mindspore/model_zoo/official/lite/object_detection/README.en.md

# demo_object_detection

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

## Deploying an Application

The following section describes how to build and execute an on-device object detecion  task on MindSpore Lite.

### 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
- Android software development kit (SDK) 26 or later
- OpenCV 4.0.0 or later (included in the sample code)

### 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)

2. Connect to an Android device and runs the object detection 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/project_structure.png)

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

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/object_detection.png)


## Detailed Description of the Sample Program  

This object detection 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 .

### Configuring MindSpore Lite Dependencies

In Android studio, the compiled mindpool-lite-x.x.x-mindata-armxx-cpu package (including ` libmindspot- lite.so `The library file and related header files, which can contain multiple compatible architectures), are unzipped and placed in the 'app / SRC / main / CPP' directory of the app project, and the` build.gradle `Cmake and 'arm64-v8a' and 'armeabi-v7a' are configured in the file as follows It is shown as follows:
```
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:

```
# Set MindSpore Lite Dependencies.
set(MINDSPORELITE_VERSION  mindspore-lite-1.0.0-minddata-arm64-cpu)
include_directories(${CMAKE_SOURCE_DIR}/src/main/cpp/${MINDSPORELITE_VERSION})
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)

# Link target library.       
target_link_libraries(
    ...
    mindspore-lite
    minddata-lite
    ...
)
```



In this example, the  download.gradle File configuration auto download library file,  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.

libmindspore-lite.so [libmindspore-lite.so]( https://download.mindspore.cn/model_zoo/official/lite/lib/mindspore%20version%200.7/libmindspore-lite.so)



### Downloading and Deploying a Model File

In this example, the  download.gradle File configuration auto download `ssd.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.

ssd.ms [ssd.ms]( https://download.mindspore.cn/model_zoo/official/lite/ssd_mobilenetv2_lite/ssd.ms)



### Compiling On-Device Inference Code

Call MindSpore Lite C++ APIs at the JNI layer to implement on-device inference.

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

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

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

     ```cpp
     // Buffer is the model data passed in by the Java layer
     jlong bufferLen = env->GetDirectBufferCapacity(buffer);
     char *modelBuffer = CreateLocalModelBuffer(env, buffer);  
     ```

   - Create a session.

     ```cpp
     void **labelEnv = new void *;
     MSNetWork *labelNet = new MSNetWork;
     *labelEnv = labelNet;
     
     // Create context.
     lite::Context *context = new lite::Context;
     
     context->device_ctx_.type = lite::DT_CPU;
     context->thread_num_ = numThread;  //Specify the number of threads to run inference
     
     // Create the mindspore session.
     labelNet->CreateSessionMS(modelBuffer, bufferLen, "device label", context);
     delete(context);
     
     ```

   - Load the model file and build a computational graph for inference.

     ```cpp
     void MSNetWork::CreateSessionMS(char* modelBuffer, size_t bufferLen, std::string name, mindspore::lite::Context* ctx)
     {
         CreateSession(modelBuffer, bufferLen, ctx);  
         session = mindspore::session::LiteSession::CreateSession(ctx);
         auto model = mindspore::lite::Model::Import(modelBuffer, bufferLen);
         int ret = session->CompileGraph(model);
     }
     ```

2. Pre-Process the imagedata and  convert the input image into the Tensor format of the MindSpore model. 

   ```cpp
   // Convert the Bitmap image passed in from the JAVA layer to Mat for OpenCV processing
       LiteMat lite_mat_bgr,lite_norm_mat_cut;
   
       if (!BitmapToLiteMat(env, srcBitmap, lite_mat_bgr)){
           MS_PRINT("BitmapToLiteMat error");
           return NULL;
       }
       int srcImageWidth = lite_mat_bgr.width_;
       int srcImageHeight = lite_mat_bgr.height_;
       if(!PreProcessImageData(lite_mat_bgr, lite_norm_mat_cut)){
           MS_PRINT("PreProcessImageData error");
           return NULL;
       }
       ImgDims inputDims;
       inputDims.channel =lite_norm_mat_cut.channel_;
       inputDims.width = lite_norm_mat_cut.width_;
       inputDims.height = lite_norm_mat_cut.height_;
   
