forked from mindspore-Ecosystem/mindspore
update new 410 category modles and update readme and fixed cpp
update new 410 category modles on branch r1.0 update new 410 category modles and update readme english cherry-pick r1.0 branch
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@ -1,17 +1,17 @@
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## Demo_image_classification
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# Demo of Image Classification
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The following describes how to use the MindSpore Lite C++ APIs (Android JNIs) and MindSpore Lite image classification models to perform on-device inference, classify the content captured by a device camera, and display the most possible classification result on the application's image preview screen.
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### 运行依赖
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### Running Dependencies
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- Android Studio 3.2 or later (Android 4.0 or later is recommended.)
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- Native development kit (NDK) 21.3
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- CMake 3.10.2 [CMake](https://cmake.org/download)
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- Android software development kit (SDK) 26 or later
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- JDK 1.8 or later [JDK]( https://www.oracle.com/downloads/otn-pub/java/JDK/)
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- JDK 1.8 or later
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### 构建与运行
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### Building and Running
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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.)
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@ -35,9 +35,7 @@ The following describes how to use the MindSpore Lite C++ APIs (Android JNIs) an
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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.
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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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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.
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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.
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@ -60,7 +58,7 @@ app
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│ | └── MindSporeNetnative.h # header file
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│ |
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│ ├── java # application code at the Java layer
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│ │ └── com.huawei.himindsporedemo
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│ │ └── com.mindspore.himindsporedemo
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│ │ ├── gallery.classify # implementation related to image processing and MindSpore JNI calling
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│ │ │ └── ...
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│ │ └── widget # implementation related to camera enabling and drawing
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@ -190,30 +188,46 @@ The inference code process is as follows. For details about the complete code, s
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Convert the image data to be detected into the Tensor format of the MindSpore model.
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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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BitmapToMat(env, srcBitmap, matImageSrc);
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// Processing such as zooming the picture size.
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matImgPreprocessed = PreProcessImageData(matImageSrc);
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ImgDims inputDims;
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inputDims.channel = matImgPreprocessed.channels();
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inputDims.width = matImgPreprocessed.cols;
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inputDims.height = matImgPreprocessed.rows;
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float *dataHWC = new float[inputDims.channel * inputDims.width * inputDims.height]
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// Copy the image data to be detected to the dataHWC array.
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// The dataHWC[image_size] array here is the intermediate variable of the input MindSpore model tensor.
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float *ptrTmp = reinterpret_cast<float *>(matImgPreprocessed.data);
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for(int i = 0; i < inputDims.channel * inputDims.width * inputDims.height; i++){
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dataHWC[i] = ptrTmp[i];
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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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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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// Assign dataHWC[image_size] to the input tensor variable.
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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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if (msInputs.size() == 0) {
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MS_PRINT("MindSpore error, msInputs.size() equals 0.");
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return NULL;
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}
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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 dataHWC to the model 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. Perform inference on the input tensor based on the model, obtain the output tensor, and perform post-processing.
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@ -240,28 +254,45 @@ The inference code process is as follows. For details about the complete code, s
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- Perform post-processing of the output data.
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```cpp
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std::string ProcessRunnetResult(std::unordered_map<std::string,
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mindspore::tensor::MSTensor *> msOutputs, int runnetRet) {
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std::string ProcessRunnetResult(const int RET_CATEGORY_SUM, const char *const labels_name_map[],
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std::unordered_map<std::string, mindspore::tensor::MSTensor *> msOutputs) {
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// Get the branch of the model output.
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// Use iterators to get map elements.
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std::unordered_map<std::string, mindspore::tensor::MSTensor *>::iterator iter;
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iter = msOutputs.begin();
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// The mobilenetv2.ms model output just one branch.
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auto outputTensor = iter->second;
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int tensorNum = outputTensor->ElementsNum();
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MS_PRINT("Number of tensor elements:%d", tensorNum);
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// Get a pointer to the first score.
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float *temp_scores = static_cast<float * >(outputTensor->MutableData());
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float *temp_scores = static_cast<float *>(outputTensor->MutableData());
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float scores[RET_CATEGORY_SUM];
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for (int i = 0; i < RET_CATEGORY_SUM; ++i) {
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if (temp_scores[i] > 0.5) {
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MS_PRINT("MindSpore scores[%d] : [%f]", i, temp_scores[i]);
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}
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scores[i] = temp_scores[i];
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}
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float unifiedThre = 0.5;
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float probMax = 1.0;
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for (size_t i = 0; i < RET_CATEGORY_SUM; ++i) {
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float threshold = g_thres_map[i];
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float tmpProb = scores[i];
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if (tmpProb < threshold) {
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tmpProb = tmpProb / threshold * unifiedThre;
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} else {
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tmpProb = (tmpProb - threshold) / (probMax - threshold) * unifiedThre + unifiedThre;
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}
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scores[i] = tmpProb;
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}
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for (int i = 0; i < RET_CATEGORY_SUM; ++i) {
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if (scores[i] > 0.5) {
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MS_PRINT("MindSpore scores[%d] : [%f]", i, scores[i]);
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}
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}
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// Score for each category.
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// Converted to text information that needs to be displayed in the APP.
