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# MindSpore Lite 端侧骨骼检测demo（Android）
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本示例程序演示了如何在端侧利用MindSpore Lite API以及MindSpore Lite骨骼检测模型完成端侧推理，对设备摄像头捕获的内容进行检测，并在App图像预览界面中显示连续目标检测结果。
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## 运行依赖
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- Android Studio >= 3.2 (推荐4.0以上版本)
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## 构建与运行
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1. 在Android Studio中加载本示例源码。
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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。
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> Android SDK Tools为默认安装项，取消`Hide Obsolete Packages`选框之后可看到。
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>
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> 使用过程中若出现问题，可参考第4项解决。
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2. 连接Android设备，运行该应用程序。
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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。
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> 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。
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>
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> Android Studio连接设备调试操作，可参考<https://developer.android.com/studio/run/device?hl=zh-cn>。
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>
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> 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”。
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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。
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如下图所示，识别出的概率最高的物体是植物。
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4. Demo部署问题解决方案。
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4.1 NDK、CMake、JDK等工具问题：
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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。
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- NDK >= 21.3 [NDK](https://developer.android.google.cn/ndk/downloads?hl=zh-cn)
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- CMake >= 3.10.2 [CMake](https://cmake.org/download)
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- Android SDK >= 26 [SDK](https://developer.microsoft.com/zh-cn/windows/downloads/windows-10-sdk/)
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- JDK >= 1.8 [JDK](https://www.oracle.com/cn/java/technologies/javase/javase-jdk8-downloads.html)
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4.2 NDK版本不匹配问题：
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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。
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4.3 Android Studio版本问题：
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在`工具栏-help-Checkout for Updates`中更新Android Studio版本。
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4.4 Gradle下依赖项安装过慢问题：
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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。
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## 示例程序详细说明
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骨骼检测Android示例程序通过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，在[Runtime](https://www.mindspore.cn/tutorial/lite/zh-CN/master/use/runtime.html)中完成模型推理的过程。
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### 示例程序结构
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```text
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├── app
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│ ├── build.gradle # 其他Android配置文件
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│ ├── download.gradle # APP构建时由gradle自动从HuaWei Server下载依赖的库文件及模型文件
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│ ├── proguard-rules.pro
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│ └── src
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│ ├── main
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│ │ ├── AndroidManifest.xml # Android配置文件
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│ │ ├── java # java层应用代码
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│ │ │ └── com
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│ │ │ └── mindspore
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│ │ │ └── posenetdemo # 图像处理及推理流程实现
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│ │ │ ├── CameraDataDealListener.java
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│ │ │ ├── ImageUtils.java
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│ │ │ ├── MainActivity.java
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│ │ │ ├── PoseNetFragment.java
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│ │ │ ├── Posenet.java #
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│ │ │ └── TestActivity.java
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│ │ └── res # 存放Android相关的资源文件
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│ └── test
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└── ...
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```
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### 下载及部署模型文件
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从MindSpore Model Hub中下载模型文件，本示例程序中使用的目标检测模型文件为`posenet_model.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。
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> 若下载失败请手动下载模型文件，posenet_model.ms [下载链接](https://download.mindspore.cn/model_zoo/official/lite/posenet_lite/posenet_model.ms)。
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### 编写端侧推理代码
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在骨骼检测demo中，使用Java API实现端测推理。相比于C++ API，Java API可以直接在Java Class中调用，无需实现JNI层的相关代码，具有更好的便捷性。
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- 本实例通过识别鼻子眼睛等身体特征、获取身体特征位置、计算结果的置信分数，来实现骨骼检测的目的。
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```java
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public enum BodyPart {
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NOSE,
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LEFT_EYE,
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RIGHT_EYE,
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LEFT_EAR,
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RIGHT_EAR,
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LEFT_SHOULDER,
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RIGHT_SHOULDER,
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LEFT_ELBOW,
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RIGHT_ELBOW,
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LEFT_WRIST,
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RIGHT_WRIST,
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LEFT_HIP,
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RIGHT_HIP,
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LEFT_KNEE,
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RIGHT_KNEE,
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LEFT_ANKLE,
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RIGHT_ANKLE
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}
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public class Position {
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int x;
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int y;
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}
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public class KeyPoint {
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BodyPart bodyPart = BodyPart.NOSE;
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Position position = new Position();
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float score = 0.0f;
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}
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public class Person {
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List<KeyPoint> keyPoints;
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float score = 0.0f;
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}
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```
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骨骼检测demo推理代码流程如下，完整代码请参见：`src/main/java/com/mindspore/posenetdemo/Posenet.java`。
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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。
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- 加载模型：从文件系统中读取MindSpore Lite模型，并进行模型解析。
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```java
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// Load the .ms model.
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model = new Model();
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if (!model.loadModel(mContext, "posenet_model.ms")) {
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Log.e("MS_LITE", "Load Model failed");
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return false;
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}
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```
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- 创建配置上下文：创建配置上下文`MSConfig`，保存会话所需的一些基本配置参数，用于指导图编译和图执行。
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```java
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// Create and init config.
