【行人检测】检测图片中的行人

    【行人检测】检测图片中的行人

    在Opencv3.4.0中自带行人检测（视频中的）的例子，在安装路径下的 

            ..\opencv3_4\opencv\sources\samples\cpp\peopledetect.cpp

    本节在其基础上稍加改动，便可运行。附录部分为程序中用到的几个关键函数的参数解析。

    【运行环境】VS2017+Opencv3.4.0+windows

    主要步骤：

    1.声明一个hog特征说明符（HOGDescriptor hog）

    2.设置SVM检测器

    3.进行多尺度检测

    4.将检测结果（矩形）画出来

    完整程序：

// 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
#include
#include
#include // include hog
#include

using namespace std;
using namespace cv;



void detectAndDraw(HOGDescriptor &hog,Mat &img)
{
	vector 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;
}

运行结果：

-------------------------------------------  附录  ----------------------------------------------

setSVMDetector()函数：

/**@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);

getDefaultPeopleDetector()函数：

 /** @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 getDefaultPeopleDetector();

detectMultiScale()函数详解：

 /** @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& foundLocations,
                                  double hitThreshold = 0, Size winStride = Size(),
                                  Size padding = Size(), double scale = 1.05,
                                  double finalThreshold = 2.0, bool useMeanshiftGrouping = false) const;

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参考：

https://blog.csdn.net/masibuaa/article/details/16003847