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std::string categoryScore = "";
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}
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return categoryScore;
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}
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```
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```
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- NDK 21.3
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- CMake 3.10.2 [CMake](https://cmake.org/download)
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- Android SDK >= 26
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- JDK >= 1.8 [JDK]( https://www.oracle.com/downloads/otn-pub/java/JDK/)
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- JDK >= 1.8
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### 构建与运行
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@ -68,7 +68,7 @@ app
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| | └── MsNetWork.cpp # MindSpre接口封装
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│ |
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│ ├── java # java层应用代码
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│ │ └── com.huawei.himindsporedemo
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│ │ └── com.mindspore.himindsporedemo
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│ │ ├── gallery.classify # 图像处理及MindSpore JNI调用相关实现
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│ │ │ └── ...
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│ │ └── widget # 开启摄像头及绘制相关实现
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@ -202,30 +202,46 @@ target_link_libraries(
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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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BitmapToMat(env, srcBitmap, matImageSrc);
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// Processing such as zooming the picture size.
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matImgPreprocessed = PreProcessImageData(matImageSrc);
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ImgDims inputDims;
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inputDims.channel = matImgPreprocessed.channels();
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inputDims.width = matImgPreprocessed.cols;
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inputDims.height = matImgPreprocessed.rows;
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float *dataHWC = new float[inputDims.channel * inputDims.width * inputDims.height]
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// Copy the image data to be detected to the dataHWC array.
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// The dataHWC[image_size] array here is the intermediate variable of the input MindSpore model tensor.
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float *ptrTmp = reinterpret_cast<float *>(matImgPreprocessed.data);
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for(int i = 0; i < inputDims.channel * inputDims.width * inputDims.height; i++){
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dataHWC[i] = ptrTmp[i];
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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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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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// Assign dataHWC[image_size] to the input tensor variable.
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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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if (msInputs.size() == 0) {
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MS_PRINT("MindSpore error, msInputs.size() equals 0.");
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return NULL;
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}
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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 dataHWC to the model 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. 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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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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std::string resultStr = ProcessRunnetResult(::RET_CATEGORY_SUM,