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msConfig = new MSConfig();
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if (!msConfig.init(DeviceType.DT_CPU, NUM_THREADS, CpuBindMode.MID_CPU)) {
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Log.e("MS_LITE", "Init context failed");
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return false;
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}
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```
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- 创建会话：创建`LiteSession`，并调用`init`方法将上一步得到`MSConfig`配置到会话中。
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```java
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// Create the MindSpore lite session.
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session = new LiteSession();
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if (!session.init(msConfig)) {
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Log.e("MS_LITE", "Create session failed");
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msConfig.free();
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return false;
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}
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msConfig.free();
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```
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- 加载模型文件并构建用于推理的计算图
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```java
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// Complile graph.
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if (!session.compileGraph(model)) {
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Log.e("MS_LITE", "Compile graph failed");
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model.freeBuffer();
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return false;
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}
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// Note: when use model.freeBuffer(), the model can not be complile graph again.
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model.freeBuffer();
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```
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2. 输入数据： Java目前支持`byte[]`或者`ByteBuffer`两种类型的数据，设置输入Tensor的数据。
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- 在输入数据之前，需要对存储图像信息的Bitmap进行解读分析与转换。
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```java
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/**
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* Scale the image to a byteBuffer of [-1,1] values.
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*/
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private ByteBuffer initInputArray(Bitmap bitmap) {
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final int bytesPerChannel = 4;
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final int inputChannels = 3;
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final int batchSize = 1;
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ByteBuffer inputBuffer = ByteBuffer.allocateDirect(
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batchSize * bytesPerChannel * bitmap.getHeight() * bitmap.getWidth() * inputChannels
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);
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inputBuffer.order(ByteOrder.nativeOrder());
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inputBuffer.rewind();
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final float mean = 128.0f;
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final float std = 128.0f;
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int[] intValues = new int[bitmap.getWidth() * bitmap.getHeight()];
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bitmap.getPixels(intValues, 0, bitmap.getWidth(), 0, 0, bitmap.getWidth(), bitmap.getHeight());
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int pixel = 0;
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for (int y = 0; y < bitmap.getHeight(); y++) {
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for (int x = 0; x < bitmap.getWidth(); x++) {
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int value = intValues[pixel++];
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inputBuffer.putFloat(((float) (value >> 16 & 0xFF) - mean) / std);
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inputBuffer.putFloat(((float) (value >> 8 & 0xFF) - mean) / std);
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inputBuffer.putFloat(((float) (value & 0xFF) - mean) / std);
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}
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}
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return inputBuffer;
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}
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```
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- 通过`ByteBuffer`输入数据。
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```java
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long estimationStartTimeNanos = SystemClock.elapsedRealtimeNanos();
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ByteBuffer inputArray = this.initInputArray(bitmap);
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List<MSTensor> inputs = session.getInputs();
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if (inputs.size() != 1) {
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return null;
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}
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Log.i("posenet", String.format("Scaling to [-1,1] took %.2f ms",
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1.0f * (SystemClock.elapsedRealtimeNanos() - estimationStartTimeNanos) / 1_000_000));
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MSTensor inTensor = inputs.get(0);
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inTensor.setData(inputArray);
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long inferenceStartTimeNanos = SystemClock.elapsedRealtimeNanos();
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```
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3. 对输入Tensor按照模型进行推理，获取输出Tensor，并进行后处理。
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- 使用`runGraph`进行模型推理。
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```java
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// Run graph to infer results.
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if (!session.runGraph()) {
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Log.e("MS_LITE", "Run graph failed");
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return null;
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}
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lastInferenceTimeNanos = SystemClock.elapsedRealtimeNanos() - inferenceStartTimeNanos;
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Log.i(
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"posenet",
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String.format("Interpreter took %.2f ms", 1.0f * lastInferenceTimeNanos / 1_000_000)
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);
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```
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- 通过输出Tensor得到推理结果。
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```java
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// Get output tensor values.
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List<MSTensor> heatmaps_list = session.getOutputsByNodeName("Conv2D-27");
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if (heatmaps_list == null) {
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return null;
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}
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MSTensor heatmaps_tensors = heatmaps_list.get(0);
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float[] heatmaps_results = heatmaps_tensors.getFloatData();
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int[] heatmapsShape = heatmaps_tensors.getShape(); //1, 9, 9 ,17
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float[][][][] heatmaps = new float[heatmapsShape[0]][][][];
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for (int x = 0; x < heatmapsShape[0]; x++) { // heatmapsShape[0] =1
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float[][][] arrayThree = new float[heatmapsShape[1]][][];
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for (int y = 0; y < heatmapsShape[1]; y++) { // heatmapsShape[1] = 9
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float[][] arrayTwo = new float[heatmapsShape[2]][];
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for (int z = 0; z < heatmapsShape[2]; z++) { //heatmapsShape[2] = 9
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float[] arrayOne = new float[heatmapsShape[3]]; //heatmapsShape[3] = 17
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for (int i = 0; i < heatmapsShape[3]; i++) {
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int n = i + z * heatmapsShape[3] + y * heatmapsShape[2] * heatmapsShape[3] + x * heatmapsShape[1] * heatmapsShape[2] * heatmapsShape[3];
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arrayOne[i] = heatmaps_results[n]; //1*9*9*17 ??
