!9322 Add Posenet Demo README

From: @liuxiao78
Reviewed-by: @zhang_xue_tong,@zhanghaibo5
Signed-off-by: @zhang_xue_tong

model_zoo/official/lite/image_classification/README.en.md

@@ -1,13 +1,12 @@
-# Demo of Image Classification
+## Demo of 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.
 
-
 ### Running Dependencies
 
 - Android Studio 3.2 or later (Android 4.0 or later is recommended.)
 - Native development kit (NDK) 21.3
-- CMake 3.10.2 [CMake](https://cmake.org/download) 
+- CMake 3.10.2 [CMake](https://cmake.org/download)
 - Android software development kit (SDK) 26 or later
 - JDK 1.8 or later
 
@@ -21,9 +20,7 @@ The following describes how to use the MindSpore Lite C++ APIs (Android JNIs) an
 
     ![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`.
+    If you have any Android Studio configuration problem when trying this demo, please refer to item 5 to resolve it.
-
-    ![project_structure](images/project_structure.png)
 
 2. Connect to an Android device and runs the image classification application.
 
@@ -39,13 +36,24 @@ The following describes how to use the MindSpore Lite C++ APIs (Android JNIs) an
 
     ![result](images/app_result.jpg)
 
+4. The solutions of Android Studio configuration problems:
+
+    |      | Warning                                                         | Solution                                                     |
+    | ---- | ------------------------------------------------------------ | ------------------------------------------------------------ |
+    | 1    | Gradle sync failed: NDK not configured.                      | Specify the installed ndk directory in local.properties：ndk.dir={ndk的安装目录} |
+    | 2    | Requested NDK version did not match the version requested by ndk.dir | Manually download corresponding [NDK Version](https://developer.android.com/ndk/downloads)，and specify the sdk directory in Project Structure - Android NDK location.（You can refer to the figure below.） |
+    | 3    | This version of Android Studio cannot open this project, please retry with Android Studio or newer. | Update Android Studio Version in Tools - help - Checkout for Updates.                 |
+    | 4    | SSL peer shut down incorrectly                               | Run this demo again.                                                     |
+
+    ![project_structure](images/project_structure.png)
+
 ## 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/tutorial/lite/en/master/use/runtime.html).
 
 ### Sample Program Structure
 
-```
+```text
 app
 │
 ├── src/main
@@ -58,12 +66,12 @@ app
 │   |   └── MindSporeNetnative.h # header file
 │   |
 │   ├── java # application code at the Java layer
-│   │   └── com.mindspore.himindsporedemo 
+│   │   └── com.mindspore.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
 │
@@ -84,7 +92,7 @@ Note: if the automatic download fails, please manually download the relevant lib
 
 mindspore-lite-1.0.1-runtime-arm64-cpu.tar.gz [Download link](https://ms-release.obs.cn-north-4.myhuaweicloud.com/1.0.1/lite/android_aarch64/mindspore-lite-1.0.1-runtime-arm64-cpu.tar.gz)
 
-```
+```text
 android{
     defaultConfig{
         externalNativeBuild{
@@ -93,7 +101,7 @@ android{
             }
         }
 
-        ndk{ 
+        ndk{
             abiFilters'armeabi-v7a', 'arm64-v8a'  
         }
     }
@@ -102,7 +110,7 @@ android{
 
 Create a link to the `.so` library file in the `app/CMakeLists.txt` file:
 
-```
+```text
 # ============== 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)
@@ -120,7 +128,7 @@ 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.       
+# Link target library.
 target_link_libraries(
     ...
      # --- mindspore ---
@@ -132,7 +140,7 @@ target_link_libraries(
 
 ### 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.
+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.
 
@@ -142,11 +150,11 @@ mobilenetv2.ms [mobilenetv2.ms]( https://download.mindspore.cn/model_zoo/officia
 
 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`. 
+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.
+    - Load a model file. Create and configure the context for model inference.
 
      ```cpp
      // Buffer is the model data passed in by the Java layer
@@ -154,24 +162,24 @@ The inference code process is as follows. For details about the complete code, s
      char *modelBuffer = CreateLocalModelBuffer(env, buffer);  
      ```
 
-   - Create a session.
+    - 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.
+    - 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)
@@ -183,7 +191,7 @@ The inference code process is as follows. For details about the complete code, s
      }
      ```
 
-2. Convert the input image into the Tensor format of the MindSpore 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.
 
@@ -230,9 +238,9 @@ The inference code process is as follows. For details about the complete code, s
          inputDims.channel * inputDims.width * inputDims.height * sizeof(float));
     ```
 
-3. Perform inference on the input tensor based on the model, obtain the output tensor, and perform post-processing.    
+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.
+    - Perform graph execution and on-device inference.
 
