# 图像预测库的使用 1. 下载源码(https://github.com/PaddlePaddle/Paddle-Lite),打开LITE_WITH_CV=ON,编译full_publish模式 example: ```shell set BUILD_WITH_CV=ON or LITE_WITH_CV=ON ./lite/tools/build.sh --arm_os=android --arm_abi=armv8 --arm_lang=gcc --android_stl=c++_static full_publish ``` 2. 准备模型和优化模型 example: ```shell wget http://paddle-inference-dist.bj.bcebos.com/mobilenet_v1.tar.gz tar zxvf mobilenet_v1.tar.gz ./lite/tools/build.sh build_optimize_tool ./build.model_optimize_tool/lite/api/model_optimize_tool --optimize_out_type=naive_buffer --optimize_out=model_dir --model_dir=model_dir --prefer_int8_kernel=false ``` 3. 编译并运行完整test_model_cv demo example: ```shell cd inference_lite_lib.android.armv8/demo/cxx/test_cv ``` - 修改MakeFile, 注释编译test_img_propress 语句 ```shell test_model_cv: fetch_opencv test_model_cv.o $(CC) $(SYSROOT_LINK) $(CXXFLAGS_LINK) test_model_cv.o -o test_model_cv $(CXX_LIBS) $(LDFLAGS) test_model_cv.o: test_model_cv.cc $(CC) $(SYSROOT_COMPLILE) $(CXX_DEFINES) $(CXX_INCLUDES) $(CXX_FLAGS) -o test_model_cv.o -c test_model_cv.cc #test_img_propress: fetch_opencv test_img_propress.o # $(CC) $(SYSROOT_LINK) $(CXXFLAGS_LINK) test_img_propress.o -o test_img_propress $(CXX_LIBS) $(LDFLAGS) #test_img_propress.o: test_img_propress.cc # $(CC) $(SYSROOT_COMPLILE) $(CXX_DEFINES) $(CXX_INCLUDES) $(CXX_FLAGS) -o test_img_propress.o -c test_img_propress.cc .PHONY: clean clean: rm -f test_model_cv.o rm -f test_model_cv #rm -f test_img_propress.o #rm -f test_img_propress ``` - 修改../../..//cxx/include/paddle_image_preprocess.h, 修改paddle_api.h头文件的路径 ```shell origin: #include "lite/api/paddle_api.h" #include "lite/api/paddle_place.h" now: #include "paddle_api.h" #include "paddle_place.h" ``` - 测试模型必须是优化后的模型 ```shell make adb -s device_id push mobilenet_v1 /data/local/tmp/ adb -s device_id push test_model_cv /data/local/tmp/ adb -s device_id push test.jpg /data/local/tmp/ adb -s device_id push ../../../cxx/lib/libpaddle_full_api_shared.so /data/local/tmp/ adb -s device_id shell chmod +x /data/local/tmp/test_model_cv adb -s device_id shell "export LD_LIBRARY_PATH=/data/local/tmp/:$LD_LIBRARY_PATH && /data/local/tmp/test_model_cv /data/local/tmp/mobilenet_v1 /data/local/tmp/test.jpg 1 3 224 224 " ``` 运行成功将在控制台输出部分预测结果 4. 编译并运行完整test_img_preprocess demo example: ```shell cd inference_lite_lib.android.armv8/demo/cxx/test_cv ``` - 修改MakeFile, 注释编译test_model_cv 语句 ```shell #test_model_cv: fetch_opencv test_model_cv.o # $(CC) $(SYSROOT_LINK) $(CXXFLAGS_LINK) test_model_cv.o -o test_model_cv $(CXX_LIBS) $(LDFLAGS) #test_model_cv.o: test_model_cv.cc # $(CC) $(SYSROOT_COMPLILE) $(CXX_DEFINES) $(CXX_INCLUDES) $(CXX_FLAGS) -o test_model_cv.o -c test_model_cv.cc test_img_propress: fetch_opencv test_img_propress.o $(CC) $(SYSROOT_LINK) $(CXXFLAGS_LINK) test_img_propress.o -o test_img_propress $(CXX_LIBS) $(LDFLAGS) test_img_propress.o: test_img_propress.cc $(CC) $(SYSROOT_COMPLILE) $(CXX_DEFINES) $(CXX_INCLUDES) $(CXX_FLAGS) -o test_img_propress.o -c test_img_propress.cc .PHONY: clean clean: #rm -f test_model_cv.o #rm -f test_model_cv rm -f test_img_propress.o rm -f test_img_propress ``` - 修改../../..//cxx/include/paddle_image_preprocess.h, 修改paddle_api.h头文件的路径 ```shell origin: #include "lite/api/paddle_api.h" #include "lite/api/paddle_place.h" now: #include "paddle_api.h" #include "paddle_place.h" ``` - 测试模型必须是优化后的模型 ```shell make adb -s device_id push mobilenet_v1 /data/local/tmp/ adb -s device_id push test_img_propress /data/local/tmp/ adb -s device_id push test.jpg /data/local/tmp/ adb -s device_id push ../../../cxx/lib/libpaddle_full_api_shared.so /data/local/tmp/ adb -s device_id shell chmod +x /data/local/tmp/test_model_cv adb -s device_id shell "export LD_LIBRARY_PATH=/data/local/tmp/:$LD_LIBRARY_PATH && /data/local/tmp/test_img_propress /data/local/tmp/test.jpg /data/local/tmp/ 3 3 1 3 224 224 /data/local/tmp/mobilenet_v1 " adb -s device_id pull /data/local/tmp/resize.jpg ./ adb -s device_id pull /data/local/tmp/convert.jpg ./ adb -s device_id pull /data/local/tmp/flip.jpg ./ adb -s device_id pull /data/local/tmp/rotate.jpg ./ ``` 运行成功将在控制台输出OpenCV 和 Padlle-lite的耗时;同时,将在test_cv目录下看到生成的图像预处理结果图: 如:resize.jpg、convert.jpg等