diff --git a/model_zoo/official/lite/image_classification/.gitignore b/model_zoo/official/lite/image_classification/.gitignore index 59c69550a3..475603d8a0 100644 --- a/model_zoo/official/lite/image_classification/.gitignore +++ b/model_zoo/official/lite/image_classification/.gitignore @@ -1,6 +1,8 @@ # MindSpore build/ mindspore/lib +app/src/main/assets/model/ +app/src/main/cpp/mindspore-lite-0.7.0-minddata-arm64-cpu output *.ir mindspore/ccsrc/schema/inner/* diff --git a/model_zoo/official/lite/image_classification/README.en.md b/model_zoo/official/lite/image_classification/README.en.md new file mode 100644 index 0000000000..8dc1605f9d --- /dev/null +++ b/model_zoo/official/lite/image_classification/README.en.md @@ -0,0 +1,278 @@ +## Demo_image_classification + +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. + + +### 运行依赖 + +- Android Studio 3.2 or later (Android 4.0 or later is recommended.) +- Native development kit (NDK) 21.3 +- CMake 3.10.2 [CMake](https://cmake.org/download) +- Android software development kit (SDK) 26 or later +- JDK 1.8 or later [JDK]( https://www.oracle.com/downloads/otn-pub/java/JDK/) + +### 构建与运行 + +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) + + (Optional) If an NDK version issue occurs during the installation, manually download the corresponding [NDK version](https://developer.android.com/ndk/downloads) (the version used in the sample code is 21.3). Specify the SDK location in `Android NDK location` of `Project Structure`. + + ![project_structure](images/project_structure.png) + +2. Connect to an Android device and runs the image classification application. + + Connect to the Android device through a USB cable for debugging. Click `Run 'app'` to run the sample project on your device. + + ![run_app](images/run_app.PNG) + + For details about how to connect the Android Studio to a device for debugging, see . + + The mobile phone needs to be turn on "USB debugging mode" before Android Studio can recognize the mobile phone. Huawei mobile phones generally turn on "USB debugging model" in Settings > system and update > developer Options > USB debugging. + +3. 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。 + + Continue the installation on the Android device. After the installation is complete, you can view the content captured by a camera and the inference result. + + ![result](images/app_result.jpg) + +## Detailed Description of the Sample Program + +This image classification sample program on the Android device includes a Java layer and a JNI layer. At the Java layer, the Android Camera 2 API is used to enable a camera to obtain image frames and process images. At the JNI layer, the model inference process is completed in [Runtime](https://www.mindspore.cn/lite/tutorial/en/master/use/runtime.html). + +### Sample Program Structure + +``` +app +│ +├── src/main +│ ├── assets # resource files +| | └── mobilenetv2.ms # model file +│ | +│ ├── cpp # main logic encapsulation classes for model loading and prediction +| | | +| | ├── MindSporeNetnative.cpp # JNI methods related to MindSpore calling +│ | └── MindSporeNetnative.h # header file +│ | +│ ├── java # application code at the Java layer +│ │ └── com.huawei.himindsporedemo +│ │ ├── gallery.classify # implementation related to image processing and MindSpore JNI calling +│ │ │ └── ... +│ │ └── widget # implementation related to camera enabling and drawing +│ │ └── ... +│ │ +│ ├── res # resource files related to Android +│ └── AndroidManifest.xml # Android configuration file +│ +├── CMakeList.txt # CMake compilation entry file +│ +├── build.gradle # Other Android configuration file +├── download.gradle # MindSpore version download +└── ... +``` + +### Configuring MindSpore Lite Dependencies + +When MindSpore C++ APIs are called at the Android JNI layer, related library files are required. You can use MindSpore Lite [source code compilation](https://www.mindspore.cn/lite/tutorial/en/master/build.html) to generate the MindSpore Lite version.　 + +``` +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 Dependencies. ============= +include_directories(${CMAKE_SOURCE_DIR}/src/main/cpp) +include_directories(${CMAKE_SOURCE_DIR}/src/main/cpp/${MINDSPORELITE_VERSION}/third_party/flatbuffers/include) +include_directories(${CMAKE_SOURCE_DIR}/src/main/cpp/${MINDSPORELITE_VERSION}) +include_directories(${CMAKE_SOURCE_DIR}/src/main/cpp/${MINDSPORELITE_VERSION}/include) +include_directories(${CMAKE_SOURCE_DIR}/src/main/cpp/${MINDSPORELITE_VERSION}/include/ir/dtype) +include_directories(${CMAKE_SOURCE_DIR}/src/main/cpp/${MINDSPORELITE_VERSION}/include/schema) + +add_library(mindspore-lite SHARED IMPORTED ) +add_library(minddata-lite SHARED IMPORTED ) + +set_target_properties(mindspore-lite PROPERTIES IMPORTED_LOCATION + ${CMAKE_SOURCE_DIR}/src/main/cpp/${MINDSPORELITE_VERSION}/lib/libmindspore-lite.so) +set_target_properties(minddata-lite PROPERTIES IMPORTED_LOCATION + ${CMAKE_SOURCE_DIR}/src/main/cpp/${MINDSPORELITE_VERSION}/lib/libminddata-lite.so) +# --------------- MindSpore Lite set End. -------------------- + +# Link target library. +target_link_libraries( + ... + # --- mindspore --- + minddata-lite + mindspore-lite + ... +) +``` + +* In this example, the download.gradle File configuration auto download MindSpore Lite version, placed in the 'app / src / main/cpp/mindspore_lite_x.x.x-minddata-arm64-cpu' directory. + + Note: if the automatic download fails, please manually download the relevant library files and put them in the corresponding location. + + MindSpore Lite version [MindSpore Lite version]( 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 `mobilenetv2.