->publish(maybe_rgb_message.value()); + camera_model_transmitter_->publish(maybe_camera_model_out_message.value()); + } + return GXF_SUCCESS; +} + +gxf_result_t FoundationposeSampling::stop() noexcept { + CHECK_CUDA_ERRORS(cudaFree(erode_depth_device_)); + CHECK_CUDA_ERRORS(cudaFree(bilateral_filter_depth_device_)); + return GXF_SUCCESS; +} + +} // namespace isaac_ros +} // namespace nvidia diff --git a/isaac_ros_gxf_extensions/gxf_isaac_foundationpose/gxf/foundationpose/foundationpose_sampling.cu b/isaac_ros_gxf_extensions/gxf_isaac_foundationpose/gxf/foundationpose/foundationpose_sampling.cu new file mode 100644 index 0000000..131fbd7 --- /dev/null +++ b/isaac_ros_gxf_extensions/gxf_isaac_foundationpose/gxf/foundationpose/foundationpose_sampling.cu @@ -0,0 +1,164 @@ +// SPDX-FileCopyrightText: NVIDIA CORPORATION & AFFILIATES +// Copyright (c) 2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. +// +// SPDX-License-Identifier: Apache-2.0 + +#include +#include "foundationpose_sampling.cu.hpp" + +namespace nvidia { +namespace isaac_ros { + +__global__ void erode_depth_kernel( + float* depth, float* out, int H, int W, int radius, float depth_diff_thres, float ratio_thres, + float zfar) { + int h = blockIdx.y * blockDim.y + threadIdx.y; + int w = blockIdx.x * blockDim.x + threadIdx.x; + + if (w >= W || h >= H) { + return; + } + + float d_ori = depth[h * W + w]; + + // Check the validity of the depth value + if (d_ori < 0.1f || d_ori >= zfar) { + out[h * W + w] = 0.0f; + return; + } + + float bad_cnt = 0.0f; + float total = 0.0f; + + // Loop over the neighboring pixels + for (int u = w - radius; u <= w + radius; u++) { + if (u < 0 || u >= W) { + continue; + } + for (int v = h - radius; v <= h + radius; v++) { + if (v < 0 || v >= H) { + continue; + } + float cur_depth = depth[v * W + u]; + + total += 1.0f; + + if (cur_depth < 0.1f || cur_depth >= zfar || fabsf(cur_depth - d_ori) > depth_diff_thres) { + bad_cnt += 1.0f; + } + } + } + + // Check the ratio of bad pixels + if ((bad_cnt / total) > ratio_thres) { + out[h * W + w] = 0.0f; + } else { + out[h * W + w] = d_ori; + } +} + +__global__ void bilateral_filter_depth_kernel( + float* depth, float* out, int H, int W, float zfar, int radius, float sigmaD, float sigmaR) { + int h = blockIdx.y * blockDim.y + threadIdx.y; + int w = blockIdx.x * blockDim.x + threadIdx.x; + + if (w >= W || h >= H) { + return; + } + + out[h * W + w] = 0.0f; + + // Compute the mean depth of the neighboring pixels + float mean_depth = 0.0f; + int num_valid = 0; + for (int u = w - radius; u <= w + radius; u++) { + if (u < 0 || u >= W) { + continue; + } + for (int v = h - radius; v <= h + radius; v++) { + if (v < 0 || v >= H) { + continue; + } + // Get the current depth value + float cur_depth = depth[v * W + u]; + if (cur_depth >= 0.1f && cur_depth < zfar) { + num_valid++; + mean_depth += cur_depth; + } + } + } + + // Check if there are any valid pixels + if (num_valid == 0) { + return; + } + + mean_depth /= (float)num_valid; + + float depthCenter = depth[h * W + w]; + float sum_weight = 0.0f; + float sum = 0.0f; + + // Loop over the neighboring pixels again + for (int u = w - radius; u <= w + radius; u++) { + if (u < 0 || u >= W) { + continue; + } + for (int v = h - radius; v <= h + radius; v++) { + if (v < 0 || v >= H) { + continue; + } + float cur_depth = depth[v * W + u]; + if (cur_depth >= 0.1f && cur_depth < zfar && fabsf(cur_depth - mean_depth) < 0.01f) { + float weight = expf( + -((float)((u - w) * (u - w) + (v - h) * (v - h))) / (2.0f * sigmaD * sigmaD) - + (depthCenter - cur_depth) * (depthCenter - cur_depth) / (2.0f * sigmaR * sigmaR)); + sum_weight += weight; + sum += weight * cur_depth; + } + } + } + + // Check if the sum of weights is positive and the number of valid pixels is positive + if (sum_weight > 0.0f && num_valid > 0) { + out[h * W + w] = sum / sum_weight; + } +} + +uint16_t ceil_div(uint16_t numerator, uint16_t denominator) { + uint32_t accumulator = numerator + denominator - 1; + return accumulator / denominator; +} + +void erode_depth( + cudaStream_t stream, float* depth, float* out, int H, int W, int radius, float depth_diff_thres, float ratio_thres, + float zfar) { + dim3 block(16, 16); + dim3 grid(ceil_div(W, 16), ceil_div(H, 16), 1); + + erode_depth_kernel<<>>( + depth, out, H, W, radius, depth_diff_thres, ratio_thres, zfar); +} + +void bilateral_filter_depth( + cudaStream_t stream, float* depth, float* out, int H, int W, float zfar, int radius, float sigmaD, float sigmaR) { + dim3 block(16, 16); + dim3 grid(ceil_div(W, 16), ceil_div(H, 16), 1); + + bilateral_filter_depth_kernel<<>>(depth, out, H, W, zfar, radius, sigmaD, sigmaR); +} + +} // namespace isaac_ros +} // namespace nvidia \ No newline at end of file diff --git a/isaac_ros_gxf_extensions/gxf_isaac_foundationpose/gxf/foundationpose/foundationpose_sampling.cu.hpp b/isaac_ros_gxf_extensions/gxf_isaac_foundationpose/gxf/foundationpose/foundationpose_sampling.cu.hpp new file mode 100644 index 0000000..0fe9838 --- /dev/null +++ b/isaac_ros_gxf_extensions/gxf_isaac_foundationpose/gxf/foundationpose/foundationpose_sampling.cu.hpp @@ -0,0 +1,40 @@ +// SPDX-FileCopyrightText: NVIDIA CORPORATION & AFFILIATES +// Copyright (c) 2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. +// +// SPDX-License-Identifier: Apache-2.0 + +#ifndef NVIDIA_ISAAC_ROS_EXTENSIONS_FOUNDATIONPOSE_SAMPLING_CUDA_HPP_ +#define NVIDIA_ISAAC_ROS_EXTENSIONS_FOUNDATIONPOSE_SAMPLING_CUDA_HPP_ + +#include +#include + +#include "cuda.h" +#include "cuda_runtime.h" + +namespace nvidia { +namespace isaac_ros { + +void erode_depth( + cudaStream_t stream, float* depth, float* out, int H, int W, int radius = 2, float depth_diff_thres = 0.001, + float ratio_thres = 0.8, float zfar = 100); +void bilateral_filter_depth( + cudaStream_t stream, float* depth, float* out, int H, int W, float zfar = 100, int radius = 2, float sigmaD = 2, + float sigmaR = 100000); + +} // namespace isaac_ros +} // namespace nvidia + +#endif // NVIDIA_ISAAC_ROS_EXTENSIONS_FOUNDATIONPOSE_SAMPLING_CUDA_HPP_ \ No newline at end of file diff --git a/isaac_ros_gxf_extensions/gxf_isaac_foundationpose/gxf/foundationpose/foundationpose_sampling.hpp b/isaac_ros_gxf_extensions/gxf_isaac_foundationpose/gxf/foundationpose/foundationpose_sampling.hpp new file mode 100644 index 0000000..0ac4895 --- /dev/null +++ b/isaac_ros_gxf_extensions/gxf_isaac_foundationpose/gxf/foundationpose/foundationpose_sampling.hpp @@ -0,0 +1,72 @@ +// SPDX-FileCopyrightText: NVIDIA CORPORATION & AFFILIATES +// Copyright (c) 2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. +// +// SPDX-License-Identifier: Apache-2.0 + +#ifndef NVIDIA_ISAAC_ROS_EXTENSIONS_FOUNDATIONPOSE_SAMPLING_HPP_ +#define NVIDIA_ISAAC_ROS_EXTENSIONS_FOUNDATIONPOSE_SAMPLING_HPP_ + +#include +#include + +#include "gxf/cuda/cuda_stream.hpp" +#include "gxf/cuda/cuda_stream_pool.hpp" +#include "gxf/core/entity.hpp" +#include "gxf/core/gxf.h" +#include "gxf/core/parameter.hpp" +#include "gxf/core/parameter_parser_std.hpp" +#include "gxf/std/codelet.hpp" +#include "gxf/std/memory_buffer.hpp" +#include "gxf/std/receiver.hpp" +#include "gxf/std/transmitter.hpp" + +#include "foundationpose_sampling.cu.hpp" + +namespace nvidia { +namespace isaac_ros { + +// GXF codelet generate initial pose estimations on an icosphere. +class FoundationposeSampling : public gxf::Codelet { + public: + gxf_result_t start() noexcept override; + gxf_result_t tick() noexcept override; + gxf_result_t stop() noexcept override; + gxf_result_t registerInterface(gxf::Registrar* registrar) noexcept override; + + private: + gxf::Parameter> point_cloud_receiver_; + gxf::Parameter> depth_receiver_; + gxf::Parameter> rgb_receiver_; + gxf::Parameter> segmentation_receiver_; + + gxf::Parameter> posearray_transmitter_; + gxf::Parameter> point_cloud_transmitter_; + gxf::Parameter> rgb_transmitter_; + gxf::Parameter> camera_model_transmitter_; + gxf::Parameter> allocator_; + gxf::Parameter> cuda_stream_pool_; + gxf::Parameter max_hypothesis_; + gxf::Parameter min_depth_; + gxf::Handle stream_; + + float* erode_depth_device_; + float* bilateral_filter_depth_device_; + bool cached_ = false; +}; + +} // namespace isaac_ros +} // namespace nvidia + +#endif // NVIDIA_ISAAC_ROS_EXTENSIONS_FOUNDATIONPOSE_SAMPLING_HPP_ diff --git a/isaac_ros_gxf_extensions/gxf_isaac_foundationpose/gxf/foundationpose/foundationpose_transformation.cpp b/isaac_ros_gxf_extensions/gxf_isaac_foundationpose/gxf/foundationpose/foundationpose_transformation.cpp new file mode 100644 index 0000000..37459d0 --- /dev/null +++ b/isaac_ros_gxf_extensions/gxf_isaac_foundationpose/gxf/foundationpose/foundationpose_transformation.cpp @@ -0,0 +1,390 @@ +// SPDX-FileCopyrightText: NVIDIA CORPORATION & AFFILIATES +// Copyright (c) 2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. +// +// SPDX-License-Identifier: Apache-2.0 + +#include "foundationpose_transformation.hpp" +#include "foundationpose_utils.hpp" + +#include + +#include +#include +#include + +#include "gxf/multimedia/camera.hpp" +#include "gxf/multimedia/video.hpp" +#include "gxf/std/tensor.hpp" +#include "gxf/std/timestamp.hpp" + +namespace nvidia { +namespace isaac_ros { + +namespace { + +constexpr char kNamePoses[] = "poses"; +constexpr char kNameTranslations[] = "output_tensor1"; +constexpr char kNameRotations[] = "output_tensor2"; +constexpr size_t kTransformationMatrixSize = 4; +constexpr int kNumBatches = 6; + +} // namespace + +gxf_result_t FoundationposeTransformation::registerInterface(gxf::Registrar* registrar) noexcept { + gxf::Expected result; + + result &= registrar->parameter( + poses_receiver_, "poses_input", "Pose Array Input", "The pose detections array as a tensor"); + + result &= registrar->parameter( + refined_poses_receiver_, "refined_poses_input", "Refined Pose detla from refinere network", + "The poses detection array as two tensors"); + + result &= registrar->parameter( + sliced_pose_array_transmitter_, "sliced_output", "Pose_Array output", "Sliced ouput poses as a pose array"); + + result &= registrar->parameter( + batched_pose_array_transmitter_, "batched_output", "Pose_Array output", "Batched ouput poses as a pose array"); + + result &= registrar->parameter( + mesh_file_path_, "mesh_file_path", "obj mesh file path", "Path to your object mesh file"); + + result &= registrar->parameter( + rot_normalizer_, "rot_normalizer", "Rotation Normalizer", "Rotation Normalizer"); + + result &= registrar->parameter( + mode_, "mode", "Transformation Mode", "Tracking or Pose Estimation"); + + result &= registrar->parameter( + refine_iterations_, "refine_iterations", "refine iterations", + "Number of iterations on the refine network (render->refine->transoformation)", 1); + + result &= registrar->parameter( + allocator_, "allocator", "Allocator", "Output Allocator"); + + result &= registrar->parameter( + cuda_stream_pool_, "cuda_stream_pool", "Cuda Stream Pool", + "Instance of gxf::CudaStreamPool to allocate CUDA stream."); + + // Only used for iterative refinement + result &= registrar->parameter( + iterative_sliced_pose_array_transmitter_, "iterative_sliced_output", "Iterative sliced output", + "Sliced ouput poses as a pose array sent back to refine pipeline", + gxf::Registrar::NoDefaultParameter(), GXF_PARAMETER_FLAGS_OPTIONAL); + + result &= registrar->parameter( + iterative_poses_receiver_, "iterative_poses_input", "Iterative sliced input", + "Sliced ouput poses as a pose array sent back to itself", + gxf::Registrar::NoDefaultParameter(), GXF_PARAMETER_FLAGS_OPTIONAL); + + return gxf::ToResultCode(result); +} + +gxf_result_t FoundationposeTransformation::start() noexcept { + // Validate input parameters + if (refine_iterations_ < 1) { + GXF_LOG_ERROR("Refine iterations should be at least 1"); + return GXF_FAILURE; + } + + Assimp::Importer importer; + const aiScene* scene = importer.ReadFile( + mesh_file_path_.get(), + aiProcess_Triangulate | aiProcess_JoinIdenticalVertices | aiProcess_SortByPType); + + if (!scene || scene->mFlags & AI_SCENE_FLAGS_INCOMPLETE || !scene->mRootNode) { + GXF_LOG_ERROR("[FoundationposeTransformation] Error while loading mesh file ERROR::ASSIMP::"); + GXF_LOG_ERROR(importer.GetErrorString()); + return GXF_FAILURE; + } + + if (scene->mNumMeshes == 0) { + GXF_LOG_ERROR("[FoundationposeTransformation] No mesh was found in the mesh file"); + return GXF_FAILURE; + } + + // Only take the first mesh + const aiMesh* mesh = scene->mMeshes[0]; + mesh_diameter_ = CalcMeshDiameter(mesh); + + // Get cuda stream from stream pool + auto maybe_stream = cuda_stream_pool_.get()->allocateStream(); + if (!maybe_stream) { return gxf::ToResultCode(maybe_stream); } + + cuda_stream_handle_ = std::move(maybe_stream.value()); + if (!cuda_stream_handle_->stream()) { + GXF_LOG_ERROR("Error: allocated stream is not initialized!"); + return GXF_FAILURE; + } + if (!cuda_stream_handle_.is_null()) { + cuda_stream_ = cuda_stream_handle_->stream().value(); + } + return GXF_SUCCESS; +} + +gxf_result_t FoundationposeTransformation::tick() noexcept { + GXF_LOG_DEBUG("[FoundationposeTransformation] Tick FoundationposeTransformation"); + + const auto maybe_refined_poses_message = refined_poses_receiver_->receive(); + if (!maybe_refined_poses_message) { + GXF_LOG_ERROR("[FoundationposeTransformation] Refined pose message is not valid"); + return gxf::ToResultCode(maybe_refined_poses_message); + } + + gxf::Entity pose_message; + if (iteration_count_ == 0) { + const auto maybe_pose_message = poses_receiver_->receive(); + if (!maybe_pose_message) { + GXF_LOG_ERROR("[FoundationposeTransformation] Pose message is not valid"); + return gxf::ToResultCode(maybe_pose_message); + } + pose_message = maybe_pose_message.value(); + } else { + auto iterative_pose_receiver = iterative_poses_receiver_.try_get(); + if (!iterative_pose_receiver) { + GXF_LOG_ERROR("[FoundationposeTransformation] Failed to get iterative pose receiver."); + return gxf::ToResultCode(iterative_pose_receiver); + } + auto maybe_pose_message = iterative_pose_receiver.value()->receive(); + if (!maybe_pose_message) { + GXF_LOG_ERROR("[FoundationposeTransformation] Failed to receive iterative pose message"); + return maybe_pose_message.error(); + } + pose_message = maybe_pose_message.value(); + } + + auto maybe_rotation_tensor = maybe_refined_poses_message.value().get(kNameRotations); + if (!maybe_rotation_tensor) { + GXF_LOG_ERROR("[FoundationposeTransformation] Failed to get rotation tensor."); + return gxf::ToResultCode(maybe_rotation_tensor); + } + auto rotation_tensor = maybe_rotation_tensor.value(); + + auto maybe_translation_tensor = + maybe_refined_poses_message.value().get(kNameTranslations); + if (!maybe_translation_tensor) { + GXF_LOG_ERROR("[FoundationposeTransformation] Failed to get translation tensor."); + return gxf::ToResultCode(maybe_translation_tensor); + } + + auto maybe_pose_tensor = pose_message.get(kNamePoses); + if (!maybe_pose_tensor) { + GXF_LOG_ERROR("[FoundationposeTransformation] Failed to get poses from message"); + return maybe_pose_tensor.error(); + } + + auto rotations_handle = maybe_rotation_tensor.value(); + auto translations_handle = maybe_translation_tensor.value(); + auto poses_handle = maybe_pose_tensor.value(); + + const uint32_t pose_nums = poses_handle->shape().dimension(0); + const uint32_t pose_rows = poses_handle->shape().dimension(1); + const uint32_t pose_cols = poses_handle->shape().dimension(2); + + const uint32_t rotation_nums = rotations_handle->shape().dimension(0); + const uint32_t rotation_shape = rotations_handle->shape().dimension(1); + + const uint32_t translations_nums = translations_handle->shape().dimension(0); + const uint32_t translations_shape = translations_handle->shape().dimension(1); + + // Check the size are equal + if (pose_nums == 0) { + GXF_LOG_ERROR("[FoundationposeTransformation] Zero poses received by transformation node"); + return GXF_FAILURE; + } + + // Check the size are equal + if (pose_rows != kTransformationMatrixSize || pose_cols != kTransformationMatrixSize) { + GXF_LOG_ERROR("[FoundationposeTransformation] The transformation matrix is not 4x4!"); + return GXF_FAILURE; + } + + // Check the size are equal + if (pose_nums != rotation_nums || rotation_nums != translations_nums) { + GXF_LOG_ERROR("[FoundationposeTransformation] The batch size of received poses are not equal!"); + return GXF_FAILURE; + } + + // Create a vector to hold N Eigen 4x4 matrices + std::vector B_in_cam(pose_nums * pose_rows * pose_cols); + std::vector trans_delta(pose_nums * translations_shape); + std::vector rot_delta(pose_nums * rotation_shape); + std::vector trans_delta_vec(pose_nums); + std::vector rot_mat_delta(pose_nums); + + CHECK_CUDA_ERRORS(cudaMemcpyAsync( + B_in_cam.data(), poses_handle->pointer(), poses_handle->size(), + cudaMemcpyDeviceToHost, cuda_stream_)); + CHECK_CUDA_ERRORS(cudaMemcpyAsync( + trans_delta.data(), translations_handle->pointer(), translations_handle->size(), + cudaMemcpyDeviceToHost, cuda_stream_)); + CHECK_CUDA_ERRORS(cudaMemcpyAsync( + rot_delta.data(), rotations_handle->pointer(), translations_handle->size(), + cudaMemcpyDeviceToHost, cuda_stream_)); + CHECK_CUDA_ERRORS(cudaStreamSynchronize(cuda_stream_)); + + // Reconstruct into vector + for (size_t index = 0; index < pose_nums; index++) { + Eigen::Map cur_trans_delta(trans_delta.data() + index * 3, 3); + Eigen::Map cur_rot_delta(rot_delta.data() + index * 3, 3); + + trans_delta_vec[index] = cur_trans_delta.array() * (mesh_diameter_ / 2); + // Transform rotation vector into matrix through Rodrigues transform + auto normalized_vect = (cur_rot_delta.array().tanh() * rot_normalizer_).matrix(); + Eigen::AngleAxisf rot_delta_angle_axis(normalized_vect.norm(), normalized_vect.normalized()); + rot_mat_delta[index] = rot_delta_angle_axis.toRotationMatrix().transpose(); + } + + // Pose array is colum-major + std::vector refined_pose; + refined_pose.reserve(pose_nums * kTransformationMatrixSize * kTransformationMatrixSize); + for (size_t index = 0; index < pose_nums; index++) { + // Contruct matrix from vector + Eigen::Map cur_pose( + B_in_cam.data() + index * kTransformationMatrixSize * kTransformationMatrixSize, + kTransformationMatrixSize, kTransformationMatrixSize); + // Add the last column of B_in_cam[i] with trans_delta[i] + cur_pose.col(3).head(3) += trans_delta_vec[index]; + + // Extract the top-left 3x3 part of B_in_cam[i] + Eigen::Matrix3f top_left_3x3 = cur_pose.block<3, 3>(0, 0); + // Multiply the 3x3 part with rot_mat_delta[i] + Eigen::Matrix3f result_3x3 = rot_mat_delta[index] * top_left_3x3; + // Place the result back into the top-left 3x3 part of B_in_cam[i] + cur_pose.block<3, 3>(0, 0) = result_3x3; + + // flatten matrix back to vector and insert + std::vector pose_data(cur_pose.data(), cur_pose.data() + cur_pose.size()); + refined_pose.insert(refined_pose.end(), pose_data.begin(), pose_data.end()); + } + if (iteration_count_ == refine_iterations_ - 1) { + // Prepare batched poses and sent to the final decoder + batched_refined_pose_.insert(batched_refined_pose_.end(), refined_pose.begin(), refined_pose.end()); + } + + // Allocate output message + auto maybe_output_message = gxf::Entity::New(context()); + if (!maybe_output_message) { + GXF_LOG_ERROR("[FoundationposeTransformation] Failed to allocate PoseArray Message"); + return gxf::ToResultCode(maybe_output_message); + } + auto output_message = maybe_output_message.value(); + + auto maybe_added_timestamp = + AddInputTimestampToOutput(output_message, pose_message); + if (!maybe_added_timestamp) { + GXF_LOG_ERROR("[FoundationposeTransformation] Failed to add timestamp"); + return gxf::ToResultCode(maybe_added_timestamp); + } + + auto maybe_pose_arrays = output_message.add(kNamePoses); + if (!maybe_pose_arrays) { + GXF_LOG_ERROR("[FoundationposeTransformation] Failed to allocate output poses"); + return gxf::ToResultCode(maybe_pose_arrays); + } + auto pose_arrays = maybe_pose_arrays.value(); + + std::array pose_arrays_shape{ + static_cast(pose_nums), kTransformationMatrixSize, kTransformationMatrixSize}; + // Initializing GXF tensor + auto result = pose_arrays->reshape( + nvidia::gxf::Shape{pose_arrays_shape, 3}, nvidia::gxf::MemoryStorageType::kDevice, allocator_); + if (!result) { + GXF_LOG_ERROR("[FoundationposeTransformation] Failed to reshape pose array tensor"); + return gxf::ToResultCode(result); + } + CHECK_CUDA_ERRORS(cudaMemcpyAsync( + pose_arrays->pointer(), refined_pose.data(), pose_arrays->size(), + cudaMemcpyHostToDevice, cuda_stream_)); + CHECK_CUDA_ERRORS(cudaStreamSynchronize(cuda_stream_)); + + if (mode_.get() == "tracking") { + GXF_LOG_DEBUG ("[FoundationposeTransformation] Tracking mode, directly publish output."); + return gxf::ToResultCode(sliced_pose_array_transmitter_->publish(std::move(output_message))); + } + + // During iteration: send the sliced message back to refine render + // On the last iteration but not all messages are accumulated: send the slice message to score render + // On the last iteration and all messages are accumulated: send the batched message to decoder + if (iteration_count_ < refine_iterations_ - 1) { + GXF_LOG_DEBUG ("[FoundationposeTransformation] Publish iterative sliced poses to render."); + auto iterative_sliced_pose_array_transmitter = iterative_sliced_pose_array_transmitter_.try_get(); + if (!iterative_sliced_pose_array_transmitter) { + GXF_LOG_ERROR("[FoundationposeTransformation] Failed to get iterative pose array transmitter."); + return gxf::ToResultCode(iterative_sliced_pose_array_transmitter); + } + iterative_sliced_pose_array_transmitter.value()->publish(std::move(output_message)); + } else if (received_batches_ < kNumBatches - 1) { + // Expected more batches, only publish current sliced message to score render. + GXF_LOG_DEBUG ("[FoundationposeTransformation] Publish sliced poses to render."); + sliced_pose_array_transmitter_->publish(std::move(output_message)); + } else { + // Accumulated all batcheds, publish batched message to decoder and current sliced message to score render. + GXF_LOG_DEBUG ("[FoundationposeTransformation] Publish batched poses to decoder."); + auto maybe_batched_output_message = gxf::Entity::New(context()); + if (!maybe_batched_output_message) { + GXF_LOG_ERROR("[FoundationposeTransformation] Failed to allocate PoseArray Message"); + return gxf::ToResultCode(maybe_batched_output_message); + } + auto batched_output_message = maybe_batched_output_message.value(); + + auto maybe_added_timestamp = + AddInputTimestampToOutput(batched_output_message, pose_message); + if (!maybe_added_timestamp) { + GXF_LOG_ERROR("[FoundationposeTransformation] Failed to add timestamp"); + return gxf::ToResultCode(maybe_added_timestamp); + } + + auto maybe_batched_pose_arrays = batched_output_message.add(kNamePoses); + if (!maybe_batched_pose_arrays) { + GXF_LOG_ERROR("[FoundationposeTransformation] Failed to allocate output poses"); + return gxf::ToResultCode(maybe_batched_pose_arrays); + } + auto batched_pose_arrays = maybe_batched_pose_arrays.value(); + + std::array pose_arrays_shape{ + static_cast(pose_nums * kNumBatches), kTransformationMatrixSize, kTransformationMatrixSize}; + + // Initializing GXF tensor + auto result = batched_pose_arrays->reshape( + nvidia::gxf::Shape{pose_arrays_shape, 3}, nvidia::gxf::MemoryStorageType::kDevice, allocator_); + if (!result) { + GXF_LOG_ERROR("[FoundationposeTransformation] Failed to reshape pose array tensor"); + return gxf::ToResultCode(result); + } + CHECK_CUDA_ERRORS(cudaMemcpyAsync( + batched_pose_arrays->pointer(), batched_refined_pose_.data(), batched_pose_arrays->size(), + cudaMemcpyHostToDevice, cuda_stream_)); + batched_refined_pose_.clear(); + + sliced_pose_array_transmitter_->publish(std::move(output_message)); + batched_pose_array_transmitter_->publish(std::move(batched_output_message)); + } + + // Update iteration and batch counters + received_batches_ += 1; + if (received_batches_ == kNumBatches) { + received_batches_ = 0; + iteration_count_ += 1; + GXF_LOG_DEBUG ("[FoundationposeTransformation] %d refine iterations are finished", iteration_count_); + } + if (iteration_count_ == refine_iterations_) { + iteration_count_ = 0; + } + return GXF_SUCCESS; +} + +} // namespace isaac_ros +} // namespace nvidia diff --git a/isaac_ros_gxf_extensions/gxf_isaac_foundationpose/gxf/foundationpose/foundationpose_transformation.hpp b/isaac_ros_gxf_extensions/gxf_isaac_foundationpose/gxf/foundationpose/foundationpose_transformation.hpp new file mode 100644 index 0000000..112be43 --- /dev/null +++ b/isaac_ros_gxf_extensions/gxf_isaac_foundationpose/gxf/foundationpose/foundationpose_transformation.hpp @@ -0,0 +1,69 @@ +// SPDX-FileCopyrightText: NVIDIA CORPORATION & AFFILIATES +// Copyright (c) 2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. +// +// SPDX-License-Identifier: Apache-2.0 + +#ifndef NVIDIA_ISAAC_ROS_EXTENSIONS_FOUNDATIONPOSE_TRANSFORMATION_HPP_ +#define NVIDIA_ISAAC_ROS_EXTENSIONS_FOUNDATIONPOSE_TRANSFORMATION_HPP_ + +#include "gxf/cuda/cuda_stream.hpp" +#include "gxf/cuda/cuda_stream_pool.hpp" +#include "gxf/core/entity.hpp" +#include "gxf/core/gxf.h" +#include "gxf/core/parameter.hpp" +#include "gxf/std/codelet.hpp" +#include "gxf/std/memory_buffer.hpp" +#include "gxf/core/parameter_parser_std.hpp" +#include "gxf/std/receiver.hpp" +#include "gxf/std/transmitter.hpp" + +namespace nvidia { +namespace isaac_ros { + +// GXF codelet refine the pose estimations using the delta received from refine network +class FoundationposeTransformation : public gxf::Codelet { + public: + gxf_result_t start() noexcept override; + gxf_result_t tick() noexcept override; + gxf_result_t stop() noexcept