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## MindSpore Lite 端侧目标检测demo（Android）
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本示例程序演示了如何在端侧利用MindSpore Lite C++ API（Android JNI）以及MindSpore Lite 目标检测模型完成端侧推理，实现对图库或者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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- NDK 21.3
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- CMake 3.10
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- Android SDK >= 26
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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，并安装相应的SDK（指定SDK版本后，由Android Studio自动安装）。
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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。
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（可选）若安装时出现NDK版本问题，可手动下载相应的[NDK版本](https://developer.android.com/ndk/downloads?hl=zh-cn)（本示例代码使用的NDK版本为21.3），并在`Project Structure`的`Android NDK location`设置中指定SDK的位置。
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2. 连接Android设备，运行目标检测示例应用程序。
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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。
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* 注：编译过程中Android Studio会自动下载MindSpore Lite、OpenCV、模型文件等相关依赖项，编译过程需做耐心等待。
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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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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，检测出图中内容是鼠标。
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## 示例程序详细说明
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本端侧目标检测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（针对推理结果画框）等功能；JNI层在[Runtime](https://www.mindspore.cn/tutorial/zh-CN/master/use/lite_runtime.html)中完成模型推理的过程。
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> 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。
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### 示例程序结构
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```
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app
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├── libs # 存放demo jni层编译出的库文件
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│ └── arm64-v8a
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│ │── libmlkit-label-MS.so #
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├── src/main
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│ ├── assets # 资源文件
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| | └── ssd.ms # 存放模型文件
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│ |
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│ ├── cpp # 模型加载和预测主要逻辑封装类
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| | ├── mindspore-lite-...-cpu # minspore源码编译出的调用包,包含demo jni层依赖的库文件及相关的头文件
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| | | └── ...
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│ | |
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| | ├── MindSporeNetnative.cpp # MindSpore调用相关的JNI方法
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│ ├── java # java层应用代码
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│ │ └── com.huawei.himindsporedemo
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│ │ ├── help # 图像处理及MindSpore JNI调用相关实现
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│ │ │ └── ...
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│ │ └── obejctdetect # 开启摄像头及绘制相关实现
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│ │ └── ...
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│ │
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│ ├── res # 存放Android相关的资源文件
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│ └── AndroidManifest.xml # Android配置文件
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│
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├── CMakeList.txt # cmake编译入口文件
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│
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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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└── ...
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```
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### 配置MindSpore Lite依赖项
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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/lite/docs/zh-CN/master/deploy.html)生成`libmindspore-lite.so`库文件。
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在Android Studio中将编译完成的`libmindspore-lite.so`库文件（可包含多个兼容架构），分别放置在APP工程的`app/libs/arm64-v8a`（ARM64）或`app/libs/armeabi-v7a`（ARM32）目录下，并在app的`build.gradle`文件中配置CMake编译支持，以及`arm64-v8a`和`armeabi-v7a`的编译支持，如下所示：
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```
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android{
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defaultConfig{
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externalNativeBuild{
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cmake{
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arguments "-DANDROID_STL=c++_shared"
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}
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}
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ndk{
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abiFilters 'arm64-v8a'
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}
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}
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}
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```
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在`app/CMakeLists.txt`文件中建立`.so`库文件链接，如下所示。
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```
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# Set MindSpore Lite Dependencies.
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include_directories(${CMAKE_SOURCE_DIR}/src/main/cpp/include/MindSpore)
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add_library(mindspore-lite SHARED IMPORTED )
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set_target_properties(mindspore-lite PROPERTIES
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IMPORTED_LOCATION "${CMAKE_SOURCE_DIR}/libs/libmindspore-lite.so")
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# Link target library.
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target_link_libraries(
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...
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mindspore-lite
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minddata-lite
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...