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. + +mobilenetv2.ms [mobilenetv2.ms]( https://download.mindspore.cn/model_zoo/official/lite/mobilenetv2_openimage_lite/mobilenetv2.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. + mindspore::lite::Context *context = new mindspore::lite::Context; + context->thread_num_ = num_thread; + + // 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. Convert the input image into the Tensor format of the MindSpore model. + + Convert the image data to be detected into the Tensor format of the MindSpore model. + + ```cpp + // Convert the Bitmap image passed in from the JAVA layer to Mat for OpenCV processing + BitmapToMat(env, srcBitmap, matImageSrc); + // Processing such as zooming the picture size. + matImgPreprocessed = PreProcessImageData(matImageSrc); + + ImgDims inputDims; + inputDims.channel = matImgPreprocessed.channels(); + inputDims.width = matImgPreprocessed.cols; + inputDims.height = matImgPreprocessed.rows; + float *dataHWC = new float[inputDims.channel * inputDims.width * inputDims.height] + + // Copy the image data to be detected to the dataHWC array. + // The dataHWC[image_size] array here is the intermediate variable of the input MindSpore model tensor. + float *ptrTmp = reinterpret_cast(matImgPreprocessed.data); + for(int i = 0; i < inputDims.channel * inputDims.width * inputDims.height; i++){ + dataHWC[i] = ptrTmp[i]; + } + + // Assign dataHWC[image_size] to the input tensor variable. + auto msInputs = mSession->GetInputs(); + auto inTensor = msInputs.front(); + memcpy(inTensor->MutableData(), dataHWC, + inputDims.channel * inputDims.width * inputDims.height * sizeof(float)); + delete[] (dataHWC); + ``` + +3. Perform inference on the input tensor based on the model, obtain the output tensor, and perform post-processing. + + - Perform graph execution and on-device inference. + + ```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(); + std::unordered_map msOutputs; + for (const auto &name : names) { + auto temp_dat =mSession->GetOutputByTensorName(name); + msOutputs.insert(std::pair {name, temp_dat}); + } + std::string retStr = ProcessRunnetResult(msOutputs, ret); + ``` + + - Perform post-processing of the output data. + + ```cpp + std::string ProcessRunnetResult(std::unordered_map msOutputs, int runnetRet) { + + std::unordered_map::iterator iter; + iter = msOutputs.begin(); + + // The mobilenetv2.ms model output just one branch. + auto outputTensor = iter->second; + int tensorNum = outputTensor->ElementsNum(); + MS_PRINT("Number of tensor elements:%d", tensorNum); + + // Get a pointer to the first score. + float *temp_scores = static_cast(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]; + } + + // Score for each category. + // Converted to text information that needs to be displayed in the APP. + std::string categoryScore = ""; + for (int i = 0; i < RET_CATEGORY_SUM; ++i) { + categoryScore += labels_name_map[i]; + categoryScore += ":"; + std::string score_str = std::to_string(scores[i]); + categoryScore += score_str; + categoryScore += ";"; + } + return categoryScore; + } + ``` + diff --git a/model_zoo/official/lite/image_classification/README.md b/model_zoo/official/lite/image_classification/README.md index ab2510854c..e33c67b5f2 100644 --- a/model_zoo/official/lite/image_classification/README.md +++ b/model_zoo/official/lite/image_classification/README.md @@ -1,283 +1,280 @@ -## MindSpore Lite 端侧图像分类demo（Android） - -本示例程序演示了如何在端侧利用MindSpore Lite C++ API（Android JNI）以及MindSpore Lite 图像分类模型完成端侧推理，实现对设备摄像头捕获的内容进行分类，并在App图像预览界面中显示出最可能的分类结果。 - - -### 运行依赖 - -- Android Studio >= 3.2 (推荐4.0以上版本) -- NDK 21.3 -- CMake 3.10 -- Android SDK >= 26 -- OpenCV >= 4.0.0 - -### 构建与运行 - -1. 在Android Studio中加载本示例源码，并安装相应的SDK（指定SDK版本后，由Android Studio自动安装）。 - - ![start_home](images/home.png) - - 启动Android Studio后，点击`File->Settings->System Settings->Android SDK`，勾选相应的SDK。如下图所示，勾选后，点击`OK`，Android Studio即可自动安装SDK。 - - ![start_sdk](images/sdk_management.png) - - （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。 - - ![project_structure](images/project_structure.png) - -2. 连接Android设备，运行图像分类应用程序。 - - 通过USB连接Android设备调试，点击`Run 'app'`即可在您的设备上运行本示例项目。 - - * 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。 - - ![run_app](images/run_app.PNG) - - Android Studio连接设备调试操作，可参考。 - -3. 在Android设备上，点击“继续安装”，安装完即可查看到设备摄像头捕获的内容和推理结果。 - - ![install](images/install.jpg) - - 如下图所示，识别出的概率最高的物体是植物。 - - ![result](images/app_result.jpg) - - -## 示例程序详细说明 - -本端侧图像分类Android示例程序分为JAVA层和JNI层，其中，JAVA层主要通过Android Camera 2 API实现摄像头获取图像帧，以及相应的图像处理等功能；JNI层完成模型推理的过程。 - -> 此处详细说明示例程序的JNI层实现，JAVA层运用Android Camera 2 API实现开启设备摄像头以及图像帧处理等功能，需读者具备一定的Android开发基础知识。 - -### 示例程序结构 - -``` -app -| -├── libs # 存放demo jni层依赖的库文件 -│ └── arm64-v8a -│ ├── libopencv_java4.so # opencv -│ ├── libmlkit-label-MS.so # ndk编译生成的库文件 -│ └── libmindspore-lite.so # mindspore lite -| -├── src/main -│ ├── assets # 资源文件 -| | └── mobilenetv2.ms # 存放模型文件 -│ | -│ ├── cpp # 模型加载和预测主要逻辑封装类 -| | ├── include # 存放MindSpore调用相关的头文件 -| | | └── ... -│ | | -| | ├── MindSporeNetnative.cpp # MindSpore调用相关的JNI方法 -│ | └── MindSporeNetnative.h # 头文件 -│ | -│ ├── java # java层应用代码 -│ │ └── com.huawei.himindsporedemo -│ │ ├── gallery.classify # 图像处理及MindSpore JNI调用相关实现 -│ │ │ └── ... -│ │ └── obejctdetect # 开启摄像头及绘制相关实现 -│ │ └── ... -│ │ -│ ├── res # 存放Android相关的资源文件 -│ └── AndroidManifest.xml # Android配置文件 -│ -├── CMakeList.txt # cmake编译入口文件 -│ -├── build.gradle # 其他Android配置文件 -├── download.gradle # APP构建时由gradle自动从HuaWei Server下载依赖的库文件及模型文件 -└── ... -``` - -### 配置MindSpore Lite依赖项 - -Android JNI层调用MindSpore C++ API时，需要相关库文件支持。