diff --git a/official/lite/image_segmentation/README.md b/official/lite/image_segmentation/README.md
index 1b4d3239705351f21d4d56b6b3000e7ea0dad5ef..995ab16c220deb0b2c1548a2ce8708546910308e 100644
--- a/official/lite/image_segmentation/README.md
+++ b/official/lite/image_segmentation/README.md
@@ -6,73 +6,50 @@
## 运行依赖
- Android Studio >= 3.2 (推荐4.0以上版本)
+- Android SDK >= 26 （Android Studio默认安装）
+- JDK >= 1.8 （Android Studio默认安装）
## 构建与运行
-1. 在Android Studio中加载本示例源码。
+1. 在Android Studio中加载本[示例源码](https://gitee.com/mindspore/models/tree/master/official/lite/image_segmentation)，并安装相应的SDK（指定SDK版本后，由Android Studio自动安装）。
- 
+ 
- 启动Android Studio后，点击`File->Settings->System Settings->Android SDK`，勾选相应的`SDK Tools`。如下图所示，勾选后，点击`OK`，Android Studio即可自动安装SDK。
+2. 连接Android设备，运行图像分割应用程序。
- 
+ 通过USB连接Android设备调试，点击`Run 'app'`即可在你的设备上运行本示例项目。
- > Android SDK Tools为默认安装项，取消`Hide Obsolete Packages`选框之后可看到。
- >
- > 使用过程中若出现问题，可参考第4项解决。
+ 
-2. 连接Android设备，运行该应用程序。
+ Android Studio连接设备调试操作，可参考<https://developer.android.com/studio/run/device?hl=zh-cn>。
- 通过USB连接Android手机。待成功识别到设备后，点击`Run 'app'`即可在您的手机上运行本示例项目。
+ 手机需开启“USB调试模式”，Android Studio才能识别到手机。 华
- > 编译过程中Android Studio会自动下载MindSpore Lite、模型文件等相关依赖项，编译过程需做耐心等待。
- >
- > Android Studio连接设备调试操作，可参考<https://developer.android.com/studio/run/device?hl=zh-cn>。
- >
- > 手机需开启“USB调试模式”，Android Studio 才能识别到手机。 华为手机一般在设置->系统和更新->开发人员选项->USB调试中开始“USB调试模型”。
+3. 在Android设备上，点击“继续安装”，安装完即可查看到本地相册以及设备摄像头拍照的头像图片进行分割推理的结果。
- 
+ 
-3. 在Android设备上，点击“继续安装”，安装完即可查看到设备摄像头捕获的内容和推理结果。
+ 运行结果如下图所示（以选取相册某张头像图片为例）。
- 
+ 
- 如下图所示，识别出的概率最高的物体是植物。
+ 选取相册带有头像图片。
- 
+ 
-4. Demo部署问题解决方案。
+ 选择九宫格中不同的背景图片，即可对人像的背景进行替换分割。
- 4.1 NDK、CMake、JDK等工具问题：
-
- 如果Android Studio内安装的工具出现无法识别等问题，可重新从相应官网下载和安装，并配置路径。
-
- - NDK >= 21.3 [NDK](https://developer.android.google.cn/ndk/downloads?hl=zh-cn)
- - CMake >= 3.10.2 [CMake](https://cmake.org/download)
- - Android SDK >= 26 [SDK](https://developer.microsoft.com/zh-cn/windows/downloads/windows-10-sdk/)
- - JDK >= 1.8 [JDK](http://jdk.java.net)
-
- 
-
- 4.2 NDK版本不匹配问题：
-
- 打开`Android SDK`，点击`Show Package Details`，根据报错信息选择安装合适的NDK版本。
- 
-
- 4.3 Android Studio版本问题：
-
- 在`工具栏-help-Checkout for Updates`中更新Android Studio版本。
-
- 4.4 Gradle下依赖项安装过慢问题：
-
- 如图所示， 打开Demo根目录下`build.gradle`文件，加入华为镜像源地址：`maven {url 'https://developer.huawei.com/repo/'}`，修改classpath为4.0.0，点击`sync`进行同步。下载完成后，将classpath版本复原，再次进行同步。
- 
+ <table>
+ <tr>
+ <td><center><img src="https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/website-images/master/docs/lite/docs/source_zh_cn/images/segmentation3.png"><br>图1 白色背景</br> </center></td>
