[Feature] Add large image demo with sahi

demo/large_image_demo.py

@@ -0,0 +1,203 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+"""Perform MMYOLO inference on large images (as satellite imagery) as:
+
+```shell
+wget -P checkpoint https://download.openmmlab.com/mmyolo/v0/yolov5/yolov5_m-v61_syncbn_fast_8xb16-300e_coco/yolov5_m-v61_syncbn_fast_8xb16-300e_coco_20220917_204944-516a710f.pth syncbn_fast_8xb16-300e_coco/yolov5_m-v61_syncbn_fast_8xb16-300e_coco_20220917_204944-516a710f.pth   syncbn_fast_8xb16-300e_coco/yolov5_m-v61_syncbn_fast_8xb16-300e_coco_20220917_204944-516a710f.pth  # noqa: E501, E261.
+
+python demo/large_image_demo.py \
+    demo/large_image.jpg \
+    configs/yolov5/yolov5_m-v61_syncbn_fast_8xb16-300e_coco.py \
+    checkpoint/yolov5_m-v61_syncbn_fast_8xb16-300e_coco_20220917_204944-516a710f.pth \
+```
+"""
+
+import os
+from argparse import ArgumentParser
+
+import mmcv
+from mmdet.apis import inference_detector, init_detector
+from mmengine.logging import print_log
+from mmengine.utils import ProgressBar
+from sahi.slicing import slice_image
+
+from mmyolo.registry import VISUALIZERS
+from mmyolo.utils import register_all_modules, switch_to_deploy
+from mmyolo.utils.large_image import merge_results_by_nms
+from mmyolo.utils.misc import get_file_list
+
+
+def parse_args():
+    parser = ArgumentParser(
+        description='Perform MMYOLO inference on large images.')
+    parser.add_argument(
+        'img', help='Image path, include image file, dir and URL.')
+    parser.add_argument('config', help='Config file')
+    parser.add_argument('checkpoint', help='Checkpoint file')
+    parser.add_argument(
+        '--out-dir', default='./output', help='Path to output file')
+    parser.add_argument(
+        '--device', default='cuda:0', help='Device used for inference')
+    parser.add_argument(
+        '--show', action='store_true', help='Show the detection results')
+    parser.add_argument(
+        '--deploy',
+        action='store_true',
+        help='Switch model to deployment mode')
+    parser.add_argument(
+        '--score-thr', type=float, default=0.3, help='Bbox score threshold')
+    parser.add_argument(
+        '--patch-size', type=int, default=640, help='The size of patches')
+    parser.add_argument(
+        '--patch-overlap-ratio',
+        type=int,
+        default=0.25,
+        help='Ratio of overlap between two patches')
+    parser.add_argument(
+        '--merge-iou-thr',
+        type=float,
+        default=0.25,
+        help='IoU threshould for merging results')
+    parser.add_argument(
+        '--merge-nms-type',
+        type=str,
+        default='nms',
+        help='NMS type for merging results')
+    parser.add_argument(
+        '--batch-size',
+        type=int,
+        default=1,
+        help='Batch size, must greater than or equal to 1')
+    parser.add_argument(
+        '--debug',
+        action='store_true',
+        help='Export debug images at each stage for 1 input')
+    args = parser.parse_args()
+    return args
+
+
+def main():
+    args = parse_args()
+
+    # register all modules in mmdet into the registries
+    register_all_modules()
+
+    # build the model from a config file and a checkpoint file
+    model = init_detector(args.config, args.checkpoint, device=args.device)
+
+    if args.deploy:
+        switch_to_deploy(model)
+
+    if not os.path.exists(args.out_dir) and not args.show:
+        os.mkdir(args.out_dir)
+
+    # init visualizer
+    visualizer = VISUALIZERS.build(model.cfg.visualizer)
+    visualizer.dataset_meta = model.dataset_meta
+
+    # get file list
+    files, source_type = get_file_list(args.img)
+
+    # if debug, only process the first file
+    if args.debug:
+        files = files[:1]
+
+    # start detector inference
+    print(f'Performing inference on {len(files)} images... \
+This may take a while.')