       // Get the mindsore inference environment which created in loadModel().
       void **labelEnv = reinterpret_cast<void **>(netEnv);
       if (labelEnv == nullptr) {
           MS_PRINT("MindSpore error, labelEnv is a nullptr.");
           return NULL;
       }
       MSNetWork *labelNet = static_cast<MSNetWork *>(*labelEnv);
   
       auto mSession = labelNet->session;
       if (mSession == nullptr) {
           MS_PRINT("MindSpore error, Session is a nullptr.");
           return NULL;
       }
       MS_PRINT("MindSpore get session.");
   
       auto msInputs = mSession->GetInputs();
       auto inTensor = msInputs.front();
   
       float *dataHWC = reinterpret_cast<float *>(lite_norm_mat_cut.data_ptr_);
       // copy input Tensor
       memcpy(inTensor->MutableData(), dataHWC,
              inputDims.channel * inputDims.width * inputDims.height * sizeof(float));
       delete[] (dataHWC);
   ```

3. The input image shall be NHWC(1:300:300:3).

   ```cpp
    bool PreProcessImageData(const LiteMat &lite_mat_bgr, LiteMat *lite_norm_mat_ptr) {
        bool ret = false;
        LiteMat lite_mat_resize;
        LiteMat &lite_norm_mat_cut = *lite_norm_mat_ptr;
        ret = ResizeBilinear(lite_mat_bgr, lite_mat_resize, 300, 300);
        if (!ret) {
            MS_PRINT("ResizeBilinear error");
            return false;
        }
        LiteMat lite_mat_convert_float;
        ret = ConvertTo(lite_mat_resize, lite_mat_convert_float, 1.0 / 255.0);
        if (!ret) {
            MS_PRINT("ConvertTo error");
            return false;
        }

        float means[3] = {0.485, 0.456, 0.406};
        float vars[3] = {1.0 / 0.229, 1.0 / 0.224, 1.0 / 0.225};
        SubStractMeanNormalize(lite_mat_convert_float, lite_norm_mat_cut, means, vars);
        return true;
    }

   ```

4. 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.

   ```cpp
   // After the model and image tensor data is loaded, run inference.
   auto status = mSession->RunGraph();
   ```

   Obtain the output data.

   ```cpp
    auto names = mSession->GetOutputTensorNames();
       typedef std::unordered_map<std::string,
               std::vector<mindspore::tensor::MSTensor *>> Msout;
    std::unordered_map<std::string,
               mindspore::tensor::MSTensor *> msOutputs;
    for (const auto &name : names) {
           auto temp_dat =mSession->GetOutputByTensorName(name);  
           msOutputs.insert(std::pair<std::string, mindspore::tensor::MSTensor *> {name, temp_dat});  
       }
   std::string retStr = ProcessRunnetResult(msOutputs, ret);
   ```

   The model output the object category scores (1:1917:81) and the object detection location offset (1:1917:4).  The location offset can be calcalation the object location in getDefaultBoxes function . 

   ```cpp
   void SSDModelUtil::getDefaultBoxes() {
       float fk[6] = {0.0, 0.0, 0.0, 0.0, 0.0, 0.0};
       std::vector<struct WHBox> all_sizes;
       struct Product mProductData[19 * 19] = {0};
   
       for (int i = 0; i < 6; i++) {
           fk[i] = config.model_input_height / config.steps[i];
       }
       float scale_rate =
               (config.max_scale - config.min_scale) / (sizeof(config.num_default) / sizeof(int) - 1);
       float scales[7] = {0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0};
       for (int i = 0; i < sizeof(config.num_default) / sizeof(int); i++) {
           scales[i] = config.min_scale + scale_rate * i;
       }
   
       for (int idex = 0; idex < sizeof(config.feature_size) / sizeof(int); idex++) {
           float sk1 = scales[idex];
           float sk2 = scales[idex + 1];
           float sk3 = sqrt(sk1 * sk2);
           struct WHBox tempWHBox;
   
           all_sizes.clear();
   
           if (idex == 0) {
               float w = sk1 * sqrt(2);
               float h = sk1 / sqrt(2);
   
               tempWHBox.boxw = 0.1;
               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);
               }
           }
       }
   }
   ```

   - The higher scores  and location  of category can be calcluted by the nonMaximumSuppression function.

     ```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;
                     }
                 }
             }
         }
     }
     ```

   - For the targets whose probability is greater than the threshold value, the output rectangle box needs to be restored to the original size after the rectangular box is filtered by NMS algorithm.

```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];
        }
    }

    // NMS processing.
    ssd_boxes_decode(tmpBox, decodedBoxes);
  //  const float nms_threshold = 0.6;
    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;
}
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] > 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;
}
```