+ <td><center><img src="https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/website-images/master/docs/lite/docs/source_zh_cn/images/segmentation4.png"><br>图2 蓝色背景</br> </center></td>
+ <td><center><img src="https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/website-images/master/docs/lite/docs/source_zh_cn/images/segmentation5.png"><br>图3 油画背景</br> </center></td>
+ </tr>
+ </table>
## 示例程序详细说明
-本端侧图像分割Android示例程序使用Java实现，Java层主要通过Android Camera 2 API实现摄像头获取图像帧，进行相应的图像处理，之后调用Java API 完成模型推理。
-
-> 此处详细说明示例程序的Java层图像处理及模型推理实现，Java层运用Android Camera 2 API实现开启设备摄像头以及图像帧处理等功能，需读者具备一定的Android开发基础知识。
+本端侧图像分割Android示例程序使用Java层，需读者具备一定的Android开发基础知识。
### 示例程序结构
@@ -80,24 +57,20 @@
app
├── src/main
│ ├── assets # 资源文件
-| | └── deeplabv3.ms # 存放模型文件
+| | └── segment_model.ms # 存放的模型文件
│ |
│ ├── java # java层应用代码
│ │ └── com.mindspore.imagesegmentation
-│ │ ├── help # 图像处理及MindSpore Java调用相关实现
-│ │ │ └── ImageUtils # 图像预处理
-│ │ │ └── ModelTrackingResult # 推理数据后处理
-│ │ │ └── TrackingMobile # 模型加载、构建计算图和推理
-│ │ └── BitmapUtils # 图像处理
-│ │ └── MainActivity # 交互主页面
-│ │ └── OnBackgroundImageListener # 获取相册图像
-│ │ └── StyleRecycleViewAdapter # 获取相册图像
+│ │ ├── help # 图像处理
+│ │ │ └── ...
+│ │ └── ... Android页面展示以及逻辑处理
│ │
│ ├── res # 存放Android相关的资源文件
│ └── AndroidManifest.xml # Android配置文件
│
-├── CMakeList.txt # cmake编译入口文件
-│
+├── libs # Android库项目的二进制归档文件
+| └── mindspore-lite-version.aar # MindSpore Lite针对Android版本的归档文件
+|
├── build.gradle # 其他Android配置文件
├── download.gradle # 工程依赖文件下载
└── ...
@@ -105,7 +78,7 @@ app
### 配置MindSpore Lite依赖项
-Android 调用MindSpore Java API时，需要相关库文件支持。可通过MindSpore Lite[源码编译](https://www.mindspore.cn/lite/docs/zh-CN/master/use/build.html)生成`mindspore-lite-{version}-minddata-{os}-{device}.tar.gz`库文件包并解压缩（包含`libmindspore-lite.so`库文件和相关头文件），在本例中需使用生成带图像预处理模块的编译命令。
+Android调用MindSpore Android AAR时，需要相关库文件支持。可通过MindSpore Lite[源码编译](https://www.mindspore.cn/lite/docs/zh-CN/master/use/build.html)生成`mindspore-lite-maven-{version}.zip`库文件包并解压缩（包含`mindspore-lite-{version}.aar`库文件）。
> version：输出件版本号，与所编译的分支代码对应的版本一致。
>
@@ -113,245 +86,170 @@ Android 调用MindSpore Java API时，需要相关库文件支持。可通过Min
>
> os：输出件应部署的操作系统。
-本示例中，build过程由download.gradle文件自动下载MindSpore Lite 版本文件，并放置在`app/src/main/cpp/`目录下。
-
-> 若自动下载失败，请手动下载相关库文件，解压并放在对应位置：
-
- mindspore-lite-1.0.1-runtime-arm64-cpu.tar.gz [下载链接](https://ms-release.obs.cn-north-4.myhuaweicloud.com/1.0.1/lite/android_aarch64/mindspore-lite-1.0.1-runtime-arm64-cpu.tar.gz)
-
-在app的`build.gradle`文件中配置CMake编译支持，以及`arm64-v8a`的编译支持，如下所示：
-
-```text
-android{
- defaultConfig{
- externalNativeBuild{
- cmake{
- arguments "-DANDROID_STL=c++_shared"
- }
- }
-
- ndk{
- abiFilters 'arm64-v8a'
- }
- }
-}
-```
-
-在`app/CMakeLists.txt`文件中建立`.so`库文件链接，如下所示。