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::labels_name_map, msOutputs);
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```
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- 输出数据的后续处理。
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MS_PRINT("Number of tensor elements:%d", tensorNum);
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// Get a pointer to the first score.
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float *temp_scores = static_cast<float * >(outputTensor->MutableData());
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float *temp_scores = static_cast<float *>(outputTensor->MutableData());
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float scores[RET_CATEGORY_SUM];
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for (int i = 0; i < RET_CATEGORY_SUM; ++i) {
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if (temp_scores[i] > 0.5) {
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MS_PRINT("MindSpore scores[%d] : [%f]", i, temp_scores[i]);
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}
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scores[i] = temp_scores[i];
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}
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float unifiedThre = 0.5;
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float probMax = 1.0;
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for (size_t i = 0; i < RET_CATEGORY_SUM; ++i) {
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float threshold = g_thres_map[i];
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float tmpProb = scores[i];
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if (tmpProb < threshold) {
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tmpProb = tmpProb / threshold * unifiedThre;
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} else {
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tmpProb = (tmpProb - threshold) / (probMax - threshold) * unifiedThre + unifiedThre;
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}
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scores[i] = tmpProb;
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}
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for (int i = 0; i < RET_CATEGORY_SUM; ++i) {
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if (scores[i] > 0.5) {
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MS_PRINT("MindSpore scores[%d] : [%f]", i, scores[i]);
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}
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}
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// Score for each category.
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// Converted to text information that needs to be displayed in the APP.
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std::string categoryScore = "";
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}
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return categoryScore;
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}
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```
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using mindspore::dataset::LDataType;
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#define MS_PRINT(format, ...) __android_log_print(ANDROID_LOG_INFO, "MSJNI", format, ##__VA_ARGS__)
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static const int RET_CATEGORY_SUM = 601;
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static const int RET_CATEGORY_SUM = 410;
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static const char *labels_name_map[RET_CATEGORY_SUM] = {
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{"Tortoise"}, {"Container"}, {"Magpie"}, {"Seaturtle"}, {"Football"}, {"Ambulance"}, {"Ladder"},
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{"Toothbrush"}, {"Syringe"}, {"Sink"}, {"Toy"}, {"Organ(MusicalInstrument) "}, {"Cassettedeck"},
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{"Apple"}, {"Humaneye"}, {"Cosmetics"}, {"Paddle"}, {"Snowman"}, {"Beer"}, {"Chopsticks"},
