README.md

# C++ Demo

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1. 环境准备
   - 一台可以编译PaddleLite的电脑
   - 一台armv7或armv8架构的安卓手机

2. 人脸识别和佩戴口罩判断的Demo

参考[源码编译](https://paddlepaddle.github.io/Paddle-Lite/v2.2.0/source_compile/)准备编译环境。

执行下面命令，下载PaddleLite代码。
```shell
git clone https://github.com/PaddlePaddle/Paddle-Lite.git
cd Paddle-Lite
```

进入PaddleLite根目录，编译预测库。
```shell
./lite/tools/build.sh \
    --arm_os=android \
    --arm_abi=armv8 \
    --arm_lang=gcc \
    --android_stl=c++_static \
    --build_extra=ON \
    --shutdown_log=OFF \
    tiny_publish
```

进入编译目录，下载模型和图片的压缩包，编译可执行文件。
```shell
cd build.lite.android.armv8.gcc/inference_lite_lib.android.armv8/demo/cxx/mask_detection
wget https://paddle-inference-dist.bj.bcebos.com/mask_detection.tar.gz
tar zxvf mask_detection.tar.gz
make
```

当然，大家也可以通过PaddleHub下载人脸检测模型和口罩佩戴判断模型。
```
# 下载paddlehub以后，通过python执行以下代码
import paddlehub as hub
pyramidbox_lite_mask = hub.Module(name="pyramidbox_lite_mask")
# 将模型保存在test_program文件夹之中
pyramidbox_lite_mask.processor.save_inference_model(dirname="test_program") 
通过以上命令，可以获得人脸检测和口罩佩戴判断模型，分别存储在pyramidbox_lite和mask_detector之中。文件夹中的__model__是模型结构文件，__param__文件是权重文件。
```

电脑连接安卓手机，将可执行文件、测试图片、模型文件、预测库push到安卓手机上。
```
adb push mask_detection /data/local/tmp/
adb push test.jpg /data/local/tmp/
adb push face_detection /data/local/tmp
adb push mask_classification /data/local/tmp
adb push ../../../cxx/lib/libpaddle_light_api_shared.so /data/local/tmp/
adb shell chmod +x /data/local/tmp/mask_detection
```

进入安卓手机，执行demo。
```
adb shell
cd /data/local/tmp
export LD_LIBRARY_PATH=/data/local/tmp/:$LD_LIBRARY_PATH 
./mask_detection face_detection mask_classification test.jpg
```

回到电脑端，将结果取出，查看如下效果图。
```
adb pull /data/local/tmp/test_mask_detection_result.jpg ./
```

![test_mask_detection_result](https://user-images.githubusercontent.com/7383104/74279176-6200cd00-4d55-11ea-9fc0-83cfc2b3b37d.jpg)

3. 编译并运行全量api的demo(注：当编译模式为tiny_pubish时将不存在该demo)
```shell
cd inference_lite_lib.android.armv8/demo/cxx/mobile_full
wget http://paddle-inference-dist.bj.bcebos.com/mobilenet_v1.tar.gz
tar zxvf mobilenet_v1.tar.gz
make
adb push mobilenet_v1 /data/local/tmp/
adb push mobilenetv1_full_api /data/local/tmp/
adb push ../../../cxx/lib/libpaddle_full_api_shared.so /data/local/tmp/
adb shell chmod +x /data/local/tmp/mobilenetv1_full_api
adb shell "export LD_LIBRARY_PATH=/data/local/tmp/:$LD_LIBRARY_PATH && 
/data/local/tmp/mobilenetv1_full_api --model_dir=/data/local/tmp/mobilenet_v1 --optimized_model_dir=/data/local/tmp/mobilenet_v1.opt"
```
运行成功将在控制台输出预测结果的前10个类别的预测概率

4. 编译并运行轻量级api的demo
```shell
cd ../mobile_light
make
adb push mobilenetv1_light_api /data/local/tmp/
adb push ../../../cxx/lib/libpaddle_light_api_shared.so /data/local/tmp/
adb shell chmod +x /data/local/tmp/mobilenetv1_light_api
adb shell "export LD_LIBRARY_PATH=/data/local/tmp/:$LD_LIBRARY_PATH && 
/data/local/tmp/mobilenetv1_light_api /data/local/tmp/mobilenet_v1.opt"
```
运行成功将在控制台输出预测结果的前10个类别的预测概率

