摘要: 轉載請注明出處，樓燚(yì)航的blog，http://www.cnblogs.com/louyihang-loves-baiyan/

github上的代碼鏈接,求給星星:)?https://github.com/YihangLou/FasterRCNN-Encapsulation-Cplusplus

在上一篇文章中，我們是將對caffe的調用隔離了出來，可以說相當于原來caffe源碼下的tools中cpp文件使用相同，然后自己寫了個CMakeLists.txt進行編譯。這里是進一步將代碼進行分離，封裝成libfaster_rcnn.so文件進行使用。對于部分接口，我可能做了一些改動。
目錄結構
├── CMakeLists.txt
├── lib
│?? ├── CMakeLists.txt
│?? ├── faster_rcnn.cpp
│?? ├── faster_rcnn.hpp
├── main.cpp
├── pbs_cxx_faster_rcnn_demo.job

在這里main.cpp就是直接調用faster_rcnn.cpp的接口，他的內容也很簡單,只是在之前的基礎上，再加上libfaster_rcnn.so這個動態庫文件

#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; }

可以看到這里只是include了faster_rcnn.hpp頭文件，其對應的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)

對于faster_rcnn.hpp和faster_rcnn.cpp?，我們需要將他們編譯成動態庫，下面是他們對應的CMakeLists.txt，在文件中，可以看到跟上面這個區別是用了add_library語句，并且加入了SHARED關鍵字，SHARED代表動態庫。其次，在編譯動態庫的過程中，是不需要鏈接的，但是我們知道這個庫是依賴別的很多個庫的，所以在最后形成可執行文件也就是上面這個CMakeLists.txt，我們需要添加這個動態庫所依賴的那些動態庫，至此就OK了。編譯的話，非常傻瓜cmake .然后在執行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})

首先將原來的cpp文件中的聲明提取出來，比較簡單，就是hpp文件對應cpp文件。如下：

#ifndef FASTER_RCNN_HPP #define FASTER_RCNN_HPP #include <stdio.h> // for snprintf #include <string> #include <vector> #include <math.h> #include <fstream> #include <boost/python.hpp> #include "caffe/caffe.hpp" #include "gpu_nms.hpp" #include <opencv2/core/core.hpp> #include <opencv2/highgui/highgui.hpp> #include <opencv2/imgproc/imgproc.hpp> using namespace caffe; using namespace std;#define max(a, b) (((a)>(b)) ? (a) :(b)) #define min(a, b) (((a)<(b)) ? (a) :(b))//background and car const int class_num=2;/* * === Class ====================================================================== * Name: Detector * Description: FasterRCNN CXX Detector * ===================================================================================== */ class Detector { public:Detector(const string& model_file, const string& weights_file);void Detect(const string& im_name);void bbox_transform_inv(const int num, const float* box_deltas, const float* pred_cls, float* boxes, float* pred, int img_height, int img_width);void vis_detections(cv::Mat image, int* keep, int num_out, float* sorted_pred_cls, float CONF_THRESH);void boxes_sort(int num, const float* pred, float* sorted_pred);private:shared_ptr<Net<float> > net_;Detector(){} };//Using for box sort struct Info {float score;const float* head; }; bool compare(const Info& Info1, const Info& Info2) {return Info1.score > Info2.score; } #endif