      ```cpp
      // After the model and image tensor data is loaded, run inference.
@@ -305,5 +313,5 @@ The inference code process is as follows. For details about the complete code, s
       }
         return categoryScore;
      }
-     
+
      ```

model_zoo/official/lite/image_classification/README.md

@@ -2,18 +2,17 @@
 
 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) 
+- CMake 3.10.2   [CMake](https://cmake.org/download)
 - Android SDK >= 26
-- JDK >= 1.8 
+- JDK >= 1.8
 
 ### 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）。 
+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)
 
@@ -21,15 +20,13 @@
 
     ![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。
+    使用过程中若出现Android Studio配置问题，可参考第5项解决。
-
-    ![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。
+    > 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)
 
@@ -45,6 +42,16 @@
 
     ![result](images/app_result.jpg)
 
+4. Android Studio 配置问题解决方案可参考下表：
+
+    |      | 报错                                                         | 解决方案                                                     |
+    | ---- | ------------------------------------------------------------ | ------------------------------------------------------------ |
+    | 1    | Gradle sync failed: NDK not configured.                      | 在local.properties中指定安装的ndk目录：ndk.dir={ndk的安装目录} |
+    | 2    | Requested NDK version did not match the version requested by ndk.dir | 可手动下载相应的[NDK版本](https://developer.android.com/ndk/downloads?hl=zh-cn)，并在Project Structure - Android NDK location设置中指定SDK的位置（可参考下图完成） |
+    | 3    | This version of Android Studio cannot open this project, please retry with Android Studio or newer. | 在工具栏-help-Checkout for Updates中更新版本                 |
+    | 4    | SSL peer shut down incorrectly                               | 重新构建                                                     |
+
+    ![project_structure](images/project_structure.png)
 
 ## 示例程序详细说明  
 
@@ -54,7 +61,7 @@
 
 ### 示例程序结构
 
-```
+```text
 app
 ├── src/main
 │   ├── assets # 资源文件
@@ -68,12 +75,12 @@ app
 |   |   └── MsNetWork.cpp # MindSpre接口封装
 │   |
 │   ├── java # java层应用代码
-│   │   └── com.mindspore.himindsporedemo 
+│   │   └── com.mindspore.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
 │
@@ -96,13 +103,13 @@ Android JNI层调用MindSpore C++ API时，需要相关库文件支持。可通
 
 本示例中，build过程由download.gradle文件自动下载MindSpore Lite 版本文件，并放置在`app/src/main/cpp/`目录下。
 
-* 注：若自动下载失败，请手动下载相关库文件，解压并放在对应位置：
+> 若自动下载失败，请手动下载相关库文件，解压并放在对应位置：
 
   mindspore-lite-1.0.1-runtime-arm64-cpu.tar.gz [下载链接](https://ms-release.obs.cn-north-4.myhuaweicloud.com/1.0.1/lite/android_aarch64/mindspore-lite-1.0.1-runtime-arm64-cpu.tar.gz)
 
 在app的`build.gradle`文件中配置CMake编译支持，以及`arm64-v8a`的编译支持，如下所示：
 
-```
+```text
 android{
     defaultConfig{
         externalNativeBuild{
@@ -111,7 +118,7 @@ android{
             }
         }
 
-        ndk{ 
+        ndk{
             abiFilters 'arm64-v8a'
         }
     }
@@ -120,7 +127,7 @@ android{
 
 在`app/CMakeLists.txt`文件中建立`.so`库文件链接，如下所示。
 
-```
+```text
 # ============== 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)
@@ -138,7 +145,7 @@ 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.       
+# Link target library.
 target_link_libraries(
     ...
      # --- mindspore ---
@@ -152,41 +159,43 @@ target_link_libraries(
 
 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)。
+> 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`。 
+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)
         {
@@ -196,11 +205,11 @@ target_link_libraries(
             int ret = session->CompileGraph(model);
         }
         ```
-    
+
-2. 将输入图片转换为传入MindSpore模型的Tensor格式。 
+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
     if (!BitmapToLiteMat(env, srcBitmap, &lite_mat_bgr)) {
      MS_PRINT("BitmapToLiteMat error");
@@ -243,8 +252,8 @@ target_link_libraries(
     memcpy(inTensor->MutableData(), dataHWC,
          inputDims.channel * inputDims.width * inputDims.height * sizeof(float));
     ```
-    
+
-3. 对输入Tensor按照模型进行推理，获取输出Tensor，并进行后处理。    
+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。
 
@@ -254,6 +263,7 @@ target_link_libraries(
         ```
 
    - 获取输出数据。
+
         ```cpp
         auto names = mSession->GetOutputTensorNames();
         std::unordered_map<std::string,mindspore::tensor::MSTensor *> msOutputs;
@@ -264,8 +274,9 @@ target_link_libraries(
          std::string resultStr = ProcessRunnetResult(::RET_CATEGORY_SUM,
                                               ::labels_name_map, msOutputs);
         ```
-        
+
    - 输出数据的后续处理。
+
         ```cpp
         std::string ProcessRunnetResult(const int RET_CATEGORY_SUM, const char *const labels_name_map[],
                  std::unordered_map<std::string, mindspore::tensor::MSTensor *> msOutputs) {
@@ -318,5 +329,5 @@ target_link_libraries(
          }
            return categoryScore;
         }
-        
+
         ```