+    progress_bar = ProgressBar(len(files))
+    for file in files:
+        # read image
+        img = mmcv.imread(file)
+
+        # arrange slices
+        height, width = img.shape[:2]
+        sliced_image_object = slice_image(
+            img,
+            slice_height=args.patch_size,
+            slice_width=args.patch_size,
+            auto_slice_resolution=False,
+            overlap_height_ratio=args.patch_overlap_ratio,
+            overlap_width_ratio=args.patch_overlap_ratio,
+        )
+
+        # perform sliced inference
+        slice_results = []
+        start = 0
+        while True:
+            # prepare batch slices
+            end = min(start + args.batch_size, len(sliced_image_object))
+            images = []
+            for sliced_image in sliced_image_object.images[start:end]:
+                images.append(sliced_image)
+
+            # forward the model
+            slice_results.extend(inference_detector(model, images))
+
+            if end >= len(sliced_image_object):
+                break
+            start += args.batch_size
+
+        if source_type['is_dir']:
+            filename = os.path.relpath(file, args.img).replace('/', '_')
+        else:
+            filename = os.path.basename(file)
+
+        # export debug images
+        if args.debug:
+            # export sliced images
+            for i, image in enumerate(sliced_image_object.images):
+                image = mmcv.imconvert(image, 'bgr', 'rgb')
+                out_file = os.path.join(args.out_dir, 'sliced_images',
+                                        filename + f'_slice_{i}.jpg')
+
+                mmcv.imwrite(image, out_file)
+
+            # export sliced image results
+            for i, slice_result in enumerate(slice_results):
+                out_file = os.path.join(args.out_dir, 'sliced_image_results',
+                                        filename + f'_slice_{i}_result.jpg')
+                image = mmcv.imconvert(sliced_image_object.images[i], 'bgr',
+                                       'rgb')
+
+                visualizer.add_datasample(
+                    os.path.basename(out_file),
+                    image,
+                    data_sample=slice_result,
+                    draw_gt=False,
+                    show=args.show,
+                    wait_time=0,
+                    out_file=out_file,
+                    pred_score_thr=args.score_thr,
+                )
+
+        image_result = merge_results_by_nms(
+            slice_results,
+            sliced_image_object.starting_pixels,
+            src_image_shape=(height, width),
+            nms_cfg={
+                'type': args.merge_nms_type,
+                'iou_thr': args.merge_iou_thr
+            })
+
+        img = mmcv.imconvert(img, 'bgr', 'rgb')
+        out_file = None if args.show else os.path.join(args.out_dir, filename)
+
+        visualizer.add_datasample(
+            os.path.basename(out_file),
+            img,
+            data_sample=image_result,
+            draw_gt=False,
+            show=args.show,
+            wait_time=0,
+            out_file=out_file,
+            pred_score_thr=args.score_thr,
+        )
+        progress_bar.update()
+
+    if not args.show:
+        print_log(
+            f'\nResults have been saved at {os.path.abspath(args.out_dir)}')
+
+
+if __name__ == '__main__':
+    main()

docs/en/user_guides/useful_tools.md

@@ -350,6 +350,59 @@ python tools/analysis_tools/optimize_anchors.py ${CONFIG} \
     --output-dir ${OUTPUT_DIR}
 ```
 
+## Perform inference on large images
+
+First install [`sahi`](https://github.com/obss/sahi) with:
+
+```shell
+pip install -U sahi>=0.11.4
+```
+
+Perform MMYOLO inference on large images (as satellite imagery) as:
+
+```shell
+wget -P checkpoint https://download.openmmlab.com/mmyolo/v0/yolov5/yolov5_m-v61_syncbn_fast_8xb16-300e_coco/yolov5_m-v61_syncbn_fast_8xb16-300e_coco_20220917_204944-516a710f.pth
+
+python demo/large_image_demo.py \
+    demo/large_image.jpg \
+    configs/yolov5/yolov5_m-v61_syncbn_fast_8xb16-300e_coco.py \
+    checkpoint/yolov5_m-v61_syncbn_fast_8xb16-300e_coco_20220917_204944-516a710f.pth \
+```
+
+Arrange slicing parameters as:
+
+```shell
+python demo/large_image_demo.py \
+    demo/large_image.jpg \
+    configs/yolov5/yolov5_m-v61_syncbn_fast_8xb16-300e_coco.py \
+    checkpoint/yolov5_m-v61_syncbn_fast_8xb16-300e_coco_20220917_204944-516a710f.pth \
+    --patch-size 512
+    --patch-overlap-ratio 0.25
+```
+
+Export debug visuals while performing inference on large images as:
+
+```shell
+python demo/large_image_demo.py \
+    demo/large_image.jpg \
+    configs/yolov5/yolov5_m-v61_syncbn_fast_8xb16-300e_coco.py \
+    checkpoint/yolov5_m-v61_syncbn_fast_8xb16-300e_coco_20220917_204944-516a710f.pth \
+    --debug
+```
+
+[`sahi`](https://github.com/obss/sahi) citation:
+
+```
+@article{akyon2022sahi,
+  title={Slicing Aided Hyper Inference and Fine-tuning for Small Object Detection},
+  author={Akyon, Fatih Cagatay and Altinuc, Sinan Onur and Temizel, Alptekin},
+  journal={2022 IEEE International Conference on Image Processing (ICIP)},
+  doi={10.1109/ICIP46576.2022.9897990},
+  pages={966-970},
+  year={2022}
+}
+```
+
 ## Extracts a subset of COCO
 
 The training dataset of the COCO2017 dataset includes 118K images, and the validation set includes 5K images, which is a relatively large dataset. Loading JSON in debugging or quick verification scenarios will consume more resources and bring slower startup speed.