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)
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```
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本示例中，app build过程由download.gradle文件自动从华为服务器下载mindspore所编译的库及相关头文件，并放置在`src/main/cpp`工程目录下。
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* 注：若自动下载失败，请手动下载相关库文件并将其放在对应位置：
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* libmindspore-lite.so [下载链接](https://download.mindspore.cn/model_zoo/official/lite/lib/mindspore%20version%200.7/libmindspore-lite.so)
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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`ssd.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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* 注：若下载失败请手动下载模型文件，ssd.ms [下载链接](https://download.mindspore.cn/model_zoo/official/lite/ssd_mobilenetv2_lite/ssd.ms)。
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### 编写端侧推理代码
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在JNI层调用MindSpore Lite C++ API实现端测推理。
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推理代码流程如下，完整代码请参见`src/cpp/MindSporeNetnative.cpp`。
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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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- 加载模型文件：创建并配置用于模型推理的上下文
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```cpp
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// Buffer is the model data passed in by the Java layer
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jlong bufferLen = env->GetDirectBufferCapacity(buffer);
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char *modelBuffer = CreateLocalModelBuffer(env, buffer);
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```
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- 创建会话
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```cpp
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void **labelEnv = new void *;
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MSNetWork *labelNet = new MSNetWork;
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*labelEnv = labelNet;
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// Create context.
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lite::Context *context = new lite::Context;
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context->cpu_bind_mode_ = lite::NO_BIND;
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context->device_ctx_.type = lite::DT_CPU;
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context->thread_num_ = numThread; //Specify the number of threads to run inference
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// Create the mindspore session.
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labelNet->CreateSessionMS(modelBuffer, bufferLen, "device label", context);
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delete context;
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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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```cpp
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void MSNetWork::CreateSessionMS(char* modelBuffer, size_t bufferLen, std::string name, mindspore::lite::Context* ctx)
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{
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CreateSession(modelBuffer, bufferLen, ctx);
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session = mindspore::session::LiteSession::CreateSession(ctx);
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auto model = mindspore::lite::Model::Import(modelBuffer, bufferLen);
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int ret = session->CompileGraph(model); // Compile Graph
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}
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```
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2. 将输入图片转换为传入MindSpore模型的Tensor格式。
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将待检测图片数据转换为输入MindSpore模型的Tensor。
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```cpp
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// Convert the Bitmap image passed in from the JAVA layer to Mat for OpenCV processing
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LiteMat lite_mat_bgr,lite_norm_mat_cut;
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if (!BitmapToLiteMat(env, srcBitmap, lite_mat_bgr)){
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MS_PRINT("BitmapToLiteMat error");
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return NULL;
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}
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int srcImageWidth = lite_mat_bgr.width_;
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int srcImageHeight = lite_mat_bgr.height_;
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if(!PreProcessImageData(lite_mat_bgr, lite_norm_mat_cut)){
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MS_PRINT("PreProcessImageData error");
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return NULL;
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}
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ImgDims inputDims;
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inputDims.channel =lite_norm_mat_cut.channel_;
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inputDims.width = lite_norm_mat_cut.width_;
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inputDims.height = lite_norm_mat_cut.height_;
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// Get the mindsore inference environment which created in loadModel().
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void **labelEnv = reinterpret_cast<void **>(netEnv);
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if (labelEnv == nullptr) {
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MS_PRINT("MindSpore error, labelEnv is a nullptr.");
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return NULL;
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}
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MSNetWork *labelNet = static_cast<MSNetWork *>(*labelEnv);
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auto mSession = labelNet->session;
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if (mSession == nullptr) {
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MS_PRINT("MindSpore error, Session is a nullptr.");
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return NULL;
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}
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MS_PRINT("MindSpore get session.");
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auto msInputs = mSession->GetInputs();
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auto inTensor = msInputs.front();
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float *dataHWC = reinterpret_cast<float *>(lite_norm_mat_cut.data_ptr_);
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// copy input Tensor
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memcpy(inTensor->MutableData(), dataHWC,
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inputDims.channel * inputDims.width * inputDims.height * sizeof(float));
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delete[] (dataHWC);
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```
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3. 进行模型推理前，输入tensor格式为 NHWC，shape为1:300:300:3，格式为RGB, 并对输入tensor做标准化处理.
