https://www.cnblogs.com/louyihang-loves-baiyan/archive/2016/05/14/5493344.html
在上一篇文章中，我们是将对caffe的调用隔离了出来，可以说相当于原来caffe源码下的tools中cpp文件使用相同，然后自己写了个CMakeLists.txt进行编译。这里是进一步将代码进行分离，封装成libfaster_rcnn.so文件进行使用。对于部分接口，我可能做了一些改动。
目录结构
├── CMakeLists.txt
├── lib
│ ├── CMakeLists.txt
│ ├── faster_rcnn.cpp
│ ├── faster_rcnn.hpp
├── main.cpp
├── pbs_cxx_faster_rcnn_demo.job
在这里main.cpp就是直接调用faster_rcnn.cpp的接口，他的内容也很简单,只是在之前的基础上，再加上libfaster_rcnn.so这个动态库文件
#include "faster_rcnn.hpp"
int main()
{
string model_file = "/home/lyh1/workspace/py-faster-rcnn/models/pascal_voc/VGG_CNN_M_1024/faster_rcnn_alt_opt/faster_rcnn_test.pt";
string weights_file = "/home/lyh1/workspace/py-faster-rcnn/output/default/yuanzhang_car/vgg_cnn_m_1024_fast_rcnn_stage2_iter_40000.caffemodel";
int GPUID=0;
Caffe::SetDevice(GPUID);
Caffe::set_mode(Caffe::GPU);
Detector det = Detector(model_file, weights_file);
det.Detect("/home/lyh1/workspace/py-faster-rcnn/data/demo/car.jpg");
return 0;
}
可以看到这里只是include了faster_rcnn.hpp头文件，其对应的CMakeLists.txt文件如下：
#This part is used for compile faster_rcnn_demo.cpp
cmake_minimum_required (VERSION 2.8)
project (main_demo)
add_executable(main main.cpp)
include_directories ( "${PROJECT_SOURCE_DIR}/../caffe-fast-rcnn/include"
"${PROJECT_SOURCE_DIR}/../lib/nms"
"${PROJECT_SOURCE_DIR}/lib"
/share/apps/local/include
/usr/local/include
/opt/python/include/python2.7
/share/apps/opt/intel/mkl/include
/usr/local/cuda/include )
target_link_libraries(main /home/lyh1/workspace/py-faster-rcnn/faster_cxx_lib/lib/libfaster_rcnn.so
/home/lyh1/workspace/py-faster-rcnn/caffe-fast-rcnn/build/lib/libcaffe.so
/home/lyh1/workspace/py-faster-rcnn/lib/nms/gpu_nms.so
/share/apps/local/lib/libopencv_highgui.so
/share/apps/local/lib/libopencv_core.so
/share/apps/local/lib/libopencv_imgproc.so
/share/apps/local/lib/libopencv_imgcodecs.so
/share/apps/local/lib/libglog.so
/share/apps/local/lib/libboost_system.so
/share/apps/local/lib/libboost_python.so
/share/apps/local/lib/libglog.so
/opt/rh/python27/root/usr/lib64/libpython2.7.so
)
对于faster_rcnn.hpp和faster_rcnn.cpp ，我们需要将他们编译成动态库，下面是他们对应的CMakeLists.txt，在文件中，可以看到跟上面这个区别是用了add_library语句，并且加入了SHARED关键字，SHARED代表动态库。其次，在编译动态库的过程中，是不需要链接的，但是我们知道这个库是依赖别的很多个库的，所以在最后形成可执行文件也就是上面这个CMakeLists.txt，我们需要添加这个动态库所依赖的那些动态库，至此就OK了。编译的话，非常傻瓜cmake .然后在执行make即可。
cmake_minimum_required (VERSION 2.8)
SET (SRC_LIST faster_rcnn.cpp)
include_directories ( "${PROJECT_SOURCE_DIR}/../../caffe-fast-rcnn/include"
"${PROJECT_SOURCE_DIR}/../../lib/nms"
/share/apps/local/include
/usr/local/include
/opt/python/include/python2.7
/share/apps/opt/intel/mkl/include
/usr/local/cuda/include )
add_library(faster_rcnn SHARED ${SRC_LIST})
首先将原来的cpp文件中的声明提取出来，比较简单，就是hpp文件对应cpp文件。如下：