Huawei_Technology
/
mindspore
Not watched
1
0
Fork 0
Code
Releases 13 Wiki evaluate Activity
Issues 0 Pull Requests 0 Datasets Model Cloudbrain HPC
Browse Source

add readme files for image_segmentation demo.

tags/v1.1.0
wang_shaocong 5 years ago
parent
8b404b026c
commit
24c0638bde
2 changed files with 675 additions and 0 deletions
Split View
Diff Options
Show Stats Download Patch File Download Diff File
  1. +330
    -0
      model_zoo/official/lite/image_segmentation/README.en.md
  2. +345
    -0
      model_zoo/official/lite/image_segmentation/README.md

+ 330
- 0
model_zoo/official/lite/image_segmentation/README.en.md View File

@@ -0,0 +1,330 @@
## Demo of Image Segmentation

The following describes how to use the MindSpore Lite C++ APIs (Android JNIs) and MindSpore Lite image segmentation models to perform on-device inference, classify the content captured by a device camera, and display the most possible segmentation result on the application's image preview screen.

### Running Dependencies

- Android Studio 3.2 or later (Android 4.0 or later is recommended.)
- Native development kit (NDK) 21.3
- CMake 3.10.2 [CMake](https://cmake.org/download)
- Android software development kit (SDK) 26 or later
- JDK 1.8 or later

### Building and Running

1. Load the sample source code to Android Studio and install the corresponding SDK. (After the SDK version is specified, Android Studio automatically installs the SDK.)

![start_home](images/home.png)

Start Android Studio, click `File > Settings > System Settings > Android SDK`, and select the corresponding SDK. As shown in the following figure, select an SDK and click `OK`. Android Studio automatically installs the SDK.

![start_sdk](images/sdk_management.png)

If you have any Android Studio configuration problem when trying this demo, please refer to item 5 to resolve it.

2. Connect to an Android device and runs the image segmentation application.

Connect to the Android device through a USB cable for debugging. Click `Run 'app'` to run the sample project on your device.

![run_app](images/run_app.PNG)

For details about how to connect the Android Studio to a device for debugging, see <https://developer.android.com/studio/run/device?hl=zh-cn>.

The mobile phone needs to be turn on "USB debugging mode" before Android Studio can recognize the mobile phone. Huawei mobile phones generally turn on "USB debugging model" in Settings > system and update > developer Options > USB debugging.

3. Continue the installation on the Android device. After the installation is complete, you can view the content captured by a camera and the inference result.

![result](images/app_result.jpg)

4. The solutions of Android Studio configuration problems:

| | Warning | Solution |
| ---- | ------------------------------------------------------------ | ------------------------------------------------------------ |
| 1 | Gradle sync failed: NDK not configured. | Specify the installed ndk directory in local.properties：ndk.dir={ndk的安装目录} |
| 2 | Requested NDK version did not match the version requested by ndk.dir | Manually download corresponding [NDK Version](https://developer.android.com/ndk/downloads)，and specify the sdk directory in Project Structure - Android NDK location.（You can refer to the figure below.） |
| 3 | This version of Android Studio cannot open this project, please retry with Android Studio or newer. | Update Android Studio Version in Tools - help - Checkout for Updates. |
| 4 | SSL peer shut down incorrectly | Run this demo again. |

![project_structure](images/project_structure.png)

## Detailed Description of the Sample Program

This image segmentation sample program on the Android device includes a Java layer and a JNI layer. At the Java layer, the Android Camera 2 API is used to enable a camera to obtain image frames and process images. At the JNI layer, the model inference process is completed in [Runtime](https://www.mindspore.cn/tutorial/lite/en/master/use/runtime.html).

### Sample Program Structure

```text
app
│
├── src/main
│ ├── assets # resource files
| | └── deeplabv3.ms # model file
│ |
│ ├── cpp # main logic encapsulation classes for model loading and prediction
| | |
| | ├── MindSporeNetnative.cpp # JNI methods related to MindSpore calling
│ | └── MindSporeNetnative.h # header file
│ |
│ ├── java # application code at the Java layer
│ │ └── com.mindspore.himindsporedemo
│ │ ├── gallery.classify # implementation related to image processing and MindSpore JNI calling
│ │ │ └── ...
│ │ └── widget # implementation related to camera enabling and drawing
│ │ └── ...
│ │
│ ├── res # resource files related to Android
│ └── AndroidManifest.xml # Android configuration file
│
├── CMakeList.txt # CMake compilation entry file
│
├── build.gradle # Other Android configuration file
├── download.gradle # MindSpore version download
└── ...
```

### Configuring MindSpore Lite Dependencies

When MindSpore C++ APIs are called at the Android JNI layer, related library files are required. You can use MindSpore Lite [source code compilation](https://www.mindspore.cn/tutorial/lite/en/master/use/build.html) to generate the MindSpore Lite version. In this case, you need to use the compile command of generate with image preprocessing module.

