【行人检测】检测图片中的行人

 // Hog_SVM_Pedestrian.cpp: 定义控制台应用程序的入口点。

    
 //图片中的行人检测
    
  
    
 #include "stdafx.h"
    
 #include<opencv2/core/core.hpp>
    
 #include<opencv2/highgui/highgui.hpp>
    
 #include<opencv2/imgproc/imgproc.hpp>
    
 #include<opencv2/objdetect.hpp> // include hog
    
 #include<iostream>
    
  
    
 using namespace std;
    
 using namespace cv;
    
  
    
  
    
  
    
 void detectAndDraw(HOGDescriptor &hog,Mat &img)
    
 {
    
 	vector<Rect> found, found_filtered;
    
 	double t = (double)getTickCount();
    
 	
    
 	hog.detectMultiScale(img, found, 0, Size(8, 8), Size(32, 32), 1.05, 2);//多尺度检测目标,返回的矩形从大到小排列
    
 	t = (double)getTickCount() - t;
    
 	cout << "detection time = " << (t*1000. / cv::getTickFrequency()) << " ms" << endl;
    
 	cout << "detection result = " << found.size() << " Rects" << endl;
    
  
    
 	for (size_t i = 0; i < found.size(); i++)
    
 	{
    
 		Rect r = found[i];
    
  
    
 		size_t j;
    
 		// Do not add small detections inside a bigger detection. 如果有嵌套的话,则取外面最大的那个矩形框放入found_filtered中
    
 		for (j = 0; j < found.size(); j++)
    
 			if (j != i && (r & found[j]) == r)
    
 				break;
    
  
    
 		if (j == found.size())
    
 			found_filtered.push_back(r);
    
 	}
    
  
    
 	cout << "Real detection result = " << found_filtered.size() << " Rects" << endl;
    
 	for (size_t i = 0; i < found_filtered.size(); i++)
    
 	{
    
 		Rect r = found_filtered[i];
    
  
    
 		// The HOG detector returns slightly larger rectangles than the real objects,
    
 		// hog检测结果返回的矩形比实际的要大一些
    
 		// so we slightly shrink the rectangles to get a nicer output.
    
 		// r.x += cvRound(r.width*0.1);
    
 		// r.width = cvRound(r.width*0.8);
    
 		// r.y += cvRound(r.height*0.07);
    
 		// r.height = cvRound(r.height*0.8);
    
 		rectangle(img, r.tl(), r.br(), cv::Scalar(0, 255, 0), 3);
    
 	}
    
  
    
 }
    
  
    
  
    
  
    
 int main()
    
 {
    
 	Mat img = imread("pedestrian.jpg");
    
 	HOGDescriptor hog;
    
 	hog.setSVMDetector(HOGDescriptor::getDefaultPeopleDetector() ); //getDefaultPeopleDetector(): 
    
 							//Returns coefficients of the classifier trained for people detection (for 64x128 windows).								//Returns coefficients of the classifier trained for people detection (for 64x128 windows).
    
 	detectAndDraw(hog, img);
    
  
    
  
    
 	namedWindow("frame");
    
 	imshow("frame", img);
    
 	while( waitKey(10) != 27) ;
    
 	destroyWindow("show");
    
  
    
     return 0;
    
 }
    
    
    
    

 /**@brief Sets coefficients for the linear SVM classifier.设置线性SVM分类器的系数

    
     @param _svmdetector coefficients for the linear SVM classifier.
    
     */
    
     CV_WRAP virtual void setSVMDetector(InputArray _svmdetector);
    
    
    
    

  /** @brief Returns coefficients of the classifier trained for people detection (for 64x128 windows).

    
 	返回 已训练好的用于行人检测 的分类器的系数
    
     */
    
     CV_WRAP static std::vector<float> getDefaultPeopleDetector();
    
    
    
    

  /** @brief Detects objects of different sizes in the input image. The detected objects are returned as a list

    
     of rectangles.多尺度检测目标，检测到的目标以矩形list返回
    
     @param img： Matrix of the type CV_8U（单通道） or CV_8UC3（三通道） containing an image where objects are detected.
    
     @param foundLocations ：Vector of rectangles where each rectangle contains the detected object.
    
     @param hitThreshold（击中率）： Threshold for the distance between features and SVM classifying plane.
    
     Usually it is 0 and should be specfied in the detector coefficients (as the last free coefficient).
    
     But if the free coefficient is omitted (which is allowed), you can specify it manually here.
    
     @param winStride(窗口滑动步长 = cell大小)： Window stride. It must be a multiple of block stride.
    
     @param padding（填充）： Padding
    
     @param scale（检测窗口增大的系数）： Coefficient of the detection window increase.
    
     @param finalThreshold（最终的阈值）： Final threshold
    
     @param useMeanshiftGrouping(使用平均移位分组): indicates grouping algorithm
    
     */
    
     virtual void detectMultiScale(InputArray img, CV_OUT std::vector<Rect>& foundLocations,
    
                               double hitThreshold = 0, Size winStride = Size(),
    
                               Size padding = Size(), double scale = 1.05,
    
                               double finalThreshold = 2.0, bool useMeanshiftGrouping = false) const;