+本示例中，build过程由`app/download.gradle`文件自动下载MindSpore Lite版本文件，并放置在`app/libs`目录下。
-```text
-# ============== Set MindSpore Dependencies. =============
-include_directories(${CMAKE_SOURCE_DIR}/src/main/cpp)
-include_directories(${CMAKE_SOURCE_DIR}/src/main/cpp/${MINDSPORELITE_VERSION}/third_party/flatbuffers/include)
-include_directories(${CMAKE_SOURCE_DIR}/src/main/cpp/${MINDSPORELITE_VERSION})
-include_directories(${CMAKE_SOURCE_DIR}/src/main/cpp/${MINDSPORELITE_VERSION}/include)
-include_directories(${CMAKE_SOURCE_DIR}/src/main/cpp/${MINDSPORELITE_VERSION}/include/ir/dtype)
-include_directories(${CMAKE_SOURCE_DIR}/src/main/cpp/${MINDSPORELITE_VERSION}/include/schema)
-
-add_library(mindspore-lite SHARED IMPORTED )
-add_library(minddata-lite SHARED IMPORTED )
-
-set_target_properties(mindspore-lite PROPERTIES IMPORTED_LOCATION
- ${CMAKE_SOURCE_DIR}/src/main/cpp/${MINDSPORELITE_VERSION}/lib/libmindspore-lite.so)
-set_target_properties(minddata-lite PROPERTIES IMPORTED_LOCATION
- ${CMAKE_SOURCE_DIR}/src/main/cpp/${MINDSPORELITE_VERSION}/lib/libminddata-lite.so)
-# --------------- MindSpore Lite set End. --------------------
-
-# Link target library.
-target_link_libraries(
- ...
- # --- mindspore ---
- minddata-lite
- mindspore-lite
- ...
-)
-```
+> 注： 若自动下载失败，请手动下载相关库文件[mindspore-lite-{version}-android-{arch}.tar.gz](https://www.mindspore.cn/lite/docs/zh-CN/master/use/downloads.html)，解压后将其放在对应位置。
### 下载及部署模型文件
-从MindSpore Model Hub中下载模型文件，本示例程序中使用的终端图像分割模型文件为`deeplabv3.ms`，同样通过download.gradle脚本在APP构建时自动下载，并放置在`app/src/main/assets`工程目录下。
+从MindSpore Model Hub中下载模型文件，本示例程序中使用的终端图像分割模型文件为`segment_model.ms`，同样通过`app/download.gradle`脚本在APP构建时自动下载，并放置在`app/src/main/assets`工程目录下。
-> 若下载失败请手动下载模型文件，deeplabv3.ms [下载链接](https://download.mindspore.cn/model_zoo/official/lite/deeplabv3_lite/deeplabv3.ms)。
+注：若下载失败请手工下载模型文件[segment_model.ms](https://download.mindspore.cn/model_zoo/official/lite/mobile_segment_lite/segment_model.ms)。
### 编写端侧推理代码
-调用MindSpore Lite Java API实现端测推理。
-
-推理代码流程如下，完整代码请参见`src/java/TrackingMobile.java`。
-
-1. 加载MindSpore Lite模型文件，构建上下文、会话以及用于推理的计算图。
-
- - 加载模型文件：创建并配置用于模型推理的上下文
+推理代码流程如下，完整代码请参见 [src/java/com/mindspore/imagesegmentation/TrackingMobile](https://gitee.com/mindspore/models/blob/master/official/lite/image_segmentation/app/src/main/java/com/mindspore/imagesegmentation/help/TrackingMobile.java)。
- ```Java
- // Create context and load the .ms model named 'IMAGESEGMENTATIONMODEL'
- model = new Model();
- if (!model.loadModel(Context, IMAGESEGMENTATIONMODEL)) {
- Log.e(TAG, "Load Model failed");
- return;
- }
- ```
+1. 加载MindSpore Lite模型，构建上下文、会话以及用于推理的计算图。
- - 创建会话
-
- ```Java
- // Create and init config.
