#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

#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];}}
}