override { return GXF_SUCCESS; } + gxf_result_t registerInterface(gxf::Registrar* registrar) noexcept override; + + private: + gxf::Parameter> poses_receiver_; + gxf::Parameter> refined_poses_receiver_; + gxf::Parameter> iterative_poses_receiver_; + gxf::Parameter> iterative_sliced_pose_array_transmitter_; + gxf::Parameter> sliced_pose_array_transmitter_; + gxf::Parameter> batched_pose_array_transmitter_; + gxf::Parameter> allocator_; + gxf::Parameter> cuda_stream_pool_; + gxf::Parameter mesh_file_path_; + gxf::Parameter rot_normalizer_; + gxf::Parameter mode_; + gxf::Parameter refine_iterations_; + gxf::Handle cuda_stream_handle_; + + float mesh_diameter_; + int32_t received_batches_ = 0; + int32_t iteration_count_ = 0; + std::vector batched_refined_pose_; + + cudaStream_t cuda_stream_ = 0; +}; + +} // namespace isaac_ros +} // namespace nvidia + +#endif // NVIDIA_ISAAC_ROS_EXTENSIONS_FOUNDATIONPOSE_TRANSFORMATION_HPP_ diff --git a/isaac_ros_gxf_extensions/gxf_isaac_foundationpose/gxf/foundationpose/foundationpose_utils.cpp b/isaac_ros_gxf_extensions/gxf_isaac_foundationpose/gxf/foundationpose/foundationpose_utils.cpp new file mode 100644 index 0000000..0fc37c7 --- /dev/null +++ b/isaac_ros_gxf_extensions/gxf_isaac_foundationpose/gxf/foundationpose/foundationpose_utils.cpp @@ -0,0 +1,102 @@ +// SPDX-FileCopyrightText: NVIDIA CORPORATION & AFFILIATES +// Copyright (c) 2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. +// +// SPDX-License-Identifier: Apache-2.0 + +#include "foundationpose_utils.hpp" + +#include +#include + +#include "cuda.h" +#include "cuda_runtime.h" + +namespace nvidia { +std::pair FindMinMaxVertex(const aiMesh* mesh) { + Eigen::Vector3f min_vertex = {0, 0, 0}; + Eigen::Vector3f max_vertex = {0, 0, 0}; + + if (mesh->mNumVertices == 0) { + return std::pair{min_vertex, max_vertex}; + } + + min_vertex << mesh->mVertices[0].x, mesh->mVertices[0].y, mesh->mVertices[0].z; + max_vertex << mesh->mVertices[0].x, mesh->mVertices[0].y, mesh->mVertices[0].z; + + // Iterate over all vertices to find the bounding box + for (size_t v = 0; v < mesh->mNumVertices; v++) { + float vx = mesh->mVertices[v].x; + float vy = mesh->mVertices[v].y; + float vz = mesh->mVertices[v].z; + + min_vertex[0] = std::min(min_vertex[0], vx); + min_vertex[1] = std::min(min_vertex[1], vy); + min_vertex[2] = std::min(min_vertex[2], vz); + + max_vertex[0] = std::max(max_vertex[0], vx); + max_vertex[1] = std::max(max_vertex[1], vy); + max_vertex[2] = std::max(max_vertex[2], vz); + } + return std::pair{min_vertex, max_vertex}; +} + +float CalcMeshDiameter(const aiMesh* mesh) { + float max_dist = 0.0; + for (unsigned int i = 0; i < mesh->mNumVertices; ++i) { + for (unsigned int j = i + 1; j < mesh->mNumVertices; ++j) { + aiVector3D diff = mesh->mVertices[i] - mesh->mVertices[j]; + float dist = diff.Length(); + max_dist = std::max(max_dist, dist); + } + } + return max_dist; +} + +gxf::Expected AddInputTimestampToOutput(gxf::Entity& output, gxf::Entity input) { + std::string named_timestamp{"timestamp"}; + std::string unnamed_timestamp{""}; + auto maybe_input_timestamp = input.get(named_timestamp.c_str()); + + // Try to get a named timestamp from the input entity + if (!maybe_input_timestamp) { + maybe_input_timestamp = input.get(unnamed_timestamp.c_str()); + } + // If there is no named timestamp, try to get a unnamed timestamp from the input entity + if (!maybe_input_timestamp) { + GXF_LOG_ERROR("Failed to get input timestamp!"); + return gxf::ForwardError(maybe_input_timestamp); + } + + // Try to get a named timestamp from the output entity + auto maybe_output_timestamp = output.get(named_timestamp.c_str()); + // If there is no named timestamp, try to get a unnamed timestamp from the output entity + if (!maybe_output_timestamp) { + maybe_output_timestamp = output.get(unnamed_timestamp.c_str()); + } + + // If there is no unnamed timestamp also, then add a named timestamp to the output entity + if (!maybe_output_timestamp) { + maybe_output_timestamp = output.add(named_timestamp.c_str()); + if (!maybe_output_timestamp) { + GXF_LOG_ERROR("Failed to add timestamp to output message!"); + return gxf::ForwardError(maybe_output_timestamp); + } + } + + *maybe_output_timestamp.value() = *maybe_input_timestamp.value(); + return gxf::Success; +} + +} // namespace nvidia \ No newline at end of file diff --git a/isaac_ros_gxf_extensions/gxf_isaac_foundationpose/gxf/foundationpose/foundationpose_utils.hpp b/isaac_ros_gxf_extensions/gxf_isaac_foundationpose/gxf/foundationpose/foundationpose_utils.hpp new file mode 100644 index 0000000..e18c89d --- /dev/null +++ b/isaac_ros_gxf_extensions/gxf_isaac_foundationpose/gxf/foundationpose/foundationpose_utils.hpp @@ -0,0 +1,63 @@ +// SPDX-FileCopyrightText: NVIDIA CORPORATION & AFFILIATES +// Copyright (c) 2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. +// +// SPDX-License-Identifier: Apache-2.0 + +#ifndef NVIDIA_ISAAC_ROS_EXTENSIONS_FOUNDATIONPOSE_UTILS_HPP_ +#define NVIDIA_ISAAC_ROS_EXTENSIONS_FOUNDATIONPOSE_UTILS_HPP_ + +#include + +#include + +#include +#include +#include + +#include "cuda.h" +#include "cuda_runtime.h" + +#include "gxf/cuda/cuda_stream.hpp" +#include "gxf/cuda/cuda_stream_pool.hpp" +#include "gxf/core/entity.hpp" +#include "gxf/core/expected.hpp" +#include "gxf/core/gxf.h" +#include "gxf/std/timestamp.hpp" + +namespace nvidia { +namespace { +#define CHECK_CUDA_ERRORS(result) \ + { CheckCudaErrors(result, __FILE__, __LINE__); } +inline void CheckCudaErrors(cudaError_t result, const char* filename, int line_number) { + if (result != cudaSuccess) { + std::cout << "CUDA Error: " + std::string(cudaGetErrorString(result)) + + " (error code: " + std::to_string(result) + ") at " + std::string(filename) + + " in line " + std::to_string(line_number); + } +} +} // namespace + +// Finds the minimum and maximum vertex from the mesh loaded by assimp +std::pair FindMinMaxVertex(const aiMesh* mesh); + +// Calculates the diameter of the mesh loaded by assimp +float CalcMeshDiameter(const aiMesh* mesh); + +// Updates/adds timestamp entity to the ouput gxf message entity from the input gxf message entity +gxf::Expected AddInputTimestampToOutput(gxf::Entity& output, gxf::Entity input); + +} // namespace nvidia + +#endif // NVIDIA_ISAAC_ROS_EXTENSIONS_FOUNDATIONPOSE_UTILS_HPP_ \ No newline at end of file diff --git a/isaac_ros_gxf_extensions/gxf_isaac_foundationpose/gxf/foundationpose/nvdiffrast/common/common.cpp b/isaac_ros_gxf_extensions/gxf_isaac_foundationpose/gxf/foundationpose/nvdiffrast/common/common.cpp new file mode 100644 index 0000000..66dc90c --- /dev/null +++ b/isaac_ros_gxf_extensions/gxf_isaac_foundationpose/gxf/foundationpose/nvdiffrast/common/common.cpp @@ -0,0 +1,69 @@ +// SPDX-FileCopyrightText: NVIDIA CORPORATION & AFFILIATES +// Copyright (c) 2020-2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. +// +// SPDX-License-Identifier: Apache-2.0 + +#include + +//------------------------------------------------------------------------ +// Block and grid size calculators for kernel launches. + +dim3 getLaunchBlockSize(int maxWidth, int maxHeight, int width, int height) +{ + int maxThreads = maxWidth * maxHeight; + if (maxThreads <= 1 || (width * height) <= 1) + return dim3(1, 1, 1); // Degenerate. + + // Start from max size. + int bw = maxWidth; + int bh = maxHeight; + + // Optimizations for weirdly sized buffers. + if (width < bw) + { + // Decrease block width to smallest power of two that covers the buffer width. + while ((bw >> 1) >= width) + bw >>= 1; + + // Maximize height. + bh = maxThreads / bw; + if (bh > height) + bh = height; + } + else if (height < bh) + { + // Halve height and double width until fits completely inside buffer vertically. + while (bh > height) + { + bh >>= 1; + if (bw < width) + bw <<= 1; + } + } + + // Done. + return dim3(bw, bh, 1); +} + +dim3 getLaunchGridSize(dim3 blockSize, int width, int height, int depth) +{ + dim3 gridSize; + gridSize.x = (width - 1) / blockSize.x + 1; + gridSize.y = (height - 1) / blockSize.y + 1; + gridSize.z = (depth - 1) / blockSize.z + 1; + return gridSize; +} + +//------------------------------------------------------------------------ diff --git a/isaac_ros_gxf_extensions/gxf_isaac_foundationpose/gxf/foundationpose/nvdiffrast/common/common.h b/isaac_ros_gxf_extensions/gxf_isaac_foundationpose/gxf/foundationpose/nvdiffrast/common/common.h new file mode 100644 index 0000000..d44077c --- /dev/null +++ b/isaac_ros_gxf_extensions/gxf_isaac_foundationpose/gxf/foundationpose/nvdiffrast/common/common.h @@ -0,0 +1,195 @@ +// SPDX-FileCopyrightText: NVIDIA CORPORATION & AFFILIATES +// Copyright (c) 2020-2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. +// +// SPDX-License-Identifier: Apache-2.0 + +#pragma once +#include +#include + +//------------------------------------------------------------------------ +// C++ helper function prototypes. + +dim3 getLaunchBlockSize(int maxWidth, int maxHeight, int width, int height); +dim3 getLaunchGridSize(dim3 blockSize, int width, int height, int depth); + +//------------------------------------------------------------------------ +// The rest is CUDA device code specific stuff. + +#ifdef __CUDACC__ + +//------------------------------------------------------------------------ +// Helpers for CUDA vector types. + +static __device__ __forceinline__ float2& operator*= (float2& a, const float2& b) { a.x *= b.x; a.y *= b.y; return a; } +static __device__ __forceinline__ float2& operator+= (float2& a, const float2& b) { a.x += b.x; a.y += b.y; return a; } +static __device__ __forceinline__ float2& operator-= (float2& a, const float2& b) { a.x -= b.x; a.y -= b.y; return a; } +static __device__ __forceinline__ float2& operator*= (float2& a, float b) { a.x *= b; a.y *= b; return a; } +static __device__ __forceinline__ float2& operator+= (float2& a, float b) { a.x += b; a.y += b; return a; } +static __device__ __forceinline__ float2& operator-= (float2& a, float b) { a.x -= b; a.y -= b; return a; } +static __device__ __forceinline__ float2 operator* (const float2& a, const float2& b) { return make_float2(a.x * b.x, a.y * b.y); } +static __device__ __forceinline__ float2 operator+ (const float2& a, const float2& b) { return make_float2(a.x + b.x, a.y + b.y); } +static __device__ __forceinline__ float2 operator- (const float2& a, const float2& b) { return make_float2(a.x - b.x, a.y - b.y); } +static __device__ __forceinline__ float2 operator* (const float2& a, float b) { return make_float2(a.x * b, a.y * b); } +static __device__ __forceinline__ float2 operator+ (const float2& a, float b) { return make_float2(a.x + b, a.y + b); } +static __device__ __forceinline__ float2 operator- (const float2& a, float b) { return make_float2(a.x - b, a.y - b); } +static __device__ __forceinline__ float2 operator* (float a, const float2& b) { return make_float2(a * b.x, a * b.y); } +static __device__ __forceinline__ float2 operator+ (float a, const float2& b) { return make_float2(a + b.x, a + b.y); } +static __device__ __forceinline__ float2 operator- (float a, const float2& b) { return make_float2(a - b.x, a - b.y); } +static __device__ __forceinline__ float2 operator- (const float2& a) { return make_float2(-a.x, -a.y); } +static __device__ __forceinline__ float3& operator*= (float3& a, const float3& b) { a.x *= b.x; a.y *= b.y; a.z *= b.z; return a; } +static __device__ __forceinline__ float3& operator+= (float3& a, const float3& b) { a.x += b.x; a.y += b.y; a.z += b.z; return a; } +static __device__ __forceinline__ float3& operator-= (float3& a, const float3& b) { a.x -= b.x; a.y -= b.y; a.z -= b.z; return a; } +static __device__ __forceinline__ float3& operator*= (float3& a, float b) { a.x *= b; a.y *= b; a.z *= b; return a; } +static __device__ __forceinline__ float3& operator+= (float3& a, float b) { a.x += b; a.y += b; a.z += b; return a; } +static __device__ __forceinline__ float3& operator-= (float3& a, float b) { a.x -= b; a.y -= b; a.z -= b; return a; } +static __device__ __forceinline__ float3 operator* (const float3& a, const float3& b) { return make_float3(a.x * b.x, a.y * b.y, a.z * b.z); } +static __device__ __forceinline__ float3 operator+ (const float3& a, const float3& b) { return make_float3(a.x + b.x, a.y + b.y, a.z + b.z); } +static __device__ __forceinline__ float3 operator- (const float3& a, const float3& b) { return make_float3(a.x - b.x, a.y - b.y, a.z - b.z); } +static __device__ __forceinline__ float3 operator* (const float3& a, float b) { return make_float3(a.x * b, a.y * b, a.z * b); } +static __device__ __forceinline__ float3 operator+ (const float3& a, float b) { return make_float3(a.x + b, a.y + b, a.z + b); } +static __device__ __forceinline__ float3 operator- (const float3& a, float b) { return make_float3(a.x - b, a.y - b, a.z - b); } +static __device__ __forceinline__ float3 operator* (float a, const float3& b) { return make_float3(a * b.x, a * b.y, a * b.z); } +static __device__ __forceinline__ float3 operator+ (float a, const float3& b) { return make_float3(a + b.x, a + b.y, a + b.z); } +static __device__ __forceinline__ float3 operator- (float a, const float3& b) { return make_float3(a - b.x, a - b.y, a - b.z); } +static __device__ __forceinline__ float3 operator- (const float3& a) { return make_float3(-a.x, -a.y, -a.z); } +static __device__ __forceinline__ float4& operator*= (float4& a, const float4& b) { a.x *= b.x; a.y *= b.y; a.z *= b.z; a.w *= b.w; return a; } +static __device__ __forceinline__ float4& operator+= (float4& a, const float4& b) { a.x += b.x; a.y += b.y; a.z += b.z; a.w += b.w; return a; } +static __device__ __forceinline__ float4& operator-= (float4& a, const float4& b) { a.x -= b.x; a.y -= b.y; a.z -= b.z; a.w -= b.w; return a; } +static __device__ __forceinline__ float4& operator*= (float4& a, float b) { a.x *= b; a.y *= b; a.z *= b; a.w *= b; return a; } +static __device__ __forceinline__ float4& operator+= (float4& a, float b) { a.x += b; a.y += b; a.z += b; a.w += b; return a; } +static __device__ __forceinline__ float4& operator-= (float4& a, float b) { a.x -= b; a.y -= b; a.z -= b; a.w -= b; return a; } +static __device__ __forceinline__ float4 operator* (const float4& a, const float4& b) { return make_float4(a.x * b.x, a.y * b.y, a.z * b.z, a.w * b.w); } +static __device__ __forceinline__ float4 operator+ (const float4& a, const float4& b) { return make_float4(a.x + b.x, a.y + b.y, a.z + b.z, a.w + b.w); } +static __device__ __forceinline__ float4 operator- (const float4& a, const float4& b) { return make_float4(a.x - b.x, a.y - b.y, a.z - b.z, a.w - b.w); } +static __device__ __forceinline__ float4 operator* (const float4& a, float b) { return make_float4(a.x * b, a.y * b, a.z * b, a.w * b); } +static __device__ __forceinline__ float4 operator+ (const float4& a, float b) { return make_float4(a.x + b, a.y + b, a.z + b, a.w + b); } +static __device__ __forceinline__ float4 operator- (const float4& a, float b) { return make_float4(a.x - b, a.y - b, a.z - b, a.w - b); } +static __device__ __forceinline__ float4 operator* (float a, const float4& b) { return make_float4(a * b.x, a * b.y, a * b.z, a * b.w); } +static __device__ __forceinline__ float4 operator+ (float a, const float4& b) { return make_float4(a + b.x, a + b.y, a + b.z, a + b.w); } +static __device__ __forceinline__ float4 operator- (float a, const float4& b) { return make_float4(a - b.x, a - b.y, a - b.z, a - b.w); } +static __device__ __forceinline__ float4 operator- (const float4& a) { return make_float4(-a.x, -a.y, -a.z, -a.w); } +static __device__ __forceinline__ int2& operator*= (int2& a, const int2& b) { a.x *= b.x; a.y *= b.y; return a; } +static __device__ __forceinline__ int2& operator+= (int2& a, const int2& b) { a.x += b.x; a.y += b.y; return a; } +static __device__ __forceinline__ int2& operator-= (int2& a, const int2& b) { a.x -= b.x; a.y -= b.y; return a; } +static __device__ __forceinline__ int2& operator*= (int2& a, int b) { a.x *= b; a.y *= b; return a; } +static __device__ __forceinline__ int2& operator+= (int2& a, int b) { a.x += b; a.y += b; return a; } +static __device__ __forceinline__ int2& operator-= (int2& a, int b) { a.x -= b; a.y -= b; return a; } +static __device__ __forceinline__ int2 operator* (const int2& a, const int2& b) { return make_int2(a.x * b.x, a.y * b.y); } +static __device__ __forceinline__ int2 operator+ (const int2& a, const int2& b) { return make_int2(a.x + b.x, a.y + b.y); } +static __device__ __forceinline__ int2 operator- (const int2& a, const int2& b) { return make_int2(a.x - b.x, a.y - b.y); } +static __device__ __forceinline__ int2 operator* (const int2& a, int b) { return make_int2(a.x * b, a.y * b); } +static __device__ __forceinline__ int2 operator+ (const int2& a, int b) { return make_int2(a.x + b, a.y + b); } +static __device__ __forceinline__ int2 operator- (const int2& a, int b) { return make_int2(a.x - b, a.y - b); } +static __device__ __forceinline__ int2 operator* (int a, const int2& b) { return make_int2(a * b.x, a * b.y); } +static __device__ __forceinline__ int2 operator+ (int a, const int2& b) { return make_int2(a + b.x, a + b.y); } +static __device__ __forceinline__ int2 operator- (int a, const int2& b) { return make_int2(a - b.x, a - b.y); } +static __device__ __forceinline__ int2 operator- (const int2& a) { return make_int2(-a.x, -a.y); } +static __device__ __forceinline__ int3& operator*= (int3& a, const int3& b) { a.x *= b.x; a.y *= b.y; a.z *= b.z; return a; } +static __device__ __forceinline__ int3& operator+= (int3& a, const int3& b) { a.x += b.x; a.y += b.y; a.z += b.z; return a; } +static __device__ __forceinline__ int3& operator-= (int3& a, const int3& b) { a.x -= b.x; a.y -= b.y; a.z -= b.z; return a; } +static __device__ __forceinline__ int3& operator*= (int3& a, int b) { a.x *= b; a.y *= b; a.z *= b; return a; } +static __device__ __forceinline__ int3& operator+= (int3& a, int b) { a.x += b; a.y += b; a.z += b; return a; } +static __device__ __forceinline__ int3& operator-= (int3& a, int b) { a.x -= b; a.y -= b; a.z -= b; return a; } +static __device__ __forceinline__ int3 operator* (const int3& a, const int3& b) { return make_int3(a.x * b.x, a.y * b.y, a.z * b.z); } +static __device__ __forceinline__ int3 operator+ (const int3& a, const int3& b) { return make_int3(a.x + b.x, a.y + b.y, a.z + b.z); } +static __device__ __forceinline__ int3 operator- (const int3& a, const int3& b) { return make_int3(a.x - b.x, a.y - b.y, a.z - b.z); } +static __device__ __forceinline__ int3 operator* (const int3& a, int b) { return make_int3(a.x * b, a.y * b, a.z * b); } +static __device__ __forceinline__ int3 operator+ (const int3& a, int b) { return make_int3(a.x + b, a.y + b, a.z + b); } +static __device__ __forceinline__ int3 operator- (const int3& a, int b) { return make_int3(a.x - b, a.y - b, a.z - b); } +static __device__ __forceinline__ int3 operator* (int a, const int3& b) { return make_int3(a * b.x, a * b.y, a * b.z); } +static __device__ __forceinline__ int3 operator+ (int a, const int3& b) { return make_int3(a + b.x, a + b.y, a + b.z); } +static __device__ __forceinline__ int3 operator- (int a, const int3& b) { return make_int3(a - b.x, a - b.y, a - b.z); } +static __device__ __forceinline__ int3 operator- (const int3& a) { return make_int3(-a.x, -a.y, -a.z); } +static __device__ __forceinline__ int4& operator*= (int4& a, const int4& b) { a.x *= b.x; a.y *= b.y; a.z *= b.z; a.w *= b.w; return a; } +static __device__ __forceinline__ int4& operator+= (int4& a, const int4& b) { a.x += b.x; a.y += b.y; a.z += b.z; a.w += b.w; return a; } +static __device__ __forceinline__ int4& operator-= (int4& a, const int4& b) { a.x -= b.x; a.y -= b.y; a.z -= b.z; a.w -= b.w; return a; } +static __device__ __forceinline__ int4& operator*= (int4& a, int b) { a.x *= b; a.y *= b; a.z *= b; a.w *= b; return a; } +static __device__ __forceinline__ int4& operator+= (int4& a, int b) { a.x += b; a.y += b; a.z += b; a.w += b; return a; } +static __device__ __forceinline__ int4& operator-= (int4& a, int b) { a.x -= b; a.y -= b; a.z -= b; a.w -= b; return a; } +static __device__ __forceinline__ int4 operator* (const int4& a, const int4& b) { return make_int4(a.x * b.x, a.y * b.y, a.z * b.z, a.w * b.w); } +static __device__ __forceinline__ int4 operator+ (const int4& a, const int4& b) { return make_int4(a.x + b.x, a.y + b.y, a.z + b.z, a.w + b.w); } +static __device__ __forceinline__ int4 operator- (const int4& a, const int4& b) { return make_int4(a.x - b.x, a.y - b.y, a.z - b.z, a.w - b.w); } +static __device__ __forceinline__ int4 operator* (const int4& a, int b) { return make_int4(a.x * b, a.y * b, a.z * b, a.w * b); } +static __device__ __forceinline__ int4 operator+ (const int4& a, int b) { return make_int4(a.x + b, a.y + b, a.z + b, a.w + b); } +static __device__ __forceinline__ int4 operator- (const int4& a, int b) { return make_int4(a.x - b, a.y - b, a.z - b, a.w - b); } +static __device__ __forceinline__ int4 operator* (int a, const int4& b) { return make_int4(a * b.x, a * b.y, a * b.z, a * b.w); } +static __device__ __forceinline__ int4 operator+ (int a, const int4& b) { return make_int4(a + b.x, a + b.y, a + b.z, a + b.w); } +static __device__ __forceinline__ int4 operator- (int a, const int4& b) { return make_int4(a - b.x, a - b.y, a - b.z, a - b.w); } +static __device__ __forceinline__ int4 operator- (const int4& a) { return make_int4(-a.x, -a.y, -a.z, -a.w); } +static __device__ __forceinline__ uint2& operator*= (uint2& a, const uint2& b) { a.x *= b.x; a.y *= b.y; return a; } +static __device__ __forceinline__ uint2& operator+= (uint2& a, const uint2& b) { a.x += b.x; a.y += b.y; return a; } +static __device__ __forceinline__ uint2& operator-= (uint2& a, const uint2& b) { a.x -= b.x; a.y -= b.y; return a; } +static __device__ __forceinline__ uint2& operator*= (uint2& a, unsigned int b) { a.x *= b; a.y *= b; return a; } +static __device__ __forceinline__ uint2& operator+= (uint2& a, unsigned int b) { a.x += b; a.y += b; return a; } +static __device__ __forceinline__ uint2& operator-= (uint2& a, unsigned int b) { a.x -= b; a.y -= b; return a; } +static __device__ __forceinline__ uint2 operator* (const uint2& a, const uint2& b) { return make_uint2(a.x * b.x, a.y * b.y); } +static __device__ __forceinline__ uint2 operator+ (const uint2& a, const uint2& b) { return make_uint2(a.x + b.x, a.y + b.y); } +static __device__ __forceinline__ uint2 operator- (const uint2& a, const uint2& b) { return make_uint2(a.x - b.x, a.y - b.y); } +static __device__ __forceinline__ uint2 operator* (const uint2& a, unsigned int b) { return make_uint2(a.x * b, a.y * b); } +static __device__ __forceinline__ uint2 operator+ (const uint2& a, unsigned int b) { return make_uint2(a.x + b, a.y + b); } +static __device__ __forceinline__ uint2 operator- (const uint2& a, unsigned int b) { return make_uint2(a.x - b, a.y - b); } +static __device__ __forceinline__ uint2 operator* (unsigned int a, const uint2& b) { return make_uint2(a * b.x, a * b.y); } +static __device__ __forceinline__ uint2 operator+ (unsigned int a, const uint2& b) { return make_uint2(a + b.x, a + b.y); } +static __device__ __forceinline__ uint2 operator- (unsigned int a, const uint2& b) { return make_uint2(a - b.x, a - b.y); } +static __device__ __forceinline__ uint3& operator*= (uint3& a, const uint3& b) { a.x *= b.x; a.y *= b.y; a.z *= b.z; return a; } +static __device__ __forceinline__ uint3& operator+= (uint3& a, const uint3& b) { a.x += b.x; a.y += b.y; a.z += b.z; return a; } +static __device__ __forceinline__ uint3& operator-= (uint3& a, const uint3& b) { a.x -= b.x; a.y -= b.y; a.z -= b.z; return a; } +static __device__ __forceinline__ uint3& operator*= (uint3& a, unsigned int b) { a.x *= b; a.y *= b; a.z *= b; return a; } +static __device__ __forceinline__ uint3& operator+= (uint3& a, unsigned int b) { a.x += b; a.y += b; a.z += b; return a; } +static __device__ __forceinline__ uint3& operator-= (uint3& a, unsigned int b) { a.x -= b; a.y -= b; a.z -= b; return a; } +static __device__ __forceinline__ uint3 operator* (const uint3& a, const uint3& b) { return make_uint3(a.x * b.x, a.y * b.y, a.z * b.z); } +static __device__ __forceinline__ uint3 operator+ (const uint3& a, const uint3& b) { return make_uint3(a.x + b.x, a.y + b.y, a.z + b.z); } +static __device__ __forceinline__ uint3 operator- (const uint3& a, const uint3& b) { return make_uint3(a.x - b.x, a.y - b.y, a.z - b.z); } +static __device__ __forceinline__ uint3 operator* (const uint3& a, unsigned int b) { return make_uint3(a.x * b, a.y * b, a.z * b); } +static __device__ __forceinline__ uint3 operator+ (const uint3& a, unsigned int b) { return make_uint3(a.x + b, a.y + b, a.z + b); } +static __device__ __forceinline__ uint3 operator- (const uint3& a, unsigned int b) { return make_uint3(a.x - b, a.y - b, a.z - b); } +static __device__ __forceinline__ uint3 operator* (unsigned int a, const uint3& b) { return make_uint3(a * b.x, a * b.y, a * b.z); } +static __device__ __forceinline__ uint3 operator+ (unsigned int a, const uint3& b) { return make_uint3(a + b.x, a + b.y, a + b.z); } +static __device__ __forceinline__ uint3 operator- (unsigned int a, const uint3& b) { return make_uint3(a - b.x, a - b.y, a - b.z); } +static __device__ __forceinline__ uint4& operator*= (uint4& a, const uint4& b) { a.x *= b.x; a.y *= b.y; a.z *= b.z; a.w *= b.w; return a; } +static __device__ __forceinline__ uint4& operator+= (uint4& a, const uint4& b) { a.x += b.x; a.y += b.y; a.z += b.z; a.w += b.w; return a; } +static __device__ __forceinline__ uint4& operator-= (uint4& a, const uint4& b) { a.x -= b.x; a.y -= b.y; a.z -= b.z; a.w -= b.w; return a; } +static __device__ __forceinline__ uint4& operator*= (uint4& a, unsigned int b) { a.x *= b; a.y *= b; a.z *= b; a.w *= b; return a; } +static __device__ __forceinline__ uint4& operator+= (uint4& a, unsigned int b) { a.x += b; a.y += b; a.z += b; a.w += b; return a; } +static __device__ __forceinline__ uint4& operator-= (uint4& a, unsigned int b) { a.x -= b; a.y -= b; a.z -= b; a.w -= b; return a; } +static __device__ __forceinline__ uint4 operator* (const uint4& a, const uint4& b) { return make_uint4(a.x * b.x, a.y * b.y, a.z * b.z, a.w * b.w); } +static __device__ __forceinline__ uint4 operator+ (const uint4& a, const uint4& b) { return make_uint4(a.x + b.x, a.y + b.y, a.z + b.z, a.w + b.w); } +static __device__ __forceinline__ uint4 operator- (const uint4& a, const uint4& b) { return make_uint4(a.x - b.x, a.y - b.y, a.z - b.z, a.w - b.w); } +static __device__ __forceinline__ uint4 operator* (const uint4& a, unsigned int b) { return make_uint4(a.x * b, a.y * b, a.z * b, a.w * b); } +static __device__ __forceinline__ uint4 operator+ (const uint4& a, unsigned int b) { return make_uint4(a.x + b, a.y + b, a.z + b, a.w + b); } +static __device__ __forceinline__ uint4 operator- (const uint4& a, unsigned int b) { return make_uint4(a.x - b, a.y - b, a.z - b, a.w - b); } +static __device__ __forceinline__ uint4 operator* (unsigned int a, const uint4& b) { return make_uint4(a * b.x, a * b.y, a * b.z, a * b.w); } +static __device__ __forceinline__ uint4 operator+ (unsigned int a, const uint4& b) { return make_uint4(a + b.x, a + b.y, a + b.z, a + b.w); } +static __device__ __forceinline__ uint4 operator- (unsigned int a, const uint4& b) { return make_uint4(a - b.x, a - b.y, a - b.z, a - b.w); } + +template static __device__ __forceinline__ T zero_value(void); +template<> __device__ __forceinline__ float zero_value (void) { return 0.f; } +template<> __device__ __forceinline__ float2 zero_value(void) { return make_float2(0.f, 0.f); } +template<> __device__ __forceinline__ float4 zero_value(void) { return make_float4(0.f, 0.f, 0.f, 0.f); } +static __device__ __forceinline__ float3 make_float3(const float2& a, float b) { return make_float3(a.x, a.y, b); } +static __device__ __forceinline__ float4 make_float4(const float3& a, float b) { return make_float4(a.x, a.y, a.z, b); } +static __device__ __forceinline__ float4 make_float4(const float2& a, const float2& b) { return make_float4(a.x, a.y, b.x, b.y); } +static __device__ __forceinline__ int3 make_int3(const int2& a, int b) { return make_int3(a.x, a.y, b); } +static __device__ __forceinline__ int4 make_int4(const int3& a, int b) { return make_int4(a.x, a.y, a.z, b); } +static __device__ __forceinline__ int4 make_int4(const int2& a, const int2& b) { return make_int4(a.x, a.y, b.x, b.y); } +static __device__ __forceinline__ uint3 make_uint3(const uint2& a, unsigned int b) { return make_uint3(a.x, a.y, b); } +static __device__ __forceinline__ uint4 make_uint4(const uint3& a, unsigned int b) { return make_uint4(a.x, a.y, a.z, b); } +static __device__ __forceinline__ uint4 make_uint4(const uint2& a, const uint2& b) { return make_uint4(a.x, a.y, b.x, b.y); } + +template static __device__ __forceinline__ void swap(T& a, T& b) { T temp = a; a = b; b = temp; } + +//------------------------------------------------------------------------ +#endif // __CUDACC__ diff --git a/isaac_ros_gxf_extensions/gxf_isaac_foundationpose/gxf/foundationpose/nvdiffrast/common/cudaraster/CudaRaster.hpp b/isaac_ros_gxf_extensions/gxf_isaac_foundationpose/gxf/foundationpose/nvdiffrast/common/cudaraster/CudaRaster.hpp new file mode 100644 index 0000000..8daf611 --- /dev/null +++ b/isaac_ros_gxf_extensions/gxf_isaac_foundationpose/gxf/foundationpose/nvdiffrast/common/cudaraster/CudaRaster.hpp @@ -0,0 +1,72 @@ +// SPDX-FileCopyrightText: NVIDIA CORPORATION & AFFILIATES +// Copyright (c) 2009-2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. +// +// SPDX-License-Identifier: Apache-2.0 + +#pragma once + +//------------------------------------------------------------------------ +// This is a slimmed-down and modernized version of the original +// CudaRaster codebase that accompanied the HPG 2011 paper +// "High-Performance Software Rasterization on GPUs" by Laine and Karras. +// Modifications have been made to accommodate post-Volta execution model +// with warp divergence. Support for shading, blending, quad rendering, +// and supersampling have been removed as unnecessary for nvdiffrast. +//------------------------------------------------------------------------ + +// TODO: @ashwinvk Move this such that is publicly linked with +// to https://github.com/NVlabs/nvdiffrast and license should be Apache + +namespace CR +{ + +class RasterImpl; + +//------------------------------------------------------------------------ +// Interface class to isolate user from implementation details. +//------------------------------------------------------------------------ + +class CudaRaster +{ +public: + enum + { + RenderModeFlag_EnableBackfaceCulling = 1 << 0, // Enable backface culling. + }; + +public: + CudaRaster (void); + ~CudaRaster (void); + + void setViewportSize (int width, int height, int numImages); // Width and height must be multiples of tile size (8x8). + void setRenderModeFlags (unsigned int renderModeFlags); // Affects all subsequent calls to drawTriangles(). Defaults to zero. + void deferredClear (unsigned int clearColor); // Clears color and depth buffers during next call to drawTriangles(). + void setVertexBuffer (void* vertices, int numVertices); // GPU pointer managed by caller. Vertex positions in clip space as float4 (x, y, z, w). + void setIndexBuffer (void* indices, int numTriangles); // GPU pointer managed by caller. Triangle index+color quadruplets as uint4 (idx0, idx1, idx2, color). + bool drawTriangles (const int* ranges, bool peel, cudaStream_t stream); // Ranges (offsets and counts) as #triangles entries, not as bytes. If NULL, draw all triangles. Returns false in case of internal overflow. + void* getColorBuffer (void); // GPU pointer managed by CudaRaster. + void* getDepthBuffer (void); // GPU pointer managed by CudaRaster. // Swap depth and peeling buffers. + +private: + CudaRaster (const CudaRaster&); // forbidden + CudaRaster& operator= (const CudaRaster&); // forbidden + +private: + RasterImpl* m_impl; // Opaque pointer to implementation. +}; + +//------------------------------------------------------------------------ +} // namespace CR + diff --git a/isaac_ros_gxf_extensions/gxf_isaac_foundationpose/gxf/foundationpose/nvdiffrast/common/cudaraster/impl/BinRaster.inl b/isaac_ros_gxf_extensions/gxf_isaac_foundationpose/gxf/foundationpose/nvdiffrast/common/cudaraster/impl/BinRaster.inl new file mode 100644 index 0000000..4d6735c --- /dev/null +++ b/isaac_ros_gxf_extensions/gxf_isaac_foundationpose/gxf/foundationpose/nvdiffrast/common/cudaraster/impl/BinRaster.inl @@ -0,0 +1,423 @@ +// Copyright (c) 2009-2022, NVIDIA CORPORATION. All rights reserved. +// +// NVIDIA CORPORATION and its licensors retain all intellectual property +// and proprietary rights in and to this software, related documentation +// and any modifications thereto. Any use, reproduction, disclosure or +// distribution of this software and related documentation without an express +// license agreement from NVIDIA CORPORATION is strictly prohibited. + +//------------------------------------------------------------------------ + +__device__ __inline__ void binRasterImpl(const CRParams p) +{ + __shared__ volatile U32 s_broadcast [CR_BIN_WARPS + 16]; + __shared__ volatile S32 s_outOfs [CR_MAXBINS_SQR]; + __shared__ volatile S32 s_outTotal [CR_MAXBINS_SQR]; + __shared__ volatile S32 s_overIndex [CR_MAXBINS_SQR]; + __shared__ volatile S32 s_outMask [CR_BIN_WARPS][CR_MAXBINS_SQR + 1]; // +1 to avoid bank collisions + __shared__ volatile S32 s_outCount [CR_BIN_WARPS][CR_MAXBINS_SQR + 1]; // +1 to avoid bank collisions + __shared__ volatile S32 s_triBuf [CR_BIN_WARPS*32*4]; // triangle ring buffer + __shared__ volatile U32 s_batchPos; + __shared__ volatile U32 s_bufCount; + __shared__ volatile U32 s_overTotal; + __shared__ volatile U32 s_allocBase; + + const CRImageParams& ip = getImageParams(p, blockIdx.z); + CRAtomics& atomics = p.atomics[blockIdx.z]; + const U8* triSubtris = (const U8*)p.triSubtris + p.maxSubtris * blockIdx.z; + const CRTriangleHeader* triHeader = (const CRTriangleHeader*)p.triHeader + p.maxSubtris * blockIdx.z; + + S32* binFirstSeg = (S32*)p.binFirstSeg + CR_MAXBINS_SQR * CR_BIN_STREAMS_SIZE * blockIdx.z; + S32* binTotal = (S32*)p.binTotal + CR_MAXBINS_SQR * CR_BIN_STREAMS_SIZE * blockIdx.z; + S32* binSegData = (S32*)p.binSegData + p.maxBinSegs * CR_BIN_SEG_SIZE * blockIdx.z; + S32* binSegNext = (S32*)p.binSegNext + p.maxBinSegs * blockIdx.z; + S32* binSegCount = (S32*)p.binSegCount + p.maxBinSegs * blockIdx.z; + + if (atomics.numSubtris > p.maxSubtris) + return; + + // per-thread state + int thrInBlock = threadIdx.x + threadIdx.y * 32; + int batchPos = 0; + + // first 16 elements of s_broadcast are always zero + if (thrInBlock < 16) + s_broadcast[thrInBlock] = 0; + + // initialize output linked lists and offsets + if (thrInBlock < p.numBins) + { + binFirstSeg[(thrInBlock << CR_BIN_STREAMS_LOG2) + blockIdx.x] = -1; + s_outOfs[thrInBlock] = -CR_BIN_SEG_SIZE; + s_outTotal[thrInBlock] = 0; + } + + // repeat until done + for(;;) + { + // get batch + if (thrInBlock == 0) + s_batchPos = atomicAdd(&atomics.binCounter, ip.binBatchSize); + __syncthreads(); + batchPos = s_batchPos; + + // all batches done? + if (batchPos >= ip.triCount) + break; + + // per-thread state + int bufIndex = 0; + int bufCount = 0; + int batchEnd = min(batchPos + ip.binBatchSize, ip.triCount); + + // loop over batch as long as we have triangles in it + do + { + // read more triangles + while (bufCount < CR_BIN_WARPS*32 && batchPos < batchEnd) + { + // get subtriangle count + + int triIdx = batchPos + thrInBlock; + int num = 0; + if (triIdx < batchEnd) + num = triSubtris[triIdx]; + + // cumulative sum of subtriangles within each warp + U32 myIdx = __popc(__ballot_sync(~0u, num & 1) & getLaneMaskLt()); + if (__any_sync(~0u, num > 1)) + { + myIdx += __popc(__ballot_sync(~0u, num & 2) & getLaneMaskLt()) * 2; + myIdx += __popc(__ballot_sync(~0u, num & 4) & getLaneMaskLt()) * 4; + } + if (threadIdx.x == 31) // Do not assume that last thread in warp wins the write. + s_broadcast[threadIdx.y + 16] = myIdx + num; + __syncthreads(); + + // cumulative sum of per-warp subtriangle counts + // Note: cannot have more than 32 warps or this needs to sync between each step. + bool act = (thrInBlock < CR_BIN_WARPS); + U32 actMask = __ballot_sync(~0u, act); + if (threadIdx.y == 0 && act) + { + volatile U32* ptr = &s_broadcast[thrInBlock + 16]; + U32 val = *ptr; + #if (CR_BIN_WARPS > 1) + val += ptr[-1]; __syncwarp(actMask); + *ptr = val; __syncwarp(actMask); + #endif + #if (CR_BIN_WARPS > 2) + val += ptr[-2]; __syncwarp(actMask); + *ptr = val; __syncwarp(actMask); + #endif + #if (CR_BIN_WARPS > 4) + val += ptr[-4]; __syncwarp(actMask); + *ptr = val; __syncwarp(actMask); + #endif + #if (CR_BIN_WARPS > 8) + val += ptr[-8]; __syncwarp(actMask); + *ptr = val; __syncwarp(actMask); + #endif + #if (CR_BIN_WARPS > 16) + val += ptr[-16]; __syncwarp(actMask); + *ptr = val; __syncwarp(actMask); + #endif + + // initially assume that we consume everything + // only last active thread does the writes + if (threadIdx.x == CR_BIN_WARPS - 1) + { + s_batchPos = batchPos + CR_BIN_WARPS * 32; + s_bufCount = bufCount + val; + } + } + __syncthreads(); + + // skip if no subtriangles + if (num) + { + // calculate write position for first subtriangle + U32 pos = bufCount + myIdx + s_broadcast[threadIdx.y + 16 - 1]; + + // only write if entire triangle fits + if (pos + num <= CR_ARRAY_SIZE(s_triBuf)) + { + pos += bufIndex; // adjust for current start position + pos &= CR_ARRAY_SIZE(s_triBuf)-1; + if (num == 1) + s_triBuf[pos] = triIdx * 8 + 7; // single triangle + else + { + for (int i=0; i < num; i++) + { + s_triBuf[pos] = triIdx * 8 + i; + pos++; + pos &= CR_ARRAY_SIZE(s_triBuf)-1; + } + } + } else if (pos <= CR_ARRAY_SIZE(s_triBuf)) + { + // this triangle is the first that failed, overwrite total count and triangle count + s_batchPos = batchPos + thrInBlock; + s_bufCount = pos; + } + } + + // update triangle counts + __syncthreads(); + batchPos = s_batchPos; + bufCount = s_bufCount; + } + + // make every warp clear its output buffers + for (int i=threadIdx.x; i < p.numBins; i += 32) + s_outMask[threadIdx.y][i] = 0; + __syncwarp(); + + // choose our triangle + uint4 triData = make_uint4(0, 0, 0, 0); + if (thrInBlock < bufCount) + { + U32 triPos = bufIndex + thrInBlock; + triPos &= CR_ARRAY_SIZE(s_triBuf)-1; + + // find triangle + int triIdx = s_triBuf[triPos]; + int dataIdx = triIdx >> 3; + int subtriIdx = triIdx & 7; + if (subtriIdx != 7) + dataIdx = triHeader[dataIdx].misc + subtriIdx; + + // read triangle + + triData = *(((const uint4*)triHeader) + dataIdx); + } + + // setup bounding box and edge functions, and rasterize + S32 lox, loy, hix, hiy; + bool hasTri = (thrInBlock < bufCount); + U32 hasTriMask = __ballot_sync(~0u, hasTri); + if (hasTri) + { + S32 v0x = add_s16lo_s16lo(triData.x, p.widthPixels * (CR_SUBPIXEL_SIZE >> 1)); + S32 v0y = add_s16hi_s16lo(triData.x, p.heightPixels * (CR_SUBPIXEL_SIZE >> 1)); + S32 d01x = sub_s16lo_s16lo(triData.y, triData.x); + S32 d01y = sub_s16hi_s16hi(triData.y, triData.x); + S32 d02x = sub_s16lo_s16lo(triData.z, triData.x); + S32 d02y = sub_s16hi_s16hi(triData.z, triData.x); + int binLog = CR_BIN_LOG2 + CR_TILE_LOG2 + CR_SUBPIXEL_LOG2; + lox = add_clamp_0_x((v0x + min_min(d01x, 0, d02x)) >> binLog, 0, p.widthBins - 1); + loy = add_clamp_0_x((v0y + min_min(d01y, 0, d02y)) >> binLog, 0, p.heightBins - 1); + hix = add_clamp_0_x((v0x + max_max(d01x, 0, d02x)) >> binLog, 0, p.widthBins - 1); + hiy = add_clamp_0_x((v0y + max_max(d01y, 0, d02y)) >> binLog, 0, p.heightBins - 1); + + U32 bit = 1 << threadIdx.x; +#if __CUDA_ARCH__ >= 700 + bool multi = (hix != lox || hiy != loy); + if (!__any_sync(hasTriMask, multi)) + { + int binIdx = lox + p.widthBins * loy; + U32 mask = __match_any_sync(hasTriMask, binIdx); + s_outMask[threadIdx.y][binIdx] = mask; + __syncwarp(hasTriMask); + } else +#endif + { + bool complex = (hix > lox+1 || hiy > loy+1); + if (!__any_sync(hasTriMask, complex)) + { + int binIdx = lox + p.widthBins * loy; + atomicOr((U32*)&s_outMask[threadIdx.y][binIdx], bit); + if (hix > lox) atomicOr((U32*)&s_outMask[threadIdx.y][binIdx + 1], bit); + if (hiy > loy) atomicOr((U32*)&s_outMask[threadIdx.y][binIdx + p.widthBins], bit); + if (hix > lox && hiy > loy) atomicOr((U32*)&s_outMask[threadIdx.y][binIdx + p.widthBins + 1], bit); + } else + { + S32 d12x = d02x - d01x, d12y = d02y - d01y; + v0x -= lox << binLog, v0y -= loy << binLog; + + S32 t01 = v0x * d01y - v0y * d01x; + S32 t02 = v0y * d02x - v0x * d02y; + S32 t12 = d01x * d12y - d01y * d12x - t01 - t02; + S32 b01 = add_sub(t01 >> binLog, max(d01x, 0), min(d01y, 0)); + S32 b02 = add_sub(t02 >> binLog, max(d02y, 0), min(d02x, 0)); + S32 b12 = add_sub(t12 >> binLog, max(d12x, 0), min(d12y, 0)); + + int width = hix - lox + 1; + d01x += width * d01y; + d02x += width * d02y; + d12x += width * d12y; + + U8* currPtr = (U8*)&s_outMask[threadIdx.y][lox + loy * p.widthBins]; + U8* skipPtr = (U8*)&s_outMask[threadIdx.y][(hix + 1) + loy * p.widthBins]; + U8* endPtr = (U8*)&s_outMask[threadIdx.y][lox + (hiy + 1) * p.widthBins]; + int stride = p.widthBins * 4; + int ptrYInc = stride - width * 4; + + do + { + if (b01 >= 0 && b02 >= 0 && b12 >= 0) + atomicOr((U32*)currPtr, bit); + currPtr += 4, b01 -= d01y, b02 += d02y, b12 -= d12y; + if (currPtr == skipPtr) + currPtr += ptrYInc, b01 += d01x, b02 -= d02x, b12 += d12x, skipPtr += stride; + } + while (currPtr != endPtr); + } + } + } + + // count per-bin contributions + if (thrInBlock == 0) + s_overTotal = 0; // overflow counter + + // ensure that out masks are done + __syncthreads(); + + int overIndex = -1; + bool act = (thrInBlock < p.numBins); + U32 actMask = __ballot_sync(~0u, act); + if (act) + { + U8* srcPtr = (U8*)&s_outMask[0][thrInBlock]; + U8* dstPtr = (U8*)&s_outCount[0][thrInBlock]; + int total = 0; + for (int i = 0; i < CR_BIN_WARPS; i++) + { + total += __popc(*(U32*)srcPtr); + *(U32*)dstPtr = total; + srcPtr += (CR_MAXBINS_SQR + 1) * 4; + dstPtr += (CR_MAXBINS_SQR + 1) * 4; + } + + // overflow => request a new segment + int ofs = s_outOfs[thrInBlock]; + bool ovr = (((ofs - 1) >> CR_BIN_SEG_LOG2) != (((ofs - 1) + total) >> CR_BIN_SEG_LOG2)); + U32 ovrMask = __ballot_sync(actMask, ovr); + if (ovr) + { + overIndex = __popc(ovrMask & getLaneMaskLt()); + if (overIndex == 0) + s_broadcast[threadIdx.y + 16] = atomicAdd((U32*)&s_overTotal, __popc(ovrMask)); + __syncwarp(ovrMask); + overIndex += s_broadcast[threadIdx.y + 16]; + s_overIndex[thrInBlock] = overIndex; + } + } + + // sync after overTotal is ready + __syncthreads(); + + // at least one segment overflowed => allocate segments + U32 overTotal = s_overTotal; + U32 allocBase = 0; + if (overTotal > 0) + { + // allocate memory + if (thrInBlock == 0) + { + U32 allocBase = atomicAdd(&atomics.numBinSegs, overTotal); + s_allocBase = (allocBase + overTotal <= p.maxBinSegs) ? allocBase : 0; + } + __syncthreads(); + allocBase = s_allocBase; + + // did my bin overflow? + if (overIndex != -1) + { + // calculate new segment index + int segIdx = allocBase + overIndex; + + // add to linked list + if (s_outOfs[thrInBlock] < 0) + binFirstSeg[(thrInBlock << CR_BIN_STREAMS_LOG2) + blockIdx.x] = segIdx; + else + binSegNext[(s_outOfs[thrInBlock] - 1) >> CR_BIN_SEG_LOG2] = segIdx; + + // defaults + binSegNext [segIdx] = -1; + binSegCount[segIdx] = CR_BIN_SEG_SIZE; + } + } + + // concurrent emission -- each warp handles its own triangle + if (thrInBlock < bufCount) + { + int triPos = (bufIndex + thrInBlock) & (CR_ARRAY_SIZE(s_triBuf) - 1); + int currBin = lox + loy * p.widthBins; + int skipBin = (hix + 1) + loy * p.widthBins; + int endBin = lox + (hiy + 1) * p.widthBins; + int binYInc = p.widthBins - (hix - lox + 1); + + // loop over triangle's bins + do + { + U32 outMask = s_outMask[threadIdx.y][currBin]; + if (outMask & (1< 0) + idx += s_outCount[threadIdx.y-1][currBin]; + + int base = s_outOfs[currBin]; + int free = (-base) & (CR_BIN_SEG_SIZE - 1); + if (idx >= free) + idx += ((allocBase + s_overIndex[currBin]) << CR_BIN_SEG_LOG2) - free; + else + idx += base; + + binSegData[idx] = s_triBuf[triPos]; + } + + currBin++; + if (currBin == skipBin) + currBin += binYInc, skipBin += p.widthBins; + } + while (currBin != endBin); + } + + // wait all triangles to finish, then replace overflown segment offsets + __syncthreads(); + if (thrInBlock < p.numBins) + { + U32 total = s_outCount[CR_BIN_WARPS - 1][thrInBlock]; + U32 oldOfs = s_outOfs[thrInBlock]; + if (overIndex == -1) + s_outOfs[thrInBlock] = oldOfs + total; + else + { + int addr = oldOfs + total; + addr = ((addr - 1) & (CR_BIN_SEG_SIZE - 1)) + 1; + addr += (allocBase + overIndex) << CR_BIN_SEG_LOG2; + s_outOfs[thrInBlock] = addr; + } + s_outTotal[thrInBlock] += total; + } + + // these triangles are now done + int count = ::min(bufCount, CR_BIN_WARPS * 32); + bufCount -= count; + bufIndex += count; + bufIndex &= CR_ARRAY_SIZE(s_triBuf)-1; + } + while (bufCount > 0 || batchPos < batchEnd); + + // flush all bins + if (thrInBlock < p.numBins) + { + int ofs = s_outOfs[thrInBlock]; + if (ofs & (CR_BIN_SEG_SIZE-1)) + { + int seg = ofs >> CR_BIN_SEG_LOG2; + binSegCount[seg] = ofs & (CR_BIN_SEG_SIZE-1); + s_outOfs[thrInBlock] = (ofs + CR_BIN_SEG_SIZE - 1) & -CR_BIN_SEG_SIZE; + } + } + } + + // output totals + if (thrInBlock < p.numBins) + binTotal[(thrInBlock << CR_BIN_STREAMS_LOG2) + blockIdx.x] = s_outTotal[thrInBlock]; +} + +//------------------------------------------------------------------------ diff --git a/isaac_ros_gxf_extensions/gxf_isaac_foundationpose/gxf/foundationpose/nvdiffrast/common/cudaraster/impl/Buffer.cpp b/isaac_ros_gxf_extensions/gxf_isaac_foundationpose/gxf/foundationpose/nvdiffrast/common/cudaraster/impl/Buffer.cpp new file mode 100644 index 0000000..15f5bdb --- /dev/null +++ b/isaac_ros_gxf_extensions/gxf_isaac_foundationpose/gxf/foundationpose/nvdiffrast/common/cudaraster/impl/Buffer.cpp @@ -0,0 +1,103 @@ +// SPDX-FileCopyrightText: NVIDIA CORPORATION & AFFILIATES +// Copyright (c) 2009-2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. +// +// SPDX-License-Identifier: Apache-2.0 + +#include "../../framework.h" +#include "Buffer.hpp" + +using namespace CR; + +//------------------------------------------------------------------------ +// GPU buffer. +//------------------------------------------------------------------------ + +Buffer::Buffer(void) +: m_gpuPtr(NULL), + m_bytes (0) +{ + // empty +} + +Buffer::~Buffer(void) +{ + if (m_gpuPtr) + cudaFree(m_gpuPtr); // Don't throw an exception. +} + +void Buffer::reset(size_t bytes) +{ + if (bytes == m_bytes) + return; + + if (m_gpuPtr) + { + NVDR_CHECK_CUDA_ERROR(cudaFree(m_gpuPtr)); + m_gpuPtr = NULL; + } + + if (bytes > 0) + NVDR_CHECK_CUDA_ERROR(cudaMalloc(&m_gpuPtr, bytes)); + + m_bytes = bytes; +} + +void Buffer::grow(size_t bytes) +{ + if (bytes > m_bytes) + reset(bytes); +} + +//------------------------------------------------------------------------ +// Host buffer with page-locked memory. +//------------------------------------------------------------------------ + +HostBuffer::HostBuffer(void) +: m_hostPtr(NULL), + m_bytes (0) +{ + // empty +} + +HostBuffer::~HostBuffer(void) +{ + if (m_hostPtr) + cudaFreeHost(m_hostPtr); // Don't throw an exception. +} + +void HostBuffer::reset(size_t bytes) +{ + if (bytes == m_bytes) + return; + + if (m_hostPtr) + { + NVDR_CHECK_CUDA_ERROR(cudaFreeHost(m_hostPtr)); + m_hostPtr = NULL; + } + + if (bytes > 0) + NVDR_CHECK_CUDA_ERROR(cudaMallocHost(&m_hostPtr, bytes)); + + m_bytes = bytes; +} + +void HostBuffer::grow(size_t bytes) +{ + if (bytes > m_bytes) + reset(bytes); +} + +//------------------------------------------------------------------------ diff --git a/isaac_ros_gxf_extensions/gxf_isaac_foundationpose/gxf/foundationpose/nvdiffrast/common/cudaraster/impl/Buffer.hpp b/isaac_ros_gxf_extensions/gxf_isaac_foundationpose/gxf/foundationpose/nvdiffrast/common/cudaraster/impl/Buffer.hpp new file mode 100644 index 0000000..8be9de9 --- /dev/null +++ b/isaac_ros_gxf_extensions/gxf_isaac_foundationpose/gxf/foundationpose/nvdiffrast/common/cudaraster/impl/Buffer.hpp @@ -0,0 +1,64 @@ +// SPDX-FileCopyrightText: NVIDIA CORPORATION & AFFILIATES +// Copyright (c) 2009-2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. +// +// SPDX-License-Identifier: Apache-2.0 + +#pragma once +#include "Defs.hpp" + +namespace CR +{ +//------------------------------------------------------------------------ + +class Buffer +{ +public: + Buffer (void); + ~Buffer (void); + + void reset (size_t bytes); + void grow (size_t bytes); + void* getPtr (void) { return m_gpuPtr; } + size_t getSize (void) const { return m_bytes; } + + void setPtr (void* ptr) { m_gpuPtr = ptr; } + +private: + void* m_gpuPtr; + size_t m_bytes; +}; + +//------------------------------------------------------------------------ + +class HostBuffer +{ +public: + HostBuffer (void); + ~HostBuffer (void); + + void reset (size_t bytes); + void grow (size_t bytes); + void* getPtr (void) { return m_hostPtr; } + size_t getSize (void) const { return m_bytes; } + + void setPtr (void* ptr) { m_hostPtr = ptr; } + +private: + void* m_hostPtr; + size_t m_bytes; +}; + +//------------------------------------------------------------------------ +} diff --git a/isaac_ros_gxf_extensions/gxf_isaac_foundationpose/gxf/foundationpose/nvdiffrast/common/cudaraster/impl/CoarseRaster.inl b/isaac_ros_gxf_extensions/gxf_isaac_foundationpose/gxf/foundationpose/nvdiffrast/common/cudaraster/impl/CoarseRaster.inl new file mode 100644 index 0000000..9740e1e --- /dev/null +++ b/isaac_ros_gxf_extensions/gxf_isaac_foundationpose/gxf/foundationpose/nvdiffrast/common/cudaraster/impl/CoarseRaster.inl @@ -0,0 +1,730 @@ +// Copyright (c) 2009-2022, NVIDIA CORPORATION. All rights reserved. +// +// NVIDIA CORPORATION and its licensors retain all intellectual property +// and proprietary rights in and to this software, related documentation +// and any modifications thereto. Any use, reproduction, disclosure or +// distribution of this software and related documentation without an express +// license agreement from NVIDIA CORPORATION is strictly prohibited. + +//------------------------------------------------------------------------ + +__device__ __inline__ int globalTileIdx(int tileInBin, int widthTiles) +{ + int tileX = tileInBin & (CR_BIN_SIZE - 1); + int tileY = tileInBin >> CR_BIN_LOG2; + return tileX + tileY * widthTiles; +} + +//------------------------------------------------------------------------ + +__device__ __inline__ void coarseRasterImpl(const CRParams p) +{ + // Common. + + __shared__ volatile U32 s_workCounter; + __shared__ volatile U32 s_scanTemp [CR_COARSE_WARPS][48]; // 3KB + + // Input. + + __shared__ volatile U32 s_binOrder [CR_MAXBINS_SQR]; // 1KB + __shared__ volatile S32 s_binStreamCurrSeg [CR_BIN_STREAMS_SIZE]; // 0KB + __shared__ volatile S32 s_binStreamFirstTri [CR_BIN_STREAMS_SIZE]; // 0KB + __shared__ volatile S32 s_triQueue [CR_COARSE_QUEUE_SIZE]; // 4KB + __shared__ volatile S32 s_triQueueWritePos; + __shared__ volatile U32 s_binStreamSelectedOfs; + __shared__ volatile U32 s_binStreamSelectedSize; + + // Output. + + __shared__ volatile U32 s_warpEmitMask [CR_COARSE_WARPS][CR_BIN_SQR + 1]; // 16KB, +1 to avoid bank collisions + __shared__ volatile U32 s_warpEmitPrefixSum [CR_COARSE_WARPS][CR_BIN_SQR + 1]; // 16KB, +1 to avoid bank collisions + __shared__ volatile U32 s_tileEmitPrefixSum [CR_BIN_SQR + 1]; // 1KB, zero at the beginning + __shared__ volatile U32 s_tileAllocPrefixSum[CR_BIN_SQR + 1]; // 1KB, zero at the beginning + __shared__ volatile S32 s_tileStreamCurrOfs [CR_BIN_SQR]; // 1KB + __shared__ volatile U32 s_firstAllocSeg; + __shared__ volatile U32 s_firstActiveIdx; + + // Pointers and constants. + + CRAtomics& atomics = p.atomics[blockIdx.z]; + const CRTriangleHeader* triHeader = (const CRTriangleHeader*)p.triHeader + p.maxSubtris * blockIdx.z; + const S32* binFirstSeg = (const S32*)p.binFirstSeg + CR_MAXBINS_SQR * CR_BIN_STREAMS_SIZE * blockIdx.z; + const S32* binTotal = (const S32*)p.binTotal + CR_MAXBINS_SQR * CR_BIN_STREAMS_SIZE * blockIdx.z; + const S32* binSegData = (const S32*)p.binSegData + p.maxBinSegs * CR_BIN_SEG_SIZE * blockIdx.z; + const S32* binSegNext = (const S32*)p.binSegNext + p.maxBinSegs * blockIdx.z; + const S32* binSegCount = (const S32*)p.binSegCount + p.maxBinSegs * blockIdx.z; + S32* activeTiles = (S32*)p.activeTiles + CR_MAXTILES_SQR * blockIdx.z; + S32* tileFirstSeg = (S32*)p.tileFirstSeg + CR_MAXTILES_SQR * blockIdx.z; + S32* tileSegData = (S32*)p.tileSegData + p.maxTileSegs * CR_TILE_SEG_SIZE * blockIdx.z; + S32* tileSegNext = (S32*)p.tileSegNext + p.maxTileSegs * blockIdx.z; + S32* tileSegCount = (S32*)p.tileSegCount + p.maxTileSegs * blockIdx.z; + + int tileLog = CR_TILE_LOG2 + CR_SUBPIXEL_LOG2; + int thrInBlock = threadIdx.x + threadIdx.y * 32; + int emitShift = CR_BIN_LOG2 * 2 + 5; // We scan ((numEmits << emitShift) | numAllocs) over tiles. + + if (atomics.numSubtris > p.maxSubtris || atomics.numBinSegs > p.maxBinSegs) + return; + + // Initialize sharedmem arrays. + + if (thrInBlock == 0) + { + s_tileEmitPrefixSum[0] = 0; + s_tileAllocPrefixSum[0] = 0; + } + s_scanTemp[threadIdx.y][threadIdx.x] = 0; + + // Sort bins in descending order of triangle count. + + for (int binIdx = thrInBlock; binIdx < p.numBins; binIdx += CR_COARSE_WARPS * 32) + { + int count = 0; + for (int i = 0; i < CR_BIN_STREAMS_SIZE; i++) + count += binTotal[(binIdx << CR_BIN_STREAMS_LOG2) + i]; + s_binOrder[binIdx] = (~count << (CR_MAXBINS_LOG2 * 2)) | binIdx; + } + + __syncthreads(); + sortShared(s_binOrder, p.numBins); + + // Process each bin by one block. + + for (;;) + { + // Pick a bin for the block. + + if (thrInBlock == 0) + s_workCounter = atomicAdd(&atomics.coarseCounter, 1); + __syncthreads(); + + int workCounter = s_workCounter; + if (workCounter >= p.numBins) + break; + + U32 binOrder = s_binOrder[workCounter]; + bool binEmpty = ((~binOrder >> (CR_MAXBINS_LOG2 * 2)) == 0); + if (binEmpty && !p.deferredClear) + break; + + int binIdx = binOrder & (CR_MAXBINS_SQR - 1); + + // Initialize input/output streams. + + int triQueueWritePos = 0; + int triQueueReadPos = 0; + + if (thrInBlock < CR_BIN_STREAMS_SIZE) + { + int segIdx = binFirstSeg[(binIdx << CR_BIN_STREAMS_LOG2) + thrInBlock]; + s_binStreamCurrSeg[thrInBlock] = segIdx; + s_binStreamFirstTri[thrInBlock] = (segIdx == -1) ? ~0u : binSegData[segIdx << CR_BIN_SEG_LOG2]; + } + + for (int tileInBin = CR_COARSE_WARPS * 32 - 1 - thrInBlock; tileInBin < CR_BIN_SQR; tileInBin += CR_COARSE_WARPS * 32) + s_tileStreamCurrOfs[tileInBin] = -CR_TILE_SEG_SIZE; + + // Initialize per-bin state. + + int binY = idiv_fast(binIdx, p.widthBins); + int binX = binIdx - binY * p.widthBins; + int originX = (binX << (CR_BIN_LOG2 + tileLog)) - (p.widthPixels << (CR_SUBPIXEL_LOG2 - 1)); + int originY = (binY << (CR_BIN_LOG2 + tileLog)) - (p.heightPixels << (CR_SUBPIXEL_LOG2 - 1)); + int maxTileXInBin = ::min(p.widthTiles - (binX << CR_BIN_LOG2), CR_BIN_SIZE) - 1; + int maxTileYInBin = ::min(p.heightTiles - (binY << CR_BIN_LOG2), CR_BIN_SIZE) - 1; + int binTileIdx = (binX + binY * p.widthTiles) << CR_BIN_LOG2; + + // Entire block: Merge input streams and process triangles. + + if (!binEmpty) + do + { + //------------------------------------------------------------------------ + // Merge. + //------------------------------------------------------------------------ + + // Entire block: Not enough triangles => merge and queue segments. + // NOTE: The bin exit criterion assumes that we queue more triangles than we actually need. + + while (triQueueWritePos - triQueueReadPos <= CR_COARSE_WARPS * 32) + { + // First warp: Choose the segment with the lowest initial triangle index. + + bool hasStream = (thrInBlock < CR_BIN_STREAMS_SIZE); + U32 hasStreamMask = __ballot_sync(~0u, hasStream); + if (hasStream) + { + // Find the stream with the lowest triangle index. + + U32 firstTri = s_binStreamFirstTri[thrInBlock]; + U32 t = firstTri; + volatile U32* v = &s_scanTemp[0][thrInBlock + 16]; + + #if (CR_BIN_STREAMS_SIZE > 1) + v[0] = t; __syncwarp(hasStreamMask); t = ::min(t, v[-1]); __syncwarp(hasStreamMask); + #endif + #if (CR_BIN_STREAMS_SIZE > 2) + v[0] = t; __syncwarp(hasStreamMask); t = ::min(t, v[-2]); __syncwarp(hasStreamMask); + #endif + #if (CR_BIN_STREAMS_SIZE > 4) + v[0] = t; __syncwarp(hasStreamMask); t = ::min(t, v[-4]); __syncwarp(hasStreamMask); + #endif + #if (CR_BIN_STREAMS_SIZE > 8) + v[0] = t; __syncwarp(hasStreamMask); t = ::min(t, v[-8]); __syncwarp(hasStreamMask); + #endif + #if (CR_BIN_STREAMS_SIZE > 16) + v[0] = t; __syncwarp(hasStreamMask); t = ::min(t, v[-16]); __syncwarp(hasStreamMask); + #endif + v[0] = t; __syncwarp(hasStreamMask); + + // Consume and broadcast. + + bool first = (s_scanTemp[0][CR_BIN_STREAMS_SIZE - 1 + 16] == firstTri); + U32 firstMask = __ballot_sync(hasStreamMask, first); + if (first && (firstMask >> threadIdx.x) == 1u) + { + int segIdx = s_binStreamCurrSeg[thrInBlock]; + s_binStreamSelectedOfs = segIdx << CR_BIN_SEG_LOG2; + if (segIdx != -1) + { + int segSize = binSegCount[segIdx]; + int segNext = binSegNext[segIdx]; + s_binStreamSelectedSize = segSize; + s_triQueueWritePos = triQueueWritePos + segSize; + s_binStreamCurrSeg[thrInBlock] = segNext; + s_binStreamFirstTri[thrInBlock] = (segNext == -1) ? ~0u : binSegData[segNext << CR_BIN_SEG_LOG2]; + } + } + } + + // No more segments => break. + + __syncthreads(); + triQueueWritePos = s_triQueueWritePos; + int segOfs = s_binStreamSelectedOfs; + if (segOfs < 0) + break; + + int segSize = s_binStreamSelectedSize; + __syncthreads(); + + // Fetch triangles into the queue. + + for (int idxInSeg = CR_COARSE_WARPS * 32 - 1 - thrInBlock; idxInSeg < segSize; idxInSeg += CR_COARSE_WARPS * 32) + { + S32 triIdx = binSegData[segOfs + idxInSeg]; + s_triQueue[(triQueueWritePos - segSize + idxInSeg) & (CR_COARSE_QUEUE_SIZE - 1)] = triIdx; + } + } + + // All threads: Clear emit masks. + + for (int maskIdx = thrInBlock; maskIdx < CR_COARSE_WARPS * CR_BIN_SQR; maskIdx += CR_COARSE_WARPS * 32) + s_warpEmitMask[maskIdx >> (CR_BIN_LOG2 * 2)][maskIdx & (CR_BIN_SQR - 1)] = 0; + + __syncthreads(); + + //------------------------------------------------------------------------ + // Raster. + //------------------------------------------------------------------------ + + // Triangle per thread: Read from the queue. + + int triIdx = -1; + if (triQueueReadPos + thrInBlock < triQueueWritePos) + triIdx = s_triQueue[(triQueueReadPos + thrInBlock) & (CR_COARSE_QUEUE_SIZE - 1)]; + + uint4 triData = make_uint4(0, 0, 0, 0); + if (triIdx != -1) + { + int dataIdx = triIdx >> 3; + int subtriIdx = triIdx & 7; + if (subtriIdx != 7) + dataIdx = triHeader[dataIdx].misc + subtriIdx; + triData = *((uint4*)triHeader + dataIdx); + } + + // 32 triangles per warp: Record emits (= tile intersections). + + if (__any_sync(~0u, triIdx != -1)) + { + S32 v0x = sub_s16lo_s16lo(triData.x, originX); + S32 v0y = sub_s16hi_s16lo(triData.x, originY); + S32 d01x = sub_s16lo_s16lo(triData.y, triData.x); + S32 d01y = sub_s16hi_s16hi(triData.y, triData.x); + S32 d02x = sub_s16lo_s16lo(triData.z, triData.x); + S32 d02y = sub_s16hi_s16hi(triData.z, triData.x); + + // Compute tile-based AABB. + + int lox = add_clamp_0_x((v0x + min_min(d01x, 0, d02x)) >> tileLog, 0, maxTileXInBin); + int loy = add_clamp_0_x((v0y + min_min(d01y, 0, d02y)) >> tileLog, 0, maxTileYInBin); + int hix = add_clamp_0_x((v0x + max_max(d01x, 0, d02x)) >> tileLog, 0, maxTileXInBin); + int hiy = add_clamp_0_x((v0y + max_max(d01y, 0, d02y)) >> tileLog, 0, maxTileYInBin); + int sizex = add_sub(hix, 1, lox); + int sizey = add_sub(hiy, 1, loy); + int area = sizex * sizey; + + // Miscellaneous init. + + U8* currPtr = (U8*)&s_warpEmitMask[threadIdx.y][lox + (loy << CR_BIN_LOG2)]; + int ptrYInc = CR_BIN_SIZE * 4 - (sizex << 2); + U32 maskBit = 1 << threadIdx.x; + + // Case A: All AABBs are small => record the full AABB using atomics. + + if (__all_sync(~0u, sizex <= 2 && sizey <= 2)) + { + if (triIdx != -1) + { + atomicOr((U32*)currPtr, maskBit); + if (sizex == 2) atomicOr((U32*)(currPtr + 4), maskBit); + if (sizey == 2) atomicOr((U32*)(currPtr + CR_BIN_SIZE * 4), maskBit); + if (sizex == 2 && sizey == 2) atomicOr((U32*)(currPtr + 4 + CR_BIN_SIZE * 4), maskBit); + } + } + else + { + // Compute warp-AABB (scan-32). + + U32 aabbMask = add_sub(2 << hix, 0x20000 << hiy, 1 << lox) - (0x10000 << loy); + if (triIdx == -1) + aabbMask = 0; + + volatile U32* v = &s_scanTemp[threadIdx.y][threadIdx.x + 16]; + v[0] = aabbMask; __syncwarp(); aabbMask |= v[-1]; __syncwarp(); + v[0] = aabbMask; __syncwarp(); aabbMask |= v[-2]; __syncwarp(); + v[0] = aabbMask; __syncwarp(); aabbMask |= v[-4]; __syncwarp(); + v[0] = aabbMask; __syncwarp(); aabbMask |= v[-8]; __syncwarp(); + v[0] = aabbMask; __syncwarp(); aabbMask |= v[-16]; __syncwarp(); + v[0] = aabbMask; __syncwarp(); aabbMask = s_scanTemp[threadIdx.y][47]; + + U32 maskX = aabbMask & 0xFFFF; + U32 maskY = aabbMask >> 16; + int wlox = findLeadingOne(maskX ^ (maskX - 1)); + int wloy = findLeadingOne(maskY ^ (maskY - 1)); + int whix = findLeadingOne(maskX); + int whiy = findLeadingOne(maskY); + int warea = (add_sub(whix, 1, wlox)) * (add_sub(whiy, 1, wloy)); + + // Initialize edge functions. + + S32 d12x = d02x - d01x; + S32 d12y = d02y - d01y; + v0x -= lox << tileLog; + v0y -= loy << tileLog; + + S32 t01 = v0x * d01y - v0y * d01x; + S32 t02 = v0y * d02x - v0x * d02y; + S32 t12 = d01x * d12y - d01y * d12x - t01 - t02; + S32 b01 = add_sub(t01 >> tileLog, ::max(d01x, 0), ::min(d01y, 0)); + S32 b02 = add_sub(t02 >> tileLog, ::max(d02y, 0), ::min(d02x, 0)); + S32 b12 = add_sub(t12 >> tileLog, ::max(d12x, 0), ::min(d12y, 0)); + + d01x += sizex * d01y; + d02x += sizex * d02y; + d12x += sizex * d12y; + + // Case B: Warp-AABB is not much larger than largest AABB => Check tiles in warp-AABB, record using ballots. + if (__any_sync(~0u, warea * 4 <= area * 8)) + { + // Not sure if this is any faster than Case C after all the post-Volta ballot mask tracking. + bool act = (triIdx != -1); + U32 actMask = __ballot_sync(~0u, act); + if (act) + { + for (int y = wloy; y <= whiy; y++) + { + bool yIn = (y >= loy && y <= hiy); + U32 yMask = __ballot_sync(actMask, yIn); + if (yIn) + { + for (int x = wlox; x <= whix; x++) + { + bool xyIn = (x >= lox && x <= hix); + U32 xyMask = __ballot_sync(yMask, xyIn); + if (xyIn) + { + U32 res = __ballot_sync(xyMask, b01 >= 0 && b02 >= 0 && b12 >= 0); + if (threadIdx.x == 31 - __clz(xyMask)) + *(U32*)currPtr = res; + currPtr += 4, b01 -= d01y, b02 += d02y, b12 -= d12y; + } + } + currPtr += ptrYInc, b01 += d01x, b02 -= d02x, b12 += d12x; + } + } + } + } + + // Case C: General case => Check tiles in AABB, record using atomics. + + else + { + if (triIdx != -1) + { + U8* skipPtr = currPtr + (sizex << 2); + U8* endPtr = currPtr + (sizey << (CR_BIN_LOG2 + 2)); + do + { + if (b01 >= 0 && b02 >= 0 && b12 >= 0) + atomicOr((U32*)currPtr, maskBit); + currPtr += 4, b01 -= d01y, b02 += d02y, b12 -= d12y; + if (currPtr == skipPtr) + currPtr += ptrYInc, b01 += d01x, b02 -= d02x, b12 += d12x, skipPtr += CR_BIN_SIZE * 4; + } + while (currPtr != endPtr); + } + } + } + } + + __syncthreads(); + + //------------------------------------------------------------------------ + // Count. + //------------------------------------------------------------------------ + + // Tile per thread: Initialize prefix sums. + + for (int tileInBin_base = 0; tileInBin_base < CR_BIN_SQR; tileInBin_base += CR_COARSE_WARPS * 32) + { + int tileInBin = tileInBin_base + thrInBlock; + bool act = (tileInBin < CR_BIN_SQR); + U32 actMask = __ballot_sync(~0u, act); + if (act) + { + // Compute prefix sum of emits over warps. + + U8* srcPtr = (U8*)&s_warpEmitMask[0][tileInBin]; + U8* dstPtr = (U8*)&s_warpEmitPrefixSum[0][tileInBin]; + int tileEmits = 0; + for (int i = 0; i < CR_COARSE_WARPS; i++) + { + tileEmits += __popc(*(U32*)srcPtr); + *(U32*)dstPtr = tileEmits; + srcPtr += (CR_BIN_SQR + 1) * 4; + dstPtr += (CR_BIN_SQR + 1) * 4; + } + + // Determine the number of segments to allocate. + + int spaceLeft = -s_tileStreamCurrOfs[tileInBin] & (CR_TILE_SEG_SIZE - 1); + int tileAllocs = (tileEmits - spaceLeft + CR_TILE_SEG_SIZE - 1) >> CR_TILE_SEG_LOG2; + volatile U32* v = &s_tileEmitPrefixSum[tileInBin + 1]; + + // All counters within the warp are small => compute prefix sum using ballot. + + if (!__any_sync(actMask, tileEmits >= 2)) + { + U32 m = getLaneMaskLe(); + *v = (__popc(__ballot_sync(actMask, tileEmits & 1) & m) << emitShift) | __popc(__ballot_sync(actMask, tileAllocs & 1) & m); + } + + // Otherwise => scan-32 within the warp. + + else + { + U32 sum = (tileEmits << emitShift) | tileAllocs; + *v = sum; __syncwarp(actMask); if (threadIdx.x >= 1) sum += v[-1]; __syncwarp(actMask); + *v = sum; __syncwarp(actMask); if (threadIdx.x >= 2) sum += v[-2]; __syncwarp(actMask); + *v = sum; __syncwarp(actMask); if (threadIdx.x >= 4) sum += v[-4]; __syncwarp(actMask); + *v = sum; __syncwarp(actMask); if (threadIdx.x >= 8) sum += v[-8]; __syncwarp(actMask); + *v = sum; __syncwarp(actMask); if (threadIdx.x >= 16) sum += v[-16]; __syncwarp(actMask); + *v = sum; __syncwarp(actMask); + } + } + } + + // First warp: Scan-8. + + __syncthreads(); + + bool scan8 = (thrInBlock < CR_BIN_SQR / 32); + U32 scan8Mask = __ballot_sync(~0u, scan8); + if (scan8) + { + int sum = s_tileEmitPrefixSum[(thrInBlock << 5) + 32]; + volatile U32* v = &s_scanTemp[0][thrInBlock + 16]; + v[0] = sum; __syncwarp(scan8Mask); + #if (CR_BIN_SQR > 1 * 32) + sum += v[-1]; __syncwarp(scan8Mask); v[0] = sum; __syncwarp(scan8Mask); + #endif + #if (CR_BIN_SQR > 2 * 32) + sum += v[-2]; __syncwarp(scan8Mask); v[0] = sum; __syncwarp(scan8Mask); + #endif + #if (CR_BIN_SQR > 4 * 32) + sum += v[-4]; __syncwarp(scan8Mask); v[0] = sum; __syncwarp(scan8Mask); + #endif + } + + __syncthreads(); + + // Tile per thread: Finalize prefix sums. + // Single thread: Allocate segments. + + for (int tileInBin = thrInBlock; tileInBin < CR_BIN_SQR; tileInBin += CR_COARSE_WARPS * 32) + { + int sum = s_tileEmitPrefixSum[tileInBin + 1] + s_scanTemp[0][(tileInBin >> 5) + 15]; + int numEmits = sum >> emitShift; + int numAllocs = sum & ((1 << emitShift) - 1); + s_tileEmitPrefixSum[tileInBin + 1] = numEmits; + s_tileAllocPrefixSum[tileInBin + 1] = numAllocs; + + if (tileInBin == CR_BIN_SQR - 1 && numAllocs != 0) + { + int t = atomicAdd(&atomics.numTileSegs, numAllocs); + s_firstAllocSeg = (t + numAllocs <= p.maxTileSegs) ? t : 0; + } + } + + __syncthreads(); + int firstAllocSeg = s_firstAllocSeg; + int totalEmits = s_tileEmitPrefixSum[CR_BIN_SQR]; + int totalAllocs = s_tileAllocPrefixSum[CR_BIN_SQR]; + + //------------------------------------------------------------------------ + // Emit. + //------------------------------------------------------------------------ + + // Emit per thread: Write triangle index to globalmem. + + for (int emitInBin = thrInBlock; emitInBin < totalEmits; emitInBin += CR_COARSE_WARPS * 32) + { + // Find tile in bin. + + U8* tileBase = (U8*)&s_tileEmitPrefixSum[0]; + U8* tilePtr = tileBase; + U8* ptr; + + #if (CR_BIN_SQR > 128) + ptr = tilePtr + 0x80 * 4; if (emitInBin >= *(U32*)ptr) tilePtr = ptr; + #endif + #if (CR_BIN_SQR > 64) + ptr = tilePtr + 0x40 * 4; if (emitInBin >= *(U32*)ptr) tilePtr = ptr; + #endif + #if (CR_BIN_SQR > 32) + ptr = tilePtr + 0x20 * 4; if (emitInBin >= *(U32*)ptr) tilePtr = ptr; + #endif + #if (CR_BIN_SQR > 16) + ptr = tilePtr + 0x10 * 4; if (emitInBin >= *(U32*)ptr) tilePtr = ptr; + #endif + #if (CR_BIN_SQR > 8) + ptr = tilePtr + 0x08 * 4; if (emitInBin >= *(U32*)ptr) tilePtr = ptr; + #endif + #if (CR_BIN_SQR > 4) + ptr = tilePtr + 0x04 * 4; if (emitInBin >= *(U32*)ptr) tilePtr = ptr; + #endif + #if (CR_BIN_SQR > 2) + ptr = tilePtr + 0x02 * 4; if (emitInBin >= *(U32*)ptr) tilePtr = ptr; + #endif + #if (CR_BIN_SQR > 1) + ptr = tilePtr + 0x01 * 4; if (emitInBin >= *(U32*)ptr) tilePtr = ptr; + #endif + + int tileInBin = (tilePtr - tileBase) >> 2; + int emitInTile = emitInBin - *(U32*)tilePtr; + + // Find warp in tile. + + int warpStep = (CR_BIN_SQR + 1) * 4; + U8* warpBase = (U8*)&s_warpEmitPrefixSum[0][tileInBin] - warpStep; + U8* warpPtr = warpBase; + + #if (CR_COARSE_WARPS > 8) + ptr = warpPtr + 0x08 * warpStep; if (emitInTile >= *(U32*)ptr) warpPtr = ptr; + #endif + #if (CR_COARSE_WARPS > 4) + ptr = warpPtr + 0x04 * warpStep; if (emitInTile >= *(U32*)ptr) warpPtr = ptr; + #endif + #if (CR_COARSE_WARPS > 2) + ptr = warpPtr + 0x02 * warpStep; if (emitInTile >= *(U32*)ptr) warpPtr = ptr; + #endif + #if (CR_COARSE_WARPS > 1) + ptr = warpPtr + 0x01 * warpStep; if (emitInTile >= *(U32*)ptr) warpPtr = ptr; + #endif + + int warpInTile = (warpPtr - warpBase) >> (CR_BIN_LOG2 * 2 + 2); + U32 emitMask = *(U32*)(warpPtr + warpStep + ((U8*)s_warpEmitMask - (U8*)s_warpEmitPrefixSum)); + int emitInWarp = emitInTile - *(U32*)(warpPtr + warpStep) + __popc(emitMask); + + // Find thread in warp. + + int threadInWarp = 0; + int pop = __popc(emitMask & 0xFFFF); + bool pred = (emitInWarp >= pop); + if (pred) emitInWarp -= pop; + if (pred) emitMask >>= 0x10; + if (pred) threadInWarp += 0x10; + + pop = __popc(emitMask & 0xFF); + pred = (emitInWarp >= pop); + if (pred) emitInWarp -= pop; + if (pred) emitMask >>= 0x08; + if (pred) threadInWarp += 0x08; + + pop = __popc(emitMask & 0xF); + pred = (emitInWarp >= pop); + if (pred) emitInWarp -= pop; + if (pred) emitMask >>= 0x04; + if (pred) threadInWarp += 0x04; + + pop = __popc(emitMask & 0x3); + pred = (emitInWarp >= pop); + if (pred) emitInWarp -= pop; + if (pred) emitMask >>= 0x02; + if (pred) threadInWarp += 0x02; + + if (emitInWarp >= (emitMask & 1)) + threadInWarp++; + + // Figure out where to write. + + int currOfs = s_tileStreamCurrOfs[tileInBin]; + int spaceLeft = -currOfs & (CR_TILE_SEG_SIZE - 1); + int outOfs = emitInTile; + + if (outOfs < spaceLeft) + outOfs += currOfs; + else + { + int allocLo = firstAllocSeg + s_tileAllocPrefixSum[tileInBin]; + outOfs += (allocLo << CR_TILE_SEG_LOG2) - spaceLeft; + } + + // Write. + + int queueIdx = warpInTile * 32 + threadInWarp; + int triIdx = s_triQueue[(triQueueReadPos + queueIdx) & (CR_COARSE_QUEUE_SIZE - 1)]; + + tileSegData[outOfs] = triIdx; + } + + //------------------------------------------------------------------------ + // Patch. + //------------------------------------------------------------------------ + + // Allocated segment per thread: Initialize next-pointer and count. + + for (int i = CR_COARSE_WARPS * 32 - 1 - thrInBlock; i < totalAllocs; i += CR_COARSE_WARPS * 32) + { + int segIdx = firstAllocSeg + i; + tileSegNext[segIdx] = segIdx + 1; + tileSegCount[segIdx] = CR_TILE_SEG_SIZE; + } + + // Tile per thread: Fix previous segment's next-pointer and update s_tileStreamCurrOfs. + + __syncthreads(); + for (int tileInBin = CR_COARSE_WARPS * 32 - 1 - thrInBlock; tileInBin < CR_BIN_SQR; tileInBin += CR_COARSE_WARPS * 32) + { + int oldOfs = s_tileStreamCurrOfs[tileInBin]; + int newOfs = oldOfs + s_warpEmitPrefixSum[CR_COARSE_WARPS - 1][tileInBin]; + int allocLo = s_tileAllocPrefixSum[tileInBin]; + int allocHi = s_tileAllocPrefixSum[tileInBin + 1]; + + if (allocLo != allocHi) + { + S32* nextPtr = &tileSegNext[(oldOfs - 1) >> CR_TILE_SEG_LOG2]; + if (oldOfs < 0) + nextPtr = &tileFirstSeg[binTileIdx + globalTileIdx(tileInBin, p.widthTiles)]; + *nextPtr = firstAllocSeg + allocLo; + + newOfs--; + newOfs &= CR_TILE_SEG_SIZE - 1; + newOfs |= (firstAllocSeg + allocHi - 1) << CR_TILE_SEG_LOG2; + newOfs++; + } + s_tileStreamCurrOfs[tileInBin] = newOfs; + } + + // Advance queue read pointer. + // Queue became empty => bin done. + + triQueueReadPos += CR_COARSE_WARPS * 32; + } + while (triQueueReadPos < triQueueWritePos); + + // Tile per thread: Fix next-pointer and count of the last segment. + // 32 tiles per warp: Count active tiles. + + __syncthreads(); + + for (int tileInBin_base = 0; tileInBin_base < CR_BIN_SQR; tileInBin_base += CR_COARSE_WARPS * 32) + { + int tileInBin = tileInBin_base + thrInBlock; + bool act = (tileInBin < CR_BIN_SQR); + U32 actMask = __ballot_sync(~0u, act); + if (act) + { + int tileX = tileInBin & (CR_BIN_SIZE - 1); + int tileY = tileInBin >> CR_BIN_LOG2; + bool force = (p.deferredClear & tileX <= maxTileXInBin & tileY <= maxTileYInBin); + + int ofs = s_tileStreamCurrOfs[tileInBin]; + int segIdx = (ofs - 1) >> CR_TILE_SEG_LOG2; + int segCount = ofs & (CR_TILE_SEG_SIZE - 1); + + if (ofs >= 0) + tileSegNext[segIdx] = -1; + else if (force) + { + s_tileStreamCurrOfs[tileInBin] = 0; + tileFirstSeg[binTileIdx + tileX + tileY * p.widthTiles] = -1; + } + + if (segCount != 0) + tileSegCount[segIdx] = segCount; + + U32 res = __ballot_sync(actMask, ofs >= 0 | force); + if (threadIdx.x == 0) + s_scanTemp[0][(tileInBin >> 5) + 16] = __popc(res); + } + } + + // First warp: Scan-8. + // One thread: Allocate space for active tiles. + + __syncthreads(); + + bool scan8 = (thrInBlock < CR_BIN_SQR / 32); + U32 scan8Mask = __ballot_sync(~0u, scan8); + if (scan8) + { + volatile U32* v = &s_scanTemp[0][thrInBlock + 16]; + U32 sum = v[0]; + #if (CR_BIN_SQR > 1 * 32) + sum += v[-1]; __syncwarp(scan8Mask); v[0] = sum; __syncwarp(scan8Mask); + #endif + #if (CR_BIN_SQR > 2 * 32) + sum += v[-2]; __syncwarp(scan8Mask); v[0] = sum; __syncwarp(scan8Mask); + #endif + #if (CR_BIN_SQR > 4 * 32) + sum += v[-4]; __syncwarp(scan8Mask); v[0] = sum; __syncwarp(scan8Mask); + #endif + + if (thrInBlock == CR_BIN_SQR / 32 - 1) + s_firstActiveIdx = atomicAdd(&atomics.numActiveTiles, sum); + } + + // Tile per thread: Output active tiles. + + __syncthreads(); + + for (int tileInBin_base = 0; tileInBin_base < CR_BIN_SQR; tileInBin_base += CR_COARSE_WARPS * 32) + { + int tileInBin = tileInBin_base + thrInBlock; + bool act = (tileInBin < CR_BIN_SQR) && (s_tileStreamCurrOfs[tileInBin] >= 0); + U32 actMask = __ballot_sync(~0u, act); + if (act) + { + int activeIdx = s_firstActiveIdx; + activeIdx += s_scanTemp[0][(tileInBin >> 5) + 15]; + activeIdx += __popc(actMask & getLaneMaskLt()); + activeTiles[activeIdx] = binTileIdx + globalTileIdx(tileInBin, p.widthTiles); + } + } + } +} + +//------------------------------------------------------------------------ diff --git a/isaac_ros_gxf_extensions/gxf_isaac_foundationpose/gxf/foundationpose/nvdiffrast/common/cudaraster/impl/Constants.hpp b/isaac_ros_gxf_extensions/gxf_isaac_foundationpose/gxf/foundationpose/nvdiffrast/common/cudaraster/impl/Constants.hpp new file mode 100644 index 0000000..3023d73 --- /dev/null +++ b/isaac_ros_gxf_extensions/gxf_isaac_foundationpose/gxf/foundationpose/nvdiffrast/common/cudaraster/impl/Constants.hpp @@ -0,0 +1,82 @@ +// SPDX-FileCopyrightText: NVIDIA CORPORATION & AFFILIATES +// Copyright (c) 2009-2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. +// +// SPDX-License-Identifier: Apache-2.0 + +#pragma once + +//------------------------------------------------------------------------ + +#define CR_MAXVIEWPORT_LOG2 11 // ViewportSize / PixelSize. +#define CR_SUBPIXEL_LOG2 4 // PixelSize / SubpixelSize. + +#define CR_MAXBINS_LOG2 4 // ViewportSize / BinSize. +#define CR_BIN_LOG2 4 // BinSize / TileSize. +#define CR_TILE_LOG2 3 // TileSize / PixelSize. + +#define CR_COVER8X8_LUT_SIZE 768 // 64-bit entries. +#define CR_FLIPBIT_FLIP_Y 2 +#define CR_FLIPBIT_FLIP_X 3 +#define CR_FLIPBIT_SWAP_XY 4 +#define CR_FLIPBIT_COMPL 5 + +#define CR_BIN_STREAMS_LOG2 4 +#define CR_BIN_SEG_LOG2 9 // 32-bit entries. +#define CR_TILE_SEG_LOG2 5 // 32-bit entries. + +#define CR_MAXSUBTRIS_LOG2 24 // Triangle structs. Dictated by CoarseRaster. +#define CR_COARSE_QUEUE_LOG2 10 // Triangles. + +#define CR_SETUP_WARPS 2 +#define CR_SETUP_OPT_BLOCKS 8 +#define CR_BIN_WARPS 16 +#define CR_COARSE_WARPS 16 // Must be a power of two. +#define CR_FINE_MAX_WARPS 20 + +#define CR_EMBED_IMAGE_PARAMS 32 // Number of per-image parameter structs embedded in kernel launch parameter block. + +//------------------------------------------------------------------------ + +#define CR_MAXVIEWPORT_SIZE (1 << CR_MAXVIEWPORT_LOG2) +#define CR_SUBPIXEL_SIZE (1 << CR_SUBPIXEL_LOG2) +#define CR_SUBPIXEL_SQR (1 << (CR_SUBPIXEL_LOG2 * 2)) + +#define CR_MAXBINS_SIZE (1 << CR_MAXBINS_LOG2) +#define CR_MAXBINS_SQR (1 << (CR_MAXBINS_LOG2 * 2)) +#define CR_BIN_SIZE (1 << CR_BIN_LOG2) +#define CR_BIN_SQR (1 << (CR_BIN_LOG2 * 2)) + +#define CR_MAXTILES_LOG2 (CR_MAXBINS_LOG2 + CR_BIN_LOG2) +#define CR_MAXTILES_SIZE (1 << CR_MAXTILES_LOG2) +#define CR_MAXTILES_SQR (1 << (CR_MAXTILES_LOG2 * 2)) +#define CR_TILE_SIZE (1 << CR_TILE_LOG2) +#define CR_TILE_SQR (1 << (CR_TILE_LOG2 * 2)) + +#define CR_BIN_STREAMS_SIZE (1 << CR_BIN_STREAMS_LOG2) +#define CR_BIN_SEG_SIZE (1 << CR_BIN_SEG_LOG2) +#define CR_TILE_SEG_SIZE (1 << CR_TILE_SEG_LOG2) + +#define CR_MAXSUBTRIS_SIZE (1 << CR_MAXSUBTRIS_LOG2) +#define CR_COARSE_QUEUE_SIZE (1 << CR_COARSE_QUEUE_LOG2) + +//------------------------------------------------------------------------ +// When evaluating interpolated Z pixel centers, we