基于OpenCV读取摄像头进行人脸检测和人脸识别
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前段时间使用OpenCV的库函数实现了人脸检测和人脸识别，笔者的实验环境为VS2010+OpenCV2.4.4，OpenCV的环境配置网上有很多，不再赘述。检测的代码网上很多，记不清楚从哪儿copy的了，识别的代码是从OpenCV官网上找到的：http://docs.opencv.org/trunk/modules/contrib/doc/facerec/facerec_api.html
需要注意的是，opencv的FaceRecogizer目前有三个类实现了它，特征脸和fisherface方法最少训练图像为两张，而LBP可以单张图像训练。本人的实验采用的图片是100x100大小的，所以如果要添加自己的图像进行识别的话务必调整为100x100，不然会报错。当然在recog_and_draw这个函数里，笔者也将每次检测到的人脸进行了保存，拖出来重命名就可以，路径自己找吧。使用不同的方法识别时，其阈值设置也不同，LBP大概在100，其他两种方法大概在1000。本人的代码已共享，下载链接：http://download.csdn.net/detail/u010944555/6749725
ps：有人说代码的检测率不高，其实可以归结为两方面的原因，第一人脸检测率不高，这个可以通过嵌套检测嘴角、眼睛等来降低，或者背景、光照固定的话可以通过图像差分来解决；第二是识别方法本身的问题，如果想提高识别率，可以添加多张不同姿态、光照下的人脸作为训练的样本，如果有时间的话可以在采集图像时给出一个人脸框，引导用户对齐人脸进行采集，三星手机解除锁屏就有这么一个功能。
效果图：
废话不多说，上传代码。
main:
#include "stdafx.h"#include "cv.h"#include "highgui.h"#include <stdio.h>#include <stdlib.h>#include <string.h>#include <assert.h>#include <math.h>#include <float.h>#include <limits.h>#include <time.h>#include <ctype.h>#include <opencv2\contrib\contrib.hpp> #include <opencv2\core\core.hpp> #include <opencv2\highgui\highgui.hpp> #include <iostream>#include <fstream>#include <sstream>#include "detect_recog.h"using namespace std;using namespace cv;#ifdef _EiC#define WIN32#endifCvMemStorage* storage = 0;CvHaarClassifierCascade* cascade = 0;CvHaarClassifierCascade* nested_cascade = 0;int use_nested_cascade = 0;const char* cascade_name = "./data/haarcascade_frontalface_alt.xml";//别人已经训练好的人脸检测xml数据const char* nested_cascade_name = "./data/haarcascade_eye_tree_eyeglasses.xml";CvCapture* capture = 0;IplImage *frame, *frame_copy = 0;IplImage *image = 0;const char* scale_opt = "--scale="; // 分类器选项指示符号 int scale_opt_len = (int)strlen(scale_opt);const char* cascade_opt = "--cascade=";int cascade_opt_len = (int)strlen(cascade_opt);const char* nested_cascade_opt = "--nested-cascade";int nested_cascade_opt_len = (int)strlen(nested_cascade_opt);double scale = 1;int num_components = 9;double facethreshold = 9.0;//opencv的FaceRecogizer目前有三个类实现了他，特征脸和fisherface方法最少训练图像为两张，而LBP可以单张图像训练//cv::Ptr<cv::FaceRecognizer> model = cv::createEigenFaceRecognizer();//cv::Ptr<cv::FaceRecognizer> model = cv::createFisherFaceRecognizer();cv::Ptr<cv::FaceRecognizer> model = cv::createLBPHFaceRecognizer();//LBP的这个方法在单个人脸验证方面效果最好vector<Mat> images;//两个容器images,labels来存放图像数据和对应的标签vector<int> labels;int main( int argc, char** argv ){cascade = (CvHaarClassifierCascade*)cvLoad(cascade_name, 0, 0, 0); //加载分类器 if(!cascade) { fprintf( stderr, "ERROR: Could not load classifier cascade\n" );getchar(); return -1; }model->set("threshold", 2100.0);string output_folder;output_folder = string("./einfacedata");//读取你的CSV文件路径string fn_csv = string("./einfacedata/at.txt");try{//通过./einfacedata/at.txt这个文件读取里面的训练图像和类别标签read_csv(fn_csv, images, labels);}catch(cv::Exception &e){cerr<<"Error opening file "<<fn_csv<<". Reason: "<<e.msg<<endl;exit(1);}/*//read_img这个函数直接从einfacedata/trainingdata目录下读取图像数据并默认将图像置为0//所以如果用这个函数只能用来单个人脸验证if(!read_img(images, labels)){cout<< "Error in reading images!";images.clear();labels.clear();return 0;}*/cout << images.size() << ":" << labels.size()<<endl;//如果没有读到足够的图片，就退出if(images.size() <= 2){string error_message = "This demo needs at least 2 images to work.";CV_Error(CV_StsError, error_message);}//得到第一张照片的高度，在下面对图像变形到他们原始大小时需要//int height = images[0].rows;//移除最后一张图片，用于做测试//Mat testSample = images[images.size() - 1];//cv::imshow("testSample", testSample);//int testLabel = labels[labels.size() - 1];//images.pop_back();//labels.pop_back();//下面创建一个特征脸模型用于人脸识别，// 通过CSV文件读取的图像和标签训练它。