- msConfig = new MSConfig();
- if (!msConfig.init(DeviceType.DT_CPU, 2, CpuBindMode.MID_CPU)) {
- Log.e(TAG, "Init context failed");
- return;
- }
+ - 创建会话。
- // Create the MindSpore lite session.
- session = new LiteSession();
- if (!session.init(msConfig)) {
- Log.e(TAG, "Create session failed");
- msConfig.free();
- return;
- }
- msConfig.free();
- ```
+ ```java
+ // Create and init config.
+ MSContext context = new MSContext();
+ if (!context.init(2, CpuBindMode.MID_CPU, false)) {
+ Log.e(TAG, "Init context failed");
+ return;
+ }
+ if (!context.addDeviceInfo(DeviceType.DT_CPU, false, 0)) {
+ Log.e(TAG, "Add device info failed");
+ return;
+ }
+ ```
- - 构建计算图
+ - 加载模型并构建用于推理的计算图。
- ```Java
- if (!session.compileGraph(model)) {
- Log.e(TAG, "Compile graph failed");
- model.freeBuffer();
- return;
- }
- // Note: when use model.freeBuffer(), the model can not be compile graph again.
- model.freeBuffer();
- ```
+ ```java
+ MappedByteBuffer modelBuffer = loadModel(mContext, IMAGESEGMENTATIONMODEL);
+ if(modelBuffer == null) {
+ Log.e(TAG, "Load model failed");
+ return;
+ }
+ // build model.
+ boolean ret = model.build(modelBuffer, ModelType.MT_MINDIR,context);
+ if(!ret) {
+ Log.e(TAG, "Build model failed");
+ }
+ ```
2. 将输入图片转换为传入MindSpore模型的Tensor格式。
- ```Java
- List<MSTensor> inputs = session.getInputs();
- if (inputs.size() != 1) {
- Log.e(TAG, "inputs.size() != 1");
- return null;
- }
-
- // `bitmap` is the picture used to infer.
- float resource_height = bitmap.getHeight();
- float resource_weight = bitmap.getWidth();
- ByteBuffer contentArray = bitmapToByteBuffer(bitmap, imageSize, imageSize, IMAGE_MEAN, IMAGE_STD);
+ 将待检测图片数据转换为输入MindSpore模型的Tensor。
- MSTensor inTensor = inputs.get(0);
- inTensor.setData(contentArray);
- ```
+ ```java
+ List<MSTensor> inputs = model.getInputs();
+ if (inputs.size() != 1) {
+ Log.e(TAG, "inputs.size() != 1");
+ return null;
+ }
-3. 对输入Tensor按照模型进行推理，获取输出Tensor，并进行后处理。
+ float resource_height = bitmap.getHeight();
+ float resource_weight = bitmap.getWidth();
- - 图执行，端侧推理。
+ ByteBuffer contentArray = BitmapUtils.bitmapToByteBuffer(bitmap, imageSize, imageSize, IMAGE_MEAN, IMAGE_STD);
- ```Java
- // After the model and image tensor data is loaded, run inference.
- if (!session.runGraph()) {
- Log.e(TAG, "Run graph failed");
- return null;
- }
- ```
+ MSTensor inTensor = inputs.get(0);
+ inTensor.setData(contentArray);
+ ```
- - 获取输出数据。
+3. 运行会话，执行计算图。
- ```Java
- // Get output tensor values, the model only outputs one tensor.
- List<String> tensorNames = session.getOutputTensorNames();
- MSTensor output = session.getOutputByTensorName(tensorNames.front());
- if (output == null) {
- Log.e(TAG, "Can not find output " + tensorName);
- return null;
+ ```java
+ // Run graph to infer results.