可通过MindSpore Lite源码编译生成`libmindspore-lite.so`库文件。 - -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，并在应用的`build.gradle`文件中配置CMake编译支持，以及`arm64-v8a`和`armeabi-v7a`的编译支持。 - -本示例中，build过程由download.gradle文件自动从华为服务器下载libmindspore-lite.so以及OpenCV的libopencv_java4.so库文件，并放置在`app/libs/arm64-v8a`目录下。 - -* 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： - - libmindspore-lite.so [下载链接](https://download.mindspore.cn/model_zoo/official/lite/lib/mindspore%20version%200.7/libmindspore-lite.so) - - libmindspore-lite include文件 [下载链接](https://download.mindspore.cn/model_zoo/official/lite/lib/mindspore%20version%200.7/include.zip) - - libopencv_java4.so [下载链接](https://download.mindspore.cn/model_zoo/official/lite/lib/opencv%204.4.0/libopencv_java4.so) - - libopencv include文件 [下载链接](https://download.mindspore.cn/model_zoo/official/lite/lib/opencv%204.4.0/include.zip) - - - -``` -android{ - defaultConfig{ - externalNativeBuild{ - cmake{ - arguments "-DANDROID_STL=c++_shared" - } - } - - ndk{ - abiFilters 'arm64-v8a' - } - } -} -``` - -在`app/CMakeLists.txt`文件中建立`.so`库文件链接，如下所示。 - -``` -# Set MindSpore Lite Dependencies. -include_directories(${CMAKE_SOURCE_DIR}/src/main/cpp/include/MindSpore) -add_library(mindspore-lite SHARED IMPORTED ) -set_target_properties(mindspore-lite PROPERTIES - IMPORTED_LOCATION "${CMAKE_SOURCE_DIR}/libs/libmindspore-lite.so") - -# Set OpenCV Dependecies. -include_directories(${CMAKE_SOURCE_DIR}/opencv/sdk/native/jni/include) -add_library(lib-opencv SHARED IMPORTED ) -set_target_properties(lib-opencv PROPERTIES - IMPORTED_LOCATION "${CMAKE_SOURCE_DIR}/libs/libopencv_java4.so") - -# Link target library. -target_link_libraries( - ... - mindspore-lite - lib-opencv - ... -) -``` - -### 下载及部署模型文件 - -从MindSpore Model Hub中下载模型文件，本示例程序中使用的终端图像分类模型文件为`mobilenetv2.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。 - -* 注：若下载失败请手动下载模型文件，mobilenetv2.ms [下载链接](https://download.mindspore.cn/model_zoo/official/lite/mobilenetv2_openimage_lite/mobilenetv2.ms)。 - - -### 编写端侧推理代码 - -在JNI层调用MindSpore Lite C++ API实现端测推理。 - -推理代码流程如下，完整代码请参见`src/cpp/MindSporeNetnative.cpp`。 - -1. 加载MindSpore Lite模型文件，构建上下文、会话以及用于推理的计算图。 - - - 加载模型文件：创建并配置用于模型推理的上下文 - ```cpp - // Buffer is the model data passed in by the Java layer - jlong bufferLen = env->GetDirectBufferCapacity(buffer); - char *modelBuffer = CreateLocalModelBuffer(env, buffer); - ``` - - - 创建会话 - ```cpp - void **labelEnv = new void *; - MSNetWork *labelNet = new MSNetWork; - *labelEnv = labelNet; - - // Create context. - lite::Context *context = new lite::Context; - context->thread_num_ = numThread; //Specify the number of threads to run inference - - // Create the mindspore session. - labelNet->CreateSessionMS(modelBuffer, bufferLen, context); - delete(context); - - ``` - - - 加载模型文件并构建用于推理的计算图 - ```cpp - void MSNetWork::CreateSessionMS(char* modelBuffer, size_t bufferLen, 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); // Compile Graph - } - ``` - -2. 将输入图片转换为传入MindSpore模型的Tensor格式。 - - 将待检测图片数据转换为输入MindSpore模型的Tensor。 - - ```cpp - // Convert the Bitmap image passed in from the JAVA layer to Mat for OpenCV processing - BitmapToMat(env, srcBitmap, matImageSrc); - // Processing such as zooming the picture size. - matImgPreprocessed = PreProcessImageData(matImageSrc); - - ImgDims inputDims; - inputDims.channel = matImgPreprocessed.channels(); - inputDims.width = matImgPreprocessed.cols; - inputDims.height = matImgPreprocessed.rows; - float *dataHWC = new float[inputDims.channel * inputDims.width * inputDims.height] - - // Copy the image data to be detected to the dataHWC array. - // The dataHWC[image_size] array here is the intermediate variable of the input MindSpore model tensor. - float *ptrTmp = reinterpret_cast(matImgPreprocessed.data); - for(int i = 0; i < inputDims.channel * inputDims.width * inputDims.height; i++){ - dataHWC[i] = ptrTmp[i]; - } - - // Assign dataHWC[image_size] to the input tensor variable. - auto msInputs = mSession->GetInputs(); - auto inTensor = msInputs.front(); - memcpy(inTensor->MutableData(), dataHWC, - inputDims.channel * inputDims.width * inputDims.height * sizeof(float)); - delete[] (dataHWC); - ``` - -3. 对输入Tensor按照模型进行推理，获取输出Tensor，并进行后处理。 - - - 图执行，端测推理。 - - ```cpp - // After the model and image tensor data is loaded, run inference. - auto status = mSession->RunGraph(); - ``` - - - 获取输出数据。 - ```cpp - // Get the mindspore inference results. - auto msOutputs = mSession->GetOutputMapByNode(); - std::string retStr = ProcessRunnetResult(msOutputs); - ``` - - - 输出数据的后续处理。 - ```cpp - std::string ProcessRunnetResult( - std::unordered_map> msOutputs){ - - // Get the branch of the model output. - // Use iterators to get map elements. - std::unordered_map>::iterator iter; - iter = msOutputs.begin(); - - // The mobilenetv2.ms model output just one branch. - auto outputString = iter->first; - auto outputTensor = iter->second; - - float *temp_scores = static_cast(branch1_tensor[0]->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]; - } - - // Converted to text information that needs to be displayed in the APP. - std::string categoryScore = ""; - for (int i = 0; i < RET_CATEGORY_SUM; ++i) { - categoryScore += g_labels_name_map[i]; - categoryScore += ":"; - std::string score_str = std::to_string(scores[i]); - categoryScore += score_str; - categoryScore += ";"; - } - return categoryScore; - } - ``` +## MindSpore Lite 端侧图像分类demo（Android） + +本示例程序演示了如何在端侧利用MindSpore Lite C++ API（Android JNI）以及MindSpore Lite 图像分类模型完成端侧推理，实现对设备摄像头捕获的内容进行分类，并在App图像预览界面中显示出最可能的分类结果。 + + +### 运行依赖 + +- Android Studio >= 3.2 (推荐4.0以上版本) +- NDK 21.3 +- CMake 3.10.2 [CMake](https://cmake.org/download) +- Android SDK >= 26 +- JDK >= 1.8 [JDK]( https://www.oracle.com/downloads/otn-pub/java/JDK/) + +### 构建与运行 + +1. 在Android Studio中加载本示例源码，并安装相应的SDK（指定SDK版本后，由Android Studio自动安装）。 + + ![start_home](images/home.png) + + 启动Android Studio后，点击`File->Settings->System Settings->Android SDK`，勾选相应的SDK。如下图所示，勾选后，点击`OK`，Android Studio即可自动安装SDK。 + + ![start_sdk](images/sdk_management.png) + + （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。 + + ![project_structure](images/project_structure.png) + +2. 连接Android设备，运行图像分类应用程序。 + + 通过USB连接Android设备调试，点击`Run 'app'`即可在您的设备上运行本示例项目。 + + * 注：编译过程中Android Studio会自动下载MindSpore Lite、模型文件等相关依赖项，编译过程需做耐心等待。 + + ![run_app](images/run_app.PNG) + + Android Studio连接设备调试操作，可参考。 + + 手机需开启“USB调试模式”，Android Studio 才能识别到手机。 华为手机一般在设置->系统和更新->开发人员选项->USB调试中开始“USB调试模型”。 + +3. 在Android设备上，点击“继续安装”，安装完即可查看到设备摄像头捕获的内容和推理结果。 + + ![install](images/install.jpg) + + 如下图所示，识别出的概率最高的物体是植物。 + + ![result](images/app_result.jpg) + + +## 示例程序详细说明 + +本端侧图像分类Android示例程序分为JAVA层和JNI层，其中，JAVA层主要通过Android Camera 2 API实现摄像头获取图像帧，以及相应的图像处理等功能；JNI层完成模型推理的过程。 + +> 此处详细说明示例程序的JNI层实现，JAVA层运用Android Camera 2 API实现开启设备摄像头以及图像帧处理等功能，需读者具备一定的Android开发基础知识。 + +### 示例程序结构 + +``` +app +├── src/main +│ ├── assets # 资源文件 +| | └── mobilenetv2.ms # 存放模型文件 +│ | +│ ├── cpp # 模型加载和预测主要逻辑封装类 +| | ├── .. +| | ├── mindspore_lite_x.x.x-minddata-arm64-cpu #MindSpore Lite版本 +| | ├── MindSporeNetnative.cpp # MindSpore调用相关的JNI方法 +│ | └── MindSporeNetnative.h # 头文件 +| | └── MsNetWork.cpp # MindSpre接口封装 +│ | +│ ├── java # java层应用代码 +│ │ └── com.huawei.himindsporedemo +│ │ ├── gallery.classify # 图像处理及MindSpore JNI调用相关实现 +│ │ │ └── ... +│ │ └── widget # 开启摄像头及绘制相关实现 +│ │ └── ... +│ │ +│ ├── res # 存放Android相关的资源文件 +│ └── AndroidManifest.xml # Android配置文件 +│ +├── CMakeList.txt # cmake编译入口文件 +│ +├── build.gradle # 其他Android配置文件 +├── download.gradle # 工程依赖文件下载 +└── ... +``` + +### 配置MindSpore Lite依赖项 + +Android JNI层调用MindSpore C++ API时，需要相关库文件支持。可通过MindSpore Lite源码编译生成`libmindspore-lite.so`库文件。 + +本示例中，build过程由download.gradle文件自动从华为服务器下载MindSpore Lite 版本文件，并放置在`app / src / main/cpp/mindspore_lite_x.x.x-minddata-arm64-cpu`目录下。 + +* 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： + + MindSpore Lite版本 [下载链接](https://download.mindspore.cn/model_zoo/official/lite/lib/mindspore%20version%200.7/libmindspore-lite.so) + + +``` +android{ + defaultConfig{ + externalNativeBuild{ + cmake{ + arguments "-DANDROID_STL=c++_shared" + } + } + + ndk{ + abiFilters 'arm64-v8a' + } + } +} +``` + +在`app/CMakeLists.txt`文件中建立`.so`库文件链接，如下所示。 + +``` +# ============== Set MindSpore Dependencies. ============= +include_directories(${CMAKE_SOURCE_DIR}/src/main/cpp) +include_directories(${CMAKE_SOURCE_DIR}/src/main/cpp/${MINDSPORELITE_VERSION}/third_party/flatbuffers/include) +include_directories(${CMAKE_SOURCE_DIR}/src/main/cpp/${MINDSPORELITE_VERSION}) +include_directories(${CMAKE_SOURCE_DIR}/src/main/cpp/${MINDSPORELITE_VERSION}/include) +include_directories(${CMAKE_SOURCE_DIR}/src/main/cpp/${MINDSPORELITE_VERSION}/include/ir/dtype) +include_directories(${CMAKE_SOURCE_DIR}/src/main/cpp/${MINDSPORELITE_VERSION}/include/schema) + +add_library(mindspore-lite SHARED IMPORTED ) +add_library(minddata-lite SHARED IMPORTED ) + +set_target_properties(mindspore-lite PROPERTIES IMPORTED_LOCATION + ${CMAKE_SOURCE_DIR}/src/main/cpp/${MINDSPORELITE_VERSION}/lib/libmindspore-lite.so) +set_target_properties(minddata-lite PROPERTIES IMPORTED_LOCATION + ${CMAKE_SOURCE_DIR}/src/main/cpp/${MINDSPORELITE_VERSION}/lib/libminddata-lite.so) +# --------------- MindSpore Lite set End. -------------------- + +# Link target library. +target_link_libraries( + ... + # --- mindspore --- + minddata-lite + mindspore-lite + ... +) +``` + +### 下载及部署模型文件 + +从MindSpore Model Hub中下载模型文件，本示例程序中使用的终端图像分类模型文件为`mobilenetv2.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。 + +* 注：若下载失败请手动下载模型文件，mobilenetv2.ms [下载链接](https://download.mindspore.cn/model_zoo/official/lite/mobilenetv2_openimage_lite/mobilenetv2.ms)。 + + +### 编写端侧推理代码 + +在JNI层调用MindSpore Lite C++ API实现端测推理。 + +推理代码流程如下，完整代码请参见`src/cpp/MindSporeNetnative.cpp`。 + +1. 加载MindSpore Lite模型文件，构建上下文、会话以及用于推理的计算图。 + + - 加载模型文件：创建并配置用于模型推理的上下文 + ```cpp + // Buffer is the model data passed in by the Java layer + jlong bufferLen = env->GetDirectBufferCapacity(buffer); + char *modelBuffer = CreateLocalModelBuffer(env, buffer); + ``` + + - 创建会话 + ```cpp + void **labelEnv = new void *; + MSNetWork *labelNet = new MSNetWork; + *labelEnv = labelNet; + + // Create context. + lite::Context *context = new lite::Context; + context->thread_num_ = numThread; //Specify the number of threads to run inference + + // Create the mindspore session. + labelNet->CreateSessionMS(modelBuffer, bufferLen, context); + delete(context); + + ``` + + - 加载模型文件并构建用于推理的计算图 + ```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. 将输入图片转换为传入MindSpore模型的Tensor格式。 + + 将待检测图片数据转换为输入MindSpore模型的Tensor。 + + ```cpp + // Convert the Bitmap image passed in from the JAVA layer to Mat for OpenCV processing + BitmapToMat(env, srcBitmap, matImageSrc); + // Processing such as zooming the picture size. + matImgPreprocessed = PreProcessImageData(matImageSrc); + + ImgDims inputDims; + inputDims.channel = matImgPreprocessed.channels(); + inputDims.width = matImgPreprocessed.cols; + inputDims.height = matImgPreprocessed.rows; + float *dataHWC = new float[inputDims.channel * inputDims.width * inputDims.height] + + // Copy the image data to be detected to the dataHWC array. + // The dataHWC[image_size] array here is the intermediate variable of the input MindSpore model tensor. + float *ptrTmp = reinterpret_cast(matImgPreprocessed.data); + for(int i = 0; i < inputDims.channel * inputDims.width * inputDims.height; i++){ + dataHWC[i] = ptrTmp[i]; + } + + // Assign dataHWC[image_size] to the input tensor variable. + auto msInputs = mSession->GetInputs(); + auto inTensor = msInputs.front(); + memcpy(inTensor->MutableData(), dataHWC, + inputDims.channel * inputDims.width * inputDims.height * sizeof(float)); + delete[] (dataHWC); + ``` + +3. 