!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

@@ -1,8 +1,7 @@
-# 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.)
@@ -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
@@ -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{
@@ -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)
@@ -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.
 
@@ -146,7 +154,7 @@ The inference code process is as follows. For details about the complete code, s
 
 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
@@ -154,7 +162,7 @@ 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 *;
@@ -171,7 +179,7 @@ The inference code process is as follows. For details about the complete code, s
 
      ```
 
-   - 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)
@@ -232,7 +240,7 @@ The inference code process is as follows. For details about the complete code, s
 
 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.

model_zoo/official/lite/image_classification/README.md

@@ -2,7 +2,6 @@
 
 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)
@@ -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
@@ -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{
@@ -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)
@@ -152,8 +159,7 @@ 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
 
@@ -164,6 +170,7 @@ target_link_libraries(
 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);
@@ -171,6 +178,7 @@ target_link_libraries(
         ```
 
     - 创建会话
+
         ```cpp
         void **labelEnv = new void *;
         MSNetWork *labelNet = new MSNetWork;
@@ -187,6 +195,7 @@ target_link_libraries(
         ```
 
     - 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)
         {
@@ -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;
@@ -266,6 +276,7 @@ target_link_libraries(
         ```
 
    - 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) {

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

@@ -20,9 +20,7 @@ The following section describes how to build and execute an on-device object det
 
    ![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.
+    If you have any Android Studio configuration problem when trying this demo, please refer to item 5 to resolve it.
-
-   ![start_sdk](images/sdk_management.png)
 
 2. Connect to an Android device and runs the object detection application.
 
@@ -36,6 +34,16 @@ The following section describes how to build and execute an on-device object det
 
    ![result](images/object_detection.png)
 
+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  
 
@@ -51,7 +59,7 @@ 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{
@@ -69,7 +77,7 @@ android{
 
 Create a link to the `.so` library file in the `app/CMakeLists.txt` file:
 
-```
+```text
 # Set MindSpore Lite Dependencies.
 set(MINDSPORELITE_VERSION  mindspore-lite-1.0.1-runtime-arm64-cpu)
 include_directories(${CMAKE_SOURCE_DIR}/src/main/cpp/${MINDSPORELITE_VERSION})
@@ -91,14 +99,12 @@ target_link_libraries(
 
 ### Downloading and Deploying a Model File
 
-In this example, the  download.gradle File configuration auto download `ssd.ms `and placed in the 'app / libs / arm64-v8a' directory.
+In this example, the  download.gradle File configuration auto download `ssd.ms`and placed in the 'app / libs / arm64-v8a' directory.
 
 Note: if the automatic download fails, please manually download the relevant library files and put them in the corresponding location.
 
 ssd.ms [ssd.ms]( https://download.mindspore.cn/model_zoo/official/lite/ssd_mobilenetv2_lite/ssd.ms)
 
-
-
 ### Compiling On-Device Inference Code
 
 Call MindSpore Lite C++ APIs at the JNI layer to implement on-device inference.
@@ -107,7 +113,7 @@ The inference code process is as follows. For details about the complete code, s
 
 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
@@ -115,7 +121,7 @@ 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 *;
@@ -134,7 +140,7 @@ The inference code process is as follows. For details about the complete code, s
 
      ```
 
-   - 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)
@@ -546,4 +552,3 @@ The inference code process is as follows. For details about the complete code, s
         return result;
     }
     ```
-

model_zoo/official/lite/object_detection/README.md

@@ -2,7 +2,6 @@
 
 本示例程序演示了如何在端侧利用MindSpore Lite C++ API（Android JNI）以及MindSpore Lite 目标检测模型完成端侧推理，实现对图库或者设备摄像头捕获的内容进行检测，并在App图像预览界面中显示连续目标检测结果。
 
-
 ### 运行依赖
 
 - Android Studio >= 3.2 (推荐4.0以上版本)
@@ -20,14 +19,12 @@
 
     ![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，运行目标检测示例应用程序。
 
     通过USB连接Android设备调试，点击`Run 'app'`即可在你的设备上运行本示例项目。
-    * 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)
 
@@ -41,6 +38,16 @@
 
     ![result](images/object_detection.png)
 
+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  
 
@@ -50,7 +57,7 @@
 
 ### 示例程序结构
 
-```
+```text
 app
 |
 ├── libs # 存放demo jni层编译出的库文件
@@ -95,13 +102,13 @@ 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{
@@ -119,7 +126,7 @@ android{
 
 在`app/CMakeLists.txt`文件中建立`.so`库文件链接，如下所示。
 
-```
+```text
 # Set MindSpore Lite Dependencies.
 set(MINDSPORELITE_VERSION  mindspore-lite-1.0.1-runtime-arm64-cpu)
 include_directories(${CMAKE_SOURCE_DIR}/src/main/cpp/${MINDSPORELITE_VERSION})
@@ -143,8 +150,7 @@ target_link_libraries(
 
 从MindSpore Model Hub中下载模型文件，本示例程序中使用的目标检测模型文件为`ssd.ms`，同样通过`download.gradle`脚本在APP构建时自动下载，并放置在`app/src/main/assets`工程目录下。
 
-* 注：若下载失败请手动下载模型文件，ssd.ms [下载链接](https://download.mindspore.cn/model_zoo/official/lite/ssd_mobilenetv2_lite/ssd.ms)。
+> 若下载失败请手动下载模型文件，ssd.ms [下载链接](https://download.mindspore.cn/model_zoo/official/lite/ssd_mobilenetv2_lite/ssd.ms)。
-
 
 ### 编写端侧推理代码
 
@@ -155,6 +161,7 @@ target_link_libraries(
 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);
@@ -162,6 +169,7 @@ target_link_libraries(
         ```
 
     - 创建会话
+
         ```cpp
         void **labelEnv = new void *;
         MSNetWork *labelNet = new MSNetWork;
@@ -180,6 +188,7 @@ target_link_libraries(
         ```
 
     - 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)
         {
@@ -273,6 +282,7 @@ target_link_libraries(
         ```
 
    - 获取输出数据。
+
         ```cpp
         auto names = mSession->GetOutputTensorNames();
             typedef std::unordered_map<std::string,
@@ -374,7 +384,7 @@ target_link_libraries(
         }
         ```
 
-   -  通过最大值抑制将目标类型置信度较高的输出筛选出来。
+   - 通过最大值抑制将目标类型置信度较高的输出筛选出来。
 
         ```cpp
         void SSDModelUtil::nonMaximumSuppression(const YXBoxes *const decoded_boxes,
@@ -496,5 +506,3 @@ target_link_libraries(
             return result;
         }
         ```
-
-​