相應的cpp文件

#include <stdio.h> // for snprintf #include <string> #include <vector> #include <math.h> #include <fstream> #include <boost/python.hpp> #include "caffe/caffe.hpp" #include "gpu_nms.hpp" #include <opencv2/core/core.hpp> #include <opencv2/highgui/highgui.hpp> #include <opencv2/imgproc/imgproc.hpp> #include "faster_rcnn.hpp" using namespace caffe; using namespace std;/* * === FUNCTION ====================================================================== * Name: Detector * Description: Load the model file and weights file * ===================================================================================== */ //load modelfile and weights Detector::Detector(const string& model_file, const string& weights_file) {net_ = shared_ptr<Net<float> >(new Net<float>(model_file, caffe::TEST));net_->CopyTrainedLayersFrom(weights_file); }/* * === FUNCTION ====================================================================== * Name: Detect * Description: Perform detection operation * Warning the max input size should less than 1000*600 * ===================================================================================== */ //perform detection operation //input image max size 1000*600 void Detector::Detect(const string& im_name) {float CONF_THRESH = 0.8;float NMS_THRESH = 0.3;const int max_input_side=1000;const int min_input_side=600;cv::Mat cv_img = cv::imread(im_name);cv::Mat cv_new(cv_img.rows, cv_img.cols, CV_32FC3, cv::Scalar(0,0,0));if(cv_img.empty()){std::cout<<"Can not get the image file !"<<endl;return ;}int max_side = max(cv_img.rows, cv_img.cols);int min_side = min(cv_img.rows, cv_img.cols);float max_side_scale = float(max_side) / float(max_input_side);float min_side_scale = float(min_side) /float( min_input_side);float max_scale=max(max_side_scale, min_side_scale);float img_scale = 1;if(max_scale > 1){img_scale = float(1) / max_scale;}int height = int(cv_img.rows * img_scale);int width = int(cv_img.cols * img_scale);int num_out;cv::Mat cv_resized;std::cout<<"imagename "<<im_name<<endl;float im_info[3];float data_buf[height*width*3];float *boxes = NULL;float *pred = NULL;float *pred_per_class = NULL;float *sorted_pred_cls = NULL;int *keep = NULL;const float* bbox_delt;const float* rois;const float* pred_cls;int num;for (int h = 0; h < cv_img.rows; ++h ){for (int w = 0; w < cv_img.cols; ++w){cv_new.at<cv::Vec3f>(cv::Point(w, h))[0] = float(cv_img.at<cv::Vec3b>(cv::Point(w, h))[0])-float(102.9801);cv_new.at<cv::Vec3f>(cv::Point(w, h))[1] = float(cv_img.at<cv::Vec3b>(cv::Point(w, h))[1])-float(115.9465);cv_new.at<cv::Vec3f>(cv::Point(w, h))[2] = float(cv_img.at<cv::Vec3b>(cv::Point(w, h))[2])-float(122.7717);}}cv::resize(cv_new, cv_resized, cv::Size(width, height));im_info[0] = cv_resized.rows;im_info[1] = cv_resized.cols;im_info[2] = img_scale;for (int h = 0; h < height; ++h ){for (int w = 0; w < width; ++w){data_buf[(0*height+h)*width+w] = float(cv_resized.at<cv::Vec3f>(cv::Point(w, h))[0]);data_buf[(1*height+h)*width+w] = float(cv_resized.at<cv::Vec3f>(cv::Point(w, h))[1]);data_buf[(2*height+h)*width+w] = float(cv_resized.at<cv::Vec3f>(cv::Point(w, h))[2]);}}net_->blob_by_name("data")->Reshape(1, 3, height, width);net_->blob_by_name("data")->set_cpu_data(data_buf);net_->blob_by_name("im_info")->set_cpu_data(im_info);net_->ForwardFrom(0);bbox_delt = net_->blob_by_name("bbox_pred")->cpu_data();num = net_->blob_by_name("rois")->num();rois = net_->blob_by_name("rois")->cpu_data();pred_cls = net_->blob_by_name("cls_prob")->cpu_data();boxes = new float[num*4];pred = new float[num*5*class_num];pred_per_class = new float[num*5];sorted_pred_cls = new float[num*5];keep = new int[num];for (int n = 0; n < num; n++){for (int c = 0; c < 4; c++){boxes[n*4+c] = rois[n*5+c+1] / img_scale;}}bbox_transform_inv(num, bbox_delt, pred_cls, boxes, pred, cv_img.rows, cv_img.cols);for (int i = 1; i < class_num; i ++){for (int j = 0; j< num; j++){for (int k=0; k<5; k++)pred_per_class[j*5+k] = pred[(i*num+j)*5+k];}boxes_sort(num, pred_per_class, sorted_pred_cls);_nms(keep, &num_out, sorted_pred_cls, num, 5, NMS_THRESH, 0);//for visualize onlyvis_detections(cv_img, keep, num_out, sorted_pred_cls, CONF_THRESH);}cv::imwrite("vis.jpg",cv_img);delete []boxes;delete []pred;delete []pred_per_class;delete []keep;delete []sorted_pred_cls;}/* * === FUNCTION ====================================================================== * Name: vis_detections * Description: Visuallize the detection result * ===================================================================================== */ void Detector::vis_detections(cv::Mat image, int* keep, int num_out, float* sorted_pred_cls, float CONF_THRESH) {int i=0;while(sorted_pred_cls[keep[i]*5+4]>CONF_THRESH && i < num_out){if(i>=num_out)return;cv::rectangle(image,cv::Point(sorted_pred_cls[keep[i]*5+0], sorted_pred_cls[keep[i]*5+1]),cv::Point(sorted_pred_cls[keep[i]*5+2], sorted_pred_cls[keep[i]*5+3]),cv::Scalar(255,0,0));i++;} }/* * === FUNCTION ====================================================================== * Name: boxes_sort * Description: Sort the bounding box according score * ===================================================================================== */ void Detector::boxes_sort(const int num, const float* pred, float* sorted_pred) {vector<Info> my;Info tmp;for (int i = 0; i< num; i++){tmp.score = pred[i*5 + 4];tmp.head = pred + i*5;my.push_back(tmp);}std::sort(my.begin(), my.end(), compare);for (int i=0; i<num; i++){for (int j=0; j<5; j++)sorted_pred[i*5+j] = my[i].head[j];} }/* * === FUNCTION ====================================================================== * Name: bbox_transform_inv * Description: Compute bounding box regression value * ===================================================================================== */ void Detector::bbox_transform_inv(int num, const float* box_deltas, const float* pred_cls, float* boxes, float* pred, int img_height, int img_width) {float width, height, ctr_x, ctr_y, dx, dy, dw, dh, pred_ctr_x, pred_ctr_y, pred_w, pred_h;for(int i=0; i< num; i++){width = boxes[i*4+2] - boxes[i*4+0] + 1.0;height = boxes[i*4+3] - boxes[i*4+1] + 1.0;ctr_x = boxes[i*4+0] + 0.5 * width;ctr_y = boxes[i*4+1] + 0.5 * height;for (int j=0; j< class_num; j++){dx = box_deltas[(i*class_num+j)*4+0];dy = box_deltas[(i*class_num+j)*4+1];dw = box_deltas[(i*class_num+j)*4+2];dh = box_deltas[(i*class_num+j)*4+3];pred_ctr_x = ctr_x + width*dx;pred_ctr_y = ctr_y + height*dy;pred_w = width * exp(dw);pred_h = height * exp(dh);pred[(j*num+i)*5+0] = max(min(pred_ctr_x - 0.5* pred_w, img_width -1), 0);pred[(j*num+i)*5+1] = max(min(pred_ctr_y - 0.5* pred_h, img_height -1), 0);pred[(j*num+i)*5+2] = max(min(pred_ctr_x + 0.5* pred_w, img_width -1), 0);pred[(j*num+i)*5+3] = max(min(pred_ctr_y + 0.5* pred_h, img_height -1), 0);pred[(j*num+i)*5+4] = pred_cls[i*class_num+j];}} }

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