对输入Tensor按照模型进行推理，获取输出Tensor，并进行后处理。 + + - 图执行，端测推理。 + + ```cpp + // After the model and image tensor data is loaded, run inference. + auto status = mSession->RunGraph(); + ``` + + - 获取输出数据。 + ```cpp + auto names = mSession->GetOutputTensorNames(); + std::unordered_map msOutputs; + for (const auto &name : names) { + auto temp_dat =mSession->GetOutputByTensorName(name); + msOutputs.insert(std::pair {name, temp_dat}); + } + std::string retStr = ProcessRunnetResult(msOutputs, ret); + ``` + + - 输出数据的后续处理。 + ```cpp + std::string ProcessRunnetResult(std::unordered_map msOutputs, int runnetRet) { + + std::unordered_map::iterator iter; + iter = msOutputs.begin(); + + // The mobilenetv2.ms model output just one branch. + auto outputTensor = iter->second; + int tensorNum = outputTensor->ElementsNum(); + MS_PRINT("Number of tensor elements:%d", tensorNum); + + // Get a pointer to the first score. + float *temp_scores = static_cast(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]; + } + + // Score for each category. + // Converted to text information that needs to be displayed in the APP. + std::string categoryScore = ""; + for (int i = 0; i < RET_CATEGORY_SUM; ++i) { + categoryScore += labels_name_map[i]; + categoryScore += ":"; + std::string score_str = std::to_string(scores[i]); + categoryScore += score_str; + categoryScore += ";"; + } + return categoryScore; + } + ``` diff --git a/model_zoo/official/lite/image_classification/app/CMakeLists.txt b/model_zoo/official/lite/image_classification/app/CMakeLists.txt index 55ff11cd86..2912792108 100644 --- a/model_zoo/official/lite/image_classification/app/CMakeLists.txt +++ b/model_zoo/official/lite/image_classification/app/CMakeLists.txt @@ -6,39 +6,28 @@ cmake_minimum_required(VERSION 3.4.1) set(CMAKE_VERBOSE_MAKEFILE on) -set(libs ${CMAKE_SOURCE_DIR}/libs) - - set(CMAKE_LIBRARY_OUTPUT_DIRECTORY ${CMAKE_SOURCE_DIR}/libs/${ANDROID_ABI}) +set(MINDSPORELITE_VERSION mindspore-lite-0.7.0-minddata-arm64-cpu) # ============== Set MindSpore Dependencies. ============= -include_directories(${CMAKE_SOURCE_DIR}/src/main/cpp/include) -include_directories(${CMAKE_SOURCE_DIR}/src/main/cpp/include/MindSpore) -include_directories(${CMAKE_SOURCE_DIR}/src/main/cpp/include/MindSpore/flatbuffers) -include_directories(${CMAKE_SOURCE_DIR}/src/main/cpp/include/MindSpore/ir/dtype) -include_directories(${CMAKE_SOURCE_DIR}/src/main/cpp/include/MindSpore/schema) +include_directories(${CMAKE_SOURCE_DIR}/src/main/cpp) +include_directories(${CMAKE_SOURCE_DIR}/src/main/cpp/${MINDSPORELITE_VERSION}/third_party/flatbuffers/include) +include_directories(${CMAKE_SOURCE_DIR}/src/main/cpp/${MINDSPORELITE_VERSION}) +include_directories(${CMAKE_SOURCE_DIR}/src/main/cpp/${MINDSPORELITE_VERSION}/include) +include_directories(${CMAKE_SOURCE_DIR}/src/main/cpp/${MINDSPORELITE_VERSION}/include/ir/dtype) +include_directories(${CMAKE_SOURCE_DIR}/src/main/cpp/${MINDSPORELITE_VERSION}/include/schema) add_library(mindspore-lite SHARED IMPORTED ) +add_library(minddata-lite SHARED IMPORTED ) set_target_properties(mindspore-lite PROPERTIES IMPORTED_LOCATION - ${CMAKE_SOURCE_DIR}/libs/${ANDROID_ABI}/libmindspore-lite.so) + ${CMAKE_SOURCE_DIR}/src/main/cpp/${MINDSPORELITE_VERSION}/lib/libmindspore-lite.so) +set_target_properties(minddata-lite PROPERTIES IMPORTED_LOCATION + ${CMAKE_SOURCE_DIR}/src/main/cpp/${MINDSPORELITE_VERSION}/lib/libminddata-lite.so) # --------------- MindSpore Lite set End. -------------------- - -# =============== Set OpenCV Dependencies =================== - -include_directories(${CMAKE_SOURCE_DIR}/opencv/sdk/native/jni/include/) - -add_library(lib-opencv SHARED IMPORTED ) - -set_target_properties(lib-opencv PROPERTIES IMPORTED_LOCATION - ${CMAKE_SOURCE_DIR}/libs/${ANDROID_ABI}/libopencv_java4.so) - -# --------------- OpenCV set End. --------------------------- - - # Creates and names a library, sets it as either STATIC # or SHARED, and provides the relative paths to its source code. # You can define multiple libraries, and CMake builds them for you. @@ -79,10 +68,8 @@ add_definitions(-DMNN_USE_LOGCAT) target_link_libraries( # Specifies the target library. mlkit-label-MS - # --- opencv --- - lib-opencv - # --- mindspore --- + minddata-lite mindspore-lite # --- other dependencies.--- diff --git a/model_zoo/official/lite/image_classification/app/build.gradle b/model_zoo/official/lite/image_classification/app/build.gradle index 0f2dfc13fa..ed58f89cde 100644 --- a/model_zoo/official/lite/image_classification/app/build.gradle +++ b/model_zoo/official/lite/image_classification/app/build.gradle @@ -49,7 +49,7 @@ android { } } packagingOptions{ - pickFirst 'lib/arm64-v8a/libopencv_java4.so' + pickFirst 'lib/arm64-v8a/libminddata-lite.so' pickFirst 'lib/arm64-v8a/libmindspore-lite.so' pickFirst 'lib/arm64-v8a/libmlkit-label-MS.so' } diff --git a/model_zoo/official/lite/image_classification/app/download.gradle b/model_zoo/official/lite/image_classification/app/download.gradle index 62601fbbe7..52ce530181 100644 --- a/model_zoo/official/lite/image_classification/app/download.gradle +++ b/model_zoo/official/lite/image_classification/app/download.gradle @@ -1,27 +1,18 @@ /** * To download necessary library from HuaWei server. - * Including mindspore-lite .so file, opencv .so file and model file. + * Including mindspore-lite .so file, minddata-lite .so file and model file. * The libraries can be downloaded manually. */ - -def targetopenCVInclude = "src/main/cpp/include" -def targetMindSporeInclude = "src/main/cpp/include" - +def targetMindSporeInclude = "src/main/cpp/" +def mindsporeLite_Version = "mindspore-lite-0.7.0-minddata-arm64-cpu" def targetModelFile = "src/main/assets/model/mobilenetv2.ms" -def openCVLibrary_arm64 = "libs/arm64-v8a/libopencv_java4.so" -def mindSporeLibrary_arm64 = "libs/arm64-v8a/libmindspore-lite.so" -def openCVlibIncluding_arm64 = "src/main/cpp/include/opencv2/include.zip" -def mindSporeLibIncluding_arm64 = "src/main/cpp/include/MindSpore/include.zip" +def mindSporeLibrary_arm64 = "src/main/cpp/${mindsporeLite_Version}.tar.gz" def modelDownloadUrl = "https://download.mindspore.cn/model_zoo/official/lite/mobilenetv2_openimage_lite/mobilenetv2.ms" -def opencvDownloadUrl = "https://download.mindspore.cn/model_zoo/official/lite/lib/opencv%204.4.0/libopencv_java4.so" -def mindsporeLiteDownloadUrl = "https://download.mindspore.cn/model_zoo/official/lite/lib/mindspore%20version%200.7/libmindspore-lite.so" -def opencvincludeDownloadUrl = "https://download.mindspore.cn/model_zoo/official/lite/lib/opencv%204.4.0/include.zip" -def mindsporeIncludeDownloadUrl = "https://download.mindspore.cn/model_zoo/official/lite/lib/mindspore%20version%200.7/include.zip" +def mindsporeLiteDownloadUrl = "https://download.mindspore.cn/model_zoo/official/lite/lib/mindspore%20version%201.0/${mindsporeLite_Version}.tar.gz" -def cleantargetopenCVInclude = "src/main/cpp/include/opencv2" -def cleantargetMindSporeInclude = "src/main/cpp/include/MindSpore" +def cleantargetMindSporeInclude = "src/main/cpp" task downloadModelFile(type: DownloadUrlTask) { @@ -32,15 +23,6 @@ task downloadModelFile(type: DownloadUrlTask) { target = file("${targetModelFile}") } - -task downloadOpenCVLibrary(type: DownloadUrlTask) { - doFirst { - println "Downloading ${opencvDownloadUrl}" - } - sourceUrl = "${opencvDownloadUrl}" - target = file("${openCVLibrary_arm64}") -} - task downloadMindSporeLibrary(type: DownloadUrlTask) { doFirst { println "Downloading ${mindsporeLiteDownloadUrl}" @@ -49,80 +31,36 @@ task downloadMindSporeLibrary(type: DownloadUrlTask) { target = file("${mindSporeLibrary_arm64}") } -task downloadopecvIncludeLibrary(type: DownloadUrlTask) { - doFirst { - println "Downloading ${opencvincludeDownloadUrl}" - } - sourceUrl = "${opencvincludeDownloadUrl}" - target = file("${openCVlibIncluding_arm64}") -} - -task downloadMindSporeIncludeLibrary(type: DownloadUrlTask) { - doFirst { - println "Downloading ${mindsporeIncludeDownloadUrl}" - } - sourceUrl = "${mindsporeIncludeDownloadUrl}" - target = file("${mindSporeLibIncluding_arm64}") -} - -task unzipopencvInclude(type: Copy, dependsOn: 'downloadopecvIncludeLibrary') { +task unzipMindSporeInclude(type: Copy, dependsOn: 'downloadMindSporeLibrary') { doFirst { - println "Unzipping ${openCVlibIncluding_arm64}" + println "Unzipping ${mindSporeLibrary_arm64}" } - from zipTree("${openCVlibIncluding_arm64}") - into "${targetopenCVInclude}" -} - -task unzipMindSporeInclude(type: Copy, dependsOn: 'downloadMindSporeIncludeLibrary') { - doFirst { - println "Unzipping ${mindSporeLibIncluding_arm64}" - } - from zipTree("${mindSporeLibIncluding_arm64}") + from tarTree(resources.gzip("${mindSporeLibrary_arm64}")) into "${targetMindSporeInclude}" } -task cleanUnusedopencvFiles(type: Delete, dependsOn: ['unzipopencvInclude']) { - delete fileTree("${cleantargetopenCVInclude}").matching { - include "*.zip" - } -} task cleanUnusedmindsporeFiles(type: Delete, dependsOn: ['unzipMindSporeInclude']) { delete fileTree("${cleantargetMindSporeInclude}").matching { - include "*.zip" + include "*.tar.gz" } } /* * Using preBuild to download mindspore library, opencv library and model file. * Run before gradle build. */ -if (file("libs/arm64-v8a/libmindspore-lite.so").exists()){ +if (file("src/main/cpp/${mindsporeLite_Version}/lib/libmindspore-lite.so").exists()){ downloadMindSporeLibrary.enabled = false + unzipMindSporeInclude.enabled = false + cleanUnusedmindsporeFiles.enabled = false } -if (file("libs/arm64-v8a/libopencv_java4.so").exists()){ - downloadOpenCVLibrary.enabled = false -} if (file("src/main/assets/model/mobilenetv2.ms").exists()){ downloadModelFile.enabled = false } -if (file("src/main/cpp/include/MindSpore/lite_session.h").exists()){ - downloadMindSporeIncludeLibrary.enabled = false - unzipopencvInclude.enabled = false - cleanUnusedopencvFiles.enabled =false -} -if (file("src/main/cpp/include/opencv2/core.hpp").exists()){ - downloadopecvIncludeLibrary.enabled = false - unzipMindSporeInclude.enabled = false - cleanUnusedmindsporeFiles.enabled =false -} - -preBuild.dependsOn downloadMindSporeLibrary -preBuild.dependsOn downloadOpenCVLibrary preBuild.dependsOn downloadModelFile -preBuild.dependsOn unzipopencvInclude +preBuild.dependsOn downloadMindSporeLibrary preBuild.dependsOn unzipMindSporeInclude -preBuild.dependsOn cleanUnusedopencvFiles preBuild.dependsOn cleanUnusedmindsporeFiles class DownloadUrlTask extends DefaultTask { diff --git a/model_zoo/official/lite/image_classification/app/src/main/assets/model/mobilenetv2.ms b/model_zoo/official/lite/image_classification/app/src/main/assets/model/mobilenetv2.ms deleted file mode 100644 index 5fa58672c6..0000000000 Binary files a/model_zoo/official/lite/image_classification/app/src/main/assets/model/mobilenetv2.ms