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{"Humanbeard"}, {"Bird"}, {"Parkingmeter"}, {"Trafficlight"}, {"Croissant"}, {"Cucumber"},
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{"Radish"}, {"Towel"}, {"Doll"}, {"Skull"}, {"Washingmachine"}, {"Glove"}, {"Tick"}, {"Belt"},
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{"Sunglasses"}, {"Banjo"}, {"Cart"}, {"Ball"}, {"Backpack"}, {"Bicycle"}, {"Homeappliance"},
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{"Centipede"}, {"Boat"}, {"Surfboard"}, {"Boot"}, {"Headphones"}, {"Hotdog"}, {"Shorts"},
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{"Fastfood"}, {"Bus"}, {"Boy "}, {"Screwdriver"}, {"Bicyclewheel"}, {"Barge"}, {"Laptop"},
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{"Miniskirt"}, {"Drill(Tool)"}, {"Dress"}, {"Bear"}, {"Waffle"}, {"Pancake"}, {"Brownbear"},
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{"Woodpecker"}, {"Bluejay"}, {"Pretzel"}, {"Bagel"}, {"Tower"}, {"Teapot"}, {"Person"},
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{"Bowandarrow"}, {"Swimwear"}, {"Beehive"}, {"Brassiere"}, {"Bee"}, {"Bat(Animal)"},
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{"Starfish"}, {"Popcorn"}, {"Burrito"}, {"Chainsaw"}, {"Balloon"}, {"Wrench"}, {"Tent"},
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{"Vehicleregistrationplate"}, {"Lantern"}, {"Toaster"}, {"Flashlight"}, {"Billboard"},
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{"Tiara"}, {"Limousine"}, {"Necklace"}, {"Carnivore"}, {"Scissors"}, {"Stairs"},
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{"Computerkeyboard"}, {"Printer"}, {"Trafficsign"}, {"Chair"}, {"Shirt"}, {"Poster"},
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{"Cheese"}, {"Sock"}, {"Firehydrant"}, {"Landvehicle"}, {"Earrings"}, {"Tie"}, {"Watercraft"},
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{"Cabinetry"}, {"Suitcase"}, {"Muffin"}, {"Bidet"}, {"Snack"}, {"Snowmobile"}, {"Clock"},
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{"Medicalequipment"}, {"Cattle"}, {"Cello"}, {"Jetski"}, {"Camel"}, {"Coat"}, {"Suit"},
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{"Desk"}, {"Cat"}, {"Bronzesculpture"}, {"Juice"}, {"Gondola"}, {"Beetle"}, {"Cannon"},
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{"Computermouse"}, {"Cookie"}, {"Officebuilding"}, {"Fountain"}, {"Coin"}, {"Calculator"},
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{"Cocktail"}, {"Computermonitor"}, {"Box"}, {"Stapler"}, {"Christmastree"}, {"Cowboyhat"},
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{"Hikingequipment"}, {"Studiocouch"}, {"Drum"}, {"Dessert"}, {"Winerack"}, {"Drink"},
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{"Zucchini"}, {"Ladle"}, {"Humanmouth"}, {"DairyProduct"}, {"Dice"}, {"Oven"}, {"Dinosaur"},
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{"Ratchet(Device)"}, {"Couch"}, {"Cricketball"}, {"Wintermelon"}, {"Spatula"}, {"Whiteboard"},
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{"Pencilsharpener"}, {"Door"}, {"Hat"}, {"Shower"}, {"Eraser"}, {"Fedora"}, {"Guacamole"},
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{"Dagger"}, {"Scarf"}, {"Dolphin"}, {"Sombrero"}, {"Tincan"}, {"Mug"}, {"Tap"}, {"Harborseal"},
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{"Stretcher"}, {"Canopener"}, {"Goggles"}, {"Humanbody"}, {"Rollerskates"}, {"Coffeecup"},
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{"Cuttingboard"}, {"Blender"}, {"Plumbingfixture"}, {"Stopsign"}, {"Officesupplies"},
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{"Volleyball(Ball)"}, {"Vase"}, {"Slowcooker"}, {"Wardrobe"}, {"Coffee"}, {"Whisk"},
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{"Papertowel"}, {"Personalcare"}, {"Food"}, {"Sunhat"}, {"Treehouse"}, {"Flyingdisc"},
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{"Skirt"}, {"Gasstove"}, {"Saltandpeppershakers"}, {"Mechanicalfan"}, {"Facepowder"}, {"Fax"},
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{"Fruit"}, {"Frenchfries"}, {"Nightstand"}, {"Barrel"}, {"Kite"}, {"Tart"}, {"Treadmill"},
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{"Fox"}, {"Flag"}, {"Frenchhorn"}, {"Windowblind"}, {"Humanfoot"}, {"Golfcart"}, {"Jacket"},