may introduce an error +// of (+-CR_LERP_ERROR) ULPs. + +#define CR_LERP_ERROR(SAMPLES_LOG2) (2200u << (SAMPLES_LOG2)) +#define CR_DEPTH_MIN CR_LERP_ERROR(3) +#define CR_DEPTH_MAX (CR_U32_MAX - CR_LERP_ERROR(3)) + +//------------------------------------------------------------------------ diff --git a/isaac_ros_gxf_extensions/gxf_isaac_foundationpose/gxf/foundationpose/nvdiffrast/common/cudaraster/impl/CudaRaster.cpp b/isaac_ros_gxf_extensions/gxf_isaac_foundationpose/gxf/foundationpose/nvdiffrast/common/cudaraster/impl/CudaRaster.cpp new file mode 100644 index 0000000..ab60706 --- /dev/null +++ b/isaac_ros_gxf_extensions/gxf_isaac_foundationpose/gxf/foundationpose/nvdiffrast/common/cudaraster/impl/CudaRaster.cpp @@ -0,0 +1,79 @@ +// SPDX-FileCopyrightText: NVIDIA CORPORATION & AFFILIATES +// Copyright (c) 2009-2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. +// +// SPDX-License-Identifier: Apache-2.0 + +#include "Defs.hpp" +#include "../CudaRaster.hpp" +#include "RasterImpl.hpp" + +using namespace CR; + +//------------------------------------------------------------------------ +// Stub interface implementation. +//------------------------------------------------------------------------ + +CudaRaster::CudaRaster() +{ + m_impl = new RasterImpl(); +} + +CudaRaster::~CudaRaster() +{ + delete m_impl; +} + +void CudaRaster::setViewportSize(int width, int height, int numImages) +{ + m_impl->setViewportSize(Vec3i(width, height, numImages)); +} + +void CudaRaster::setRenderModeFlags(U32 flags) +{ + m_impl->setRenderModeFlags(flags); +} + +void CudaRaster::deferredClear(U32 clearColor) +{ + m_impl->deferredClear(clearColor); +} + +void CudaRaster::setVertexBuffer(void* vertices, int numVertices) +{ + m_impl->setVertexBuffer(vertices, numVertices); +} + +void CudaRaster::setIndexBuffer(void* indices, int numTriangles) +{ + m_impl->setIndexBuffer(indices, numTriangles); +} + +bool CudaRaster::drawTriangles(const int* ranges, bool peel, cudaStream_t stream) +{ + return m_impl->drawTriangles((const Vec2i*)ranges, peel, stream); +} + +void* CudaRaster::getColorBuffer(void) +{ + return m_impl->getColorBuffer(); +} + +void* CudaRaster::getDepthBuffer(void) +{ + return m_impl->getDepthBuffer(); +} + + +//------------------------------------------------------------------------ diff --git a/isaac_ros_gxf_extensions/gxf_isaac_foundationpose/gxf/foundationpose/nvdiffrast/common/cudaraster/impl/Defs.hpp b/isaac_ros_gxf_extensions/gxf_isaac_foundationpose/gxf/foundationpose/nvdiffrast/common/cudaraster/impl/Defs.hpp new file mode 100644 index 0000000..c61d0c5 --- /dev/null +++ b/isaac_ros_gxf_extensions/gxf_isaac_foundationpose/gxf/foundationpose/nvdiffrast/common/cudaraster/impl/Defs.hpp @@ -0,0 +1,99 @@ +// SPDX-FileCopyrightText: NVIDIA CORPORATION & AFFILIATES +// Copyright (c) 2009-2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. +// +// SPDX-License-Identifier: Apache-2.0 + +#pragma once +#include +#include + +namespace CR +{ +//------------------------------------------------------------------------ + +#ifndef NULL +# define NULL 0 +#endif + +#ifdef __CUDACC__ +# define CR_CUDA 1 +#else +# define CR_CUDA 0 +#endif + +#if CR_CUDA +# define CR_CUDA_FUNC __device__ __inline__ +# define CR_CUDA_CONST __constant__ +#else +# define CR_CUDA_FUNC inline +# define CR_CUDA_CONST static const +#endif + +#define CR_UNREF(X) ((void)(X)) +#define CR_ARRAY_SIZE(X) ((int)(sizeof(X) / sizeof((X)[0]))) + +//------------------------------------------------------------------------ + +typedef uint8_t U8; +typedef uint16_t U16; +typedef uint32_t U32; +typedef uint64_t U64; +typedef int8_t S8; +typedef int16_t S16; +typedef int32_t S32; +typedef int64_t S64; +typedef float F32; +typedef double F64; +typedef void (*FuncPtr)(void); + +//------------------------------------------------------------------------ + +#define CR_U32_MAX (0xFFFFFFFFu) +#define CR_S32_MIN (~0x7FFFFFFF) +#define CR_S32_MAX (0x7FFFFFFF) +#define CR_U64_MAX ((U64)(S64)-1) +#define CR_S64_MIN ((S64)-1 << 63) +#define CR_S64_MAX (~((S64)-1 << 63)) +#define CR_F32_MIN (1.175494351e-38f) +#define CR_F32_MAX (3.402823466e+38f) +#define CR_F64_MIN (2.2250738585072014e-308) +#define CR_F64_MAX (1.7976931348623158e+308) + +//------------------------------------------------------------------------ +// Misc types. + +class Vec2i +{ +public: + Vec2i(int x_, int y_) : x(x_), y(y_) {} + int x, y; +}; + +class Vec3i +{ +public: + Vec3i(int x_, int y_, int z_) : x(x_), y(y_), z(z_) {} + int x, y, z; +}; + +//------------------------------------------------------------------------ +// CUDA utilities. + +#if CR_CUDA +# define globalThreadIdx (threadIdx.x + blockDim.x * (threadIdx.y + blockDim.y * (blockIdx.x + gridDim.x * blockIdx.y))) +#endif + +//------------------------------------------------------------------------ +} // namespace CR diff --git a/isaac_ros_gxf_extensions/gxf_isaac_foundationpose/gxf/foundationpose/nvdiffrast/common/cudaraster/impl/FineRaster.inl b/isaac_ros_gxf_extensions/gxf_isaac_foundationpose/gxf/foundationpose/nvdiffrast/common/cudaraster/impl/FineRaster.inl new file mode 100644 index 0000000..69c6516 --- /dev/null +++ b/isaac_ros_gxf_extensions/gxf_isaac_foundationpose/gxf/foundationpose/nvdiffrast/common/cudaraster/impl/FineRaster.inl @@ -0,0 +1,375 @@ +// Copyright (c) 2009-2022, NVIDIA CORPORATION. All rights reserved. +// +// NVIDIA CORPORATION and its licensors retain all intellectual property +// and proprietary rights in and to this software, related documentation +// and any modifications thereto. Any use, reproduction, disclosure or +// distribution of this software and related documentation without an express +// license agreement from NVIDIA CORPORATION is strictly prohibited. + +//------------------------------------------------------------------------ +// Utility funcs. +//------------------------------------------------------------------------ + +__device__ __inline__ void initTileZMax(U32& tileZMax, bool& tileZUpd, volatile U32* tileDepth) +{ + tileZMax = CR_DEPTH_MAX; + tileZUpd = (::min(tileDepth[threadIdx.x], tileDepth[threadIdx.x + 32]) < tileZMax); +} + +__device__ __inline__ void updateTileZMax(U32& tileZMax, bool& tileZUpd, volatile U32* tileDepth, volatile U32* temp) +{ + // Entry is warp-coherent. + if (__any_sync(~0u, tileZUpd)) + { + U32 z = ::max(tileDepth[threadIdx.x], tileDepth[threadIdx.x + 32]); __syncwarp(); + temp[threadIdx.x + 16] = z; __syncwarp(); + z = ::max(z, temp[threadIdx.x + 16 - 1]); __syncwarp(); temp[threadIdx.x + 16] = z; __syncwarp(); + z = ::max(z, temp[threadIdx.x + 16 - 2]); __syncwarp(); temp[threadIdx.x + 16] = z; __syncwarp(); + z = ::max(z, temp[threadIdx.x + 16 - 4]); __syncwarp(); temp[threadIdx.x + 16] = z; __syncwarp(); + z = ::max(z, temp[threadIdx.x + 16 - 8]); __syncwarp(); temp[threadIdx.x + 16] = z; __syncwarp(); + z = ::max(z, temp[threadIdx.x + 16 - 16]); __syncwarp(); temp[threadIdx.x + 16] = z; __syncwarp(); + tileZMax = temp[47]; + tileZUpd = false; + } +} + +//------------------------------------------------------------------------ + +__device__ __inline__ void getTriangle(const CRParams& p, S32& triIdx, S32& dataIdx, uint4& triHeader, S32& segment) +{ + const CRTriangleHeader* triHeaderPtr = (const CRTriangleHeader*)p.triHeader + blockIdx.z * p.maxSubtris;; + const S32* tileSegData = (const S32*)p.tileSegData + p.maxTileSegs * CR_TILE_SEG_SIZE * blockIdx.z; + const S32* tileSegNext = (const S32*)p.tileSegNext + p.maxTileSegs * blockIdx.z; + const S32* tileSegCount = (const S32*)p.tileSegCount + p.maxTileSegs * blockIdx.z; + + if (threadIdx.x >= tileSegCount[segment]) + { + triIdx = -1; + dataIdx = -1; + } + else + { + int subtriIdx = tileSegData[segment * CR_TILE_SEG_SIZE + threadIdx.x]; + triIdx = subtriIdx >> 3; + dataIdx = triIdx; + subtriIdx &= 7; + if (subtriIdx != 7) + dataIdx = triHeaderPtr[triIdx].misc + subtriIdx; + triHeader = *((uint4*)triHeaderPtr + dataIdx); + } + + // advance to next segment + segment = tileSegNext[segment]; +} + +//------------------------------------------------------------------------ + +__device__ __inline__ bool earlyZCull(uint4 triHeader, U32 tileZMax) +{ + U32 zmin = triHeader.w & 0xFFFFF000; + return (zmin > tileZMax); +} + +//------------------------------------------------------------------------ + +__device__ __inline__ U64 trianglePixelCoverage(const CRParams& p, const uint4& triHeader, int tileX, int tileY, volatile U64* s_cover8x8_lut) +{ + int baseX = (tileX << (CR_TILE_LOG2 + CR_SUBPIXEL_LOG2)) - ((p.widthPixels - 1) << (CR_SUBPIXEL_LOG2 - 1)); + int baseY = (tileY << (CR_TILE_LOG2 + CR_SUBPIXEL_LOG2)) - ((p.heightPixels - 1) << (CR_SUBPIXEL_LOG2 - 1)); + + // extract S16 vertex positions while subtracting tile coordinates + S32 v0x = sub_s16lo_s16lo(triHeader.x, baseX); + S32 v0y = sub_s16hi_s16lo(triHeader.x, baseY); + S32 v01x = sub_s16lo_s16lo(triHeader.y, triHeader.x); + S32 v01y = sub_s16hi_s16hi(triHeader.y, triHeader.x); + S32 v20x = sub_s16lo_s16lo(triHeader.x, triHeader.z); + S32 v20y = sub_s16hi_s16hi(triHeader.x, triHeader.z); + + // extract flipbits + U32 f01 = (triHeader.w >> 6) & 0x3C; + U32 f12 = (triHeader.w >> 2) & 0x3C; + U32 f20 = (triHeader.w << 2) & 0x3C; + + // compute per-edge coverage masks + U64 c01, c12, c20; + c01 = cover8x8_exact_fast(v0x, v0y, v01x, v01y, f01, s_cover8x8_lut); + c12 = cover8x8_exact_fast(v0x + v01x, v0y + v01y, -v01x - v20x, -v01y - v20y, f12, s_cover8x8_lut); + c20 = cover8x8_exact_fast(v0x, v0y, v20x, v20y, f20, s_cover8x8_lut); + + // combine masks + return c01 & c12 & c20; +} + +//------------------------------------------------------------------------ + +__device__ __inline__ U32 scan32_value(U32 value, volatile U32* temp) +{ + __syncwarp(); + temp[threadIdx.x + 16] = value; __syncwarp(); + value += temp[threadIdx.x + 16 - 1]; __syncwarp(); temp[threadIdx.x + 16] = value; __syncwarp(); + value += temp[threadIdx.x + 16 - 2]; __syncwarp(); temp[threadIdx.x + 16] = value; __syncwarp(); + value += temp[threadIdx.x + 16 - 4]; __syncwarp(); temp[threadIdx.x + 16] = value; __syncwarp(); + value += temp[threadIdx.x + 16 - 8]; __syncwarp(); temp[threadIdx.x + 16] = value; __syncwarp(); + value += temp[threadIdx.x + 16 - 16]; __syncwarp(); temp[threadIdx.x + 16] = value; __syncwarp(); + return value; +} + +__device__ __inline__ volatile const U32& scan32_total(volatile U32* temp) +{ + return temp[47]; +} + +//------------------------------------------------------------------------ + +__device__ __inline__ S32 findBit(U64 mask, int idx) +{ + U32 x = getLo(mask); + int pop = __popc(x); + bool p = (pop <= idx); + if (p) x = getHi(mask); + if (p) idx -= pop; + int bit = p ? 32 : 0; + + pop = __popc(x & 0x0000ffffu); + p = (pop <= idx); + if (p) x >>= 16; + if (p) bit += 16; + if (p) idx -= pop; + + U32 tmp = x & 0x000000ffu; + pop = __popc(tmp); + p = (pop <= idx); + if (p) tmp = x & 0x0000ff00u; + if (p) idx -= pop; + + return findLeadingOne(tmp) + bit - idx; +} + +//------------------------------------------------------------------------ +// Single-sample implementation. +//------------------------------------------------------------------------ + +__device__ __inline__ void executeROP(U32 color, U32 depth, volatile U32* pColor, volatile U32* pDepth, U32 ropMask) +{ + atomicMin((U32*)pDepth, depth); + __syncwarp(ropMask); + bool act = (depth == *pDepth); + __syncwarp(ropMask); + U32 actMask = __ballot_sync(ropMask, act); + if (act) + { + *pDepth = 0; + __syncwarp(actMask); + atomicMax((U32*)pDepth, threadIdx.x); + __syncwarp(actMask); + if (*pDepth == threadIdx.x) + { + *pDepth = depth; + *pColor = color; + } + __syncwarp(actMask); + } +} + +//------------------------------------------------------------------------ + +__device__ __inline__ void fineRasterImpl(const CRParams p) +{ + // for 20 warps: + __shared__ volatile U64 s_cover8x8_lut[CR_COVER8X8_LUT_SIZE]; // 6KB + __shared__ volatile U32 s_tileColor [CR_FINE_MAX_WARPS][CR_TILE_SQR]; // 5KB + __shared__ volatile U32 s_tileDepth [CR_FINE_MAX_WARPS][CR_TILE_SQR]; // 5KB + __shared__ volatile U32 s_triDataIdx [CR_FINE_MAX_WARPS][64]; // 5KB CRTriangleData index + __shared__ volatile U64 s_triangleCov [CR_FINE_MAX_WARPS][64]; // 10KB coverage mask + __shared__ volatile U32 s_triangleFrag[CR_FINE_MAX_WARPS][64]; // 5KB fragment index + __shared__ volatile U32 s_temp [CR_FINE_MAX_WARPS][80]; // 6.25KB + // = 47.25KB total + + CRAtomics& atomics = p.atomics[blockIdx.z]; + const CRTriangleData* triData = (const CRTriangleData*)p.triData + blockIdx.z * p.maxSubtris; + + const S32* activeTiles = (const S32*)p.activeTiles + CR_MAXTILES_SQR * blockIdx.z; + const S32* tileFirstSeg = (const S32*)p.tileFirstSeg + CR_MAXTILES_SQR * blockIdx.z; + + volatile U32* tileColor = s_tileColor[threadIdx.y]; + volatile U32* tileDepth = s_tileDepth[threadIdx.y]; + volatile U32* triDataIdx = s_triDataIdx[threadIdx.y]; + volatile U64* triangleCov = s_triangleCov[threadIdx.y]; + volatile U32* triangleFrag = s_triangleFrag[threadIdx.y]; + volatile U32* temp = s_temp[threadIdx.y]; + + if (atomics.numSubtris > p.maxSubtris || atomics.numBinSegs > p.maxBinSegs || atomics.numTileSegs > p.maxTileSegs) + return; + + temp[threadIdx.x] = 0; // first 16 elements of temp are always zero + cover8x8_setupLUT(s_cover8x8_lut); + __syncthreads(); + + // loop over tiles + for (;;) + { + // pick a tile + if (threadIdx.x == 0) + temp[16] = atomicAdd(&atomics.fineCounter, 1); + __syncwarp(); + int activeIdx = temp[16]; + if (activeIdx >= atomics.numActiveTiles) + break; + + int tileIdx = activeTiles[activeIdx]; + S32 segment = tileFirstSeg[tileIdx]; + int tileY = tileIdx / p.widthTiles; + int tileX = tileIdx - tileY * p.widthTiles; + int px = (tileX << CR_TILE_LOG2) + (threadIdx.x & (CR_TILE_SIZE - 1)); + int py = (tileY << CR_TILE_LOG2) + (threadIdx.x >> CR_TILE_LOG2); + + // initialize per-tile state + int triRead = 0, triWrite = 0; + int fragRead = 0, fragWrite = 0; + if (threadIdx.x == 0) + triangleFrag[63] = 0; // "previous triangle" + + // deferred clear => clear tile + if (p.deferredClear) + { + tileColor[threadIdx.x] = p.clearColor; + tileDepth[threadIdx.x] = p.clearDepth; + tileColor[threadIdx.x + 32] = p.clearColor; + tileDepth[threadIdx.x + 32] = p.clearDepth; + } + else // otherwise => read tile from framebuffer + { + U32* pColor = (U32*)p.colorBuffer + p.widthPixels * p.heightPixels * blockIdx.z; + U32* pDepth = (U32*)p.depthBuffer + p.widthPixels * p.heightPixels * blockIdx.z; + tileColor[threadIdx.x] = pColor[px + p.widthPixels * py]; + tileDepth[threadIdx.x] = pDepth[px + p.widthPixels * py]; + tileColor[threadIdx.x + 32] = pColor[px + p.widthPixels * (py + 4)]; + tileDepth[threadIdx.x + 32] = pDepth[px + p.widthPixels * (py + 4)]; + } + + U32 tileZMax; + bool tileZUpd; + initTileZMax(tileZMax, tileZUpd, tileDepth); + + // process fragments + for(;;) + { + // need to queue more fragments? + if (fragWrite - fragRead < 32 && segment >= 0) + { + // update tile z - coherent over warp + updateTileZMax(tileZMax, tileZUpd, tileDepth, temp); + + // read triangles + do + { + // read triangle index and data, advance to next segment + S32 triIdx, dataIdx; + uint4 triHeader; + getTriangle(p, triIdx, dataIdx, triHeader, segment); + + // early z cull + if (triIdx >= 0 && earlyZCull(triHeader, tileZMax)) + triIdx = -1; + + // determine coverage + U64 coverage = trianglePixelCoverage(p, triHeader, tileX, tileY, s_cover8x8_lut); + S32 pop = (triIdx == -1) ? 0 : __popcll(coverage); + + // fragment count scan + U32 frag = scan32_value(pop, temp); + frag += fragWrite; // frag now holds cumulative fragment count + fragWrite += scan32_total(temp); + + // queue non-empty triangles + U32 goodMask = __ballot_sync(~0u, pop != 0); + if (pop != 0) + { + int idx = (triWrite + __popc(goodMask & getLaneMaskLt())) & 63; + triDataIdx [idx] = dataIdx; + triangleFrag[idx] = frag; + triangleCov [idx] = coverage; + } + triWrite += __popc(goodMask); + } + while (fragWrite - fragRead < 32 && segment >= 0); + } + __syncwarp(); + + // end of segment? + if (fragRead == fragWrite) + break; + + // clear triangle boundaries + temp[threadIdx.x + 16] = 0; + __syncwarp(); + + // tag triangle boundaries + if (triRead + threadIdx.x < triWrite) + { + int idx = triangleFrag[(triRead + threadIdx.x) & 63] - fragRead; + if (idx <= 32) + temp[idx + 16 - 1] = 1; + } + __syncwarp(); + + int ropLaneIdx = threadIdx.x; + U32 boundaryMask = __ballot_sync(~0u, temp[ropLaneIdx + 16]); + + // distribute fragments + bool hasFragment = (ropLaneIdx < fragWrite - fragRead); + U32 fragmentMask = __ballot_sync(~0u, hasFragment); + if (hasFragment) + { + int triBufIdx = (triRead + __popc(boundaryMask & getLaneMaskLt())) & 63; + int fragIdx = add_sub(fragRead, ropLaneIdx, triangleFrag[(triBufIdx - 1) & 63]); + U64 coverage = triangleCov[triBufIdx]; + int pixelInTile = findBit(coverage, fragIdx); + int dataIdx = triDataIdx[triBufIdx]; + + // determine pixel position + U32 pixelX = (tileX << CR_TILE_LOG2) + (pixelInTile & 7); + U32 pixelY = (tileY << CR_TILE_LOG2) + (pixelInTile >> 3); + + // depth test + U32 depth = 0; + uint4 td = *((uint4*)triData + dataIdx * (sizeof(CRTriangleData) >> 4)); + + depth = td.x * pixelX + td.y * pixelY + td.z; + bool zkill = false; + if (!zkill) + { + U32 oldDepth = tileDepth[pixelInTile]; + if (depth > oldDepth) + zkill = true; + else if (oldDepth == tileZMax) + tileZUpd = true; // we are replacing previous zmax => need to update + } + + U32 ropMask = __ballot_sync(fragmentMask, !zkill); + if (!zkill) + executeROP(td.w, depth, &tileColor[pixelInTile], &tileDepth[pixelInTile], ropMask); + } + // no need to sync, as next up is updateTileZMax that does internal warp sync + + // update counters + fragRead = ::min(fragRead + 32, fragWrite); + triRead += __popc(boundaryMask); + } + + // Write tile back to the framebuffer. + if (true) + { + int px = (tileX << CR_TILE_LOG2) + (threadIdx.x & (CR_TILE_SIZE - 1)); + int py = (tileY << CR_TILE_LOG2) + (threadIdx.x >> CR_TILE_LOG2); + U32* pColor = (U32*)p.colorBuffer + p.widthPixels * p.heightPixels * blockIdx.z; + U32* pDepth = (U32*)p.depthBuffer + p.widthPixels * p.heightPixels * blockIdx.z; + pColor[px + p.widthPixels * py] = tileColor[threadIdx.x]; + pDepth[px + p.widthPixels * py] = tileDepth[threadIdx.x]; + pColor[px + p.widthPixels * (py + 4)] = tileColor[threadIdx.x + 32]; + pDepth[px + p.widthPixels * (py + 4)] = tileDepth[threadIdx.x + 32]; + } + } +} + +//------------------------------------------------------------------------ diff --git a/isaac_ros_gxf_extensions/gxf_isaac_foundationpose/gxf/foundationpose/nvdiffrast/common/cudaraster/impl/PrivateDefs.hpp b/isaac_ros_gxf_extensions/gxf_isaac_foundationpose/gxf/foundationpose/nvdiffrast/common/cudaraster/impl/PrivateDefs.hpp new file mode 100644 index 0000000..debd1b0 --- /dev/null +++ b/isaac_ros_gxf_extensions/gxf_isaac_foundationpose/gxf/foundationpose/nvdiffrast/common/cudaraster/impl/PrivateDefs.hpp @@ -0,0 +1,152 @@ +// SPDX-FileCopyrightText: NVIDIA CORPORATION & AFFILIATES +// Copyright (c) 2009-2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. +// +// SPDX-License-Identifier: Apache-2.0 + +#pragma once +#include "Defs.hpp" +#include "Constants.hpp" + +namespace CR +{ +//------------------------------------------------------------------------ +// Projected triangle. +//------------------------------------------------------------------------ + +struct CRTriangleHeader +{ + S16 v0x; // Subpixels relative to viewport center. Valid if triSubtris = 1. + S16 v0y; + S16 v1x; + S16 v1y; + S16 v2x; + S16 v2y; + + U32 misc; // triSubtris=1: (zmin:20, f01:4, f12:4, f20:4), triSubtris>=2: (subtriBase) +}; + +//------------------------------------------------------------------------ + +struct CRTriangleData +{ + U32 zx; // zx * sampleX + zy * sampleY + zb = lerp(CR_DEPTH_MIN, CR_DEPTH_MAX, (clipZ / clipW + 1) / 2) + U32 zy; + U32 zb; + U32 id; // Triangle id. +}; + +//------------------------------------------------------------------------ +// Device-side structures. +//------------------------------------------------------------------------ + +struct CRAtomics +{ + // Setup. + S32 numSubtris; // = numTris + + // Bin. + S32 binCounter; // = 0 + S32 numBinSegs; // = 0 + + // Coarse. + S32 coarseCounter; // = 0 + S32 numTileSegs; // = 0 + S32 numActiveTiles; // = 0 + + // Fine. + S32 fineCounter; // = 0 +}; + +//------------------------------------------------------------------------ + +struct CRImageParams +{ + S32 triOffset; // First triangle index to draw. + S32 triCount; // Number of triangles to draw. + S32 binBatchSize; // Number of triangles per batch. +}; + +//------------------------------------------------------------------------ + +struct CRParams +{ + // Common. + + CRAtomics* atomics; // Work counters. Per-image. + S32 numImages; // Batch size. + S32 totalCount; // In range mode, total number of triangles to render. + S32 instanceMode; // 0 = range mode, 1 = instance mode. + + S32 numVertices; // Number of vertices in input buffer, not counting multiples in instance mode. + S32 numTriangles; // Number of triangles in input buffer. + void* vertexBuffer; // numVertices * float4(x, y, z, w) + void* indexBuffer; // numTriangles * int3(vi0, vi1, vi2) + + S32 widthPixels; // Viewport size in pixels. Must be multiple of tile size (8x8). + S32 heightPixels; + S32 widthBins; // widthPixels / CR_BIN_SIZE + S32 heightBins; // heightPixels / CR_BIN_SIZE + S32 numBins; // widthBins * heightBins + + S32 widthTiles; // widthPixels / CR_TILE_SIZE + S32 heightTiles; // heightPixels / CR_TILE_SIZE + S32 numTiles; // widthTiles * heightTiles + + U32 renderModeFlags; + S32 deferredClear; // 1 = Clear framebuffer before rendering triangles. + U32 clearColor; + U32 clearDepth; + + // These are uniform across batch. + + S32 maxSubtris; + S32 maxBinSegs; + S32 maxTileSegs; + + // Setup output / bin input. + + void* triSubtris; // maxSubtris * U8 + void* triHeader; // maxSubtris * CRTriangleHeader + void* triData; // maxSubtris * CRTriangleData + + // Bin output / coarse input. + + void* binSegData; // maxBinSegs * CR_BIN_SEG_SIZE * S32 + void* binSegNext; // maxBinSegs * S32 + void* binSegCount; // maxBinSegs * S32 + void* binFirstSeg; // CR_MAXBINS_SQR * CR_BIN_STREAMS_SIZE * (S32 segIdx), -1 = none + void* binTotal; // CR_MAXBINS_SQR * CR_BIN_STREAMS_SIZE * (S32 numTris) + + // Coarse output / fine input. + + void* tileSegData; // maxTileSegs * CR_TILE_SEG_SIZE * S32 + void* tileSegNext; // maxTileSegs * S32 + void* tileSegCount; // maxTileSegs * S32 + void* activeTiles; // CR_MAXTILES_SQR * (S32 tileIdx) + void* tileFirstSeg; // CR_MAXTILES_SQR * (S32 segIdx), -1 = none + + // Surface buffers. + + void* colorBuffer; // sizePixels.x * sizePixels.y * numImages * U32 + void* depthBuffer; // sizePixels.x * sizePixels.y * numImages * U32 + + // Per-image parameters for first images are embedded here to avoid extra memcpy for small batches. + + CRImageParams imageParamsFirst[CR_EMBED_IMAGE_PARAMS]; + const CRImageParams* imageParamsExtra; // After CR_EMBED_IMAGE_PARAMS. +}; + +//------------------------------------------------------------------------ +} diff --git a/isaac_ros_gxf_extensions/gxf_isaac_foundationpose/gxf/foundationpose/nvdiffrast/common/cudaraster/impl/RasterImpl.cpp b/isaac_ros_gxf_extensions/gxf_isaac_foundationpose/gxf/foundationpose/nvdiffrast/common/cudaraster/impl/RasterImpl.cpp new file mode 100644 index 0000000..c7a5874 --- /dev/null +++ b/isaac_ros_gxf_extensions/gxf_isaac_foundationpose/gxf/foundationpose/nvdiffrast/common/cudaraster/impl/RasterImpl.cpp @@ -0,0 +1,341 @@ +// SPDX-FileCopyrightText: NVIDIA CORPORATION & AFFILIATES +// Copyright (c) 2009-2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. +// +// SPDX-License-Identifier: Apache-2.0 + +#include "../../framework.h" +#include "PrivateDefs.hpp" +#include "Constants.hpp" +#include "RasterImpl.hpp" +#include + +// PATCH for Isaac ROS FoundationPose +#include +#include + +using namespace CR; +using std::min; +using std::max; + +//------------------------------------------------------------------------ +// Kernel prototypes and variables. + +void triangleSetupKernel (const CRParams p); +void binRasterKernel (const CRParams p); +void coarseRasterKernel (const CRParams p); +void fineRasterKernel (const CRParams p); + +//------------------------------------------------------------------------ + +RasterImpl::RasterImpl(void) +: m_renderModeFlags (0), + m_deferredClear (false), + m_clearColor (0), + m_vertexPtr (NULL), + m_indexPtr (NULL), + m_numVertices (0), + m_numTriangles (0), + m_bufferSizesReported (0), + + m_numImages (0), + m_sizePixels (0, 0), + m_sizeBins (0, 0), + m_numBins (0), + m_sizeTiles (0, 0), + m_numTiles (0), + + m_numSMs (1), + m_numCoarseBlocksPerSM (1), + m_numFineBlocksPerSM (1), + m_numFineWarpsPerBlock (1), + + m_maxSubtris (1), + m_maxBinSegs (1), + m_maxTileSegs (1) +{ + // Query relevant device attributes. + + int currentDevice = 0; + NVDR_CHECK_CUDA_ERROR(cudaGetDevice(¤tDevice)); + NVDR_CHECK_CUDA_ERROR(cudaDeviceGetAttribute(&m_numSMs, cudaDevAttrMultiProcessorCount, currentDevice)); + cudaFuncAttributes attr; + NVDR_CHECK_CUDA_ERROR(cudaFuncGetAttributes(&attr, (void*)fineRasterKernel)); + m_numFineWarpsPerBlock = min(attr.maxThreadsPerBlock / 32, CR_FINE_MAX_WARPS); + NVDR_CHECK_CUDA_ERROR(cudaOccupancyMaxActiveBlocksPerMultiprocessor(&m_numCoarseBlocksPerSM, (void*)coarseRasterKernel, 32 * CR_COARSE_WARPS, 0)); + NVDR_CHECK_CUDA_ERROR(cudaOccupancyMaxActiveBlocksPerMultiprocessor(&m_numFineBlocksPerSM, (void*)fineRasterKernel, 32 * m_numFineWarpsPerBlock, 0)); + + // Setup functions. + + NVDR_CHECK_CUDA_ERROR(cudaFuncSetCacheConfig((void*)triangleSetupKernel, cudaFuncCachePreferShared)); + NVDR_CHECK_CUDA_ERROR(cudaFuncSetCacheConfig((void*)binRasterKernel, cudaFuncCachePreferShared)); + NVDR_CHECK_CUDA_ERROR(cudaFuncSetCacheConfig((void*)coarseRasterKernel, cudaFuncCachePreferShared)); + NVDR_CHECK_CUDA_ERROR(cudaFuncSetCacheConfig((void*)fineRasterKernel, cudaFuncCachePreferShared)); +} + +//------------------------------------------------------------------------ + +RasterImpl::~RasterImpl(void) +{ + // Empty. +} + +//------------------------------------------------------------------------ + +void RasterImpl::setViewportSize(Vec3i size) +{ + if ((size.x | size.y) & (CR_TILE_SIZE - 1)) + return; // Invalid size. + + m_numImages = size.z; + m_sizePixels = Vec2i(size.x, size.y); + m_sizeTiles.x = m_sizePixels.x >> CR_TILE_LOG2; + m_sizeTiles.y = m_sizePixels.y >> CR_TILE_LOG2; + m_numTiles = m_sizeTiles.x * m_sizeTiles.y; + m_sizeBins.x = (m_sizeTiles.x + CR_BIN_SIZE - 1) >> CR_BIN_LOG2; + m_sizeBins.y = (m_sizeTiles.y + CR_BIN_SIZE - 1) >> CR_BIN_LOG2; + m_numBins = m_sizeBins.x * m_sizeBins.y; + + m_colorBuffer.reset(m_sizePixels.x * m_sizePixels.y * m_numImages * sizeof(U32)); + m_depthBuffer.reset(m_sizePixels.x * m_sizePixels.y * m_numImages * sizeof(U32)); +} + +//------------------------------------------------------------------------ + +bool RasterImpl::drawTriangles(const Vec2i* ranges, bool peel, cudaStream_t stream) +{ + bool instanceMode = (!ranges); + + int maxSubtrisSlack = 4096; // x 81B = 324KB + int maxBinSegsSlack = 256; // x 2137B = 534KB + int maxTileSegsSlack = 4096; // x 136B = 544KB + + // Resize atomics as needed. + m_crAtomics .grow(m_numImages * sizeof(CRAtomics)); + m_crAtomicsHost.grow(m_numImages * sizeof(CRAtomics)); + + // Size of these buffers doesn't depend on input. + m_binFirstSeg .grow(m_numImages * CR_MAXBINS_SQR * CR_BIN_STREAMS_SIZE * sizeof(S32)); + m_binTotal .grow(m_numImages * CR_MAXBINS_SQR * CR_BIN_STREAMS_SIZE * sizeof(S32)); + m_activeTiles .grow(m_numImages * CR_MAXTILES_SQR * sizeof(S32)); + m_tileFirstSeg .grow(m_numImages * CR_MAXTILES_SQR * sizeof(S32)); + + // Construct per-image parameters and determine worst-case buffer sizes. + m_crImageParamsHost.grow(m_numImages * sizeof(CRImageParams)); + CRImageParams* imageParams = (CRImageParams*)m_crImageParamsHost.getPtr(); + for (int i=0; i < m_numImages; i++) + { + CRImageParams& ip = imageParams[i]; + + int roundSize = CR_BIN_WARPS * 32; + int minBatches = CR_BIN_STREAMS_SIZE * 2; + int maxRounds = 32; + + ip.triOffset = instanceMode ? 