pico在lfw上测试检测率

测试pico在lfw上的检测率。

lfw给定的文件格式：

每个名称文件夹下有一张或多张图片。

#include "io.h"
std::vector<std::string> list_folders(std::string pathname)
{
	std::vector<std::string> folders;
	_finddata_t folder;

	std::string findpath = pathname + "\\*";

	long handle = _findfirst(findpath.c_str(),&folder);

	if(handle == -1)
	{
		std::cerr << "no such path" << std::endl;
		system("pause");
		exit(-1);
	}

	do
	{
		if(folder.attrib & _A_SUBDIR)
		{
            if( (strcmp(folder.name, ".") != 0) && (strcmp(folder.name, "..") != 0) )
            {
                std::string newpath = pathname + "\\" + folder.name;
                folders.push_back(newpath);
            }
		}
	}while( _findnext(handle, &folder) == 0 );

	return folders;
}

std::vector<std::string> list_files(std::string pathname)
{
	std::vector<std::string> files;
	_finddata_t file;

	std::string findpath = pathname + "\\*";

	long handle = _findfirst(findpath.c_str(),&file);

	if(handle == -1)
	{
		std::cerr << "no such path" << std::endl;
		system("pause");
		exit(-1);
	}

	do
	{
		if(!(file.attrib & _A_SUBDIR))
		{
			files.push_back(pathname + "\\" + file.name);
		}
	}while( _findnext(handle, &file) == 0 );

	return files;
}

这是大爷给我的代码，list_folders()，遍历给定路径下的文件夹。list_files()，遍历给定路径下的文件。

在此基础上：

std::string root = "C:\\Users\\zhuqian\\Desktop\\pico_face_detect\\lfw\\lfw";
	std::vector<std::string> all = list_folders(root);
	for (std::vector<std::string>::const_iterator it = all.begin(); it!=all.end();++it)
	{
		//std::cout << *it << std::endl;
		std::vector<std::string> filename = list_files(*it);
		for (std::vector<std::string>::const_iterator subit = filename.begin();subit!=filename.end();
			++subit)
		{
			IplImage* img;
			allface += 1;
			//
			img = cvLoadImage(subit->c_str(), CV_LOAD_IMAGE_COLOR);
			if(!img)
			{
				printf("# cannot load image from '%s'\n", subit->c_str());
				return 0;
			}
			process_image(img, 1, *it);
			cvReleaseImage(&img);
		}
	}
	std::cout << "lossdect: " << lossdect << std::endl;
	std::cout << "falsedect: " << falsedect << std::endl;
	std::cout << "allface" << allface << std::endl;

	std::cout << "lossdectRate: " << (double)lossdect/allface << std::endl;
	std::cout << "facedectRate: " << (double)falsedect/allface << std::endl;

其中调用的void process_image(IplImage* frame, int draw, const std::string& path)修改如下:

if(draw)
		for(i=0; i<ndetections; ++i)
			if(qs[i]>=qthreshold) // check the confidence threshold
			{
				/*cv::Rect r1(cs[i]-ss[i]/2, rs[i]-ss[i]/2, ss[i], ss[i]); //矩阵标记
				cv::Mat img(frame,0);                                   //IplImage转Mat
				cv::Mat face(img(r1));*/                     //取矩阵
				//std::stringstream s;
				//s << i;                         //int转string的方法之一
				//cv::imwrite(path + "result_" + s.str() + ".jpg", face);
				
				cvCircle(frame, cvPoint(cs[i], rs[i]), ss[i]/2, CV_RGB(255, 0, 0), 4, 8, 0); // we draw circles here since height-to-width ratio of the detected face regions is 1.0f
				
			}

	static int nnn=0;
	std::stringstream s;
	s << nnn++;

	if (ndetections==0)  //记录误检漏检率，并在项目路径下写检测后的图片，便于人眼观察。
	{
		cv::imwrite("lossdect"  + s.str() + ".jpg", cv::Mat(frame,0));
		lossdect += 1;
	}
	if (ndetections>1)
	{
		falsedect += ndetections-1;
		std::cout << path << std::endl;
		cv::imwrite("falsedect"  + s.str() + ".jpg", cv::Mat(frame,0));
	}
	else
	{
		cv::imwrite("right"  + s.str() + ".jpg", cv::Mat(frame,0));
	}

	// if the `verbose` flag is set, print the results to standard output
	if(verbose)
	{
		//
		for(i=0; i<ndetections; ++i)
			if(qs[i]>=qthreshold) // check the confidence threshold
				printf("%d %d %d %f\n", (int)rs[i], (int)cs[i], (int)ss[i], qs[i]);

		//
		//printf("# %f\n", 1000.0f*t); // use '#' to ignore this line when parsing the output of the program
	}

结果：

样本：13233. 漏检84. 误检150. （漏检率：0.6%，误检率：1.1%） 
还有参数设置，如果参数能够根据样本中人脸的大小(先验知识)作相应修改，效果会更好。