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{"Egg(Food)"}, {"Streetlight"}, {"Guitar"}, {"Pillow"}, {"Humanleg"}, {"Isopod"}, {"Grape"},
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{"Humanear"}, {"Powerplugsandsockets"}, {"Panda"}, {"Giraffe"}, {"Woman"}, {"Doorhandle"},
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{"Rhinoceros"}, {"Bathtub"}, {"Goldfish"}, {"Houseplant"}, {"Goat"}, {"Baseballbat"},
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{"Baseballglove"}, {"Mixingbowl"}, {"Marineinvertebrates"}, {"Kitchenutensil"}, {"Lightswitch"},
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{"House"}, {"Horse"}, {"Stationarybicycle"}, {"Hammer"}, {"Ceilingfan"}, {"Sofabed"},
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{"Adhesivetape "}, {"Harp"}, {"Sandal"}, {"Bicyclehelmet"}, {"Saucer"}, {"Harpsichord"},
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{"Humanhair"}, {"Heater"}, {"Harmonica"}, {"Hamster"}, {"Curtain"}, {"Bed"}, {"Kettle"},
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{"Fireplace"}, {"Scale"}, {"Drinkingstraw"}, {"Insect"}, {"Hairdryer"}, {"Kitchenware"},
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{"Indoorrower"}, {"Invertebrate"}, {"Foodprocessor"}, {"Bookcase"}, {"Refrigerator"},
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{"Wood-burningstove"}, {"Punchingbag"}, {"Commonfig"}, {"Cocktailshaker"}, {"Jaguar(Animal)"},
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{"Golfball"}, {"Fashionaccessory"}, {"Alarmclock"}, {"Filingcabinet"}, {"Artichoke"}, {"Table"},
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{"Tableware"}, {"Kangaroo"}, {"Koala"}, {"Knife"}, {"Bottle"}, {"Bottleopener"}, {"Lynx"},
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{"Lavender(Plant)"}, {"Lighthouse"}, {"Dumbbell"}, {"Humanhead"}, {"Bowl"}, {"Humidifier"},
|
||||
{"Porch"}, {"Lizard"}, {"Billiardtable"}, {"Mammal"}, {"Mouse"}, {"Motorcycle"},
|
||||
{"Musicalinstrument"}, {"Swimcap"}, {"Fryingpan"}, {"Snowplow"}, {"Bathroomcabinet"},
|
||||
{"Missile"}, {"Bust"}, {"Man"}, {"Waffleiron"}, {"Milk"}, {"Ringbinder"}, {"Plate"},
|
||||
{"Mobilephone"}, {"Bakedgoods"}, {"Mushroom"}, {"Crutch"}, {"Pitcher(Container)"}, {"Mirror"},
|
||||
{"Personalflotationdevice"}, {"Tabletennisracket"}, {"Pencilcase"}, {"Musicalkeyboard"},
|
||||
{"Scoreboard"}, {"Briefcase"}, {"Kitchenknife"}, {"Nail(Construction)"}, {"Tennisball"},
|
||||
{"Plasticbag"}, {"Oboe"}, {"Chestofdrawers"}, {"Ostrich"}, {"Piano"}, {"Girl"}, {"Plant"},
|
||||
{"Potato"}, {"Hairspray"}, {"Sportsequipment"}, {"Pasta"}, {"Penguin"}, {"Pumpkin"}, {"Pear"},
|
||||
{"Infantbed"}, {"Polarbear"}, {"Mixer"}, {"Cupboard"}, {"Jacuzzi"}, {"Pizza"}, {"Digitalclock"},
|
||||
{"Pig"}, {"Reptile"}, {"Rifle"}, {"Lipstick"}, {"Skateboard"}, {"Raven"}, {"Highheels"},
|
||||
{"Redpanda"}, {"Rose"}, {"Rabbit"}, {"Sculpture"}, {"Saxophone"}, {"Shotgun"}, {"Seafood"},
|
||||
{"Submarinesandwich"}, {"Snowboard"}, {"Sword"}, {"Pictureframe"}, {"Sushi"}, {"Loveseat"},
|
||||
{"Ski"}, {"Squirrel"}, {"Tripod"}, {"Stethoscope"}, {"Submarine"}, {"Scorpion"}, {"Segway"},
|
||||
{"Trainingbench"}, {"Snake"}, {"Coffeetable"}, {"Skyscraper"}, {"Sheep"}, {"Television"},
|
||||
{"Trombone"}, {"Tea"}, {"Tank"}, {"Taco"}, {"Telephone"}, {"Torch"}, {"Tiger"}, {"Strawberry"},
|
||||
{"Trumpet"}, {"Tree"}, {"Tomato"}, {"Train"}, {"Tool"}, {"Picnicbasket"}, {"Cookingspray"},
|
||||
{"Trousers"}, {"Bowlingequipment"}, {"Footballhelmet"}, {"Truck"}, {"Measuringcup"},
|
||||
{"Coffeemaker"}, {"Violin"}, {"Vehicle"}, {"Handbag"}, {"Papercutter"}, {"Wine"}, {"Weapon"},
|
||||
{"Wheel"}, {"Worm"}, {"Wok"}, {"Whale"}, {"Zebra"}, {"Autopart"}, {"Jug"}, {"Pizzacutter"},
|
||||
{"Cream"}, {"Monkey"}, {"Lion"}, {"Bread"}, {"Platter"}, {"Chicken"}, {"Eagle"}, {"Helicopter"},
|
||||
{"Owl"}, {"Duck"}, {"Turtle"}, {"Hippopotamus"}, {"Crocodile"}, {"Toilet"}, {"Toiletpaper"},
|
||||
{"Squid"}, {"Clothing"}, {"Footwear"}, {"Lemon"}, {"Spider"}, {"Deer"}, {"Frog"}, {"Banana"},
|
||||
{"Rocket"}, {"Wineglass"}, {"Countertop"}, {"Tabletcomputer"}, {"Wastecontainer"},
|
||||
{"Swimmingpool"}, {"Dog"}, {"Book"}, {"Elephant"}, {"Shark"}, {"Candle"}, {"Leopard"}, {"Axe"},
|
||||