mmyolo/utils/large_image.py

@@ -0,0 +1,74 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+from typing import Sequence, Tuple
+
+from mmcv.ops import batched_nms
+from mmdet.structures import DetDataSample, SampleList
+from mmengine.structures import InstanceData
+
+
+def shift_predictions(det_data_samples: SampleList,
+                      offsets: Sequence[Tuple[int, int]],
+                      src_image_shape: Tuple[int, int]) -> SampleList:
+    """Shift predictions to the original image.
+
+    Args:
+        det_data_samples (List[:obj:`DetDataSample`]): A list of patch results.
+        offsets (Sequence[Tuple[int, int]]): Positions of the left top points
+            of patches.
+        src_image_shape (Tuple[int, int]): A (height, width) tuple of the large
+            image's width and height.
+    Returns:
+        (List[:obj:`DetDataSample`]): shifted results.
+    """
+    from sahi.slicing import shift_bboxes, shift_masks
+
+    assert len(det_data_samples) == len(
+        offsets), 'The `results` should has the ' 'same length with `offsets`.'
+    shifted_predictions = []
+    for det_data_sample, offset in zip(det_data_samples, offsets):
+        pred_inst = det_data_sample.pred_instances.clone()
+
+        # shift bboxes and masks
+        pred_inst.bboxes = shift_bboxes(pred_inst.bboxes, offset)
+        if 'masks' in det_data_sample:
+            pred_inst.masks = shift_masks(pred_inst.masks, offset,
+                                          src_image_shape)
+
+        shifted_predictions.append(pred_inst.clone())
+
+    shifted_predictions = InstanceData.cat(shifted_predictions)
+
+    return shifted_predictions
+
+
+def merge_results_by_nms(results: SampleList, offsets: Sequence[Tuple[int,
+                                                                      int]],
+                         src_image_shape: Tuple[int, int],
+                         nms_cfg: dict) -> DetDataSample:
+    """Merge patch results by nms.
+
+    Args:
+        results (List[:obj:`DetDataSample`]): A list of patch results.
+        offsets (Sequence[Tuple[int, int]]): Positions of the left top points
+            of patches.
+        src_image_shape (Tuple[int, int]): A (height, width) tuple of the large
+            image's width and height.
+        nms_cfg (dict): it should specify nms type and other parameters
+            like `iou_threshold`.
+    Returns:
+        :obj:`DetDataSample`: merged results.
+    """
+    shifted_instances = shift_predictions(results, offsets, src_image_shape)
+
+    _, keeps = batched_nms(
+        boxes=shifted_instances.bboxes,
+        scores=shifted_instances.scores,
+        idxs=shifted_instances.labels,
+        nms_cfg=nms_cfg)
+    merged_instances = shifted_instances[keeps]
+
+    merged_result = results[0].clone()
+    # update items like gt_instances, ignore_instances
+    merged_result.update(results[0])
+    merged_result.pred_instances = merged_instances
+    return merged_result

requirements/sahi.txt

@@ -0,0 +1 @@
+sahi>=0.11.4