0 : ranges[i].x; + ip.triCount = instanceMode ? m_numTriangles : ranges[i].y; + ip.binBatchSize = min(max(ip.triCount / (roundSize * minBatches), 1), maxRounds) * roundSize; + + m_maxSubtris = max(m_maxSubtris, min(ip.triCount + maxSubtrisSlack, CR_MAXSUBTRIS_SIZE)); + m_maxBinSegs = max(m_maxBinSegs, max(m_numBins * CR_BIN_STREAMS_SIZE, (ip.triCount - 1) / CR_BIN_SEG_SIZE + 1) + maxBinSegsSlack); + m_maxTileSegs = max(m_maxTileSegs, max(m_numTiles, (ip.triCount - 1) / CR_TILE_SEG_SIZE + 1) + maxTileSegsSlack); + } + + // Retry until successful. + + for (;;) + { + // Allocate buffers. + m_triSubtris.reset(m_numImages * m_maxSubtris * sizeof(U8)); + m_triHeader .reset(m_numImages * m_maxSubtris * sizeof(CRTriangleHeader)); + m_triData .reset(m_numImages * m_maxSubtris * sizeof(CRTriangleData)); + + m_binSegData .reset(m_numImages * m_maxBinSegs * CR_BIN_SEG_SIZE * sizeof(S32)); + m_binSegNext .reset(m_numImages * m_maxBinSegs * sizeof(S32)); + m_binSegCount.reset(m_numImages * m_maxBinSegs * sizeof(S32)); + + m_tileSegData .reset(m_numImages * m_maxTileSegs * CR_TILE_SEG_SIZE * sizeof(S32)); + m_tileSegNext .reset(m_numImages * m_maxTileSegs * sizeof(S32)); + m_tileSegCount.reset(m_numImages * m_maxTileSegs * sizeof(S32)); + + // Report if buffers grow from last time. + size_t sizesTotal = getTotalBufferSizes(); + if (sizesTotal > m_bufferSizesReported) + { + size_t sizesMB = ((sizesTotal - 1) >> 20) + 1; // Round up. + sizesMB = ((sizesMB + 9) / 10) * 10; // 10MB granularity enough in this day and age. + //LOG(INFO) << "Internal buffers grown to " << sizesMB << " MB"; + m_bufferSizesReported = sizesMB << 20; + } + + // Launch stages. Blocks until everything is done. + launchStages(instanceMode, peel, stream); + + // Peeling iteration cannot fail, so no point checking things further. + if (peel) + break; + + // Atomics after coarse stage are now available. + CRAtomics* atomics = (CRAtomics*)m_crAtomicsHost.getPtr(); + + // Success? + bool failed = false; + for (int i=0; i < m_numImages; i++) + { + const CRAtomics& a = atomics[i]; + failed = failed || (a.numSubtris > m_maxSubtris) || (a.numBinSegs > m_maxBinSegs) || (a.numTileSegs > m_maxTileSegs); + } + if (!failed) + break; // Success! + + // If we were already at maximum capacity, no can do. + if (m_maxSubtris == CR_MAXSUBTRIS_SIZE) + return false; + + // Enlarge buffers and try again. + for (int i=0; i < m_numImages; i++) + { + const CRAtomics& a = atomics[i]; + m_maxSubtris = max(m_maxSubtris, min(a.numSubtris + maxSubtrisSlack, CR_MAXSUBTRIS_SIZE)); + m_maxBinSegs = max(m_maxBinSegs, a.numBinSegs + maxBinSegsSlack); + m_maxTileSegs = max(m_maxTileSegs, a.numTileSegs + maxTileSegsSlack); + } + } + + m_deferredClear = false; + return true; // Success. +} + +//------------------------------------------------------------------------ + +size_t RasterImpl::getTotalBufferSizes(void) const +{ + return + m_colorBuffer.getSize() + m_depthBuffer.getSize() + // Don't include atomics and image params. + m_triSubtris.getSize() + m_triHeader.getSize() + m_triData.getSize() + + m_binFirstSeg.getSize() + m_binTotal.getSize() + m_binSegData.getSize() + m_binSegNext.getSize() + m_binSegCount.getSize() + + m_activeTiles.getSize() + m_tileFirstSeg.getSize() + m_tileSegData.getSize() + m_tileSegNext.getSize() + m_tileSegCount.getSize(); +} + +//------------------------------------------------------------------------ + +void RasterImpl::launchStages(bool instanceMode, bool peel, cudaStream_t stream) +{ + CRImageParams* imageParams = (CRImageParams*)m_crImageParamsHost.getPtr(); + + // Unless peeling, initialize atomics to mostly zero. + CRAtomics* atomics = (CRAtomics*)m_crAtomicsHost.getPtr(); + if (!peel) + { + memset(atomics, 0, m_numImages * sizeof(CRAtomics)); + for (int i=0; i < m_numImages; i++) + atomics[i].numSubtris = imageParams[i].triCount; + } + + // Copy to device. If peeling, this is the state after coarse raster launch on first iteration. + NVDR_CHECK_CUDA_ERROR(cudaMemcpyAsync(m_crAtomics.getPtr(), atomics, m_numImages * sizeof(CRAtomics), cudaMemcpyHostToDevice, stream)); + + // Copy per-image parameters if there are more than fits in launch parameter block and we haven't done it already. + if (!peel && m_numImages > CR_EMBED_IMAGE_PARAMS) + { + int numImageParamsExtra = m_numImages - CR_EMBED_IMAGE_PARAMS; + m_crImageParamsExtra.grow(numImageParamsExtra * sizeof(CRImageParams)); + NVDR_CHECK_CUDA_ERROR(cudaMemcpyAsync(m_crImageParamsExtra.getPtr(), imageParams + CR_EMBED_IMAGE_PARAMS, numImageParamsExtra * sizeof(CRImageParams), cudaMemcpyHostToDevice, stream)); + } + + // Set global parameters. + CRParams p; + { + p.atomics = (CRAtomics*)m_crAtomics.getPtr(); + p.numImages = m_numImages; + p.totalCount = 0; // Only relevant in range mode. + p.instanceMode = instanceMode ? 1 : 0; + + p.numVertices = m_numVertices; + p.numTriangles = m_numTriangles; + p.vertexBuffer = m_vertexPtr; + p.indexBuffer = m_indexPtr; + + p.widthPixels = m_sizePixels.x; + p.heightPixels = m_sizePixels.y; + p.widthBins = m_sizeBins.x; + p.heightBins = m_sizeBins.y; + p.numBins = m_numBins; + + p.widthTiles = m_sizeTiles.x; + p.heightTiles = m_sizeTiles.y; + p.numTiles = m_numTiles; + + p.renderModeFlags = m_renderModeFlags; + p.deferredClear = m_deferredClear ? 1 : 0; + p.clearColor = m_clearColor; + p.clearDepth = CR_DEPTH_MAX; + + p.maxSubtris = m_maxSubtris; + p.maxBinSegs = m_maxBinSegs; + p.maxTileSegs = m_maxTileSegs; + + p.triSubtris = m_triSubtris.getPtr(); + p.triHeader = m_triHeader.getPtr(); + p.triData = m_triData.getPtr(); + p.binSegData = m_binSegData.getPtr(); + p.binSegNext = m_binSegNext.getPtr(); + p.binSegCount = m_binSegCount.getPtr(); + p.binFirstSeg = m_binFirstSeg.getPtr(); + p.binTotal = m_binTotal.getPtr(); + p.tileSegData = m_tileSegData.getPtr(); + p.tileSegNext = m_tileSegNext.getPtr(); + p.tileSegCount = m_tileSegCount.getPtr(); + p.activeTiles = m_activeTiles.getPtr(); + p.tileFirstSeg = m_tileFirstSeg.getPtr(); + + p.colorBuffer = m_colorBuffer.getPtr(); + p.depthBuffer = m_depthBuffer.getPtr(); + + memcpy(&p.imageParamsFirst, imageParams, min(m_numImages, CR_EMBED_IMAGE_PARAMS) * sizeof(CRImageParams)); + p.imageParamsExtra = (CRImageParams*)m_crImageParamsExtra.getPtr(); + } + + // Setup block sizes. + + dim3 brBlock(32, CR_BIN_WARPS); + dim3 crBlock(32, CR_COARSE_WARPS); + dim3 frBlock(32, m_numFineWarpsPerBlock); + void* args[] = {&p}; + + // Launch stages from setup to coarse and copy atomics to host only if this is not a peeling iteration. + if (!peel) + { + if (instanceMode) + { + int setupBlocks = (m_numTriangles - 1) / (32 * CR_SETUP_WARPS) + 1; + NVDR_CHECK_CUDA_ERROR(cudaLaunchKernel((void*)triangleSetupKernel, dim3(setupBlocks, 1, m_numImages), dim3(32, CR_SETUP_WARPS), args, 0, stream)); + } + else + { + for (int i=0; i < m_numImages; i++) + p.totalCount += imageParams[i].triCount; + int setupBlocks = (p.totalCount - 1) / (32 * CR_SETUP_WARPS) + 1; + NVDR_CHECK_CUDA_ERROR(cudaLaunchKernel((void*)triangleSetupKernel, dim3(setupBlocks, 1, 1), dim3(32, CR_SETUP_WARPS), args, 0, stream)); + } + NVDR_CHECK_CUDA_ERROR(cudaLaunchKernel((void*)binRasterKernel, dim3(CR_BIN_STREAMS_SIZE, 1, m_numImages), brBlock, args, 0, stream)); + NVDR_CHECK_CUDA_ERROR(cudaLaunchKernel((void*)coarseRasterKernel, dim3(m_numSMs * m_numCoarseBlocksPerSM, 1, m_numImages), crBlock, args, 0, stream)); + NVDR_CHECK_CUDA_ERROR(cudaMemcpyAsync(m_crAtomicsHost.getPtr(), m_crAtomics.getPtr(), sizeof(CRAtomics) * m_numImages, cudaMemcpyDeviceToHost, stream)); + } + + // Fine rasterizer is launched always. + NVDR_CHECK_CUDA_ERROR(cudaLaunchKernel((void*)fineRasterKernel, dim3(m_numSMs * m_numFineBlocksPerSM, 1, m_numImages), frBlock, args, 0, stream)); + NVDR_CHECK_CUDA_ERROR(cudaStreamSynchronize(stream)); +} + +//------------------------------------------------------------------------ diff --git a/isaac_ros_gxf_extensions/gxf_isaac_foundationpose/gxf/foundationpose/nvdiffrast/common/cudaraster/impl/RasterImpl.cu b/isaac_ros_gxf_extensions/gxf_isaac_foundationpose/gxf/foundationpose/nvdiffrast/common/cudaraster/impl/RasterImpl.cu new file mode 100644 index 0000000..43b1edf --- /dev/null +++ b/isaac_ros_gxf_extensions/gxf_isaac_foundationpose/gxf/foundationpose/nvdiffrast/common/cudaraster/impl/RasterImpl.cu @@ -0,0 +1,37 @@ +// Copyright (c) 2009-2022, NVIDIA CORPORATION. All rights reserved. +// +// NVIDIA CORPORATION and its licensors retain all intellectual property +// and proprietary rights in and to this software, related documentation +// and any modifications thereto. Any use, reproduction, disclosure or +// distribution of this software and related documentation without an express +// license agreement from NVIDIA CORPORATION is strictly prohibited. + +#include "../CudaRaster.hpp" +#include "PrivateDefs.hpp" +#include "Constants.hpp" +#include "Util.inl" + +namespace CR +{ + +//------------------------------------------------------------------------ +// Stage implementations. +//------------------------------------------------------------------------ + +#include "TriangleSetup.inl" +#include "BinRaster.inl" +#include "CoarseRaster.inl" +#include "FineRaster.inl" + +} + +//------------------------------------------------------------------------ +// Stage entry points. +//------------------------------------------------------------------------ + +__global__ void __launch_bounds__(CR_SETUP_WARPS * 32, CR_SETUP_OPT_BLOCKS) triangleSetupKernel (const CR::CRParams p) { CR::triangleSetupImpl(p); } +__global__ void __launch_bounds__(CR_BIN_WARPS * 32, 1) binRasterKernel (const CR::CRParams p) { CR::binRasterImpl(p); } +__global__ void __launch_bounds__(CR_COARSE_WARPS * 32, 1) coarseRasterKernel (const CR::CRParams p) { CR::coarseRasterImpl(p); } +__global__ void __launch_bounds__(CR_FINE_MAX_WARPS * 32, 1) fineRasterKernel (const CR::CRParams p) { CR::fineRasterImpl(p); } + +//------------------------------------------------------------------------ diff --git a/isaac_ros_gxf_extensions/gxf_isaac_foundationpose/gxf/foundationpose/nvdiffrast/common/cudaraster/impl/RasterImpl.hpp b/isaac_ros_gxf_extensions/gxf_isaac_foundationpose/gxf/foundationpose/nvdiffrast/common/cudaraster/impl/RasterImpl.hpp new file mode 100644 index 0000000..462621c --- /dev/null +++ b/isaac_ros_gxf_extensions/gxf_isaac_foundationpose/gxf/foundationpose/nvdiffrast/common/cudaraster/impl/RasterImpl.hpp @@ -0,0 +1,104 @@ +// SPDX-FileCopyrightText: NVIDIA CORPORATION & AFFILIATES +// Copyright (c) 2009-2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. +// +// SPDX-License-Identifier: Apache-2.0 + +#pragma once +#include "PrivateDefs.hpp" +#include "Buffer.hpp" +#include "../CudaRaster.hpp" + +namespace CR +{ +//------------------------------------------------------------------------ + +class RasterImpl +{ +public: + RasterImpl (void); + ~RasterImpl (void); + + void setViewportSize (Vec3i size); // Must be multiple of tile size (8x8). + void setRenderModeFlags (U32 flags) { m_renderModeFlags = flags; } + void deferredClear (U32 color) { m_deferredClear = true; m_clearColor = color; } + void setVertexBuffer (void* ptr, int numVertices) { m_vertexPtr = ptr; m_numVertices = numVertices; } // GPU pointer. + void setIndexBuffer (void* ptr, int numTriangles) { m_indexPtr = ptr; m_numTriangles = numTriangles; } // GPU pointer. + bool drawTriangles (const Vec2i* ranges, bool peel, cudaStream_t stream); + void* getColorBuffer (void) { return m_colorBuffer.getPtr(); } // GPU pointer. + void* getDepthBuffer (void) { return m_depthBuffer.getPtr(); } // GPU pointer. + size_t getTotalBufferSizes (void) const; + +private: + void launchStages (bool instanceMode, bool peel, cudaStream_t stream); + + // State. + + unsigned int m_renderModeFlags; + bool m_deferredClear; + unsigned int m_clearColor; + void* m_vertexPtr; + void* m_indexPtr; + int m_numVertices; // Input buffer size. + int m_numTriangles; // Input buffer size. + size_t m_bufferSizesReported; // Previously reported buffer sizes. + + // Surfaces. + + Buffer m_colorBuffer; + Buffer m_depthBuffer; + int m_numImages; + Vec2i m_sizePixels; + Vec2i m_sizeBins; + S32 m_numBins; + Vec2i m_sizeTiles; + S32 m_numTiles; + + // Launch sizes etc. + + S32 m_numSMs; + S32 m_numCoarseBlocksPerSM; + S32 m_numFineBlocksPerSM; + S32 m_numFineWarpsPerBlock; + + // Global intermediate buffers. Individual images have offsets to these. + + Buffer m_crAtomics; + HostBuffer m_crAtomicsHost; + HostBuffer m_crImageParamsHost; + Buffer m_crImageParamsExtra; + Buffer m_triSubtris; + Buffer m_triHeader; + Buffer m_triData; + Buffer m_binFirstSeg; + Buffer m_binTotal; + Buffer m_binSegData; + Buffer m_binSegNext; + Buffer m_binSegCount; + Buffer m_activeTiles; + Buffer m_tileFirstSeg; + Buffer m_tileSegData; + Buffer m_tileSegNext; + Buffer m_tileSegCount; + + // Actual buffer sizes. + + S32 m_maxSubtris; + S32 m_maxBinSegs; + S32 m_maxTileSegs; +}; + +//------------------------------------------------------------------------ +} // namespace CR + diff --git a/isaac_ros_gxf_extensions/gxf_isaac_foundationpose/gxf/foundationpose/nvdiffrast/common/cudaraster/impl/TriangleSetup.inl b/isaac_ros_gxf_extensions/gxf_isaac_foundationpose/gxf/foundationpose/nvdiffrast/common/cudaraster/impl/TriangleSetup.inl new file mode 100644 index 0000000..edb61cf --- /dev/null +++ b/isaac_ros_gxf_extensions/gxf_isaac_foundationpose/gxf/foundationpose/nvdiffrast/common/cudaraster/impl/TriangleSetup.inl @@ -0,0 +1,393 @@ +// Copyright (c) 2009-2022, NVIDIA CORPORATION. All rights reserved. +// +// NVIDIA CORPORATION and its licensors retain all intellectual property +// and proprietary rights in and to this software, related documentation +// and any modifications thereto. Any use, reproduction, disclosure or +// distribution of this software and related documentation without an express +// license agreement from NVIDIA CORPORATION is strictly prohibited. + +//------------------------------------------------------------------------ + +__device__ __inline__ void snapTriangle( + const CRParams& p, + float4 v0, float4 v1, float4 v2, + int2& p0, int2& p1, int2& p2, float3& rcpW, int2& lo, int2& hi) +{ + F32 viewScaleX = (F32)(p.widthPixels << (CR_SUBPIXEL_LOG2 - 1)); + F32 viewScaleY = (F32)(p.heightPixels << (CR_SUBPIXEL_LOG2 - 1)); + rcpW = make_float3(1.0f / v0.w, 1.0f / v1.w, 1.0f / v2.w); + p0 = make_int2(f32_to_s32_sat(v0.x * rcpW.x * viewScaleX), f32_to_s32_sat(v0.y * rcpW.x * viewScaleY)); + p1 = make_int2(f32_to_s32_sat(v1.x * rcpW.y * viewScaleX), f32_to_s32_sat(v1.y * rcpW.y * viewScaleY)); + p2 = make_int2(f32_to_s32_sat(v2.x * rcpW.z * viewScaleX), f32_to_s32_sat(v2.y * rcpW.z * viewScaleY)); + lo = make_int2(min_min(p0.x, p1.x, p2.x), min_min(p0.y, p1.y, p2.y)); + hi = make_int2(max_max(p0.x, p1.x, p2.x), max_max(p0.y, p1.y, p2.y)); +} + +//------------------------------------------------------------------------ + +__device__ __inline__ U32 cover8x8_selectFlips(S32 dx, S32 dy) // 10 instr +{ + U32 flips = 0; + if (dy > 0 || (dy == 0 && dx <= 0)) + flips ^= (1 << CR_FLIPBIT_FLIP_X) ^ (1 << CR_FLIPBIT_FLIP_Y) ^ (1 << CR_FLIPBIT_COMPL); + if (dx > 0) + flips ^= (1 << CR_FLIPBIT_FLIP_X) ^ (1 << CR_FLIPBIT_FLIP_Y); + if (::abs(dx) < ::abs(dy)) + flips ^= (1 << CR_FLIPBIT_SWAP_XY) ^ (1 << CR_FLIPBIT_FLIP_Y); + return flips; +} + +//------------------------------------------------------------------------ + +__device__ __inline__ bool prepareTriangle( + const CRParams& p, + int2 p0, int2 p1, int2 p2, int2 lo, int2 hi, + int2& d1, int2& d2, S32& area) +{ + // Backfacing or degenerate => cull. + + d1 = make_int2(p1.x - p0.x, p1.y - p0.y); + d2 = make_int2(p2.x - p0.x, p2.y - p0.y); + area = d1.x * d2.y - d1.y * d2.x; + + if (area == 0) + return false; // Degenerate. + + if (area < 0 && (p.renderModeFlags & CudaRaster::RenderModeFlag_EnableBackfaceCulling) != 0) + return false; // Backfacing. + + // AABB falls between samples => cull. + + int sampleSize = 1 << CR_SUBPIXEL_LOG2; + int biasX = (p.widthPixels << (CR_SUBPIXEL_LOG2 - 1)) - (sampleSize >> 1); + int biasY = (p.heightPixels << (CR_SUBPIXEL_LOG2 - 1)) - (sampleSize >> 1); + int lox = (int)add_add(lo.x, sampleSize - 1, biasX) & -sampleSize; + int loy = (int)add_add(lo.y, sampleSize - 1, biasY) & -sampleSize; + int hix = (hi.x + biasX) & -sampleSize; + int hiy = (hi.y + biasY) & -sampleSize; + + if (lox > hix || loy > hiy) + return false; // Between pixels. + + // AABB covers 1 or 2 samples => cull if they are not covered. + + int diff = add_sub(hix, hiy, lox) - loy; + if (diff <= sampleSize) + { + int2 t0 = make_int2(add_sub(p0.x, biasX, lox), add_sub(p0.y, biasY, loy)); + int2 t1 = make_int2(add_sub(p1.x, biasX, lox), add_sub(p1.y, biasY, loy)); + int2 t2 = make_int2(add_sub(p2.x, biasX, lox), add_sub(p2.y, biasY, loy)); + S32 e0 = t0.x * t1.y - t0.y * t1.x; + S32 e1 = t1.x * t2.y - t1.y * t2.x; + S32 e2 = t2.x * t0.y - t2.y * t0.x; + if (area < 0) + { + e0 = -e0; + e1 = -e1; + e2 = -e2; + } + + if (e0 < 0 || e1 < 0 || e2 < 0) + { + if (diff == 0) + return false; // Between pixels. + + t0 = make_int2(add_sub(p0.x, biasX, hix), add_sub(p0.y, biasY, hiy)); + t1 = make_int2(add_sub(p1.x, biasX, hix), add_sub(p1.y, biasY, hiy)); + t2 = make_int2(add_sub(p2.x, biasX, hix), add_sub(p2.y, biasY, hiy)); + e0 = t0.x * t1.y - t0.y * t1.x; + e1 = t1.x * t2.y - t1.y * t2.x; + e2 = t2.x * t0.y - t2.y * t0.x; + if (area < 0) + { + e0 = -e0; + e1 = -e1; + e2 = -e2; + } + + if (e0 < 0 || e1 < 0 || e2 < 0) + return false; // Between pixels. + } + } + + // Otherwise => proceed to output the triangle. + + return true; // Visible. +} + +//------------------------------------------------------------------------ + +__device__ __inline__ void setupTriangle( + const CRParams& p, + CRTriangleHeader* th, CRTriangleData* td, int triId, + float v0z, float v1z, float v2z, + int2 p0, int2 p1, int2 p2, float3 rcpW, + int2 d1, int2 d2, S32 area) +{ + // Swap vertices 1 and 2 if area is negative. Only executed if backface culling is + // disabled (if it is enabled, we never come here with area < 0). + + if (area < 0) + { + swap(d1, d2); + swap(p1, p2); + swap(v1z, v2z); + swap(rcpW.y, rcpW.z); + area = -area; + } + + int2 wv0; + wv0.x = p0.x + (p.widthPixels << (CR_SUBPIXEL_LOG2 - 1)); + wv0.y = p0.y + (p.heightPixels << (CR_SUBPIXEL_LOG2 - 1)); + + // Setup depth plane equation. + + F32 zcoef = (F32)(CR_DEPTH_MAX - CR_DEPTH_MIN) * 0.5f; + F32 zbias = (F32)(CR_DEPTH_MAX + CR_DEPTH_MIN) * 0.5f; + float3 zvert = make_float3( + (v0z * zcoef) * rcpW.x + zbias, + (v1z * zcoef) * rcpW.y + zbias, + (v2z * zcoef) * rcpW.z + zbias + ); + int2 zv0 = make_int2( + wv0.x - (1 << (CR_SUBPIXEL_LOG2 - 1)), + wv0.y - (1 << (CR_SUBPIXEL_LOG2 - 1)) + ); + uint3 zpleq = setupPleq(zvert, zv0, d1, d2, 1.0f / (F32)area); + + U32 zmin = f32_to_u32_sat(fminf(fminf(zvert.x, zvert.y), zvert.z) - (F32)CR_LERP_ERROR(0)); + + // Write CRTriangleData. + + *(uint4*)td = make_uint4(zpleq.x, zpleq.y, zpleq.z, triId); + + // Determine flipbits. + + U32 f01 = cover8x8_selectFlips(d1.x, d1.y); + U32 f12 = cover8x8_selectFlips(d2.x - d1.x, d2.y - d1.y); + U32 f20 = cover8x8_selectFlips(-d2.x, -d2.y); + + // Write CRTriangleHeader. + + *(uint4*)th = make_uint4( + prmt(p0.x, p0.y, 0x5410), + prmt(p1.x, p1.y, 0x5410), + prmt(p2.x, p2.y, 0x5410), + (zmin & 0xfffff000u) | (f01 << 6) | (f12 << 2) | (f20 >> 2)); +} + +//------------------------------------------------------------------------ + +__device__ __inline__ void triangleSetupImpl(const CRParams p) +{ + __shared__ F32 s_bary[CR_SETUP_WARPS * 32][18]; + F32* bary = s_bary[threadIdx.x + threadIdx.y * 32]; + + // Compute task and image indices. + + int taskIdx = threadIdx.x + 32 * (threadIdx.y + CR_SETUP_WARPS * blockIdx.x); + int imageIdx = 0; + if (p.instanceMode) + { + imageIdx = blockIdx.z; + if (taskIdx >= p.numTriangles) + return; + } + else + { + while (imageIdx < p.numImages) + { + int count = getImageParams(p, imageIdx).triCount; + if (taskIdx < count) + break; + taskIdx -= count; + imageIdx += 1; + } + if (imageIdx == p.numImages) + return; + } + + // Per-image data structures. + + const CRImageParams& ip = getImageParams(p, imageIdx); + CRAtomics& atomics = p.atomics[imageIdx]; + + const int* indexBuffer = (const int*)p.indexBuffer; + U8* triSubtris = (U8*)p.triSubtris + imageIdx * p.maxSubtris; + CRTriangleHeader* triHeader = (CRTriangleHeader*)p.triHeader + imageIdx * p.maxSubtris; + CRTriangleData* triData = (CRTriangleData*)p.triData + imageIdx * p.maxSubtris; + + // Determine triangle index. + + int triIdx = taskIdx; + if (!p.instanceMode) + triIdx += ip.triOffset; + + // Read vertex indices. + + if ((U32)triIdx >= (U32)p.numTriangles) + { + // Bad triangle index. + triSubtris[taskIdx] = 0; + return; + } + + uint4 vidx; + vidx.x = indexBuffer[triIdx * 3 + 0]; + vidx.y = indexBuffer[triIdx * 3 + 1]; + vidx.z = indexBuffer[triIdx * 3 + 2]; + vidx.w = triIdx + 1; // Triangle index. + + if (vidx.x >= (U32)p.numVertices || + vidx.y >= (U32)p.numVertices || + vidx.z >= (U32)p.numVertices) + { + // Bad vertex index. + triSubtris[taskIdx] = 0; + return; + } + + // Read vertex positions. + + const float4* vertexBuffer = (const float4*)p.vertexBuffer; + if (p.instanceMode) + vertexBuffer += p.numVertices * imageIdx; // Instance offset. + + float4 v0 = vertexBuffer[vidx.x]; + float4 v1 = vertexBuffer[vidx.y]; + float4 v2 = vertexBuffer[vidx.z]; + + // Outside view frustum => cull. + + if (v0.w < fabsf(v0.x) | v0.w < fabsf(v0.y) | v0.w < fabsf(v0.z)) + { + if ((v0.w < +v0.x & v1.w < +v1.x & v2.w < +v2.x) | + (v0.w < -v0.x & v1.w < -v1.x & v2.w < -v2.x) | + (v0.w < +v0.y & v1.w < +v1.y & v2.w < +v2.y) | + (v0.w < -v0.y & v1.w < -v1.y & v2.w < -v2.y) | + (v0.w < +v0.z & v1.w < +v1.z & v2.w < +v2.z) | + (v0.w < -v0.z & v1.w < -v1.z & v2.w < -v2.z)) + { + triSubtris[taskIdx] = 0; + return; + } + } + + // Inside depth range => try to snap vertices. + + if (v0.w >= fabsf(v0.z) & v1.w >= fabsf(v1.z) & v2.w >= fabsf(v2.z)) + { + // Inside S16 range and small enough => fast path. + // Note: aabbLimit comes from the fact that cover8x8 + // does not support guardband with maximal viewport. + + int2 p0, p1, p2, lo, hi; + float3 rcpW; + + snapTriangle(p, v0, v1, v2, p0, p1, p2, rcpW, lo, hi); + S32 loxy = ::min(lo.x, lo.y); + S32 hixy = ::max(hi.x, hi.y); + S32 aabbLimit = (1 << (CR_MAXVIEWPORT_LOG2 + CR_SUBPIXEL_LOG2)) - 1; + + if (loxy >= -32768 && hixy <= 32767 && hixy - loxy <= aabbLimit) + { + int2 d1, d2; + S32 area; + bool res = prepareTriangle(p, p0, p1, p2, lo, hi, d1, d2, area); + triSubtris[taskIdx] = res ? 