+ if (!model.predict()) {
+ Log.e(TAG, "Run graph failed");
+ return null;
}
```
- - 输出数据的后续处理。
-
- ```Java
- // Show output as pictures.
- float[] results = output.getFloatData();
-
- ByteBuffer bytebuffer_results = floatArrayToByteArray(results);
-
- Bitmap dstBitmap = convertBytebufferMaskToBitmap(bytebuffer_results, imageSize, imageSize, bitmap, dstBitmap, segmentColors);
- dstBitmap = scaleBitmapAndKeepRatio(dstBitmap, (int) resource_height, (int) resource_weight);
- ```
-
-4. 图片处理及输出数据后处理请参考如下代码。
+4. 对输出数据进行处理。
- ```Java
- Bitmap scaleBitmapAndKeepRatio(Bitmap targetBmp, int reqHeightInPixels, int reqWidthInPixels) {
- if (targetBmp.getHeight() == reqHeightInPixels && targetBmp.getWidth() == reqWidthInPixels) {
- return targetBmp;
- }
+ - 通过Tensor获取的输出数据得到其维度，批处理数，通道数等信息。
- Matrix matrix = new Matrix();
- matrix.setRectToRect(new RectF(0f, 0f, targetBmp.getWidth(), targetBmp.getHeight()),
- new RectF(0f, 0f, reqWidthInPixels, reqHeightInPixels), Matrix.ScaleToFit.FILL;
+ ```java
+ // Get output tensor values.
+ List<MSTensor> outTensors = model.getOutputs();
+ for (MSTensor output : outTensors) {
+ if (output == null) {
+ Log.e(TAG, "Can not find output " + tensorName);
+ return null;
+ }
+ float[] results = output.getFloatData();
+ float[] result = new float[output.elementsNum()];
+
+ int batch = output.getShape()[0];
+ int channel = output.getShape()[1];
+ int weight = output.getShape()[2];
+ int height = output.getShape()[3];
+ int plane = weight * height;
+ ```
+
+ - 将NCHW格式转为NHWC格式，放入到`float[] result`。
+
+ ```java
+ for (int n = 0; n < batch; n++) {
+ for (int c = 0; c < channel; c++) {
+ for (int hw = 0; hw < plane; hw++) {
+ result[n * channel * plane + hw * channel + c] = results[n * channel * plane + c * plane + hw];
+ }
+ }
+ }
+ ```
- return Bitmap.createBitmap(targetBmp, 0, 0, targetBmp.getWidth(), targetBmp.getHeight(), matrix, true);
- }
+5. 对输入Tensor按照模型进行推理，进行后处理。
- ByteBuffer bitmapToByteBuffer(Bitmap bitmapIn, int width, int height, float mean, float std) {
- Bitmap bitmap = scaleBitmapAndKeepRatio(bitmapIn, width, height);
- ByteBuffer inputImage = ByteBuffer.allocateDirect(1 * width * height * 3 * 4);
- inputImage.order(ByteOrder.nativeOrder());
- inputImage.rewind();
- int[] intValues = new int[width * height];
- bitmap.getPixels(intValues, 0, width, 0, 0, width, height);
- int pixel = 0;
- for (int y = 0; y < height; y++) {
- for (int x = 0; x < width; x++) {
- int value = intValues[pixel++];
- inputImage.putFloat(((float) (value >> 16 & 255) - mean) / std);
- inputImage.putFloat(((float) (value >> 8 & 255) - mean) / std);
- inputImage.putFloat(((float) (value & 255) - mean) / std);
- }
- }
- inputImage.rewind();
- return inputImage;
- }
+ - 将`float[] result`数据转换成ByteBuffer数据格式。
- ByteBuffer floatArrayToByteArray(float[] floats) {
- ByteBuffer buffer = ByteBuffer.allocate(4 * floats.length);
+ ```java
+ ByteBuffer buffer = ByteBuffer.allocate(4 * result.length);
FloatBuffer floatBuffer = buffer.asFloatBuffer();
- floatBuffer.put(floats);
+ floatBuffer.put(result);
return buffer;
- }
-
- Bitmap convertBytebufferMaskToBitmap(ByteBuffer inputBuffer, int imageWidth, int imageHeight, Bitmap backgroundImage, int[] colors) {
- Bitmap.Config conf = Bitmap.Config.ARGB_8888;
- Bitmap dstBitmap = Bitmap.createBitmap(imageWidth, imageHeight, conf);
- Bitmap scaledBackgroundImage = scaleBitmapAndKeepRatio(backgroundImage, imageWidth, imageHeight);
- int[][] mSegmentBits = new int[imageWidth][imageHeight];
- inputBuffer.rewind();
- for (int y = 0; y < imageHeight; y++) {
- for (int x = 0; x < imageWidth; x++) {
- float maxVal = 0f;
- mSegmentBits[x][y] = 0;
- // NUM_CLASSES is the number of labels, the value here is 21.