{"Handdryer"}, {"Soapdispenser"}, {"Porcupine"}, {"Flower"}, {"Canary"}, {"Cheetah"},
|
||||
{"Palmtree"}, {"Hamburger"}, {"Maple"}, {"Building"}, {"Fish"}, {"Lobster"},
|
||||
{"GardenAsparagus"}, {"Furniture"}, {"Hedgehog"}, {"Airplane"}, {"Spoon"}, {"Otter"}, {"Bull"},
|
||||
{"Oyster"}, {"Horizontalbar"}, {"Conveniencestore"}, {"Bomb"}, {"Bench"}, {"Icecream"},
|
||||
{"Caterpillar"}, {"Butterfly"}, {"Parachute"}, {"Orange"}, {"Antelope"}, {"Beaker"},
|
||||
{"Mothsandbutterflies"}, {"Window"}, {"Closet"}, {"Castle"}, {"Jellyfish"}, {"Goose"}, {"Mule"},
|
||||
{"Swan"}, {"Peach"}, {"Coconut"}, {"Seatbelt"}, {"Raccoon"}, {"Chisel"}, {"Fork"}, {"Lamp"},
|
||||
{"Camera"}, {"Squash(Plant)"}, {"Racket"}, {"Humanface"}, {"Humanarm"}, {"Vegetable"},
|
||||
{"Diaper"}, {"Unicycle"}, {"Falcon"}, {"Chime"}, {"Snail"}, {"Shellfish"}, {"Cabbage"},
|
||||
{"Carrot"}, {"Mango"}, {"Jeans"}, {"Flowerpot"}, {"Pineapple"}, {"Drawer"}, {"Stool"},
|
||||
{"Envelope"}, {"Cake"}, {"Dragonfly"}, {"Commonsunflower"}, {"Microwaveoven"}, {"Honeycomb"},
|
||||
{"Marinemammal"}, {"Sealion"}, {"Ladybug"}, {"Shelf"}, {"Watch"}, {"Candy"}, {"Salad"},
|
||||
{"Parrot"}, {"Handgun"}, {"Sparrow"}, {"Van"}, {"Grinder"}, {"Spicerack"}, {"Lightbulb"},
|
||||
{"Cordedphone"}, {"Sportsuniform"}, {"Tennisracket"}, {"Wallclock"}, {"Servingtray"},
|
||||
{"Kitchen&diningroomtable"}, {"Dogbed"}, {"Cakestand"}, {"Catfurniture"}, {"Bathroomaccessory"},
|
||||
{"Facialtissueholder"}, {"Pressurecooker"}, {"Kitchenappliance"}, {"Tire"}, {"Ruler"},
|
||||
{"Luggageandbags"}, {"Microphone"}, {"Broccoli"}, {"Umbrella"}, {"Pastry"}, {"Grapefruit"},
|
||||
{"Band-aid"}, {"Animal"}, {"Bellpepper"}, {"Turkey"}, {"Lily"}, {"Pomegranate"}, {"Doughnut"},
|
||||
{"Glasses"}, {"Humannose"}, {"Pen"}, {"Ant"}, {"Car"}, {"Aircraft"}, {"Humanhand"}, {"Skunk"},
|
||||
{"Teddybear"}, {"Watermelon"}, {"Cantaloupe"}, {"Dishwasher"}, {"Flute"}, {"Balancebeam"},
|
||||
{"Sandwich"}, {"Shrimp"}, {"Sewingmachine"}, {"Binoculars"}, {"Raysandskates"}, {"Ipod"},
|
||||
{"Accordion"}, {"Willow"}, {"Crab"}, {"Crown"}, {"Seahorse"}, {"Perfume"}, {"Alpaca"}, {"Taxi"},
|
||||
{"Canoe"}, {"Remotecontrol"}, {"Wheelchair"}, {"Rugbyball"}, {"Armadillo"}, {"Maracas"},
|
||||
{"Helmet"}};
|
||||
{"Herd"}, {"Safari"}, {"Bangle"}, {"Cushion"}, {"Countertop"},
|
||||
{"Prom"}, {"Branch"}, {"Sports"}, {"Sky"}, {"Community"},
|
||||
{"Wheel"}, {"Cola"}, {"Tuxedo"}, {"Flowerpot"}, {"Team"},
|
||||
{"Computer"}, {"Unicycle"}, {"Brig"}, {"Aerospace engineering"}, {"Scuba diving"},
|
||||
{"Goggles"}, {"Fruit"}, {"Badminton"}, {"Horse"}, {"Sunglasses"},
|
||||
{"Fun"}, {"Prairie"}, {"Poster"}, {"Flag"}, {"Speedboat"},
|
||||
{"Eyelash"}, {"Veil"}, {"Mobile phone"}, {"Wheelbarrow"}, {"Saucer"},
|
||||
{"Leather"}, {"Drawer"}, {"Paper"}, {"Pier"}, {"Waterfowl"},
|
||||
{"Tights"}, {"Rickshaw"}, {"Vegetable"}, {"Handrail"}, {"Ice"},
|
||||
{"Metal"}, {"Flower"}, {"Wing"}, {"Silverware"}, {"Event"},
|
||||
{"Skyline"}, {"Money"}, {"Comics"}, {"Handbag"}, {"Porcelain"},
|
||||
{"Rodeo"}, {"Curtain"}, {"Tile"}, {"Human mouth"}, {"Army"},
|
||||
{"Menu"}, {"Boat"}, {"Snowboarding"}, {"Cairn terrier"}, {"Net"},
|
||||
{"Pasteles"}, {"Cup"}, {"Rugby"}, {"Pho"}, {"Cap"},
|
||||
{"Human hair"}, {"Surfing"}, {"Loveseat"}, {"Museum"}, {"Shipwreck"},
|
||||
{"Trunk (Tree)"}, {"Plush"}, {"Monochrome"}, {"Volcano"}, {"Rock"},
|
||||
{"Pillow"}, {"Presentation"}, {"Nebula"}, {"Subwoofer"}, {"Lake"},
|
||||
{"Sledding"}, {"Bangs"}, {"Tablecloth"}, {"Necklace"}, {"Swimwear"},
|
||||
{"Standing"}, {"Jeans"}, {"Carnival"}, {"Softball"}, {"Centrepiece"},
|
||||
{"Skateboarder"}, {"Cake"}, {"Dragon"}, {"Aurora"}, {"Skiing"},
|
||||
{"Bathroom"}, {"Dog"}, {"Needlework"}, {"Umbrella"}, {"Church"},
|
||||
{"Fire"}, {"Piano"}, {"Denim"}, {"Bridle"}, {"Cabinetry"},
|
||||
{"Lipstick"}, {"Ring"}, {"Television"}, {"Roller"}, {"Seal"},
|
||||
{"Concert"}, {"Product"}, {"News"}, {"Fast food"}, {"Horn (Animal)"},
|
||||
{"Tattoo"}, {"Bird"}, {"Bridegroom"}, {"Love"}, {"Helmet"},
|
||||
{"Dinosaur"}, {"Icing"}, {"Miniature"}, {"Tire"}, {"Toy"},
|
||||
{"Icicle"}, {"Jacket"}, {"Coffee"}, {"Mosque"}, {"Rowing"},
|
||||
{"Wetsuit"}, {"Camping"}, {"Underwater"}, {"Christmas"}, {"Gelato"},
|
||||