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```cpp
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bool PreProcessImageData(LiteMat &lite_mat_bgr,LiteMat &lite_norm_mat_cut) {
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bool ret=false;
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LiteMat lite_mat_resize;
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ret = ResizeBilinear(lite_mat_bgr, lite_mat_resize, 300, 300);
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if (!ret) {
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MS_PRINT("ResizeBilinear error");
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return false;
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}
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LiteMat lite_mat_convert_float;
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ret = ConvertTo(lite_mat_resize, lite_mat_convert_float, 1.0 / 255.0);
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if (!ret) {
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MS_PRINT("ConvertTo error");
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return false;
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}
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float means[3] = {0.485, 0.456, 0.406};
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float vars[3] = {1.0 / 0.229, 1.0 / 0.224, 1.0 / 0.225};
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SubStractMeanNormalize(lite_mat_convert_float, lite_norm_mat_cut, means, vars);
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return true;
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}
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```
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4. 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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- 图执行，端测推理。
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|
```cpp
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// After the model and image tensor data is loaded, run inference.
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auto status = mSession->RunGraph();
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```
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- 获取输出数据。
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|
```cpp
|
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|
auto names = mSession->GetOutputTensorNames();
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typedef std::unordered_map<std::string,
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std::vector<mindspore::tensor::MSTensor *>> Msout;
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std::unordered_map<std::string,
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mindspore::tensor::MSTensor *> msOutputs;
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for (const auto &name : names) {
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auto temp_dat =mSession->GetOutputByTensorName(name);
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msOutputs.insert(std::pair<std::string, mindspore::tensor::MSTensor *> {name, temp_dat});
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}
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std::string retStr = ProcessRunnetResult(msOutputs, ret);