5. 编译并运行ssd目标检测的demo
```shell
cd ../ssd_detection
wget https://paddle-inference-dist.bj.bcebos.com/mobilenetv1-ssd.tar.gz
tar zxvf mobilenetv1-ssd.tar.gz
make
adb push ssd_detection /data/local/tmp/
adb push test.jpg /data/local/tmp/
adb push mobilenetv1-ssd /data/local/tmp
adb push ../../../cxx/lib/libpaddle_light_api_shared.so /data/local/tmp/
adb shell chmod +x /data/local/tmp/ssd_detection
adb shell "export LD_LIBRARY_PATH=/data/local/tmp/:$LD_LIBRARY_PATH && 
/data/local/tmp/ssd_detection /data/local/tmp/mobilenetv1-ssd /data/local/tmp/test.jpg"
adb pull /data/local/tmp/test_ssd_detection_result.jpg ./
```
运行成功将在ssd_detection目录下看到生成的目标检测结果图像: test_ssd_detection_result.jpg

6. 编译并运行yolov3目标检测的demo
```shell
cd ../yolov3_detection
wget https://paddle-inference-dist.bj.bcebos.com/mobilenetv1-yolov3.tar.gz
tar zxvf mobilenetv1-yolov3.tar.gz
make
adb push yolov3_detection /data/local/tmp/
adb push test.jpg /data/local/tmp/
adb push mobilenetv1-yolov3 /data/local/tmp
adb push ../../../cxx/lib/libpaddle_light_api_shared.so /data/local/tmp/
adb shell chmod +x /data/local/tmp/yolov3_detection
adb shell "export LD_LIBRARY_PATH=/data/local/tmp/:$LD_LIBRARY_PATH && 
/data/local/tmp/yolov3_detection /data/local/tmp/mobilenetv1-yolov3 /data/local/tmp/test.jpg"
adb pull /data/local/tmp/test_yolov3_detection_result.jpg ./
```
运行成功将在yolov3_detection目录下看到生成的目标检测结果图像: test_yolov3_detection_result.jpg

7. 编译并运行物体分类的demo
```shell
cd ../mobile_classify
wget http://paddle-inference-dist.bj.bcebos.com/mobilenet_v1.tar.gz
tar zxvf mobilenet_v1.tar.gz
./model_optimize_tool optimize model
make

adb push mobile_classify /data/local/tmp/
adb push test.jpg /data/local/tmp/
adb push labels.txt /data/local/tmp/
adb push ../../../cxx/lib/libpaddle_light_api_shared.so /data/local/tmp/
adb shell chmod +x /data/local/tmp/mobile_classify
adb shell "export LD_LIBRARY_PATH=/data/local/tmp/:$LD_LIBRARY_PATH && 
/data/local/tmp/mobile_classify /data/local/tmp/mobilenetv1opt2 /data/local/tmp/test.jpg /data/local/tmp/labels.txt"
```
运行成功将在控制台输出预测结果的前5个类别的预测概率
- 如若想看前10个类别的预测概率，在运行命令输入topk的值即可
    eg:
    ```shell
    adb shell "export LD_LIBRARY_PATH=/data/local/tmp/:$LD_LIBRARY_PATH && 
    /data/local/tmp/mobile_classify /data/local/tmp/mobilenetv1opt2/ /data/local/tmp/test.jpg /data/local/tmp/labels.txt 10"
    ```
- 如若想看其他模型的分类结果， 在运行命令输入model_dir 及其model的输入大小即可
    eg:
    ```shell
    adb shell "export LD_LIBRARY_PATH=/data/local/tmp/:$LD_LIBRARY_PATH && 
    /data/local/tmp/mobile_classify /data/local/tmp/mobilenetv2opt2/ /data/local/tmp/test.jpg /data/local/tmp/labels.txt 10 224 224"
    ```
    
8. 编译含CV预处理库模型单测demo 
```shell
cd ../test_cv
wget http://paddle-inference-dist.bj.bcebos.com/mobilenet_v1.tar.gz
tar zxvf mobilenet_v1.tar.gz
./model_optimize_tool optimize model
make
adb push test_model_cv /data/local/tmp/
adb push test.jpg /data/local/tmp/
adb push labels.txt /data/local/tmp/
adb push ../../../cxx/lib/libpaddle_full_api_shared.so /data/local/tmp/
adb shell chmod +x /data/local/tmp/test_model_cv
adb shell "export LD_LIBRARY_PATH=/data/local/tmp/:$LD_LIBRARY_PATH && 
/data/local/tmp/test_model_cv /data/local/tmp/mobilenetv1opt2 /data/local/tmp/test.jpg /data/local/tmp/labels.txt"
```
运行成功将在控制台输出预测结果的前10个类别的预测概率