and /dev/null differ diff --git a/model_zoo/official/lite/image_classification/app/src/main/cpp/MSNetWork.cpp b/model_zoo/official/lite/image_classification/app/src/main/cpp/MSNetWork.cpp index e5ddf6b31b..abce847eab 100644 --- a/model_zoo/official/lite/image_classification/app/src/main/cpp/MSNetWork.cpp +++ b/model_zoo/official/lite/image_classification/app/src/main/cpp/MSNetWork.cpp @@ -18,7 +18,7 @@ #include #include #include -#include "include/MindSpore/errorcode.h" +#include "include/errorcode.h" #define MS_PRINT(format, ...) __android_log_print(ANDROID_LOG_INFO, "MSJNI", format, ##__VA_ARGS__) @@ -54,8 +54,6 @@ int MSNetWork::ReleaseNets(void) { return 0; } -const int MSNetWork::RET_CATEGORY_SUM = 601; - const char *MSNetWork::labels_name_map[MSNetWork::RET_CATEGORY_SUM] = { {"Tortoise"}, {"Container"}, {"Magpie"}, {"Seaturtle"}, {"Football"}, {"Ambulance"}, {"Ladder"}, {"Toothbrush"}, {"Syringe"}, {"Sink"}, {"Toy"}, {"Organ(MusicalInstrument) "}, {"Cassettedeck"}, diff --git a/model_zoo/official/lite/image_classification/app/src/main/cpp/MSNetWork.h b/model_zoo/official/lite/image_classification/app/src/main/cpp/MSNetWork.h index d1c3dbace0..da04476180 100644 --- a/model_zoo/official/lite/image_classification/app/src/main/cpp/MSNetWork.h +++ b/model_zoo/official/lite/image_classification/app/src/main/cpp/MSNetWork.h @@ -52,10 +52,9 @@ class MSNetWork { int ReleaseNets(void); - private: mindspore::session::LiteSession *session; mindspore::lite::Model *model; - static const int RET_CATEGORY_SUM; + static const int RET_CATEGORY_SUM = 601; static const char *labels_name_map[RET_CATEGORY_SUM]; }; #endif diff --git a/model_zoo/official/lite/image_classification/app/src/main/cpp/MindSporeNetnative.cpp b/model_zoo/official/lite/image_classification/app/src/main/cpp/MindSporeNetnative.cpp index f58394c0bd..c0283a2dc8 100644 --- a/model_zoo/official/lite/image_classification/app/src/main/cpp/MindSporeNetnative.cpp +++ b/model_zoo/official/lite/image_classification/app/src/main/cpp/MindSporeNetnative.cpp @@ -13,104 +13,27 @@ * See the License for the specific language governing permissions and * limitations under the License. */ - +#include #include #include #include -#include -#include -#include #include #include #include #include #include +#include "include/errorcode.h" +#include "include/ms_tensor.h" #include "MindSporeNetnative.h" -#include "opencv2/core.hpp" -#include "opencv2/imgproc.hpp" #include "MSNetWork.h" +#include "lite_cv/lite_mat.h" +#include "lite_cv/image_process.h" +using mindspore::dataset::LiteMat; +using mindspore::dataset::LPixelType; +using mindspore::dataset::LDataType; #define MS_PRINT(format, ...) __android_log_print(ANDROID_LOG_INFO, "MSJNI", format, ##__VA_ARGS__) -void BitmapToMat2(JNIEnv *env, const jobject &bitmap, cv::Mat *mat, - jboolean needUnPremultiplyAlpha) { - AndroidBitmapInfo info; - void *pixels = nullptr; - cv::Mat &dst = *mat; - CV_Assert(AndroidBitmap_getInfo(env, bitmap, &info) >= 0); - CV_Assert(info.format == ANDROID_BITMAP_FORMAT_RGBA_8888 || - info.format == ANDROID_BITMAP_FORMAT_RGB_565); - CV_Assert(AndroidBitmap_lockPixels(env, bitmap, &pixels) >= 0); - CV_Assert(pixels); - dst.create(info.height, info.width, CV_8UC4); - if (info.format == ANDROID_BITMAP_FORMAT_RGBA_8888) { - cv::Mat tmp(info.height, info.width, CV_8UC4, pixels); - if (needUnPremultiplyAlpha) { - cvtColor(tmp, dst, cv::COLOR_RGBA2BGR); - } else { - tmp.copyTo(dst); - } - } else { - cv::Mat tmp(info.height, info.width, CV_8UC4, pixels); - cvtColor(tmp, dst, cv::COLOR_BGR5652RGBA); - } - AndroidBitmap_unlockPixels(env, bitmap); - return; -} - -void BitmapToMat(JNIEnv *env, const jobject &bitmap, cv::Mat *mat) { - BitmapToMat2(env, bitmap, mat, true); -} - -/** - * Processing image with resize and normalize. - */ -cv::Mat PreProcessImageData(cv::Mat input) { - cv::Mat imgFloatTmp, imgResized256, imgResized224; - int resizeWidth = 256; - int resizeHeight = 256; - float normalizMin = 1.0; - float normalizMax = 255.0; - - cv::resize(input, imgFloatTmp, cv::Size(resizeWidth, resizeHeight)); - - imgFloatTmp.convertTo(imgResized256, CV_32FC3, normalizMin / normalizMax); - - const int offsetX = 16; - const int offsetY = 16; - const int cropWidth = 224; - const int cropHeight = 224; - - // Standardization processing. - float meanR = 0.485; - float meanG = 0.456; - float meanB = 0.406; - float varR = 0.229; - float varG = 0.224; - float varB = 0.225; - - cv::Rect roi; - roi.x = offsetX; - roi.y = offsetY; - roi.width = cropWidth; - roi.height = cropHeight; - - // The final image size of the incoming model is 224*224. - imgResized256(roi).copyTo(imgResized224); - - cv::Scalar mean = cv::Scalar(meanR, meanG, meanB); - cv::Scalar var = cv::Scalar(varR, varG, varB); - cv::Mat imgResized1; - cv::Mat imgResized2; - cv::Mat imgMean(imgResized224.size(), CV_32FC3, - mean); // imgMean Each pixel channel is (0.485, 0.456, 0.406) - cv::Mat imgVar(imgResized224.size(), CV_32FC3, - var); // imgVar Each pixel channel is (0.229, 0.224, 0.225) - imgResized1 = imgResized224 - imgMean; - imgResized2 = imgResized1 / imgVar; - return imgResized2; -} - char *CreateLocalModelBuffer(JNIEnv *env, jobject modelBuffer) { jbyte *modelAddr = static_cast(env->GetDirectBufferAddress(modelBuffer)); int modelLen = static_cast(env->GetDirectBufferCapacity(modelBuffer)); @@ -126,21 +49,20 @@ char *CreateLocalModelBuffer(JNIEnv *env, jobject modelBuffer) { */ std::string ProcessRunnetResult(const int RET_CATEGORY_SUM, const char *const labels_name_map[], - std::unordered_map> msOutputs) { + std::unordered_map msOutputs) { // Get the branch of