{"Whiteboard"}, {"Field"}, {"Ragdoll"}, {"Construction"}, {"Lampshade"},
|
||||
{"Palace"}, {"Meal"}, {"Factory"}, {"Cage"}, {"Clipper (Boat)"},
|
||||
{"Gymnastics"}, {"Turtle"}, {"Human foot"}, {"Marriage"}, {"Web page"},
|
||||
{"Human beard"}, {"Fog"}, {"Wool"}, {"Cappuccino"}, {"Lighthouse"},
|
||||
{"Lego"}, {"Sparkler"}, {"Sari"}, {"Model"}, {"Temple"},
|
||||
{"Beanie"}, {"Building"}, {"Waterfall"}, {"Penguin"}, {"Cave"},
|
||||
{"Stadium"}, {"Smile"}, {"Human hand"}, {"Park"}, {"Desk"},
|
||||
{"Shetland sheepdog"}, {"Bar"}, {"Eating"}, {"Neon"}, {"Dalmatian"},
|
||||
{"Crocodile"}, {"Wakeboarding"}, {"Longboard"}, {"Road"}, {"Race"},
|
||||
{"Kitchen"}, {"Odometer"}, {"Cliff"}, {"Fiction"}, {"School"},
|
||||
{"Interaction"}, {"Bullfighting"}, {"Boxer"}, {"Gown"}, {"Aquarium"},
|
||||
{"Superhero"}, {"Pie"}, {"Asphalt"}, {"Surfboard"}, {"Cheeseburger"},
|
||||
{"Screenshot"}, {"Supper"}, {"Laugh"}, {"Lunch"}, {"Party "},
|
||||
{"Glacier"}, {"Bench"}, {"Grandparent"}, {"Sink"}, {"Pomacentridae"},
|
||||
{"Blazer"}, {"Brick"}, {"Space"}, {"Backpacking"}, {"Stuffed toy"},
|
||||
{"Sushi"}, {"Glitter"}, {"Bonfire"}, {"Castle"}, {"Marathon"},
|
||||
{"Pizza"}, {"Beach"}, {"Human ear"}, {"Racing"}, {"Sitting"},
|
||||
{"Iceberg"}, {"Shelf"}, {"Vehicle"}, {"Pop music"}, {"Playground"},
|
||||
{"Clown"}, {"Car"}, {"Rein"}, {"Fur"}, {"Musician"},
|
||||
{"Casino"}, {"Baby"}, {"Alcohol"}, {"Strap"}, {"Reef"},
|
||||
{"Balloon"}, {"Outerwear"}, {"Cathedral"}, {"Competition"}, {"Joker"},
|
||||
{"Blackboard"}, {"Bunk bed"}, {"Bear"}, {"Moon"}, {"Archery"},
|
||||
{"Polo"}, {"River"}, {"Fishing"}, {"Ferris wheel"}, {"Mortarboard"},
|
||||
{"Bracelet"}, {"Flesh"}, {"Statue"}, {"Farm"}, {"Desert"},
|
||||
{"Chain"}, {"Aircraft"}, {"Textile"}, {"Hot dog"}, {"Knitting"},
|
||||
{"Singer"}, {"Juice"}, {"Circus"}, {"Chair"}, {"Musical instrument"},
|
||||
{"Room"}, {"Crochet"}, {"Sailboat"}, {"Newspaper"}, {"Santa claus"},
|
||||
{"Swamp"}, {"Skyscraper"}, {"Skin"}, {"Rocket"}, {"Aviation"},
|
||||
{"Airliner"}, {"Garden"}, {"Ruins"}, {"Storm"}, {"Glasses"},
|
||||
{"Balance"}, {"Nail (Body part)"}, {"Rainbow"}, {"Soil "}, {"Vacation "},
|
||||
{"Moustache"}, {"Doily"}, {"Food"}, {"Bride "}, {"Cattle"},
|
||||
{"Pocket"}, {"Infrastructure"}, {"Train"}, {"Gerbil"}, {"Fireworks"},
|
||||
{"Pet"}, {"Dam"}, {"Crew"}, {"Couch"}, {"Bathing"},
|
||||
{"Quilting"}, {"Motorcycle"}, {"Butterfly"}, {"Sled"}, {"Watercolor paint"},
|
||||
{"Rafting"}, {"Monument"}, {"Lightning"}, {"Sunset"}, {"Bumper"},
|
||||
{"Shoe"}, {"Waterskiing"}, {"Sneakers"}, {"Tower"}, {"Insect"},
|
||||
{"Pool"}, {"Placemat"}, {"Airplane"}, {"Plant"}, {"Jungle"},
|
||||
{"Armrest"}, {"Duck"}, {"Dress"}, {"Tableware"}, {"Petal"},
|
||||
{"Bus"}, {"Hanukkah"}, {"Forest"}, {"Hat"}, {"Barn"},
|
||||
{"Tubing"}, {"Snorkeling"}, {"Cool"}, {"Cookware and bakeware"}, {"Cycling"},
|
||||
{"Swing (Seat)"}, {"Muscle"}, {"Cat"}, {"Skateboard"}, {"Star"},
|
||||
{"Toe"}, {"Junk"}, {"Bicycle"}, {"Bedroom"}, {"Person"},
|
||||
{"Sand"}, {"Canyon"}, {"Tie"}, {"Twig"}, {"Sphynx"},
|
||||
{"Supervillain"}, {"Nightclub"}, {"Ranch"}, {"Pattern"}, {"Shorts"},
|
||||
{"Himalayan"}, {"Wall"}, {"Leggings"}, {"Windsurfing"}, {"Deejay"},
|
||||
{"Dance"}, {"Van"}, {"Bento"}, {"Sleep"}, {"Wine"},
|
||||
{"Picnic"}, {"Leisure"}, {"Dune"}, {"Crowd"}, {"Kayak"},
|
||||
{"Ballroom"}, {"Selfie"}, {"Graduation"}, {"Frigate"}, {"Mountain"},
|
||||
{"Dude"}, {"Windshield"}, {"Skiff"}, {"Class"}, {"Scarf"},
|
||||
{"Bull"}, {"Soccer"}, {"Bag"}, {"Basset hound"}, {"Tractor"},
|
||||
{"Swimming"}, {"Running"}, {"Track"}, {"Helicopter"}, {"Pitch"},
|
||||
{"Clock"}, {"Song"}, {"Jersey"}, {"Stairs"}, {"Flap"},
|
||||
{"Jewellery"}, {"Bridge"}, {"Cuisine"}, {"Bread"}, {"Caving"},
|
||||
{"Shell"}, {"Wreath"}, {"Roof"}, {"Cookie"}, {"Canoe"}};
|
||||
|
||||
static float g_thres_map[RET_CATEGORY_SUM] = {
|
||||
0.23, 0.03, 0.10, 0.13, 0.03,
|
||||
0.10, 0.06, 0.09, 0.09, 0.05,
|
||||
0.01, 0.04, 0.01, 0.27, 0.05,
|
||||
0.16, 0.01, 0.16, 0.04, 0.13,
|
||||
0.09, 0.18, 0.10, 0.65, 0.08,
|
||||
0.04, 0.08, 0.01, 0.05, 0.20,
|
||||
0.01, 0.16, 0.10, 0.10, 0.10,
|
||||
0.02, 0.24, 0.08, 0.10, 0.53,
|
||||
0.07, 0.05, 0.07, 0.27, 0.02,
|
||||
0.01, 0.71, 0.01, 0.06, 0.06,
|
||||
0.03, 0.96, 0.03, 0.94, 0.05,