In this example, the build process automatically downloads the `mindspore-lite-1.0.1-runtime-arm64-cpu` by the `app/download.gradle` file and saves in the `app/src/main/cpp` directory.

Note: if the automatic download fails, please manually download the relevant library files and put them in the corresponding location.

mindspore-lite-1.0.1-runtime-arm64-cpu.tar.gz [Download link](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)

```text
android{
defaultConfig{
externalNativeBuild{
cmake{
arguments "-DANDROID_STL=c++_shared"
}
}

ndk{
abiFilters'armeabi-v7a', 'arm64-v8a'
}
}
}
```

Create a link to the `.so` library file in the `app/CMakeLists.txt` file:

```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
...
)
```

### Downloading and Deploying a Model File

In this example, the download.gradle File configuration auto download `deeplabv3.ms`and placed in the 'app/libs/arm64-v8a' directory.

Note: if the automatic download fails, please manually download the relevant library files and put them in the corresponding location.

deeplabv3.ms [deeplabv3.ms]( https://download.mindspore.cn/model_zoo/official/lite/deeplabv3_openimage_lite/deeplabv3.ms)

### Compiling On-Device Inference Code

Call MindSpore Lite Java APIs to implement on-device inference.

The inference code process is as follows. For details about the complete code, see `src/java/TrackingMobile.java`.

1. Load the MindSpore Lite model file and build the context, session, and computational graph for inference.

- Load a model file. Import and configure the context for model inference.

```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;
}
```

- Create a session.

```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;
}

// Create the MindSpore lite session.
session = new LiteSession();
if (!session.init(msConfig)) {
Log.e(TAG, "Create session failed");
msConfig.free();
return;
}
msConfig.free();
```

- Compile graph for inference.

```Java
if (!session.compileGraph(model)) {
Log.e(TAG, "Compile graph failed");
model.freeBuffer();
return;
}
// Note: when use model.freeBuffer(), the model can not be complile graph again.
model.freeBuffer();
```

2. Convert the input image into the Tensor format of the MindSpore model.

```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);

MSTensor inTensor = inputs.get(0);
inTensor.setData(contentArray);
```

3. Perform inference on the input tensor based on the model, obtain the output tensor, and perform post-processing.

- Perform graph execution and on-device inference.

```Java
// After the model and image tensor data is loaded, run inference.
if (!session.runGraph()) {
Log.e(TAG, "Run graph failed");
return null;
}
```

- Obtain the output data.

```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;
}
```

- Perform post-processing of the output data.

```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. The process of image and output data can refer to methods showing bellow.

```Java
Bitmap scaleBitmapAndKeepRatio(Bitmap targetBmp, int reqHeightInPixels, int reqWidthInPixels) {
if (targetBmp.getHeight() == reqHeightInPixels && targetBmp.getWidth() == reqWidthInPixels) {
return targetBmp;
}

Matrix matrix = new Matrix();
matrix.setRectToRect(new RectF(0f, 0f, targetBmp.getWidth(), targetBmp.getHeight()),
new RectF(0f, 0f, reqWidthInPixels, reqHeightInPixels), Matrix.ScaleToFit.FILL;

return Bitmap.createBitmap(targetBmp, 0, 0, targetBmp.getWidth(), targetBmp.getHeight(), matrix, true);
}

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;
}

ByteBuffer floatArrayToByteArray(float[] floats) {
ByteBuffer buffer = ByteBuffer.allocate(4 * floats.length);
FloatBuffer floatBuffer = buffer.asFloatBuffer();
floatBuffer.put(floats);
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 wether a pixel belongs to a person whose label is 15.
if (i == 15) {
mSegmentBits[x][y] = i;
} else {
mSegmentBits[x][y] = 0;
}
}
}
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;
}
```

+ 345
- 0
model_zoo/official/lite/image_segmentation/README.md View File

@@ -0,0 +1,345 @@
## MindSpore Lite 端侧图像分割demo（Android）

本示例程序演示了如何在端侧利用MindSpore Lite C++ API（Android JNI）以及MindSpore Lite 图像分割模型完成端侧推理，实现对设备摄像头捕获的内容进行分割，并在App图像预览界面中显示出最可能的分割结果。

### 运行依赖

- Android Studio >= 3.2 (推荐4.0以上版本)
- NDK 21.3
- CMake 3.10.2 [CMake](https://cmake.org/download)
- Android SDK >= 26
- JDK >= 1.8

### 构建与运行

1. 在Android Studio中加载本示例源码，并安装相应的SDK（指定SDK版本后，由Android Studio自动安装）。

![start_home](images/home.png)

启动Android Studio后，点击`File->Settings->System Settings->Android SDK`，勾选相应的SDK。如下图所示，勾选后，点击`OK`，Android Studio即可自动安装SDK。

![start_sdk](images/sdk_management.png)

使用过程中若出现Android Studio配置问题，可参考第5项解决。

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'`即可在您的设备上运行本示例项目。

> 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。

![run_app](images/run_app.PNG)

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”，安装完即可查看到设备摄像头捕获的内容和推理结果。

![install](images/install.jpg)

如下图所示，识别出的概率最高的物体是植物。

![result](images/app_result.jpg)

4. Android Studio 配置问题解决方案可参考下表：

| | 报错 | 解决方案 |
| ---- | ------------------------------------------------------------ | ------------------------------------------------------------ |
| 1 | Gradle sync failed: NDK not configured. | 在local.properties中指定安装的ndk目录：ndk.dir={ndk的安装目录} |
| 2 | Requested NDK version did not match the version requested by ndk.dir | 可手动下载相应的[NDK版本](https://developer.android.com/ndk/downloads?hl=zh-cn)，并在Project Structure - Android NDK location设置中指定SDK的位置（可参考下图完成） |
| 3 | This version of Android Studio cannot open this project, please retry with Android Studio or newer. | 在工具栏-help-Checkout for Updates中更新版本 |
| 4 | SSL peer shut down incorrectly | 重新构建 |

![project_structure](images/project_structure.png)

## 示例程序详细说明

本端侧图像分割Android示例程序分为JAVA层和JNI层，其中，JAVA层主要通过Android Camera 2 API实现摄像头获取图像帧，以及相应的图像处理等功能；JNI层完成模型推理的过程。

> 此处详细说明示例程序的JNI层实现，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。

### 示例程序结构