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}
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arrayTwo[z] = arrayOne;
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}
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arrayThree[y] = arrayTwo;
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}
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heatmaps[x] = arrayThree;
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}
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List<MSTensor> offsets_list = session.getOutputsByNodeName("Conv2D-28");
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if (offsets_list == null) {
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return null;
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}
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MSTensor offsets_tensors = offsets_list.get(0);
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float[] offsets_results = offsets_tensors.getFloatData();
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int[] offsetsShapes = offsets_tensors.getShape();
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float[][][][] offsets = new float[offsetsShapes[0]][][][];
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for (int x = 0; x < offsetsShapes[0]; x++) {
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float[][][] offsets_arrayThree = new float[offsetsShapes[1]][][];
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for (int y = 0; y < offsetsShapes[1]; y++) {
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float[][] offsets_arrayTwo = new float[offsetsShapes[2]][];
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for (int z = 0; z < offsetsShapes[2]; z++) {
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float[] offsets_arrayOne = new float[offsetsShapes[3]];
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for (int i = 0; i < offsetsShapes[3]; i++) {
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int n = i + z * offsetsShapes[3] + y * offsetsShapes[2] * offsetsShapes[3] + x * offsetsShapes[1] * offsetsShapes[2] * offsetsShapes[3];
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offsets_arrayOne[i] = offsets_results[n];
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}
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offsets_arrayTwo[z] = offsets_arrayOne;
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}
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offsets_arrayThree[y] = offsets_arrayTwo;
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}
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offsets[x] = offsets_arrayThree;
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}
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```
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- 对输出节点的数据进行处理，得到骨骼检测demo的返回值`person`，实现功能。
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`Conv2D-27`中，`heatmaps`存储`height`、`weight`、`numKeypoints`三种参数，可用于求出`keypointPosition`位置信息。
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`Conv2D-28`中，`offset`代表位置坐标的偏移量，与`keypointPosition`结合可获取`confidenceScores`置信分数，用于判断模型推理结果。
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通过`keypointPosition`与`confidenceScores`，获取`person.keyPoints`和`person.score`，得到模型的返回值`person`。
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```java
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int height = ((Object[]) heatmaps[0]).length; //9
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int width = ((Object[]) heatmaps[0][0]).length; //9
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int numKeypoints = heatmaps[0][0][0].length; //17
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// Finds the (row, col) locations of where the keypoints are most likely to be.
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Pair[] keypointPositions = new Pair[numKeypoints];
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for (int i = 0; i < numKeypoints; i++) {
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keypointPositions[i] = new Pair(0, 0);
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}
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for (int keypoint = 0; keypoint < numKeypoints; keypoint++) {
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float maxVal = heatmaps[0][0][0][keypoint];
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int maxRow = 0;
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int maxCol = 0;
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for (int row = 0; row < height; row++) {
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for (int col = 0; col < width; col++) {
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if (heatmaps[0][row][col][keypoint] > maxVal) {
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maxVal = heatmaps[0][row][col][keypoint];
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maxRow = row;
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maxCol = col;
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}
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}
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}
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keypointPositions[keypoint] = new Pair(maxRow, maxCol);
|
|
|
}
|
|
|
|
|
|
// Calculating the x and y coordinates of the keypoints with offset adjustment.
|
|
|
int[] xCoords = new int[numKeypoints];
|
|
|
int[] yCoords = new int[numKeypoints];
|
|
|
float[] confidenceScores = new float[numKeypoints];
|
|
|
for (int i = 0; i < keypointPositions.length; i++) {
|
|
|
Pair position = keypointPositions[i];
|
|
|
int positionY = (int) position.first;
|
|
|
int positionX = (int) position.second;
|
|
|
|
|
|
yCoords[i] = (int) ((float) positionY / (float) (height - 1) * bitmap.getHeight() + offsets[0][positionY][positionX][i]);
|
|
|
xCoords[i] = (int) ((float) positionX / (float) (width - 1) * bitmap.getWidth() + offsets[0][positionY][positionX][i + numKeypoints]);
|
|
|
confidenceScores[i] = sigmoid(heatmaps[0][positionY][positionX][i]);
|
|
|
}
|
|
|
|
|
|
Person person = new Person();
|
|
|
KeyPoint[] keypointList = new KeyPoint[numKeypoints];
|
|
|
for (int i = 0; i < numKeypoints; i++) {
|
|
|
keypointList[i] = new KeyPoint();
|
|
|
}
|
|
|
|
|
|
float totalScore = 0.0f;
|
|
|
for (int i = 0; i < keypointList.length; i++) {
|
|
|
keypointList[i].position.x = xCoords[i];
|
|
|
keypointList[i].position.y = yCoords[i];
|
|
|
keypointList[i].score = confidenceScores[i];
|
|
|
totalScore += confidenceScores[i];
|
|
|
}
|
|
|
person.keyPoints = Arrays.asList(keypointList);
|
|
|
person.score = totalScore / numKeypoints;
|
|
|
|
|
|
return person;
|
|
|
```
|
