Merge pull request opencv#24680 from AleksandrPanov:update_android_mo…

…bilenet_tutorial

Update Android mobilenet tutorial

doc/tutorials/dnn/dnn_android/dnn_android.markdown

@@ -1,107 +1 @@
-# How to run deep networks on Android device {#tutorial_dnn_android}
-
-@tableofcontents
-
-@prev_tutorial{tutorial_dnn_openvino}
-@next_tutorial{tutorial_dnn_yolo}
-
-|    |    |
-| -: | :- |
-| Original author | Dmitry Kurtaev |
-| Compatibility | OpenCV >= 3.3 |
-
-## Introduction
-In this tutorial you'll know how to run deep learning networks on Android device
-using OpenCV deep learning module.
-
-Tutorial was written for the following versions of corresponding software:
-- Android Studio 2.3.3
-- OpenCV 3.3.0+
-
-## Requirements
-
-- Download and install Android Studio from https://developer.android.com/studio.
-
-- Get the latest pre-built OpenCV for Android release from https://github.com/opencv/opencv/releases and unpack it (for example, `opencv-4.X.Y-android-sdk.zip`).
-
-- Download MobileNet object detection model from https://github.com/chuanqi305/MobileNet-SSD. We need a configuration file `MobileNetSSD_deploy.prototxt` and weights `MobileNetSSD_deploy.caffemodel`.
-
-## Create an empty Android Studio project
-- Open Android Studio. Start a new project. Let's call it `opencv_mobilenet`.
-![](1_start_new_project.png)
-
-- Keep default target settings.
-![](2_start_new_project.png)
-
-- Use "Empty Activity" template. Name activity as `MainActivity` with a
-corresponding layout `activity_main`.
-![](3_start_new_project.png)
-
-  ![](4_start_new_project.png)
-
-- Wait until a project was created. Go to `Run->Edit Configurations`.
-Choose `USB Device` as target device for runs.
-![](5_setup.png)
-Plug in your device and run the project. It should be installed and launched
-successfully before we'll go next.
-@note Read @ref tutorial_android_dev_intro in case of problems.
-
-![](6_run_empty_project.png)
-
-## Add OpenCV dependency
-
-- Go to `File->New->Import module` and provide a path to `unpacked_OpenCV_package/sdk/java`. The name of module detects automatically.
-Disable all features that Android Studio will suggest you on the next window.
-![](7_import_module.png)
-
-  ![](8_import_module.png)
-
-- Open two files:
-
-  1. `AndroidStudioProjects/opencv_mobilenet/app/build.gradle`
-
-  2. `AndroidStudioProjects/opencv_mobilenet/openCVLibrary330/build.gradle`
-
-  Copy both `compileSdkVersion` and `buildToolsVersion` from the first file to
-  the second one.
-
-  `compileSdkVersion 14` -> `compileSdkVersion 26`
-
-  `buildToolsVersion "25.0.0"` -> `buildToolsVersion "26.0.1"`
-
-- Make the project. There is no errors should be at this point.
-
-- Go to `File->Project Structure`. Add OpenCV module dependency.
-![](9_opencv_dependency.png)
-
-  ![](10_opencv_dependency.png)
-
-- Install once an appropriate OpenCV manager from `unpacked_OpenCV_package/apk`
-to target device.
-@code
-adb install OpenCV_3.3.0_Manager_3.30_armeabi-v7a.apk
-@endcode
-
-- Congratulations! We're ready now to make a sample using OpenCV.
-
-## Make a sample
-Our sample will takes pictures from a camera, forwards it into a deep network and
-receives a set of rectangles, class identifiers and confidence values in `[0, 1]`
-range.
-
-- First of all, we need to add a necessary widget which displays processed
-frames. Modify `app/src/main/res/layout/activity_main.xml`:
-@include android/mobilenet-objdetect/res/layout/activity_main.xml
-
-- Put downloaded `MobileNetSSD_deploy.prototxt` and `MobileNetSSD_deploy.caffemodel`
-into `app/build/intermediates/assets/debug` folder.
-
-- Modify `/app/src/main/AndroidManifest.xml` to enable full-screen mode, set up
-a correct screen orientation and allow to use a camera.
-@include android/mobilenet-objdetect/gradle/AndroidManifest.xml
-
-- Replace content of `app/src/main/java/org/opencv/samples/opencv_mobilenet/MainActivity.java`:
-@include android/mobilenet-objdetect/src/org/opencv/samples/opencv_mobilenet/MainActivity.java
-
-- Launch an application and make a fun!
-![](11_demo.jpg)
+The page was moved to @ref tutorial_android_dnn_intro

doc/tutorials/dnn/dnn_openvino/dnn_openvino.markdown

@@ -2,7 +2,7 @@ OpenCV usage with OpenVINO {#tutorial_dnn_openvino}
 =====================
 