- for (int i = 0; i < NUM_CLASSES; i++) {
- float value = inputBuffer.getFloat((y * imageWidth * NUM_CLASSES + x * NUM_CLASSES + i) * 4);
- if (i == 0 || value > maxVal) {
- maxVal = value;
- // Check whether a pixel belongs to a person whose label is 15.
- if (i == 15) {
- mSegmentBits[x][y] = i;
- } else {
+ ```
+
+ - 将ByteBuffer数据格式转成Bitmap。
+
+ 通过推理出来的数据在Bitmap每个像素坐标进行比对。如果坐标数据等于PERSON，坐标点颜色不变。反之，则改成透明色（如下图所示）。
+
+ ```java
+ Bitmap.Config conf = Bitmap.Config.ARGB_8888;
+ Bitmap maskBitmap = Bitmap.createBitmap(imageWidth, imageHeight, conf);
+ Bitmap scaledBackgroundImage =
+ BitmapUtils.scaleBitmapAndKeepRatio(backgroundImage, imageWidth, imageHeight);
+ int[][] mSegmentBits = new int[imageWidth][imageHeight];
+ inputBuffer.rewind();
+ for (int y = 0; y < imageHeight; y++) {
+ for (int x = 0; x < imageWidth; x++) {
+ float maxVal = 0f;
mSegmentBits[x][y] = 0;
- }
+ for (int i = 0; i < NUM_CLASSES; i++) {
+ float value = inputBuffer.getFloat((y * imageWidth * NUM_CLASSES + x * NUM_CLASSES + i) * 4);
+ if (i == 0 || value > maxVal) {
+ maxVal = value;
+ if (i == PERSON) {
+ mSegmentBits[x][y] = i;
+ } else {
+ mSegmentBits[x][y] = 0;
+ }
+ }
+ }
+ maskBitmap.setPixel(x, y, mSegmentBits[x][y] == 0 ? colors[0] : scaledBackgroundImage.getPixel(x, y));
}
- }
- itemsFound.add(mSegmentBits[x][y]);
-
- int newPixelColor = ColorUtils.compositeColors(
- colors[mSegmentBits[x][y] == 0 ? 0 : 1],
- scaledBackgroundImage.getPixel(x, y)
- );
- dstBitmap.setPixel(x, y, mSegmentBits[x][y] == 0 ? colors[0] : scaledBackgroundImage.getPixel(x, y));
}
- }
- return dstBitmap;
- }
+ ```
+
+ <table>
+ <tr>
+ <td><center><img src="https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/website-images/master/docs/lite/docs/source_zh_cn/images/segmentation6.png"><br>图1 推理前</br></center></td>
+ <td><center><img src="https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/website-images/master/docs/lite/docs/source_zh_cn/images/segmentation7.png"><br>图2 推理后</br></center></td>
+ </tr>
+ </table>
+
+6. 将推理后的图片与选择的背景图片相结合。
+
+ ```java
+ MainActivity.this.imgPreview.setDrawingCacheEnabled(true);
+ MainActivity.this.imgPreview.setBackground(isDemo ? getDrawable(IMAGES[selectedPosition]) : customBack);
+ MainActivity.this.imgPreview.setImageBitmap(foreground);
+ MainActivity.this.imgPreview.setDrawingCacheEnabled(false);
```
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