1 : 0; + + if (res) + setupTriangle( + p, + &triHeader[taskIdx], &triData[taskIdx], vidx.w, + v0.z, v1.z, v2.z, + p0, p1, p2, rcpW, + d1, d2, area); + + return; + } + } + + // Clip to view frustum. + + float4 ov0 = v0; + float4 od1 = make_float4(v1.x - v0.x, v1.y - v0.y, v1.z - v0.z, v1.w - v0.w); + float4 od2 = make_float4(v2.x - v0.x, v2.y - v0.y, v2.z - v0.z, v2.w - v0.w); + int numVerts = clipTriangleWithFrustum(bary, &ov0.x, &v1.x, &v2.x, &od1.x, &od2.x); + + // Count non-culled subtriangles. + + v0.x = ov0.x + od1.x * bary[0] + od2.x * bary[1]; + v0.y = ov0.y + od1.y * bary[0] + od2.y * bary[1]; + v0.z = ov0.z + od1.z * bary[0] + od2.z * bary[1]; + v0.w = ov0.w + od1.w * bary[0] + od2.w * bary[1]; + v1.x = ov0.x + od1.x * bary[2] + od2.x * bary[3]; + v1.y = ov0.y + od1.y * bary[2] + od2.y * bary[3]; + v1.z = ov0.z + od1.z * bary[2] + od2.z * bary[3]; + v1.w = ov0.w + od1.w * bary[2] + od2.w * bary[3]; + float4 tv1 = v1; + + int numSubtris = 0; + for (int i = 2; i < numVerts; i++) + { + v2.x = ov0.x + od1.x * bary[i * 2 + 0] + od2.x * bary[i * 2 + 1]; + v2.y = ov0.y + od1.y * bary[i * 2 + 0] + od2.y * bary[i * 2 + 1]; + v2.z = ov0.z + od1.z * bary[i * 2 + 0] + od2.z * bary[i * 2 + 1]; + v2.w = ov0.w + od1.w * bary[i * 2 + 0] + od2.w * bary[i * 2 + 1]; + + int2 p0, p1, p2, lo, hi, d1, d2; + float3 rcpW; + S32 area; + + snapTriangle(p, v0, v1, v2, p0, p1, p2, rcpW, lo, hi); + if (prepareTriangle(p, p0, p1, p2, lo, hi, d1, d2, area)) + numSubtris++; + + v1 = v2; + } + + triSubtris[taskIdx] = numSubtris; + + // Multiple subtriangles => allocate. + + int subtriBase = taskIdx; + if (numSubtris > 1) + { + subtriBase = atomicAdd(&atomics.numSubtris, numSubtris); + triHeader[taskIdx].misc = subtriBase; + if (subtriBase + numSubtris > p.maxSubtris) + numVerts = 0; + } + + // Setup subtriangles. + + v1 = tv1; + for (int i = 2; i < numVerts; i++) + { + v2.x = ov0.x + od1.x * bary[i * 2 + 0] + od2.x * bary[i * 2 + 1]; + v2.y = ov0.y + od1.y * bary[i * 2 + 0] + od2.y * bary[i * 2 + 1]; + v2.z = ov0.z + od1.z * bary[i * 2 + 0] + od2.z * bary[i * 2 + 1]; + v2.w = ov0.w + od1.w * bary[i * 2 + 0] + od2.w * bary[i * 2 + 1]; + + int2 p0, p1, p2, lo, hi, d1, d2; + float3 rcpW; + S32 area; + + snapTriangle(p, v0, v1, v2, p0, p1, p2, rcpW, lo, hi); + if (prepareTriangle(p, p0, p1, p2, lo, hi, d1, d2, area)) + { + setupTriangle( + p, + &triHeader[subtriBase], &triData[subtriBase], vidx.w, + v0.z, v1.z, v2.z, + p0, p1, p2, rcpW, + d1, d2, area); + + subtriBase++; + } + + v1 = v2; + } +} + +//------------------------------------------------------------------------ diff --git a/isaac_ros_gxf_extensions/gxf_isaac_foundationpose/gxf/foundationpose/nvdiffrast/common/cudaraster/impl/Util.inl b/isaac_ros_gxf_extensions/gxf_isaac_foundationpose/gxf/foundationpose/nvdiffrast/common/cudaraster/impl/Util.inl new file mode 100644 index 0000000..f8faeba --- /dev/null +++ b/isaac_ros_gxf_extensions/gxf_isaac_foundationpose/gxf/foundationpose/nvdiffrast/common/cudaraster/impl/Util.inl @@ -0,0 +1,452 @@ +// Copyright (c) 2009-2022, NVIDIA CORPORATION. All rights reserved. +// +// NVIDIA CORPORATION and its licensors retain all intellectual property +// and proprietary rights in and to this software, related documentation +// and any modifications thereto. Any use, reproduction, disclosure or +// distribution of this software and related documentation without an express +// license agreement from NVIDIA CORPORATION is strictly prohibited. + +#include "PrivateDefs.hpp" + +namespace CR +{ +//------------------------------------------------------------------------ + +template __device__ __inline__ void swap(T& a, T& b) { T t = a; a = b; b = t; } + +__device__ __inline__ U32 getLo (U64 a) { return __double2loint(__longlong_as_double(a)); } +__device__ __inline__ S32 getLo (S64 a) { return __double2loint(__longlong_as_double(a)); } +__device__ __inline__ U32 getHi (U64 a) { return __double2hiint(__longlong_as_double(a)); } +__device__ __inline__ S32 getHi (S64 a) { return __double2hiint(__longlong_as_double(a)); } +__device__ __inline__ U64 combineLoHi (U32 lo, U32 hi) { return __double_as_longlong(__hiloint2double(hi, lo)); } +__device__ __inline__ S64 combineLoHi (S32 lo, S32 hi) { return __double_as_longlong(__hiloint2double(hi, lo)); } +__device__ __inline__ U32 getLaneMaskLt (void) { U32 r; asm("mov.u32 %0, %lanemask_lt;" : "=r"(r)); return r; } +__device__ __inline__ U32 getLaneMaskLe (void) { U32 r; asm("mov.u32 %0, %lanemask_le;" : "=r"(r)); return r; } +__device__ __inline__ U32 getLaneMaskGt (void) { U32 r; asm("mov.u32 %0, %lanemask_gt;" : "=r"(r)); return r; } +__device__ __inline__ U32 getLaneMaskGe (void) { U32 r; asm("mov.u32 %0, %lanemask_ge;" : "=r"(r)); return r; } +__device__ __inline__ int findLeadingOne (U32 v) { U32 r; asm("bfind.u32 %0, %1;" : "=r"(r) : "r"(v)); return r; } +__device__ __inline__ bool singleLane (void) { return ((::__ballot_sync(~0u, true) & getLaneMaskLt()) == 0); } + +__device__ __inline__ void add_add_carry (U32& rlo, U32 alo, U32 blo, U32& rhi, U32 ahi, U32 bhi) { U64 r = combineLoHi(alo, ahi) + combineLoHi(blo, bhi); rlo = getLo(r); rhi = getHi(r); } +__device__ __inline__ S32 f32_to_s32_sat (F32 a) { S32 v; asm("cvt.rni.sat.s32.f32 %0, %1;" : "=r"(v) : "f"(a)); return v; } +__device__ __inline__ U32 f32_to_u32_sat (F32 a) { U32 v; asm("cvt.rni.sat.u32.f32 %0, %1;" : "=r"(v) : "f"(a)); return v; } +__device__ __inline__ U32 f32_to_u32_sat_rmi (F32 a) { U32 v; asm("cvt.rmi.sat.u32.f32 %0, %1;" : "=r"(v) : "f"(a)); return v; } +__device__ __inline__ U32 f32_to_u8_sat (F32 a) { U32 v; asm("cvt.rni.sat.u8.f32 %0, %1;" : "=r"(v) : "f"(a)); return v; } +__device__ __inline__ S64 f32_to_s64 (F32 a) { S64 v; asm("cvt.rni.s64.f32 %0, %1;" : "=l"(v) : "f"(a)); return v; } +__device__ __inline__ S32 add_s16lo_s16lo (S32 a, S32 b) { S32 v; asm("vadd.s32.s32.s32 %0, %1.h0, %2.h0;" : "=r"(v) : "r"(a), "r"(b)); return v; } +__device__ __inline__ S32 add_s16hi_s16lo (S32 a, S32 b) { S32 v; asm("vadd.s32.s32.s32 %0, %1.h1, %2.h0;" : "=r"(v) : "r"(a), "r"(b)); return v; } +__device__ __inline__ S32 add_s16lo_s16hi (S32 a, S32 b) { S32 v; asm("vadd.s32.s32.s32 %0, %1.h0, %2.h1;" : "=r"(v) : "r"(a), "r"(b)); return v; } +__device__ __inline__ S32 add_s16hi_s16hi (S32 a, S32 b) { S32 v; asm("vadd.s32.s32.s32 %0, %1.h1, %2.h1;" : "=r"(v) : "r"(a), "r"(b)); return v; } +__device__ __inline__ S32 sub_s16lo_s16lo (S32 a, S32 b) { S32 v; asm("vsub.s32.s32.s32 %0, %1.h0, %2.h0;" : "=r"(v) : "r"(a), "r"(b)); return v; } +__device__ __inline__ S32 sub_s16hi_s16lo (S32 a, S32 b) { S32 v; asm("vsub.s32.s32.s32 %0, %1.h1, %2.h0;" : "=r"(v) : "r"(a), "r"(b)); return v; } +__device__ __inline__ S32 sub_s16lo_s16hi (S32 a, S32 b) { S32 v; asm("vsub.s32.s32.s32 %0, %1.h0, %2.h1;" : "=r"(v) : "r"(a), "r"(b)); return v; } +__device__ __inline__ S32 sub_s16hi_s16hi (S32 a, S32 b) { S32 v; asm("vsub.s32.s32.s32 %0, %1.h1, %2.h1;" : "=r"(v) : "r"(a), "r"(b)); return v; } +__device__ __inline__ S32 sub_u16lo_u16lo (U32 a, U32 b) { S32 v; asm("vsub.s32.u32.u32 %0, %1.h0, %2.h0;" : "=r"(v) : "r"(a), "r"(b)); return v; } +__device__ __inline__ S32 sub_u16hi_u16lo (U32 a, U32 b) { S32 v; asm("vsub.s32.u32.u32 %0, %1.h1, %2.h0;" : "=r"(v) : "r"(a), "r"(b)); return v; } +__device__ __inline__ S32 sub_u16lo_u16hi (U32 a, U32 b) { S32 v; asm("vsub.s32.u32.u32 %0, %1.h0, %2.h1;" : "=r"(v) : "r"(a), "r"(b)); return v; } +__device__ __inline__ S32 sub_u16hi_u16hi (U32 a, U32 b) { S32 v; asm("vsub.s32.u32.u32 %0, %1.h1, %2.h1;" : "=r"(v) : "r"(a), "r"(b)); return v; } +__device__ __inline__ U32 add_b0 (U32 a, U32 b) { U32 v; asm("vadd.u32.u32.u32 %0, %1.b0, %2;" : "=r"(v) : "r"(a), "r"(b)); return v; } +__device__ __inline__ U32 add_b1 (U32 a, U32 b) { U32 v; asm("vadd.u32.u32.u32 %0, %1.b1, %2;" : "=r"(v) : "r"(a), "r"(b)); return v; } +__device__ __inline__ U32 add_b2 (U32 a, U32 b) { U32 v; asm("vadd.u32.u32.u32 %0, %1.b2, %2;" : "=r"(v) : "r"(a), "r"(b)); return v; } +__device__ __inline__ U32 add_b3 (U32 a, U32 b) { U32 v; asm("vadd.u32.u32.u32 %0, %1.b3, %2;" : "=r"(v) : "r"(a), "r"(b)); return v; } +__device__ __inline__ U32 vmad_b0 (U32 a, U32 b, U32 c) { U32 v; asm("vmad.u32.u32.u32 %0, %1.b0, %2, %3;" : "=r"(v) : "r"(a), "r"(b), "r"(c)); return v; } +__device__ __inline__ U32 vmad_b1 (U32 a, U32 b, U32 c) { U32 v; asm("vmad.u32.u32.u32 %0, %1.b1, %2, %3;" : "=r"(v) : "r"(a), "r"(b), "r"(c)); return v; } +__device__ __inline__ U32 vmad_b2 (U32 a, U32 b, U32 c) { U32 v; asm("vmad.u32.u32.u32 %0, %1.b2, %2, %3;" : "=r"(v) : "r"(a), "r"(b), "r"(c)); return v; } +__device__ __inline__ U32 vmad_b3 (U32 a, U32 b, U32 c) { U32 v; asm("vmad.u32.u32.u32 %0, %1.b3, %2, %3;" : "=r"(v) : "r"(a), "r"(b), "r"(c)); return v; } +__device__ __inline__ U32 vmad_b0_b3 (U32 a, U32 b, U32 c) { U32 v; asm("vmad.u32.u32.u32 %0, %1.b0, %2.b3, %3;" : "=r"(v) : "r"(a), "r"(b), "r"(c)); return v; } +__device__ __inline__ U32 vmad_b1_b3 (U32 a, U32 b, U32 c) { U32 v; asm("vmad.u32.u32.u32 %0, %1.b1, %2.b3, %3;" : "=r"(v) : "r"(a), "r"(b), "r"(c)); return v; } +__device__ __inline__ U32 vmad_b2_b3 (U32 a, U32 b, U32 c) { U32 v; asm("vmad.u32.u32.u32 %0, %1.b2, %2.b3, %3;" : "=r"(v) : "r"(a), "r"(b), "r"(c)); return v; } +__device__ __inline__ U32 vmad_b3_b3 (U32 a, U32 b, U32 c) { U32 v; asm("vmad.u32.u32.u32 %0, %1.b3, %2.b3, %3;" : "=r"(v) : "r"(a), "r"(b), "r"(c)); return v; } +__device__ __inline__ U32 add_mask8 (U32 a, U32 b) { U32 v; U32 z=0; asm("vadd.u32.u32.u32 %0.b0, %1, %2, %3;" : "=r"(v) : "r"(a), "r"(b), "r"(z)); return v; } +__device__ __inline__ U32 sub_mask8 (U32 a, U32 b) { U32 v; U32 z=0; asm("vsub.u32.u32.u32 %0.b0, %1, %2, %3;" : "=r"(v) : "r"(a), "r"(b), "r"(z)); return v; } +__device__ __inline__ S32 max_max (S32 a, S32 b, S32 c) { S32 v; asm("vmax.s32.s32.s32.max %0, %1, %2, %3;" : "=r"(v) : "r"(a), "r"(b), "r"(c)); return v; } +__device__ __inline__ S32 min_min (S32 a, S32 b, S32 c) { S32 v; asm("vmin.s32.s32.s32.min %0, %1, %2, %3;" : "=r"(v) : "r"(a), "r"(b), "r"(c)); return v; } +__device__ __inline__ S32 max_add (S32 a, S32 b, S32 c) { S32 v; asm("vmax.s32.s32.s32.add %0, %1, %2, %3;" : "=r"(v) : "r"(a), "r"(b), "r"(c)); return v; } +__device__ __inline__ S32 min_add (S32 a, S32 b, S32 c) { S32 v; asm("vmin.s32.s32.s32.add %0, %1, %2, %3;" : "=r"(v) : "r"(a), "r"(b), "r"(c)); return v; } +__device__ __inline__ U32 add_add (U32 a, U32 b, U32 c) { U32 v; asm("vadd.u32.u32.u32.add %0, %1, %2, %3;" : "=r"(v) : "r"(a), "r"(b), "r"(c)); return v; } +__device__ __inline__ U32 sub_add (U32 a, U32 b, U32 c) { U32 v; asm("vsub.u32.u32.u32.add %0, %1, %2, %3;" : "=r"(v) : "r"(a), "r"(b), "r"(c)); return v; } +__device__ __inline__ U32 add_sub (U32 a, U32 b, U32 c) { U32 v; asm("vsub.u32.u32.u32.add %0, %1, %2, %3;" : "=r"(v) : "r"(a), "r"(c), "r"(b)); return v; } +__device__ __inline__ S32 add_clamp_0_x (S32 a, S32 b, S32 c) { S32 v; asm("vadd.u32.s32.s32.sat.min %0, %1, %2, %3;" : "=r"(v) : "r"(a), "r"(b), "r"(c)); return v; } +__device__ __inline__ S32 add_clamp_b0 (S32 a, S32 b, S32 c) { S32 v; asm("vadd.u32.s32.s32.sat %0.b0, %1, %2, %3;" : "=r"(v) : "r"(a), "r"(b), "r"(c)); return v; } +__device__ __inline__ S32 add_clamp_b2 (S32 a, S32 b, S32 c) { S32 v; asm("vadd.u32.s32.s32.sat %0.b2, %1, %2, %3;" : "=r"(v) : "r"(a), "r"(b), "r"(c)); return v; } +__device__ __inline__ U32 prmt (U32 a, U32 b, U32 c) { U32 v; asm("prmt.b32 %0, %1, %2, %3;" : "=r"(v) : "r"(a), "r"(b), "r"(c)); return v; } +__device__ __inline__ S32 u32lo_sext (U32 a) { U32 v; asm("cvt.s16.u32 %0, %1;" : "=r"(v) : "r"(a)); return v; } +__device__ __inline__ U32 slct (U32 a, U32 b, S32 c) { U32 v; asm("slct.u32.s32 %0, %1, %2, %3;" : "=r"(v) : "r"(a), "r"(b), "r"(c)); return v; } +__device__ __inline__ S32 slct (S32 a, S32 b, S32 c) { S32 v; asm("slct.s32.s32 %0, %1, %2, %3;" : "=r"(v) : "r"(a), "r"(b), "r"(c)); return v; } +__device__ __inline__ F32 slct (F32 a, F32 b, S32 c) { F32 v; asm("slct.f32.s32 %0, %1, %2, %3;" : "=f"(v) : "f"(a), "f"(b), "r"(c)); return v; } +__device__ __inline__ U32 isetge (S32 a, S32 b) { U32 v; asm("set.ge.u32.s32 %0, %1, %2;" : "=r"(v) : "r"(a), "r"(b)); return v; } +__device__ __inline__ F64 rcp_approx (F64 a) { F64 v; asm("rcp.approx.ftz.f64 %0, %1;" : "=d"(v) : "d"(a)); return v; } +__device__ __inline__ F32 fma_rm (F32 a, F32 b, F32 c) { F32 v; asm("fma.rm.f32 %0, %1, %2, %3;" : "=f"(v) : "f"(a), "f"(b), "f"(c)); return v; } +__device__ __inline__ U32 idiv_fast (U32 a, U32 b); + +__device__ __inline__ uint3 setupPleq (float3 values, int2 v0, int2 d1, int2 d2, F32 areaRcp); + +__device__ __inline__ void cover8x8_setupLUT (volatile U64* lut); +__device__ __inline__ U64 cover8x8_exact_fast (S32 ox, S32 oy, S32 dx, S32 dy, U32 flips, volatile const U64* lut); // Assumes viewport <= 2^11, subpixels <= 2^4, no guardband. +__device__ __inline__ U64 cover8x8_lookupMask (S64 yinit, U32 yinc, U32 flips, volatile const U64* lut); + +__device__ __inline__ U64 cover8x8_exact_noLUT (S32 ox, S32 oy, S32 dx, S32 dy); // optimized reference implementation, does not require look-up table +__device__ __inline__ U64 cover8x8_conservative_noLUT (S32 ox, S32 oy, S32 dx, S32 dy); +__device__ __inline__ U64 cover8x8_generateMask_noLUT (S32 curr, S32 dx, S32 dy); + +template __device__ __inline__ void sortShared(T* ptr, int numItems); // Assumes that numItems <= threadsInBlock. Must sync before & after the call. + +__device__ __inline__ const CRImageParams& getImageParams(const CRParams& p, int idx) +{ + return (idx < CR_EMBED_IMAGE_PARAMS) ? p.imageParamsFirst[idx] : p.imageParamsExtra[idx - CR_EMBED_IMAGE_PARAMS]; +} + +//------------------------------------------------------------------------ + +__device__ __inline__ int clipPolygonWithPlane(F32* baryOut, const F32* baryIn, int numIn, F32 v0, F32 v1, F32 v2) +{ + int numOut = 0; + if (numIn >= 3) + { + int ai = (numIn - 1) * 2; + F32 av = v0 + v1 * baryIn[ai + 0] + v2 * baryIn[ai + 1]; + for (int bi = 0; bi < numIn * 2; bi += 2) + { + F32 bv = v0 + v1 * baryIn[bi + 0] + v2 * baryIn[bi + 1]; + if (av * bv < 0.0f) + { + F32 bc = av / (av - bv); + F32 ac = 1.0f - bc; + baryOut[numOut + 0] = baryIn[ai + 0] * ac + baryIn[bi + 0] * bc; + baryOut[numOut + 1] = baryIn[ai + 1] * ac + baryIn[bi + 1] * bc; + numOut += 2; + } + if (bv >= 0.0f) + { + baryOut[numOut + 0] = baryIn[bi + 0]; + baryOut[numOut + 1] = baryIn[bi + 1]; + numOut += 2; + } + ai = bi; + av = bv; + } + } + return (numOut >> 1); +} + +//------------------------------------------------------------------------ + +__device__ __inline__ int clipTriangleWithFrustum(F32* bary, const F32* v0, const F32* v1, const F32* v2, const F32* d1, const F32* d2) +{ + int num = 3; + bary[0] = 0.0f, bary[1] = 0.0f; + bary[2] = 1.0f, bary[3] = 0.0f; + bary[4] = 0.0f, bary[5] = 1.0f; + + if ((v0[3] < fabsf(v0[0])) | (v1[3] < fabsf(v1[0])) | (v2[3] < fabsf(v2[0]))) + { + F32 temp[18]; + num = clipPolygonWithPlane(temp, bary, num, v0[3] + v0[0], d1[3] + d1[0], d2[3] + d2[0]); + num = clipPolygonWithPlane(bary, temp, num, v0[3] - v0[0], d1[3] - d1[0], d2[3] - d2[0]); + } + if ((v0[3] < fabsf(v0[1])) | (v1[3] < fabsf(v1[1])) | (v2[3] < fabsf(v2[1]))) + { + F32 temp[18]; + num = clipPolygonWithPlane(temp, bary, num, v0[3] + v0[1], d1[3] + d1[1], d2[3] + d2[1]); + num = clipPolygonWithPlane(bary, temp, num, v0[3] - v0[1], d1[3] - d1[1], d2[3] - d2[1]); + } + if ((v0[3] < fabsf(v0[2])) | (v1[3] < fabsf(v1[2])) | (v2[3] < fabsf(v2[2]))) + { + F32 temp[18]; + num = clipPolygonWithPlane(temp, bary, num, v0[3] + v0[2], d1[3] + d1[2], d2[3] + d2[2]); + num = clipPolygonWithPlane(bary, temp, num, v0[3] - v0[2], d1[3] - d1[2], d2[3] - d2[2]); + } + return num; +} + +//------------------------------------------------------------------------ + +__device__ __inline__ U32 idiv_fast(U32 a, U32 b) +{ + return f32_to_u32_sat_rmi(((F32)a + 0.5f) / (F32)b); +} + +//------------------------------------------------------------------------ + +__device__ __inline__ U32 toABGR(float4 color) +{ + // 11 instructions: 4*FFMA, 4*F2I, 3*PRMT + U32 x = f32_to_u32_sat_rmi(fma_rm(color.x, (1 << 24) * 255.0f, (1 << 24) * 0.5f)); + U32 y = f32_to_u32_sat_rmi(fma_rm(color.y, (1 << 24) * 255.0f, (1 << 24) * 0.5f)); + U32 z = f32_to_u32_sat_rmi(fma_rm(color.z, (1 << 24) * 255.0f, (1 << 24) * 0.5f)); + U32 w = f32_to_u32_sat_rmi(fma_rm(color.w, (1 << 24) * 255.0f, (1 << 24) * 0.5f)); + return prmt(prmt(x, y, 0x0073), prmt(z, w, 0x0073), 0x5410); +} + +//------------------------------------------------------------------------ +// v0 = subpixels relative to the bottom-left sampling point + +__device__ __inline__ uint3 setupPleq(float3 values, int2 v0, int2 d1, int2 d2, F32 areaRcp) +{ + F32 mx = fmaxf(fmaxf(values.x, values.y), values.z); + int sh = ::min(::max((__float_as_int(mx) >> 23) - (127 + 22), 0), 8); + S32 t0 = (U32)values.x >> sh; + S32 t1 = ((U32)values.y >> sh) - t0; + S32 t2 = ((U32)values.z >> sh) - t0; + + U32 rcpMant = (__float_as_int(areaRcp) & 0x007FFFFF) | 0x00800000; + int rcpShift = (23 + 127) - (__float_as_int(areaRcp) >> 23); + + uint3 pleq; + S64 xc = ((S64)t1 * d2.y - (S64)t2 * d1.y) * rcpMant; + S64 yc = ((S64)t2 * d1.x - (S64)t1 * d2.x) * rcpMant; + pleq.x = (U32)(xc >> (rcpShift - (sh + CR_SUBPIXEL_LOG2))); + pleq.y = (U32)(yc >> (rcpShift - (sh + CR_SUBPIXEL_LOG2))); + + S32 centerX = (v0.x * 2 + min_min(d1.x, d2.x, 0) + max_max(d1.x, d2.x, 0)) >> (CR_SUBPIXEL_LOG2 + 1); + S32 centerY = (v0.y * 2 + min_min(d1.y, d2.y, 0) + max_max(d1.y, d2.y, 0)) >> (CR_SUBPIXEL_LOG2 + 1); + S32 vcx = v0.x - (centerX << CR_SUBPIXEL_LOG2); + S32 vcy = v0.y - (centerY << CR_SUBPIXEL_LOG2); + + pleq.z = t0 << sh; + pleq.z -= (U32)(((xc >> 13) * vcx + (yc >> 13) * vcy) >> (rcpShift - (sh + 13))); + pleq.z -= pleq.x * centerX + pleq.y * centerY; + return pleq; +} + +//------------------------------------------------------------------------ + +__device__ __inline__ void cover8x8_setupLUT(volatile U64* lut) +{ + for (S32 lutIdx = threadIdx.x + blockDim.x * threadIdx.y; lutIdx < CR_COVER8X8_LUT_SIZE; lutIdx += blockDim.x * blockDim.y) + { + int half = (lutIdx < (12 << 5)) ? 0 : 1; + int yint = (lutIdx >> 5) - half * 12 - 3; + U32 shape = ((lutIdx >> 2) & 7) << (31 - 2); + S32 slctSwapXY = lutIdx << (31 - 1); + S32 slctNegX = lutIdx << (31 - 0); + S32 slctCompl = slctSwapXY ^ slctNegX; + + U64 mask = 0; + int xlo = half * 4; + int xhi = xlo + 4; + for (int x = xlo; x < xhi; x++) + { + int ylo = slct(0, ::max(yint, 0), slctCompl); + int yhi = slct(::min(yint, 8), 8, slctCompl); + for (int y = ylo; y < yhi; y++) + { + int xx = slct(x, y, slctSwapXY); + int yy = slct(y, x, slctSwapXY); + xx = slct(xx, 7 - xx, slctNegX); + mask |= (U64)1 << (xx + yy * 8); + } + yint += shape >> 31; + shape <<= 1; + } + lut[lutIdx] = mask; + } +} + +//------------------------------------------------------------------------ + +__device__ __inline__ U64 cover8x8_exact_fast(S32 ox, S32 oy, S32 dx, S32 dy, U32 flips, volatile const U64* lut) // 52 instr +{ + F32 yinitBias = (F32)(1 << (31 - CR_MAXVIEWPORT_LOG2 - CR_SUBPIXEL_LOG2 * 2)); + F32 yinitScale = (F32)(1 << (32 - CR_SUBPIXEL_LOG2)); + F32 yincScale = 65536.0f * 65536.0f; + + S32 slctFlipY = flips << (31 - CR_FLIPBIT_FLIP_Y); + S32 slctFlipX = flips << (31 - CR_FLIPBIT_FLIP_X); + S32 slctSwapXY = flips << (31 - CR_FLIPBIT_SWAP_XY); + + // Evaluate cross product. + + S32 t = ox * dy - oy * dx; + F32 det = (F32)slct(t, t - dy * (7 << CR_SUBPIXEL_LOG2), slctFlipX); + if (flips >= (1 << CR_FLIPBIT_COMPL)) + det = -det; + + // Represent Y as a function of X. + + F32 xrcp = 1.0f / (F32)::abs(slct(dx, dy, slctSwapXY)); + F32 yzero = det * yinitScale * xrcp + yinitBias; + S64 yinit = f32_to_s64(slct(yzero, -yzero, slctFlipY)); + U32 yinc = f32_to_u32_sat((F32)::abs(slct(dy, dx, slctSwapXY)) * xrcp * yincScale); + + // Lookup. + + return cover8x8_lookupMask(yinit, yinc, flips, lut); +} + +//------------------------------------------------------------------------ + +__device__ __inline__ U64 cover8x8_lookupMask(S64 yinit, U32 yinc, U32 flips, volatile const U64* lut) +{ + // First half. + + U32 yfrac = getLo(yinit); + U32 shape = add_clamp_0_x(getHi(yinit) + 4, 0, 11); + add_add_carry(yfrac, yfrac, yinc, shape, shape, shape); + add_add_carry(yfrac, yfrac, yinc, shape, shape, shape); + add_add_carry(yfrac, yfrac, yinc, shape, shape, shape); + int oct = flips & ((1 << CR_FLIPBIT_FLIP_X) | (1 << CR_FLIPBIT_SWAP_XY)); + U64 mask = *(U64*)((U8*)lut + oct + (shape << 5)); + + // Second half. + + add_add_carry(yfrac, yfrac, yinc, shape, shape, shape); + shape = add_clamp_0_x(getHi(yinit) + 4, __popc(shape & 15), 11); + add_add_carry(yfrac, yfrac, yinc, shape, shape, shape); + add_add_carry(yfrac, yfrac, yinc, shape, shape, shape); + add_add_carry(yfrac, yfrac, yinc, shape, shape, shape); + mask |= *(U64*)((U8*)lut + oct + (shape << 5) + (12 << 8)); + return (flips >= (1 << CR_FLIPBIT_COMPL)) ? ~mask : mask; +} + +//------------------------------------------------------------------------ + +__device__ __inline__ U64 cover8x8_exact_noLUT(S32 ox, S32 oy, S32 dx, S32 dy) +{ + S32 curr = ox * dy - oy * dx; + if (dy > 0 || (dy == 0 && dx <= 0)) curr--; // exclusive + return cover8x8_generateMask_noLUT(curr, dx, dy); +} + +//------------------------------------------------------------------------ + +__device__ __inline__ U64 cover8x8_conservative_noLUT(S32 ox, S32 oy, S32 dx, S32 dy) +{ + S32 curr = ox * dy - oy * dx; + if (dy > 0 || (dy == 0 && dx <= 0)) curr--; // exclusive + curr += (::abs(dx) + ::abs(dy)) << (CR_SUBPIXEL_LOG2 - 1); + return cover8x8_generateMask_noLUT(curr, dx, dy); +} + +//------------------------------------------------------------------------ + +__device__ __inline__ U64 cover8x8_generateMask_noLUT(S32 curr, S32 dx, S32 dy) +{ + curr += (dx - dy) * (7 << CR_SUBPIXEL_LOG2); + S32 stepX = dy << (CR_SUBPIXEL_LOG2 + 1); + S32 stepYorig = -dx - dy * 7; + S32 stepY = stepYorig << (CR_SUBPIXEL_LOG2 + 1); + + U32 hi = isetge(curr, 0); + U32 frac = curr + curr; + for (int i = 62; i >= 32; i--) + add_add_carry(frac, frac, ((i & 7) == 7) ? stepY : stepX, hi, hi, hi); + + U32 lo = 0; + for (int i = 31; i >= 0; i--) + add_add_carry(frac, frac, ((i & 7) == 7) ? stepY : stepX, lo, lo, lo); + + lo ^= lo >> 1, hi ^= hi >> 1; + lo ^= lo >> 2, hi ^= hi >> 2; + lo ^= lo >> 4, hi ^= hi >> 4; + lo ^= lo >> 8, hi ^= hi >> 8; + lo ^= lo >> 16, hi ^= hi >> 16; + + if (dy < 0) + { + lo ^= 0x55AA55AA; + hi ^= 0x55AA55AA; + } + if (stepYorig < 0) + { + lo ^= 0xFF00FF00; + hi ^= 0x00FF00FF; + } + if ((hi & 1) != 0) + lo = ~lo; + + return combineLoHi(lo, hi); +} + +//------------------------------------------------------------------------ + +template __device__ __inline__ void sortShared(T* ptr, int numItems) +{ + int thrInBlock = threadIdx.x + threadIdx.y * blockDim.x; + int range = 16; + + // Use transposition sort within each 16-wide subrange. + + int base = thrInBlock * 2; + bool act = (base < numItems - 1); + U32 actMask = __ballot_sync(~0u, act); + if (act) + { + bool tryOdd = (base < numItems - 2 && (~base & (range - 2)) != 0); + T mid = ptr[base + 1]; + + for (int iter = 0; iter < range; iter += 2) + { + // Evens. + + T tmp = ptr[base + 0]; + if (tmp > mid) + { + ptr[base + 0] = mid; + mid = tmp; + } + __syncwarp(actMask); + + // Odds. + + if (tryOdd) + { + tmp = ptr[base + 2]; + if (mid > tmp) + { + ptr[base + 2] = mid; + mid = tmp; + } + } + __syncwarp(actMask); + } + ptr[base + 1] = mid; + } + + // Multiple subranges => Merge hierarchically. + + for (; range < numItems; range <<= 1) + { + // Assuming that we would insert the current item into the other + // subrange, use binary search to find the appropriate slot. + + __syncthreads(); + + T item; + int slot; + if (thrInBlock < numItems) + { + item = ptr[thrInBlock]; + slot = (thrInBlock & -range) ^ range; + if (slot < numItems) + { + T tmp = ptr[slot]; + bool inclusive = ((thrInBlock & range) != 0); + if (tmp < item || (inclusive && tmp == item)) + { + for (int step = (range >> 1); step != 0; step >>= 1) + { + int probe = slot + step; + if (probe < numItems) + { + tmp = ptr[probe]; + if (tmp < item || (inclusive && tmp == item)) + slot = probe; + } + } + slot++; + } + } + } + + // Store the item at an appropriate place. + + __syncthreads(); + + if (thrInBlock < numItems) + ptr[slot + (thrInBlock & (range * 2 - 1)) - range] = item; + } +} + +//------------------------------------------------------------------------ +} diff --git a/isaac_ros_gxf_extensions/gxf_isaac_foundationpose/gxf/foundationpose/nvdiffrast/common/framework.h b/isaac_ros_gxf_extensions/gxf_isaac_foundationpose/gxf/foundationpose/nvdiffrast/common/framework.h new file mode 100644 index 0000000..cccbd7b --- /dev/null +++ b/isaac_ros_gxf_extensions/gxf_isaac_foundationpose/gxf/foundationpose/nvdiffrast/common/framework.h @@ -0,0 +1,23 @@ +// SPDX-FileCopyrightText: NVIDIA CORPORATION & AFFILIATES +// Copyright (c) 2020-2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. +// +// SPDX-License-Identifier: Apache-2.0 + +#pragma once + +//------------------------------------------------------------------------ + +// PATCH for Isaac ROS FoundationPose +#define NVDR_CHECK_CUDA_ERROR(CUDA_CALL) do { cudaError_t err = CUDA_CALL; } while(0) \ No newline at end of file diff --git a/isaac_ros_gxf_extensions/gxf_isaac_foundationpose/gxf/foundationpose/nvdiffrast/common/interpolate.cu b/isaac_ros_gxf_extensions/gxf_isaac_foundationpose/gxf/foundationpose/nvdiffrast/common/interpolate.cu new file mode 100644 index 0000000..15e65b3 --- /dev/null +++ b/isaac_ros_gxf_extensions/gxf_isaac_foundationpose/gxf/foundationpose/nvdiffrast/common/interpolate.cu @@ -0,0 +1,86 @@ +// Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +// +// NVIDIA CORPORATION and its licensors retain all intellectual property +// and proprietary rights in and to this software, related documentation +// and any modifications thereto. Any use, reproduction, disclosure or +// distribution of this software and related documentation without an express +// license agreement from NVIDIA CORPORATION is strictly prohibited. + +#include "common.h" +#include "interpolate.h" + +//------------------------------------------------------------------------ +// Forward kernel. + +template +static __forceinline__ __device__ void InterpolateFwdKernelTemplate(const InterpolateKernelParams p) +{ + // Calculate pixel position. + int px = blockIdx.x * blockDim.x + threadIdx.x; + int py = blockIdx.y * blockDim.y + threadIdx.y; + int pz = blockIdx.z; + if (px >= p.width || py >= p.height || pz >= p.depth) + return; + + // Pixel index. + int pidx = px + p.width * (py + p.height * pz); + + // Output ptrs. + float* out = p.out + pidx * p.numAttr; + + // Fetch rasterizer output. + float4 r = ((float4*)p.rast)[pidx]; + int triIdx = (int)r.w - 1; + bool triValid = (triIdx >= 0 && triIdx < p.numTriangles); + + // If no geometry in entire warp, zero the output and exit. + // Otherwise force barys to zero and output with live threads. + if (__all_sync(0xffffffffu, !triValid)) + { + for (int i=0; i < p.numAttr; i++) + out[i] = 0.f; + return; + } + + // Fetch vertex indices. + int vi0 = triValid ? p.tri[triIdx * 3 + 0] : 0; + int vi1 = triValid ? p.tri[triIdx * 3 + 1] : 0; + int vi2 = triValid ? p.tri[triIdx * 3 + 2] : 0; + + // Bail out if corrupt indices. + if (vi0 < 0 || vi0 >= p.numVertices || + vi1 < 0 || vi1 >= p.numVertices || + vi2 < 0 || vi2 >= p.numVertices) + return; + + // In instance mode, adjust vertex indices by minibatch index unless broadcasting. + if (p.instance_mode && !p.attrBC) + { + vi0 += pz * p.numVertices; + vi1 += pz * p.numVertices; + vi2 += pz * p.numVertices; + } + + // Pointers to attributes. + const float* a0 = p.attr + vi0 * p.numAttr; + const float* a1 = p.attr + vi1 * p.numAttr; + const float* a2 = p.attr + vi2 * p.numAttr; + + // Barys. If no triangle, force all to zero -> output is zero. + float b0 = triValid ? r.x : 0.f; + float b1 = triValid ? r.y : 0.f; + float b2 = triValid ? (1.f - r.x - r.y) : 0.f; + + // Interpolate and write attributes. + for (int i=0; i < p.numAttr; i++) + out[i] = b0*a0[i] + b1*a1[i] + b2*a2[i]; + + // No diff attrs? Exit. + if (!ENABLE_DA) + return; +} + +// Template specializations. +__global__ void InterpolateFwdKernel (const InterpolateKernelParams p) { InterpolateFwdKernelTemplate(p); } + +//------------------------------------------------------------------------ diff --git a/isaac_ros_gxf_extensions/gxf_isaac_foundationpose/gxf/foundationpose/nvdiffrast/common/interpolate.h b/isaac_ros_gxf_extensions/gxf_isaac_foundationpose/gxf/foundationpose/nvdiffrast/common/interpolate.h new file mode 100644 index 0000000..2c8f930 --- /dev/null +++ b/isaac_ros_gxf_extensions/gxf_isaac_foundationpose/gxf/foundationpose/nvdiffrast/common/interpolate.h @@ -0,0 +1,45 @@ +// SPDX-FileCopyrightText: NVIDIA CORPORATION & AFFILIATES +// Copyright (c) 2020-2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. +// +// SPDX-License-Identifier: Apache-2.0 + +#pragma once + +//------------------------------------------------------------------------ +// Constants and helpers. + +#define IP_FWD_MAX_KERNEL_BLOCK_WIDTH 8 +#define IP_FWD_MAX_KERNEL_BLOCK_HEIGHT 8 + +//------------------------------------------------------------------------ +// CUDA kernel params. + +struct InterpolateKernelParams +{ + const int* tri; // Incoming triangle buffer. + const float* attr; // Incoming attribute buffer. + const float* rast; // Incoming rasterizer output buffer. + float* out; // Outgoing interpolated attributes. + int numTriangles; // Number of triangles. + int numVertices; // Number of vertices. + int numAttr; // Number of total vertex attributes. + int width; // Image width. + int height; // Image height. + int depth; // Minibatch size. + int attrBC; // 0=normal, 1=attr is broadcast. + int instance_mode; // 0=normal, 1=instance mode. +}; + +//------------------------------------------------------------------------ diff --git a/isaac_ros_gxf_extensions/gxf_isaac_foundationpose/gxf/foundationpose/nvdiffrast/common/rasterize.cu b/isaac_ros_gxf_extensions/gxf_isaac_foundationpose/gxf/foundationpose/nvdiffrast/common/rasterize.cu new file mode 100644 index 0000000..71e0eb4 --- /dev/null +++ b/isaac_ros_gxf_extensions/gxf_isaac_foundationpose/gxf/foundationpose/nvdiffrast/common/rasterize.cu @@ -0,0 +1,92 @@ +// Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +// +// NVIDIA CORPORATION and its licensors retain all intellectual property +// and proprietary rights in and to this software, related documentation +// and any modifications thereto. Any use, reproduction, disclosure or +// distribution of this software and related documentation without an express +// license agreement from NVIDIA CORPORATION is strictly prohibited. + +#include "common.h" +#include "rasterize.h" + +//------------------------------------------------------------------------ +// Cuda forward rasterizer pixel shader kernel. + +__global__ void RasterizeCudaFwdShaderKernel(const RasterizeCudaFwdShaderParams p) +{ + // Calculate pixel position. + int px = blockIdx.x * blockDim.x + threadIdx.x; + int py = blockIdx.y * blockDim.y + threadIdx.y; + int pz = blockIdx.z; + if (px >= p.width || py >= p.height || pz >= p.depth) + return; + + // Pixel index. + int pidx = px + p.width * (py + p.height * pz); + + // Fetch triangle idx. + int triIdx = p.in_idx[pidx] - 1; + if (triIdx < 0 || triIdx >= p.numTriangles) + { + // No or corrupt triangle. + ((float4*)p.out)[pidx] = make_float4(0.0, 0.0, 0.0, 0.0); // Clear out. + return; + } + + // Fetch vertex indices. + int vi0 = p.tri[triIdx * 3 + 0]; + int vi1 = p.tri[triIdx * 3 + 1]; + int vi2 = p.tri[triIdx * 3 + 2]; + + // Bail out if vertex indices are corrupt. + if (vi0 < 0 || vi0 >= p.numVertices || + vi1 < 0 || vi1 >= p.numVertices || + vi2 < 0 || vi2 >= p.numVertices) + return; + + // In instance mode, adjust vertex indices by minibatch index. + if (p.instance_mode) + { + vi0 += pz * p.numVertices; + vi1 += pz * p.numVertices; + vi2 += pz * p.numVertices; + } + + // Fetch vertex positions. + float4 p0 = ((float4*)p.pos)[vi0]; + float4 p1 = ((float4*)p.pos)[vi1]; + float4 p2 = ((float4*)p.pos)[vi2]; + + // Evaluate edge functions. + float fx = p.xs * (float)px + p.xo; + float fy = p.ys * (float)py + p.yo; + float p0x = p0.x - fx * p0.w; + float p0y = p0.y - fy * p0.w; + float p1x = p1.x - fx * p1.w; + float p1y = p1.y - fy * p1.w; + float p2x = p2.x - fx * p2.w; + float p2y = p2.y - fy * p2.w; + float a0 = p1x*p2y - p1y*p2x; + float a1 = p2x*p0y - p2y*p0x; + float a2 = p0x*p1y - p0y*p1x; + + // Perspective correct, normalized barycentrics. + float iw = 1.f / (a0 + a1 + a2); + float b0 = a0 * iw; + float b1 = a1 * iw; + + // Compute z/w for depth buffer. + float z = p0.z * a0 + p1.z * a1 + p2.z * a2; + float w = p0.w * a0 + p1.w * a1 + p2.w * a2; + float zw = z / w; + + // Clamps to avoid NaNs. + b0 = __saturatef(b0); // Clamp to [+0.0, 1.0]. + b1 = __saturatef(b1); // Clamp to [+0.0, 1.0]. + zw = fmaxf(fminf(zw, 1.f), -1.f); + + // Emit output. + ((float4*)p.out)[pidx] = make_float4(b0, b1, zw, (float)(triIdx + 1)); +} + +//------------------------------------------------------------------------ diff --git a/isaac_ros_gxf_extensions/gxf_isaac_foundationpose/gxf/foundationpose/nvdiffrast/common/rasterize.h b/isaac_ros_gxf_extensions/gxf_isaac_foundationpose/gxf/foundationpose/nvdiffrast/common/rasterize.h new file mode 100644 index 0000000..c70e521 --- /dev/null +++ b/isaac_ros_gxf_extensions/gxf_isaac_foundationpose/gxf/foundationpose/nvdiffrast/common/rasterize.h @@ -0,0 +1,46 @@ +// SPDX-FileCopyrightText: NVIDIA CORPORATION & AFFILIATES +// Copyright (c) 2020-2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. +// +// SPDX-License-Identifier: Apache-2.0 + +#pragma once + +//------------------------------------------------------------------------ +// Constants and helpers. + +#define RAST_CUDA_FWD_SHADER_KERNEL_BLOCK_WIDTH 8 +#define RAST_CUDA_FWD_SHADER_KERNEL_BLOCK_HEIGHT 8 +#define RAST_GRAD_MAX_KERNEL_BLOCK_WIDTH 8 +#define RAST_GRAD_MAX_KERNEL_BLOCK_HEIGHT 8 + +//------------------------------------------------------------------------ +// CUDA forward rasterizer shader kernel params. + +struct RasterizeCudaFwdShaderParams +{ + const float* pos; // Vertex positions. + const int* tri; // Triangle indices. + const int* in_idx; // Triangle idx buffer from rasterizer. + float* out; // Main output buffer. + int numTriangles; // Number of triangles. + int numVertices; // Number of vertices. + int width; // Image width. + int height; // Image height. + int depth; // Size of minibatch. + int instance_mode; // 1 if in instance rendering mode. + float xs, xo, ys, yo; // Pixel position to clip-space x, y transform. +}; + +//------------------------------------------------------------------------ diff --git a/isaac_ros_gxf_extensions/gxf_isaac_foundationpose/gxf/foundationpose/nvdiffrast/common/texture.cu b/isaac_ros_gxf_extensions/gxf_isaac_foundationpose/gxf/foundationpose/nvdiffrast/common/texture.cu new file mode 100644 index 0000000..97c11ee --- /dev/null +++ b/isaac_ros_gxf_extensions/gxf_isaac_foundationpose/gxf/foundationpose/nvdiffrast/common/texture.cu @@ -0,0 +1,180 @@ +// Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +// +// NVIDIA CORPORATION and its licensors retain all intellectual property +// and proprietary rights in and to this software, related documentation +// and any modifications thereto. Any use, reproduction, disclosure or +// distribution of this software and related documentation without an express +// license agreement from NVIDIA CORPORATION is strictly prohibited. + +#include "common.h" +#include "texture.h" + +//------------------------------------------------------------------------ +// Memory access and math helpers. + +template static __device__ __forceinline__ T lerp (const T& a, const T& b, float c) { return a + c * (b - a); } +template static __device__ __forceinline__ T bilerp(const T& a, const T& b, const T& c, const T& d, const float2& e) { return lerp(lerp(a, b, e.x), lerp(c, d, e.x), e.y); } + + +template +static __device__ __forceinline__ float2 indexTextureLinear(const TextureKernelParams& p, float3 uv, int tz, int4& tcOut, int level) +{ + // Mip level size. + int2 sz = mipLevelSize(p, level); + int w = sz.x; + int h = sz.y; + + // Compute texture-space u, v. + float u = uv.x; + float v = uv.y; + bool clampU = false; + bool clampV = false; + + // Cube map indexing. + int face = 0; + + if (p.boundaryMode == TEX_BOUNDARY_MODE_WRAP) + { + // Wrap. + u = u - (float)__float2int_rd(u); + v = v - (float)__float2int_rd(v); + } + + // Move to texel space. + u = u * (float)w - 0.5f; + v = v * (float)h - 0.5f; + + if (p.boundaryMode == TEX_BOUNDARY_MODE_CLAMP) + { + // Clamp to center of edge texels. + u = fminf(fmaxf(u, 0.f), w - 1.f); + v = fminf(fmaxf(v, 0.f), h - 1.f); + clampU = (u == 0.f || u == w - 1.f); + clampV = (v == 0.f || v == h - 1.f); + } + + // Compute texel coordinates and weights. + int iu0 = __float2int_rd(u); + int iv0 = __float2int_rd(v); + int iu1 = iu0 + (clampU ? 0 : 1); // Ensure zero u/v gradients with clamped. + int iv1 = iv0 + (clampV ? 0 : 1); + u -= (float)iu0; + v -= (float)iv0; + + // Wrap overflowing texel indices. + if (!CUBE_MODE && p.boundaryMode == TEX_BOUNDARY_MODE_WRAP) + { + if (iu0 < 0) iu0 += w; + if (iv0 < 0) iv0 += h; + if (iu1 >= w) iu1 -= w; + if (iv1 >= h) iv1 -= h; + } + + // Coordinates with tz folded in. + int iu0z = iu0 + tz * w * h; + int iu1z = iu1 + tz * w * h; + tcOut.x = iu0z + w * iv0; + tcOut.y = iu1z + w * iv0; + tcOut.z = iu0z + w * iv1; + tcOut.w = iu1z + w * iv1; + + // Invalidate texture addresses outside unit square if we are in zero mode. + if (!CUBE_MODE && p.boundaryMode == TEX_BOUNDARY_MODE_ZERO) + { + bool iu0_out = (iu0 < 0 || iu0 >= w); + bool iu1_out = (iu1 < 0 || iu1 >= w); + bool iv0_out = (iv0 < 0 || iv0 >= h); + bool iv1_out = (iv1 < 0 || iv1 >= h); + if (iu0_out || iv0_out) tcOut.x = -1; + if (iu1_out || iv0_out) tcOut.y = -1; + if (iu0_out || iv1_out) tcOut.z = -1; + if (iu1_out || iv1_out) tcOut.w = -1; + } + + // All done. + return make_float2(u, v); +} + +//------------------------------------------------------------------------ +// Texel fetch and accumulator helpers that understand cube map corners. + +template +static __device__ __forceinline__ void fetchQuad(T& a00, T& a10, T& a01, T& a11, const float* pIn, int4 tc, bool corner) +{ + // For invalid cube map uv, tc will be all negative, and all texel values will be zero. + if (corner) + { + T avg = zero_value(); + if (tc.x >= 0) avg += (a00 = *((const T*)&pIn[tc.x])); + if (tc.y >= 0) avg += (a10 = *((const T*)&pIn[tc.y])); + if (tc.z >= 0) avg += (a01 = *((const T*)&pIn[tc.z])); + if (tc.w >= 0) avg += (a11 = *((const T*)&pIn[tc.w])); + avg *= 0.33333333f; + if (tc.x < 0) a00 = avg; + if (tc.y < 0) a10 = avg; + if (tc.z < 0) a01 = avg; + if (tc.w < 0) a11 = avg; + } + else + { + a00 = (tc.x >= 0) ? *((const T*)&pIn[tc.x]) : zero_value(); + a10 = (tc.y >= 0) ? *((const T*)&pIn[tc.y]) : zero_value(); + a01 = (tc.z >= 0) ? *((const T*)&pIn[tc.z]) : zero_value(); + a11 = (tc.w >= 0) ? *((const T*)&pIn[tc.w]) : zero_value(); + } +} + + +//------------------------------------------------------------------------ +// Forward kernel. + +template +static __forceinline__ __device__ void TextureFwdKernelTemplate(const TextureKernelParams p) +{ + // Calculate pixel position. + int px = blockIdx.x * blockDim.x + threadIdx.x; + int py = blockIdx.y * blockDim.y + threadIdx.y; + int pz = blockIdx.z; + int tz = (p.texDepth == 1) ? 0 : pz; + if (px >= p.imgWidth || py >= p.imgHeight || pz >= p.n) + return; + + // Pixel index. + int pidx = px + p.imgWidth * (py + p.imgHeight * pz); + + // Output ptr. + float* pOut = p.out + pidx * p.channels; + + // Get UV. + float3 uv; + uv = make_float3(((const float2*)p.uv)[pidx], 0.f); + + // Calculate mip level. In 'linear' mode these will all stay zero. + float flevel = 0.f; // Fractional level. + int level0 = 0; // Discrete level 0. + int level1 = 0; // Discrete level 1. + + // Get texel indices and pointer for level 0. + int4 tc0 = make_int4(0, 0, 0, 0); + float2 uv0 = indexTextureLinear(p, uv, tz, tc0, level0); + const float* pIn0 = p.tex[level0]; + bool corner0 = CUBE_MODE && ((tc0.x | tc0.y | tc0.z | tc0.w) < 0); + tc0 *= p.channels; + + // Bilinear fetch. + if (FILTER_MODE == TEX_MODE_LINEAR || FILTER_MODE == TEX_MODE_LINEAR_MIPMAP_NEAREST) + { + // Interpolate. + for (int i=0; i < p.channels; i += C, tc0 += C) + { + T a00, a10, a01, a11; + fetchQuad(a00, a10, a01, a11, pIn0, tc0, corner0); + *((T*)&pOut[i]) = bilerp(a00, a10, a01, a11, uv0); + } + return; // Exit. + } +} + +// Template specializations. +__global__ void TextureFwdKernelLinear1 (const TextureKernelParams p) { TextureFwdKernelTemplate(p); } +//------------------------------------------------------------------------ diff --git a/isaac_ros_gxf_extensions/gxf_isaac_foundationpose/gxf/foundationpose/nvdiffrast/common/texture.h b/isaac_ros_gxf_extensions/gxf_isaac_foundationpose/gxf/foundationpose/nvdiffrast/common/texture.h new file mode 100644 index 0000000..a102a8e --- /dev/null +++ b/isaac_ros_gxf_extensions/gxf_isaac_foundationpose/gxf/foundationpose/nvdiffrast/common/texture.h @@ -0,0 +1,85 @@ +// SPDX-FileCopyrightText: NVIDIA CORPORATION & AFFILIATES +// Copyright (c) 2020-2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. +// +// SPDX-License-Identifier: Apache-2.0 + +#pragma once +#include "framework.h" + +//------------------------------------------------------------------------ +// Constants. + +#define TEX_FWD_MAX_KERNEL_BLOCK_WIDTH 8 +#define TEX_FWD_MAX_KERNEL_BLOCK_HEIGHT 8 +#define TEX_FWD_MAX_MIP_KERNEL_BLOCK_WIDTH 8 +#define TEX_FWD_MAX_MIP_KERNEL_BLOCK_HEIGHT 8 +#define TEX_GRAD_MAX_KERNEL_BLOCK_WIDTH 8 +#define TEX_GRAD_MAX_KERNEL_BLOCK_HEIGHT 8 +#define TEX_GRAD_MAX_MIP_KERNEL_BLOCK_WIDTH 8 +#define TEX_GRAD_MAX_MIP_KERNEL_BLOCK_HEIGHT 8 +#define TEX_MAX_MIP_LEVEL 16 // Currently a texture cannot be larger than 2 GB because we use 32-bit indices everywhere. +#define TEX_MODE_NEAREST 0 // Nearest on base level. +#define TEX_MODE_LINEAR 1 // Bilinear on base level. +#define TEX_MODE_LINEAR_MIPMAP_NEAREST 2 // Bilinear on nearest mip level. +#define TEX_MODE_LINEAR_MIPMAP_LINEAR 3 // Trilinear. +#define TEX_MODE_COUNT 4 +#define TEX_BOUNDARY_MODE_CUBE 0 // Cube map mode. +#define TEX_BOUNDARY_MODE_WRAP 1 // Wrap (u, v). +#define TEX_BOUNDARY_MODE_CLAMP 2 // Clamp (u, v). +#define TEX_BOUNDARY_MODE_ZERO 3 // Pad with zeros. +#define TEX_BOUNDARY_MODE_COUNT 4 + +//------------------------------------------------------------------------ +// CUDA kernel params. + +struct TextureKernelParams +{ + const float* tex[TEX_MAX_MIP_LEVEL]; // Incoming texture buffer with mip levels. + const float* uv; // Incoming texcoord buffer. + const float* mipLevelBias; // Incoming mip level bias or NULL. + float* out; // Outgoing texture data. + float* gradTex[TEX_MAX_MIP_LEVEL]; // Outgoing texture gradients with mip levels. + float* gradUV; // Outgoing texcoord gradient. + float* gradUVDA; // Outgoing texcoord pixel differential gradient. + float* gradMipLevelBias; // Outgoing mip level bias gradient. + int enableMip; // If true, we have uv_da and/or mip_level_bias input(s), and a mip tensor. + int filterMode; // One of the TEX_MODE_ constants. + int boundaryMode; // One of the TEX_BOUNDARY_MODE_ contants. + int texConst; // If true, texture is known to be constant. + int mipLevelLimit; // Mip level limit coming from the op. + int channels; // Number of texture channels. + int imgWidth; // Image width. + int imgHeight; // Image height. + int texWidth; // Texture width. + int texHeight; // Texture height. + int texDepth; // Texture depth. + int n; // Minibatch size. + int mipLevelMax; // Maximum mip level index. Zero if mips disabled. + int mipLevelOut; // Mip level being calculated in builder kernel. +}; + +//------------------------------------------------------------------------ +// C++ helper function prototypes. + +// ISAAC ROS patch +// void raiseMipSizeError(NVDR_CTX_ARGS, const TextureKernelParams& p); +// int calculateMipInfo(NVDR_CTX_ARGS, TextureKernelParams& p, int* mipOffsets); + +//------------------------------------------------------------------------ +// Macros. + +#define mipLevelSize(p, i) make_int2(((p).texWidth >> (i)) > 1 ? ((p).texWidth >> (i)) : 1, ((p).texHeight >> (i)) > 1 ? ((p).texHeight >> (i)) : 1) + +//------------------------------------------------------------------------ diff --git a/isaac_ros_gxf_extensions/gxf_isaac_foundationpose/gxf/foundationpose/render_receptive_scheduling_term.cpp b/isaac_ros_gxf_extensions/gxf_isaac_foundationpose/gxf/foundationpose/render_receptive_scheduling_term.cpp new file mode 100644 index 0000000..7d67ae3 --- /dev/null +++ b/isaac_ros_gxf_extensions/gxf_isaac_foundationpose/gxf/foundationpose/render_receptive_scheduling_term.cpp @@ -0,0 +1,49 @@ +// SPDX-FileCopyrightText: NVIDIA CORPORATION & AFFILIATES +// Copyright (c) 2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. +// +// SPDX-License-Identifier: Apache-2.0 + +#include "render_receptive_scheduling_term.hpp" + +namespace nvidia { +namespace isaac_ros { + +gxf_result_t RenderReceptiveSchedulingTerm::registerInterface( + nvidia::gxf::Registrar* registrar) { + gxf::Expected result; + result &= registrar->parameter(render_impl_, "render_impl", + "Render Implementation Handle"); + return gxf::ToResultCode(result); +} + +gxf_result_t RenderReceptiveSchedulingTerm::check_abi(int64_t timestamp, + gxf::SchedulingConditionType* type, int64_t* target_timestamp) const { + auto maybe_is_accepting_request = render_impl_.get()->isAcceptingRequest(); + if (!maybe_is_accepting_request) { + GXF_LOG_ERROR("Render isAcceptingRequest had unexpected return"); + return GXF_FAILURE; + } + const auto& is_accepting_request = maybe_is_accepting_request.value(); + *type = is_accepting_request ? gxf::SchedulingConditionType::READY : + gxf::SchedulingConditionType::WAIT; + return GXF_SUCCESS; +} + +gxf_result_t RenderReceptiveSchedulingTerm::onExecute_abi(int64_t dt) { + return GXF_SUCCESS; +} + +} // namespace isaac_ros +} // namespace nvidia diff --git a/isaac_ros_gxf_extensions/gxf_isaac_foundationpose/gxf/foundationpose/render_receptive_scheduling_term.hpp b/isaac_ros_gxf_extensions/gxf_isaac_foundationpose/gxf/foundationpose/render_receptive_scheduling_term.hpp new file mode 100644 index 0000000..4e36950 --- /dev/null +++ b/isaac_ros_gxf_extensions/gxf_isaac_foundationpose/gxf/foundationpose/render_receptive_scheduling_term.hpp @@ -0,0 +1,71 @@ +// SPDX-FileCopyrightText: NVIDIA CORPORATION & AFFILIATES +// Copyright (c) 2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. +// +// SPDX-License-Identifier: Apache-2.0 + +#ifndef NVIDIA_ISAAC_ROS_EXTENSIONS_RENDER_RECEPTIVE_SCHEDULING_TERM_HPP_ +#define NVIDIA_ISAAC_ROS_EXTENSIONS_RENDER_RECEPTIVE_SCHEDULING_TERM_HPP_ + +#pragma once +#include +#include + +#include "foundationpose_render.hpp" +#include "gxf/core/handle.hpp" +#include "gxf/std/scheduling_term.hpp" + +namespace nvidia { +namespace isaac_ros { +/** + * @brief Scheduling term which permits execution only when the Render Component is able to + * accept new requests. + * + */ +class RenderReceptiveSchedulingTerm : public nvidia::gxf::SchedulingTerm { + public: + /** + * @brief Register Parameters. + * + * @param registrar + * @return gxf_result_t + */ + gxf_result_t registerInterface(nvidia::gxf::Registrar* registrar) override; + /** + * @brief Only when an render can accept new input, SchedulingCondition is + * Ready + * + * @param[in] timestamp Unused + * @param[out] type Ready (if render can accept new incoming input) or Wait (otherwise) + * @param[out] target_timestamp Unmodified + * @return gxf_result_t + */ + gxf_result_t check_abi(int64_t timestamp, nvidia::gxf::SchedulingConditionType* type, + int64_t* target_timestamp) const override; + /** + * @brief Returns success + * + * @param dt Unused + * @return gxf_result_t + */ + gxf_result_t onExecute_abi(int64_t dt) override; + + private: + nvidia::gxf::Parameter> render_impl_; +}; + +} // namespace isaac_ros +} // namespace nvidia + +#endif // NVIDIA_ISAAC_ROS_EXTENSIONS_RENDER_RECEPTIVE_SCHEDULING_TERM_HPP_ \ No newline at end of file diff --git a/isaac_ros_gxf_extensions/gxf_isaac_foundationpose/package.xml b/isaac_ros_gxf_extensions/gxf_isaac_foundationpose/package.xml new file mode 100644 index 0000000..4049297 --- /dev/null +++ b/isaac_ros_gxf_extensions/gxf_isaac_foundationpose/package.xml @@ -0,0 +1,46 @@ + + + + + + + gxf_isaac_foundationpose + 3.0.0 + GXF extension containing Foundation Pose estimation related components. + + Isaac ROS Maintainers + Apache-2.0 + https://developer.nvidia.com/isaac-ros-gems/ + Yuankun Zhu + + ament_cmake_auto + + isaac_ros_common + isaac_ros_gxf + isaac_ros_nitros_detection3_d_array_type + + gxf_isaac_gems + + ament_lint_auto + ament_lint_common + + + ament_cmake + + diff --git a/resources/rosbags/centerpose_rosbag/centerpose_rosbag_0.db3 b/resources/rosbags/centerpose_rosbag/centerpose_rosbag_0.db3 deleted file mode 100644 index d4dc062..0000000 --- a/resources/rosbags/centerpose_rosbag/centerpose_rosbag_0.db3 +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:56909d823ff920b7c3053623314a9ce8ff9c37aebbcba239316252721e9ed892 -size 187826176 diff --git a/resources/rosbags/centerpose_rosbag/metadata.yaml b/resources/rosbags/centerpose_rosbag/metadata.yaml deleted file mode 100644 index 3149b8e..0000000 --- a/resources/rosbags/centerpose_rosbag/metadata.yaml +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:819011003dffabf69fdcbfa7405c2e6612e27c102ff626e6bf3237210568fd2e -size 1471 diff --git a/resources/rosbags/dope_rosbag/dope_rosbag_0.db3 b/resources/rosbags/dope_rosbag/dope_rosbag_0.db3 deleted file mode 100644 index 83373ba..0000000 --- a/resources/rosbags/dope_rosbag/dope_rosbag_0.db3 +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:171e40ff3e38ddb8f9911178560dfc50a42e5ef1f41381c1e48314e5036063a3 -size 6250496 diff --git a/resources/rosbags/dope_rosbag/metadata.yaml b/resources/rosbags/dope_rosbag/metadata.yaml deleted file mode 100644 index 39741b5..0000000 --- a/resources/rosbags/dope_rosbag/metadata.yaml +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:4a447dcedd0b585d708f2a75b71b647799be4f693791664fd6a17ff2c9de47e7 -size 1447 diff --git a/resources/rosbags/foundationpose_tracking.bag/foundationpose_tracking.bag_0.db3 b/resources/rosbags/foundationpose_tracking.bag/foundationpose_tracking.bag_0.db3 new file mode 100644 index 0000000..32bcdd2 --- /dev/null +++ b/resources/rosbags/foundationpose_tracking.bag/foundationpose_tracking.bag_0.db3 @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:32a94df3ad3ef216e71db911bf9fed7cb0e654bad4353d49f117fb5e8349ed93 +size 1586929664 diff --git a/resources/rosbags/foundationpose_tracking.bag/metadata.yaml b/resources/rosbags/foundationpose_tracking.bag/metadata.yaml new file mode 100644 index 0000000..066f171 --- /dev/null +++ b/resources/rosbags/foundationpose_tracking.bag/metadata.yaml @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:6487f34d0a7b49e016d55ffc53772ec12b91813c8e83bfb84c55db2afa5efb35 +size 1075 diff --git a/test/models/dummy_refine_model.onnx b/test/models/dummy_refine_model.onnx new file mode 100644 index 0000000..9c28c46 --- /dev/null +++ b/test/models/dummy_refine_model.onnx @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:c30640164abb17e6cdb000c39918aefe7470cbdbc7a66f851b5459f3fb3ef5c3 +size 68130876 diff --git a/test/models/dummy_score_model.onnx b/test/models/dummy_score_model.onnx new file mode 100644 index 0000000..c18ef2a --- /dev/null +++ b/test/models/dummy_score_model.onnx @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:0687c73ba1e51e312a9b957c938e7a5cbcd19870f43846ae8bf46a733be6ce04 +size 63902165