 @prev_tutorial{tutorial_dnn_halide_scheduling}
-@next_tutorial{tutorial_dnn_android}
+@next_tutorial{tutorial_dnn_yolo}
 
 |    |    |
 | -: | :- |

doc/tutorials/dnn/dnn_yolo/dnn_yolo.markdown

@@ -3,7 +3,7 @@ YOLO DNNs  {#tutorial_dnn_yolo}
 
 @tableofcontents
 
-@prev_tutorial{tutorial_dnn_android}
+@prev_tutorial{tutorial_dnn_openvino}
 @next_tutorial{tutorial_dnn_javascript}
 
 |    |    |

doc/tutorials/dnn/table_of_content_dnn.markdown

@@ -5,7 +5,6 @@ Deep Neural Networks (dnn module) {#tutorial_table_of_content_dnn}
 -   @subpage tutorial_dnn_halide
 -   @subpage tutorial_dnn_halide_scheduling
 -   @subpage tutorial_dnn_openvino
--   @subpage tutorial_dnn_android
 -   @subpage tutorial_dnn_yolo
 -   @subpage tutorial_dnn_javascript
 -   @subpage tutorial_dnn_custom_layers

doc/tutorials/introduction/android_binary_package/android_dnn_intro.markdown

@@ -0,0 +1,85 @@
+# How to run deep networks on Android device {#tutorial_android_dnn_intro}
+
+@tableofcontents
+
+@prev_tutorial{tutorial_dev_with_OCV_on_Android}
+@next_tutorial{tutorial_android_ocl_intro}
+
+@see @ref tutorial_table_of_content_dnn
+
+|    |    |
+| -: | :- |
+| Original author | Dmitry Kurtaev |
+| Compatibility | OpenCV >= 4.9 |
+
+## Introduction
+In this tutorial you'll know how to run deep learning networks on Android device
+using OpenCV deep learning module.
+Tutorial was written for Android Studio Android Studio 2022.2.1.
+
+## Requirements
+
+- Download and install Android Studio from https://developer.android.com/studio.
+
+- Get the latest pre-built OpenCV for Android release from https://github.com/opencv/opencv/releases
+and unpack it (for example, `opencv-4.X.Y-android-sdk.zip`).
+
+- Download MobileNet object detection model from https://github.com/chuanqi305/MobileNet-SSD.
+Configuration file `MobileNetSSD_deploy.prototxt` and model weights `MobileNetSSD_deploy.caffemodel`
+are required.
+
+## Create an empty Android Studio project and add OpenCV dependency
+
+Use @ref tutorial_dev_with_OCV_on_Android tutorial to initialize your project and add OpenCV.
+
+## Make an app
+
+Our sample will takes pictures from a camera, forwards it into a deep network and
+receives a set of rectangles, class identifiers and confidence values in range [0, 1].
+
+- First of all, we need to add a necessary widget which displays processed
+frames. Modify `app/src/main/res/layout/activity_main.xml`:
+@include android/mobilenet-objdetect/res/layout/activity_main.xml
+
+- Modify `/app/src/main/AndroidManifest.xml` to enable full-screen mode, set up
+a correct screen orientation and allow to use a camera.
+@code{.xml}
+<?xml version="1.0" encoding="utf-8"?>
+<manifest xmlns:android="http://schemas.android.com/apk/res/android">
+
+    <application
+        android:label="@string/app_name">
+@endcode
+@snippet android/mobilenet-objdetect/gradle/AndroidManifest.xml mobilenet_tutorial
+
+- Replace content of `app/src/main/java/com/example/myapplication/MainActivity.java` and set a custom package name if necessary:
+
+@snippet android/mobilenet-objdetect/src/org/opencv/samples/opencv_mobilenet/MainActivity.java mobilenet_tutorial_package
+@snippet android/mobilenet-objdetect/src/org/opencv/samples/opencv_mobilenet/MainActivity.java mobilenet_tutorial
+
+- Put downloaded `deploy.prototxt` and `mobilenet_iter_73000.caffemodel`
+into `app/src/main/res/raw` folder. OpenCV DNN model is mainly designed to load ML and DNN models
+from file. Modern Android does not allow it without extra permissions, but provides Java API to load
+bytes from resources. The sample uses alternative DNN API that initializes a model from in-memory
+buffer rather than a file. The following function reads model file from resources and converts it to
+`MatOfBytes` (analog of `std::vector<char>` in C++ world) object suitable for OpenCV Java API:
+
+@snippet android/mobilenet-objdetect/src/org/opencv/samples/opencv_mobilenet/MainActivity.java mobilenet_tutorial_resource
+
+And then the network initialization is done with the following lines:
+
+@snippet android/mobilenet-objdetect/src/org/opencv/samples/opencv_mobilenet/MainActivity.java init_model_from_memory
+
+See also [Android documentation on resources](https://developer.android.com/guide/topics/resources/providing-resources.html)
+
+- Take a look how DNN model input is prepared and inference result is interpreted:
+
+@snippet android/mobilenet-objdetect/src/org/opencv/samples/opencv_mobilenet/MainActivity.java mobilenet_handle_frame
+
+`Dnn.blobFromImage` converts camera frame to neural network input tensor. Resize and statistical
+normalization are applied. Each line of network output tensor contains information on one detected
+object in the following order: confidence in range [0, 1], class id, left, top, right, bottom box
+coordinates. All coordinates are in range [0, 1] and should be scaled to image size before rendering.
+
+- Launch an application and make a fun!
+![](images/11_demo.jpg)

doc/tutorials/introduction/android_binary_package/android_ocl_intro.markdown

@@ -1,7 +1,7 @@
 Use OpenCL in Android camera preview based CV application {#tutorial_android_ocl_intro}
 =====================================
 