the model output. // Use iterators to get map elements. - std::unordered_map>::iterator iter; + std::unordered_map::iterator iter; iter = msOutputs.begin(); // The mobilenetv2.ms model output just one branch. auto outputTensor = iter->second; - int tensorNum = outputTensor[0]->ElementsNum(); + int tensorNum = outputTensor->ElementsNum(); MS_PRINT("Number of tensor elements:%d", tensorNum); // Get a pointer to the first score. - float *temp_scores = static_cast(outputTensor[0]->MutableData()); + float *temp_scores = static_cast(outputTensor->MutableData()); float scores[RET_CATEGORY_SUM]; for (int i = 0; i < RET_CATEGORY_SUM; ++i) { @@ -163,6 +85,72 @@ ProcessRunnetResult(const int RET_CATEGORY_SUM, const char *const labels_name_ma return categoryScore; } +bool BitmapToLiteMat(JNIEnv *env, const jobject &srcBitmap, LiteMat *lite_mat) { + bool ret = false; + AndroidBitmapInfo info; + void *pixels = nullptr; + LiteMat &lite_mat_bgr = *lite_mat; + AndroidBitmap_getInfo(env, srcBitmap, &info); + if (info.format != ANDROID_BITMAP_FORMAT_RGBA_8888) { + MS_PRINT("Image Err, Request RGBA"); + return false; + } + AndroidBitmap_lockPixels(env, srcBitmap, &pixels); + if (info.stride == info.width*4) { + ret = InitFromPixel(reinterpret_cast(pixels), + LPixelType::RGBA2RGB, LDataType::UINT8, + info.width, info.height, lite_mat_bgr); + if (!ret) { + MS_PRINT("Init From RGBA error"); + } + } else { + unsigned char *pixels_ptr = new unsigned char[info.width*info.height*4]; + unsigned char *ptr = pixels_ptr; + unsigned char *data = reinterpret_cast(pixels); + for (int i = 0; i < info.height; i++) { + memcpy(ptr, data, info.width*4); + ptr += info.width*4; + data += info.stride; + } + ret = InitFromPixel(reinterpret_cast(pixels_ptr), + LPixelType::RGBA2RGB, LDataType::UINT8, + info.width, info.height, lite_mat_bgr); + if (!ret) { + MS_PRINT("Init From RGBA error"); + } + delete[] (pixels_ptr); + } + AndroidBitmap_unlockPixels(env, srcBitmap); + return ret; +} + +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, 256, 256); + 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; + } + LiteMat lite_mat_cut; + ret = Crop(lite_mat_convert_float, lite_mat_cut, 16, 16, 224, 224); + if (!ret) { + MS_PRINT("Crop 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_cut, lite_norm_mat_cut, means, vars); + return true; +} + /** * The Java layer reads the model into MappedByteBuffer or ByteBuffer to load the model. @@ -170,9 +158,9 @@ ProcessRunnetResult(const int RET_CATEGORY_SUM, const char *const labels_name_ma extern "C" JNIEXPORT jlong JNICALL Java_com_mindspore_himindsporedemo_gallery_classify_TrackingMobile_loadModel(JNIEnv *env, - jobject thiz, - jobject model_buffer, - jint num_thread) { + jobject thiz, + jobject model_buffer, + jint num_thread) { if (nullptr == model_buffer) { MS_PRINT("error, buffer is nullptr!"); return (jlong) nullptr; @@ -220,16 +208,23 @@ Java_com_mindspore_himindsporedemo_gallery_classify_TrackingMobile_loadModel(JNI */ extern "C" JNIEXPORT jstring JNICALL Java_com_mindspore_himindsporedemo_gallery_classify_TrackingMobile_runNet(JNIEnv *env, jclass type, - jlong netEnv, - jobject srcBitmap) { - cv::Mat matImageSrc; - BitmapToMat(env, srcBitmap, &matImageSrc); - cv::Mat matImgPreprocessed = PreProcessImageData(matImageSrc); + jlong netEnv, + jobject srcBitmap) { + LiteMat lite_mat_bgr, lite_norm_mat_cut; + + if (!BitmapToLiteMat(env, srcBitmap, &lite_mat_bgr)) { + MS_PRINT("BitmapToLiteMat error"); + return NULL; + } + if (!PreProcessImageData(lite_mat_bgr, &lite_norm_mat_cut)) { + MS_PRINT("PreProcessImageData error"); + return NULL; + } ImgDims inputDims; - inputDims.channel = matImgPreprocessed.channels(); - inputDims.width = matImgPreprocessed.cols; - inputDims.height = matImgPreprocessed.rows; + 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(netEnv); @@ -253,17 +248,10 @@ Java_com_mindspore_himindsporedemo_gallery_classify_TrackingMobile_runNet(JNIEnv } auto inTensor = msInputs.front(); - // dataHWC is the tensor format. - float *dataHWC = new float[inputDims.channel * inputDims.width * inputDims.height]; - float *ptrTmp = reinterpret_cast(matImgPreprocessed.data); - for (int i = 0; i < inputDims.channel * inputDims.width * inputDims.height; ++i) { - dataHWC[i] = ptrTmp[i]; - } - + float *dataHWC = reinterpret_cast(lite_norm_mat_cut.data_ptr_); // Copy dataHWC to the model input tensor. memcpy(inTensor->MutableData(), dataHWC, inputDims.channel * inputDims.width * inputDims.height * sizeof(float)); - delete[] (dataHWC); // After the model and image tensor data is loaded, run inference. auto status = mSession->RunGraph(); @@ -277,7 +265,12 @@ Java_com_mindspore_himindsporedemo_gallery_classify_TrackingMobile_runNet(JNIEnv * Get the mindspore inference results. * Return the map of output node name and MindSpore Lite MSTensor. */ - auto msOutputs = mSession->GetOutputMapByNode(); + auto names = mSession->GetOutputTensorNames(); + std::unordered_map msOutputs; + for (const auto &name : names) { + auto temp_dat = mSession->GetOutputByTensorName(name); + msOutputs.insert(std::pair {name, temp_dat}); + } std::string resultStr = ProcessRunnetResult(MSNetWork::RET_CATEGORY_SUM, MSNetWork::labels_name_map, msOutputs); @@ -288,8 +281,8 @@ Java_com_mindspore_himindsporedemo_gallery_classify_TrackingMobile_runNet(JNIEnv extern "C" JNIEXPORT jboolean JNICALL Java_com_mindspore_himindsporedemo_gallery_classify_TrackingMobile_unloadModel(JNIEnv *env, - jclass type, - jlong netEnv) { + jclass type, + jlong netEnv) { MS_PRINT("MindSpore release net."); void **labelEnv = reinterpret_cast(netEnv); if (labelEnv == nullptr) {