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|
```
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|
|
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- 模型有2个输出，输出1是目标的类别置信度，维度为1：1917: 81； 输出2是目标的矩形框坐标偏移量，维度为1:1917:4。 为了得出目标的实际矩形框，需要根据偏移量计算出矩形框的位置。这部分在 getDefaultBoxes中实现。
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|
```cpp
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void SSDModelUtil::getDefaultBoxes() {
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float fk[6] = {0.0, 0.0, 0.0, 0.0, 0.0, 0.0};
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std::vector<struct WHBox> all_sizes;
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struct Product mProductData[19 * 19] = {0};
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for (int i = 0; i < 6; i++) {
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fk[i] = config.model_input_height / config.steps[i];
|
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}
|
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|
float scale_rate =
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(config.max_scale - config.min_scale) / (sizeof(config.num_default) / sizeof(int) - 1);
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|
float scales[7] = {0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0};
|
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|
for (int i = 0; i < sizeof(config.num_default) / sizeof(int); i++) {
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|
scales[i] = config.min_scale + scale_rate * i;
|
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|
|
|
}
|
|
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|
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|
|
for (int idex = 0; idex < sizeof(config.feature_size) / sizeof(int); idex++) {
|
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|
|
float sk1 = scales[idex];
|
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|
|
float sk2 = scales[idex + 1];
|
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|
|
float sk3 = sqrt(sk1 * sk2);
|
|
|
|
|
|
struct WHBox tempWHBox;
|
|
|
|
|
|
|
|
|
|
|
|
all_sizes.clear();
|
|
|
|
|
|
|
|
|
|
|
|
if (idex == 0) {
|
|
|
|
|
|
float w = sk1 * sqrt(2);
|
|
|
|
|
|
float h = sk1 / sqrt(2);
|
|
|
|
|
|
|
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|
|
tempWHBox.boxw = 0.1;
|
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|
|
|
|
tempWHBox.boxh = 0.1;
|
|
|
|
|
|
all_sizes.push_back(tempWHBox);
|
|
|
|
|
|
|
|
|
|
|
|
tempWHBox.boxw = w;
|
|
|
|
|
|
tempWHBox.boxh = h;
|
|
|
|
|
|
all_sizes.push_back(tempWHBox);
|
|
|
|
|
|
|
|
|
|
|
|
tempWHBox.boxw = h;
|
|
|
|
|
|
tempWHBox.boxh = w;
|
|
|
|
|
|
all_sizes.push_back(tempWHBox);
|
|
|
|
|
|
|
|
|
|
|
|
} else {
|
|
|
|
|
|
tempWHBox.boxw = sk1;
|
|
|
|
|
|
tempWHBox.boxh = sk1;
|
|
|
|
|
|
all_sizes.push_back(tempWHBox);
|
|
|
|
|
|
|
|
|
|
|
|
for (int j = 0; j < sizeof(config.aspect_ratios[idex]) / sizeof(int); j++) {
|
|
|
|
|
|
float w = sk1 * sqrt(config.aspect_ratios[idex][j]);
|
|
|
|
|
|
float h = sk1 / sqrt(config.aspect_ratios[idex][j]);
|
|
|
|
|
|
tempWHBox.boxw = w;
|
|
|
|
|
|
tempWHBox.boxh = h;
|
|
|
|
|
|
all_sizes.push_back(tempWHBox);
|
|
|
|
|
|
tempWHBox.boxw = h;
|
|
|
|
|
|
tempWHBox.boxh = w;