-@prev_tutorial{tutorial_dev_with_OCV_on_Android}
+@prev_tutorial{tutorial_android_dnn_intro}
 @next_tutorial{tutorial_macos_install}
 
 |    |    |

doc/tutorials/introduction/android_binary_package/dev_with_OCV_on_Android.markdown

@@ -2,7 +2,7 @@ Android Development with OpenCV {#tutorial_dev_with_OCV_on_Android}
 ===============================
 
 @prev_tutorial{tutorial_O4A_SDK}
-@next_tutorial{tutorial_android_ocl_intro}
+@next_tutorial{tutorial_android_dnn_intro}
 
 |    |    |
 | -: | :- |

doc/tutorials/introduction/table_of_content_introduction.markdown

@@ -23,6 +23,7 @@ Introduction to OpenCV {#tutorial_table_of_content_introduction}
 -   @subpage tutorial_android_dev_intro
 -   @subpage tutorial_O4A_SDK
 -   @subpage tutorial_dev_with_OCV_on_Android
+-   @subpage tutorial_android_dnn_intro
 -   @subpage tutorial_android_ocl_intro
 
 ##### Other platforms

samples/android/mobilenet-objdetect/gradle/AndroidManifest.xml

@@ -5,7 +5,7 @@
     <application
         android:label="@string/app_name"
         android:icon="@drawable/icon">
-
+        <!-- //! [mobilenet_tutorial] -->
         <activity
                   android:exported="true"
                   android:name=".MainActivity"
@@ -25,3 +25,4 @@
     <uses-feature android:name="android.hardware.camera.front.autofocus" android:required="false"/>
 
 </manifest>
+<!-- //! [mobilenet_tutorial] -->

samples/android/mobilenet-objdetect/src/org/opencv/samples/opencv_mobilenet/MainActivity.java

@@ -1,5 +1,11 @@
 package org.opencv.samples.opencv_mobilenet;
-
+/*
+// snippet was added for Android tutorial
+//! [mobilenet_tutorial_package]
+package com.example.myapplication;
+//! [mobilenet_tutorial_package]
+*/
+//! [mobilenet_tutorial]
 import android.content.Context;
 import android.content.res.AssetManager;
 import android.os.Bundle;
@@ -47,16 +53,17 @@ protected void onCreate(Bundle savedInstanceState) {
             return;
         }
 
+        //! [init_model_from_memory]
         mModelBuffer = loadFileFromResource(R.raw.mobilenet_iter_73000);
         mConfigBuffer = loadFileFromResource(R.raw.deploy);
         if (mModelBuffer == null || mConfigBuffer == null) {
             Log.e(TAG, "Failed to load model from resources");
         } else
             Log.i(TAG, "Model files loaded successfully");
 
-
         net = Dnn.readNet("caffe", mModelBuffer, mConfigBuffer);
         Log.i(TAG, "Network loaded successfully");
+        //! [init_model_from_memory]
 
         setContentView(R.layout.activity_main);
 
@@ -106,6 +113,7 @@ public Mat onCameraFrame(CvCameraViewFrame inputFrame) {
         Imgproc.cvtColor(frame, frame, Imgproc.COLOR_RGBA2RGB);
 
         // Forward image through network.
+        //! [mobilenet_handle_frame]
         Mat blob = Dnn.blobFromImage(frame, IN_SCALE_FACTOR,
                 new Size(IN_WIDTH, IN_HEIGHT),
                 new Scalar(MEAN_VAL, MEAN_VAL, MEAN_VAL), /*swapRB*/false, /*crop*/false);
@@ -143,11 +151,14 @@ public Mat onCameraFrame(CvCameraViewFrame inputFrame) {
                         Imgproc.FONT_HERSHEY_SIMPLEX, 0.5, new Scalar(0, 0, 0));
             }
         }
+        //! [mobilenet_handle_frame]
+
         return frame;
     }
 
     public void onCameraViewStopped() {}
 
+    //! [mobilenet_tutorial_resource]
     private MatOfByte loadFileFromResource(int id) {
        byte[] buffer;
         try {
@@ -167,6 +178,7 @@ private MatOfByte loadFileFromResource(int id) {
 
         return new MatOfByte(buffer);
     }
+    //! [mobilenet_tutorial_resource]
 
     private static final String TAG = "OpenCV-MobileNet";
     private static final String[] classNames = {"background",
@@ -181,3 +193,4 @@ private MatOfByte loadFileFromResource(int id) {
     private Net                  net;
     private CameraBridgeViewBase mOpenCvCameraView;
 }
+//! [mobilenet_tutorial]