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

由于需要把FasterRCNN做的工程化，因此這里需要對Caffe進行封裝。其實封裝聽起來感覺很高深的樣子，其實就是將自己在caffe上再調用的接口做成一個動態庫，同時將Caffe的庫連著Caffe的那些庫依賴一起做成自己工程的庫依賴就可以了。如果你只是直接使用Caffe的話，那么到時候直接鏈接到caffe下面build目錄中的libcaffe.so或者libcaffe.a就可以了。如果有對Caffe有C++代碼的改動，操作也是一樣的，但是如果用了Python 模塊比如使用了Python Layer，那么在使用中，還需要為Caffe指定其Python模塊的位置。

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

好了，首先在這里我參照http://blog.csdn.net/xyy19920105/article/details/50440957，感謝他的分享，讓我們很受用。首先寫成一個C++版本。一開始的版本是這樣的，當時只有一個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> using namespace caffe; using namespace std;#define max(a, b) (((a)>(b)) ? (a) :(b)) #define min(a, b) (((a)<(b)) ? (a) :(b)) 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 Detection(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(){} };/* * === 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); }//Using for box sort struct Info {float score;const float* head; }; bool compare(const Info& Info1, const Info& Info2) {return Info1.score > Info2.score; }/* * === 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::Detection(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);vis_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];}}}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.Detection("/home/lyh1/workspace/py-faster-rcnn/data/demo/car.jpg"); return 0; }

這個文件對應的CMakeLists.txt，其中對編譯部分加了相應的注釋。此處需要添加$PYTHONPATH，原因是因為我們在使用中是C++接口調用的Caffe，但是在Caffe運行中調用了Python，因此需要告訴Caffe我們自己的Python模塊的路徑，比如這里去模型的定義文件中看，里面用了py-faster-rcnn根目錄下的lib中的rpn文件夾下的proposal模塊，因此需要在$PYTHONPATH中加入這個模塊路徑...\py-faster-rcnn\lib以及Caffe的Python接口路徑...\py-faster-rcnn\caffe-fast-rcnn\python，下面是我在~.bashrc中添加$PYTHONPATH，可供參考。添加之后執行source ~/.bashrc，對bash立即生效，提醒一下.bashrc是在你啟動一個bash的時候會被立即執行的。要么你直接在當前bash中直接source，否則需要再打開一個新的bash。還有一點，順帶提一下，加入到$PYTHONPATH之后，如果你系統中有多個caffe，那么你得想一下怎么防止沖突，當時我就給自己埋了個坑，別的caffe調用PYTHON接口時，就直接調到這個加入到$PYTHONPATH中的這個caffe了，所以各種報錯，如果可以話可以考慮用Docker來處理這種東西。

如圖添加

還是接下來講一下這個CMakeList.txt。我也是第一次用，之前用Autotools，真是各種麻煩，對Linux下的手動編譯都有一些陰影。用Cmake真的是比較傻瓜式的，能把東西梳理的比較清晰。

#This part is used for compile faster_rcnn_demo.cpp #這里是版本要求，根據自己項目而定，我用了默認的 cmake_minimum_required (VERSION 2.8) #我們的工程的名字 project (faster_rcnn_demo)#添加我們要生成的可執行文件的名字，以及相應的源碼文件 add_executable(faster_rcnn_demo faster_rcnn_demo.cpp) #這里添加這個faster_rcnn_demo.cpp所依賴的頭文件路徑 #首先是Caffe目錄的include #其次是用了gpu_nms.cu，所以也要添加相應的頭文件gpu_nms.hpp在py-faster-rcnn根目錄下的lib/nms中 #下面就是幾個Caffe的依賴項，包括Python #值得注意的是boost/python。hpp的頭文件路徑也要加入 #還有opencv的路徑，cuda路徑，線性代數庫路徑相應的都要添加 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 ) #在這里值得說一下，target_link_libraries 語句中 生成的目標是可執行文件 后面緊跟的得是動態庫的完整路徑，否則會出錯 #我一開始用的是Link_directoreis然后在后面直接加入了動態庫的路徑，結果他一直報錯，提示找不到庫，ORZ真是跪了， #所以要么在這里直接加入完整路徑或者同通過另一條語句find_library()，這種方式也比較好，直接去指定路徑查找，返回相應的絕對路徑也可避免直接添加地址的問題 #gpu_nms.so 在py-faster-rcnn根目錄下的lib\nms中，直接make就會生成這個so文件 target_link_libraries(faster_rcnn_demo /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)

編譯的時候比較坑爹，還會到這個問題

但是我用find命令查找了一下整個caffe的工程目錄下居然沒有這個caffe.pb.h 后來Google一下之后才知道需要手動生成，解決辦法如下用protoc命令手動生成，并放到include文件夾下

protoc src/caffe/proto/caffe.proto --cpp_out=. mkdir include/caffe/proto mv src/caffe/proto/caffe.pb.h include/caffe/proto

最后cmake .然后make編譯成功，運行正常。
接下來就是這么打包成動態庫了，具體操作在下一個博文中

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