|
|
|
|
|
|
all_sizes.push_back(tempWHBox);
|
|
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
|
|
tempWHBox.boxw = sk3;
|
|
|
|
|
|
tempWHBox.boxh = sk3;
|
|
|
|
|
|
all_sizes.push_back(tempWHBox);
|
|
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
|
|
for (int i = 0; i < config.feature_size[idex]; i++) {
|
|
|
|
|
|
for (int j = 0; j < config.feature_size[idex]; j++) {
|
|
|
|
|
|
mProductData[i * config.feature_size[idex] + j].x = i;
|
|
|
|
|
|
mProductData[i * config.feature_size[idex] + j].y = j;
|
|
|
|
|
|
}
|
|
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
|
|
int productLen = config.feature_size[idex] * config.feature_size[idex];
|
|
|
|
|
|
|
|
|
|
|
|
for (int i = 0; i < productLen; i++) {
|
|
|
|
|
|
for (int j = 0; j < all_sizes.size(); j++) {
|
|
|
|
|
|
struct NormalBox tempBox;
|
|
|
|
|
|
float cx = (mProductData[i].y + 0.5) / fk[idex];
|
|
|
|
|
|
float cy = (mProductData[i].x + 0.5) / fk[idex];
|
|
|
|
|
|
tempBox.y = cy;
|
|
|
|
|
|
tempBox.x = cx;
|
|
|
|
|
|
tempBox.h = all_sizes[j].boxh;
|
|
|
|
|
|
tempBox.w = all_sizes[j].boxw;
|
|
|
|
|
|
mDefaultBoxes.push_back(tempBox);
|
|
|
|
|
|
}
|
|
|
|
|
|
}
|
|
|
|
|
|
}
|
|
|
|
|
|
}
|
|
|
|
|
|
```
|
|
|
|
|
|
|
|
|
|
|
|
- 通过最大值抑制将目标类型置信度较高的输出筛选出来。
|
|
|
|
|
|
|
|
|
|
|
|
```cpp
|
|
|
|
|
|
void SSDModelUtil::nonMaximumSuppression(const YXBoxes *const decoded_boxes,
|
|
|
|
|
|
const float *const scores,
|
|
|
|
|
|
const std::vector<int> &in_indexes,
|
|
|
|
|
|
std::vector<int> &out_indexes, const float nmsThreshold,
|
|
|
|
|
|
const int count, const int max_results) {
|
|
|
|
|
|
int nR = 0; //number of results
|
|
|
|
|
|
std::vector<bool> del(count, false);
|
|
|
|
|
|
for (size_t i = 0; i < in_indexes.size(); i++) {
|
|
|
|
|
|
if (!del[in_indexes[i]]) {
|
|
|
|
|
|
out_indexes.push_back(in_indexes[i]);
|
|
|
|
|
|
if (++nR == max_results) {
|
|
|
|
|
|
break;
|
|
|
|
|
|
}
|
|
|
|
|
|
for (size_t j = i + 1; j < in_indexes.size(); j++) {
|
|
|
|
|
|
const auto boxi = decoded_boxes[in_indexes[i]], boxj = decoded_boxes[in_indexes[j]];
|
|
|
|
|
|
float a[4] = {boxi.xmin, boxi.ymin, boxi.xmax, boxi.ymax};
|
|
|
|
|
|
float b[4] = {boxj.xmin, boxj.ymin, boxj.xmax, boxj.ymax};
|
|
|
|
|
|
if (IOU(a, b) > nmsThreshold) {
|
|
|
|
|
|
del[in_indexes[j]] = true;
|
|
|
|
|
|
}
|
|
|
|
|
|
}
|
|
|
|
|
|
}
|
|
|
|
|
|
}
|
|
|
|
|
|
}
|
|
|
|
|
|
```
|
|
|
|
|
|
|
|
|
|
|
|
- 对每类的概率大于阈值，通过NMS算法筛选出矩形框后， 还需要将输出的矩形框恢复到原图尺寸。
|
|
|
|
|
|
|
|
|
|
|
|
```cpp
|
|
|
|
|
|
std::string SSDModelUtil::getDecodeResult(float *branchScores, float *branchBoxData) {
|
|
|
|
|
|
std::string result = "";
|
|
|
|
|
|
NormalBox tmpBox[1917] = {0};
|
|
|
|
|
|
float mScores[1917][81] = {0};
|
|
|
|
|
|
float outBuff[1917][7] = {0};
|
|
|
|
|
|
float scoreWithOneClass[1917] = {0};
|
|
|
|
|
|
int outBoxNum = 0;
|
|
|
|
|
|
YXBoxes decodedBoxes[1917] = {0};
|
|
|
|
|
|
|
|
|
|
|
|
// Copy branch outputs box data to tmpBox.
|
|
|
|
|
|
for (int i = 0; i < 1917; ++i) {
|
|
|
|
|
|
tmpBox[i].y = branchBoxData[i * 4 + 0];
|
|
|
|
|
|
tmpBox[i].x = branchBoxData[i * 4 + 1];
|
|
|
|
|
|
tmpBox[i].h = branchBoxData[i * 4 + 2];
|
|
|
|
|
|
tmpBox[i].w = branchBoxData[i * 4 + 3];
|
|
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
|
|
// Copy branch outputs score to mScores.