```text
app
├── src/main
│ ├── assets # 资源文件
| | └── deeplabv3.ms # 存放模型文件
│ |
│ ├── cpp # 模型加载和预测主要逻辑封装类
| | ├── ..
| | ├── mindspore_lite_x.x.x-minddata-arm64-cpu #MindSpore Lite版本
| | ├── MindSporeNetnative.cpp # MindSpore调用相关的JNI方法
│ | └── MindSporeNetnative.h # 头文件
| | └── MsNetWork.cpp # MindSpre接口封装
│ |
│ ├── java # java层应用代码
│ │ └── com.mindspore.himindsporedemo
│ │ ├── gallery.classify # 图像处理及MindSpore JNI调用相关实现
│ │ │ └── ...
│ │ └── widget # 开启摄像头及绘制相关实现
│ │ └── ...
│ │
│ ├── 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
│
├── build.gradle # 其他Android配置文件
├── download.gradle # 工程依赖文件下载
└── ...
```

### 配置MindSpore Lite依赖项

Android JNI层调用MindSpore C++ API时，需要相关库文件支持。可通过MindSpore Lite[源码编译](https://www.mindspore.cn/tutorial/lite/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。

> version：输出件版本号，与所编译的分支代码对应的版本一致。
>
> device：当前分为cpu（内置CPU算子）和gpu（内置CPU和GPU算子）。
>
> 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。

```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
...
)
```

### 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。

> 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_openimage_lite/deeplabv3.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

```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;
}
```

- 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;
}

// Create the MindSpore lite session.
session = new LiteSession();
if (!session.init(msConfig)) {
Log.e(TAG, "Create session failed");
msConfig.free();
return;
}
msConfig.free();
```

- 构建计算图

```Java
if (!session.compileGraph(model)) {
Log.e(TAG, "Compile graph failed");
model.freeBuffer();
return;
}
// Note: when use model.freeBuffer(), the model can not be complile graph again.
model.freeBuffer();
```

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);

MSTensor inTensor = inputs.get(0);
inTensor.setData(contentArray);
```

3. 对输入Tensor按照模型进行推理，获取输出Tensor，并进行后处理。

- 图执行，端侧推理。

```Java
// After the model and image tensor data is loaded, run inference.
if (!session.runGraph()) {
Log.e(TAG, "Run graph failed");
return null;
}
```

- 获取输出数据。

```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;
}
```

- 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。

```Java
Bitmap scaleBitmapAndKeepRatio(Bitmap targetBmp, int reqHeightInPixels, int reqWidthInPixels) {
if (targetBmp.getHeight() == reqHeightInPixels && targetBmp.getWidth() == reqWidthInPixels) {
return targetBmp;
}

Matrix matrix = new Matrix();
matrix.setRectToRect(new RectF(0f, 0f, targetBmp.getWidth(), targetBmp.getHeight()),
new RectF(0f, 0f, reqWidthInPixels, reqHeightInPixels), Matrix.ScaleToFit.FILL;

return Bitmap.createBitmap(targetBmp, 0, 0, targetBmp.getWidth(), targetBmp.getHeight(), matrix, true);
}

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;
}

ByteBuffer floatArrayToByteArray(float[] floats) {
ByteBuffer buffer = ByteBuffer.allocate(4 * floats.length);
FloatBuffer floatBuffer = buffer.asFloatBuffer();
floatBuffer.put(floats);
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 wether a pixel belongs to a person whose label is 15.
if (i == 15) {
mSegmentBits[x][y] = i;
} else {
mSegmentBits[x][y] = 0;
}
}
}
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;
}
```

Write Preview
Loading…
Cancel
Save