|
||||
0.03, 0.14, 0.09, 0.03, 0.11,
|
||||
0.50, 0.16, 0.07, 0.07, 0.06,
|
||||
0.07, 0.08, 0.10, 0.29, 0.03,
|
||||
0.05, 0.11, 0.03, 0.03, 0.03,
|
||||
0.01, 0.11, 0.07, 0.03, 0.49,
|
||||
0.12, 0.30, 0.10, 0.15, 0.02,
|
||||
0.06, 0.17, 0.01, 0.04, 0.07,
|
||||
0.06, 0.02, 0.19, 0.20, 0.14,
|
||||
0.35, 0.15, 0.01, 0.10, 0.13,
|
||||
0.43, 0.11, 0.12, 0.32, 0.01,
|
||||
0.22, 0.51, 0.02, 0.04, 0.14,
|
||||
0.04, 0.35, 0.35, 0.01, 0.54,
|
||||
0.04, 0.02, 0.03, 0.02, 0.38,
|
||||
0.13, 0.19, 0.06, 0.01, 0.02,
|
||||
0.06, 0.03, 0.04, 0.01, 0.10,
|
||||
0.01, 0.07, 0.07, 0.07, 0.33,
|
||||
0.08, 0.04, 0.06, 0.07, 0.07,
|
||||
0.11, 0.02, 0.32, 0.48, 0.14,
|
||||
0.01, 0.01, 0.04, 0.05, 0.04,
|
||||
0.16, 0.50, 0.11, 0.03, 0.04,
|
||||
0.02, 0.55, 0.17, 0.13, 0.84,
|
||||
0.18, 0.03, 0.16, 0.02, 0.06,
|
||||
0.03, 0.11, 0.96, 0.36, 0.68,
|
||||
0.02, 0.08, 0.02, 0.01, 0.03,
|
||||
0.05, 0.14, 0.09, 0.06, 0.03,
|
||||
0.20, 0.15, 0.62, 0.03, 0.10,
|
||||
0.08, 0.02, 0.02, 0.06, 0.03,
|
||||
0.04, 0.01, 0.10, 0.05, 0.04,
|
||||
0.02, 0.07, 0.03, 0.32, 0.11,
|
||||
0.03, 0.02, 0.03, 0.01, 0.03,
|
||||
0.03, 0.25, 0.20, 0.19, 0.03,
|
||||
0.11, 0.03, 0.02, 0.03, 0.15,
|
||||
0.14, 0.06, 0.11, 0.03, 0.02,
|
||||
0.02, 0.52, 0.03, 0.02, 0.02,
|
||||
0.02, 0.09, 0.56, 0.01, 0.22,
|
||||
0.01, 0.48, 0.14, 0.10, 0.08,
|
||||
0.73, 0.39, 0.09, 0.10, 0.85,
|
||||
0.31, 0.03, 0.05, 0.01, 0.01,
|
||||
0.01, 0.10, 0.28, 0.02, 0.03,
|
||||
0.04, 0.03, 0.07, 0.14, 0.20,
|
||||
0.10, 0.01, 0.05, 0.37, 0.12,
|
||||
0.04, 0.44, 0.04, 0.26, 0.08,
|
||||
0.07, 0.27, 0.10, 0.03, 0.01,
|
||||
0.03, 0.16, 0.41, 0.16, 0.34,
|
||||
0.04, 0.30, 0.04, 0.05, 0.18,
|
||||
0.33, 0.03, 0.21, 0.03, 0.04,
|
||||
0.22, 0.01, 0.04, 0.02, 0.01,
|
||||
0.06, 0.02, 0.08, 0.87, 0.11,
|
||||
0.15, 0.05, 0.14, 0.09, 0.08,
|
||||
0.22, 0.09, 0.07, 0.06, 0.06,
|
||||
0.05, 0.43, 0.70, 0.03, 0.07,
|
||||
0.06, 0.07, 0.14, 0.04, 0.01,
|
||||
0.03, 0.05, 0.65, 0.06, 0.04,
|
||||
0.23, 0.06, 0.75, 0.10, 0.01,
|
||||
0.63, 0.41, 0.09, 0.01, 0.01,
|
||||
0.18, 0.10, 0.03, 0.01, 0.05,
|
||||
0.13, 0.18, 0.03, 0.23, 0.01,
|
||||
0.04, 0.03, 0.38, 0.90, 0.21,
|
||||
0.18, 0.10, 0.48, 0.08, 0.46,
|
||||
0.03, 0.01, 0.02, 0.03, 0.10,
|
||||
0.01, 0.09, 0.01, 0.01, 0.01,
|
||||
0.10, 0.41, 0.01, 0.06, 0.75,
|
||||
0.08, 0.01, 0.01, 0.08, 0.21,
|
||||
0.06, 0.02, 0.05, 0.02, 0.05,
|
||||
0.09, 0.12, 0.03, 0.06, 0.11,
|
||||
0.03, 0.01, 0.01, 0.06, 0.84,
|
||||
0.04, 0.81, 0.39, 0.02, 0.29,
|
||||
0.77, 0.07, 0.06, 0.22, 0.23,
|
||||
0.23, 0.01, 0.02, 0.13, 0.04,
|
||||
0.19, 0.04, 0.08, 0.27, 0.09,
|
||||
0.06, 0.01, 0.03, 0.21, 0.04,
|
||||
};
|
||||
|
||||
char *CreateLocalModelBuffer(JNIEnv *env, jobject modelBuffer) {
|
||||
jbyte *modelAddr = static_cast<jbyte *>(env->GetDirectBufferAddress(modelBuffer));
|
||||
|
|
@ -160,15 +234,30 @@ std::string ProcessRunnetResult(const int RET_CATEGORY_SUM, const char *const la
|
|||
|
||||
// Get a pointer to the first score.
|
||||
float *temp_scores = static_cast<float *>(outputTensor->MutableData());
|
||||
|
||||
float scores[RET_CATEGORY_SUM];
|
||||
for (int i = 0; i < RET_CATEGORY_SUM; ++i) {
|
||||
if (temp_scores[i] > 0.5) {
|
||||
MS_PRINT("MindSpore scores[%d] : [%f]", i, temp_scores[i]);
|
||||
}
|
||||
scores[i] = temp_scores[i];
|
||||
}
|
||||
|
||||
float unifiedThre = 0.5;
|
||||
float probMax = 1.0;
|
||||
for (size_t i = 0; i < RET_CATEGORY_SUM; ++i) {
|
||||
float threshold = g_thres_map[i];
|
||||
float tmpProb = scores[i];
|
||||
if (tmpProb < threshold) {
|
||||
tmpProb = tmpProb / threshold * unifiedThre;
|
||||
} else {
|
||||
tmpProb = (tmpProb - threshold) / (probMax - threshold) * unifiedThre + unifiedThre;
|
||||
}
|
||||
scores[i] = tmpProb;
|
||||
}
|
||||
|
||||
for (int i = 0; i < RET_CATEGORY_SUM; ++i) {
|
||||
if (scores[i] > 0.5) {
|
||||
MS_PRINT("MindSpore scores[%d] : [%f]", i, scores[i]);
|
||||
}
|
||||
}
|
||||
|
||||
// Score for each category.
|
||||
// Converted to text information that needs to be displayed in the APP.
|
||||
std::string categoryScore = "";
|
||||
|
|
|
|||
|
|
@ -1,4 +1,4 @@
|
|||
# demo_object_detection
|
||||
# Demo of 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.
|
||||
|
||||
|
|
|
|||
Loading…
Reference in New Issue