|
|
|
|
|
|
for (int i = 0; i < 1917; ++i) {
|
|
|
|
|
|
for (int j = 0; j < 81; ++j) {
|
|
|
|
|
|
mScores[i][j] = branchScores[i * 81 + j];
|
|
|
|
|
|
}
|
|
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
|
|
ssd_boxes_decode(tmpBox, decodedBoxes);
|
|
|
|
|
|
const float nms_threshold = 0.3;
|
|
|
|
|
|
for (int i = 1; i < 81; i++) {
|
|
|
|
|
|
std::vector<int> in_indexes;
|
|
|
|
|
|
for (int j = 0; j < 1917; j++) {
|
|
|
|
|
|
scoreWithOneClass[j] = mScores[j][i];
|
|
|
|
|
|
// if (mScores[j][i] > 0.1) {
|
|
|
|
|
|
if (mScores[j][i] > g_thres_map[i]) {
|
|
|
|
|
|
in_indexes.push_back(j);
|
|
|
|
|
|
}
|
|
|
|
|
|
}
|
|
|
|
|
|
if (in_indexes.size() == 0) {
|
|
|
|
|
|
continue;
|
|
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
|
|
sort(in_indexes.begin(), in_indexes.end(),
|
|
|
|
|
|
[&](int a, int b) { return scoreWithOneClass[a] > scoreWithOneClass[b]; });
|
|
|
|
|
|
std::vector<int> out_indexes;
|
|
|
|
|
|
|
|
|
|
|
|
nonMaximumSuppression(decodedBoxes, scoreWithOneClass, in_indexes, out_indexes,
|
|
|
|
|
|
nms_threshold);
|
|
|
|
|
|
for (int k = 0; k < out_indexes.size(); k++) {
|
|
|
|
|
|
outBuff[outBoxNum][0] = out_indexes[k]; //image id
|
|
|
|
|
|
outBuff[outBoxNum][1] = i; //labelid
|
|
|
|
|
|
outBuff[outBoxNum][2] = scoreWithOneClass[out_indexes[k]]; //scores
|
|
|
|
|
|
outBuff[outBoxNum][3] =
|
|
|
|
|
|
decodedBoxes[out_indexes[k]].xmin * inputImageWidth / 300;
|
|
|
|
|
|
outBuff[outBoxNum][4] =
|
|
|
|
|
|
decodedBoxes[out_indexes[k]].ymin * inputImageHeight / 300;
|
|
|
|
|
|
outBuff[outBoxNum][5] =
|
|
|
|
|
|
decodedBoxes[out_indexes[k]].xmax * inputImageWidth / 300;
|
|
|
|
|
|
outBuff[outBoxNum][6] =
|
|
|
|
|
|
decodedBoxes[out_indexes[k]].ymax * inputImageHeight / 300;
|
|
|
|
|
|
outBoxNum++;
|
|
|
|
|
|
}
|
|
|
|
|
|
}
|
|
|
|
|
|
MS_PRINT("outBoxNum %d", outBoxNum);
|
|
|
|
|
|
|
|
|
|
|
|
for (int i = 0; i < outBoxNum; ++i) {
|
|
|
|
|
|
std::string tmpid_str = std::to_string(outBuff[i][0]);
|
|
|
|
|
|
result += tmpid_str; // image ID
|
|
|
|
|
|
result += "_";
|
|
|
|
|
|
// tmpid_str = std::to_string(outBuff[i][1]);
|
|
|
|
|
|
MS_PRINT("label_classes i %d, outBuff %d",i, (int) outBuff[i][1]);
|
|
|
|
|
|
tmpid_str = label_classes[(int) outBuff[i][1]];
|
|
|
|
|
|
result += tmpid_str; // label id
|
|
|
|
|
|
result += "_";
|
|
|
|
|
|
tmpid_str = std::to_string(outBuff[i][2]);
|
|
|
|
|
|
result += tmpid_str; // scores
|
|
|
|
|
|
result += "_";
|
|
|
|
|
|
tmpid_str = std::to_string(outBuff[i][3]);
|
|
|
|
|
|
result += tmpid_str; // xmin
|
|
|
|
|
|
result += "_";
|
|
|
|
|
|
tmpid_str = std::to_string(outBuff[i][4]);
|
|
|
|
|
|
result += tmpid_str; // ymin
|
|
|
|
|
|
result += "_";
|
|
|
|
|
|
tmpid_str = std::to_string(outBuff[i][5]);
|
|
|
|
|
|
result += tmpid_str; // xmax
|
|
|
|
|
|
result += "_";
|
|
|
|
|
|
tmpid_str = std::to_string(outBuff[i][6]);
|
|
|
|
|
|
result += tmpid_str; // ymax
|
|
|
|
|
|
result += ";";
|
|
|
|
|
|
}
|
|
|
|
|
|
return result;
|
|
|
|
|
|
}
|
|
|
|
|
|
```
|
|
|
|
|
|
|
|
|
|
|
|
|