//进行训练model->train(images, labels); storage = cvCreateMemStorage(0); // 创建内存存储器 capture = cvCaptureFromCAM(0); // 创建视频读取结构 cvNamedWindow( "result", 1 ); if( capture ) // 如过是视频或摄像头采集图像，则循环处理每一帧 { for(;;) { if( !cvGrabFrame( capture )) break; frame = cvRetrieveFrame( capture ); if( !frame ) break; if( !frame_copy ) frame_copy = cvCreateImage( cvSize(640,480),IPL_DEPTH_8U, frame->nChannels ); if( frame->origin == IPL_ORIGIN_TL ) cvCopy( frame, frame_copy, 0 ); else cvFlip( frame, frame_copy, 0 ); //detect_and_draw( frame_copy ); // 如果调用这个函数，只是实现人脸检测//cout << frame_copy->width << "x" << frame_copy->height << endl;recog_and_draw( frame_copy );//该函数实现人脸检测和识别 if( cvWaitKey( 100 ) >= 0 )//esc键值好像是100 goto _cleanup_; } cvWaitKey(0);_cleanup_: // 标记使用，在汇编里用过，C语言，我还没见用过 cvReleaseImage( &frame_copy ); cvReleaseCapture( &capture ); } cvDestroyWindow("result"); return 0;}detect_recog.cpp:
#include "stdafx.h"#include "cv.h"#include "highgui.h"#include <stdio.h>#include <stdlib.h>#include <string.h>#include <assert.h>#include <math.h>#include <float.h>#include <limits.h>#include <time.h>#include <ctype.h>#include "detect_recog.h"#include <opencv2\contrib\contrib.hpp> #include <opencv2\core\core.hpp> #include <opencv2\highgui\highgui.hpp> #include <iostream>#include <fstream>#include <sstream>#include <stdio.h>#include <io.h> #include <direct.h> using namespace std;using namespace cv;//检测并圈出人脸，并将检测到的人脸进行判断属于训练图像中的哪一类void recog_and_draw( IplImage* img ) { static CvScalar colors[] = { {{0,0,255}}, {{0,128,255}}, {{0,255,255}}, {{0,255,0}}, {{255,128,0}}, {{255,255,0}}, {{255,0,0}}, {{255,0,255}} }; IplImage *gray, *small_img; int i, j; gray = cvCreateImage( cvSize(img->width,img->height), 8, 1 ); small_img = cvCreateImage( cvSize( cvRound (img->width/scale), cvRound (img->height/scale)), 8, 1 ); cvCvtColor( img, gray, CV_BGR2GRAY ); // 彩色RGB图像转为灰度图像 cvResize( gray, small_img, CV_INTER_LINEAR ); cvEqualizeHist( small_img, small_img ); // 直方图均衡化 cvClearMemStorage( storage ); if( cascade ) { double t = (double)cvGetTickCount(); CvSeq* faces = cvHaarDetectObjects( small_img, cascade, storage, 1.1, 2, 0 //|CV_HAAR_FIND_BIGGEST_OBJECT //|CV_HAAR_DO_ROUGH_SEARCH |CV_HAAR_DO_CANNY_PRUNING //|CV_HAAR_SCALE_IMAGE , cvSize(30, 30) ); t = (double)cvGetTickCount() - t; // 统计检测使用时间 //printf( "detection time = %gms\n", t/((double)cvGetTickFrequency()*1000.) ); for( i = 0; i < (faces ? faces->total : 0); i++ ) { CvRect* r = (CvRect*)cvGetSeqElem( faces, i ); // 将faces数据从CvSeq转为CvRect CvMat small_img_roi; CvSeq* nested_objects; CvPoint center; CvScalar color = colors[i%8]; // 使用不同颜色绘制各个face，共八种色 int radius; center.x = cvRound((r->x + r->width*0.5)*scale); // 找出faces中心 center.y = cvRound((r->y + r->height*0.5)*scale); radius = cvRound((r->width + r->height)*0.25*scale); cvGetSubRect( small_img, &small_img_roi, *r );//截取检测到的人脸区域作为识别的图像IplImage *result;CvRect roi;roi = *r;result = cvCreateImage( cvSize(r->width, r->height), img->depth, img->nChannels );cvSetImageROI(img,roi);// 创建子图像cvCopy(img,result);cvResetImageROI(img);IplImage *resizeRes;CvSize dst_cvsize;dst_cvsize.width=(int)(100);dst_cvsize.height=(int)(100);resizeRes=cvCreateImage(dst_cvsize,result->depth,result->nChannels);//检测到的区域可能不是100x100大小，所以需要插值处理到统一大小，图像的大小可以自己指定的cvResize(result,resizeRes,CV_INTER_NN);IplImage* img1 = cvCreateImage(cvGetSize(resizeRes), IPL_DEPTH_8U, 1);//创建目标图像cvCvtColor(resizeRes,img1,CV_BGR2GRAY);//cvCvtColor(src,des,CV_BGR2GRAY) cvShowImage( "resize", resizeRes );cvCircle( img, center, radius, color, 3, 8, 0 ); // 从中心位置画圆，圈出脸部区域int predictedLabel = -1;Mat test = img1;//images[images.size() - 1] = test;model->train(images, labels);//如果调用read_img函数时 chdir将默认目录做了更改，所以output.jpg自己找一下吧imwrite("../ouput.jpg",test);//在这里对人脸进行判别double predicted_confidence = 0.0;model->predict(test,predictedLabel,predicted_confidence);if(predictedLabel == 0)cvText(img, "yes", r->x+r->width*0.5, r->y); elsecvText(img, "no", r->x+r->width*0.5, r->y); //cout << "predict:"<<model->predict(test) << endl;cout << "predict:"<< predictedLabel << "\nconfidence:" << predicted_confidence << endl; if( !nested_cascade ) continue; nested_objects = cvHaarDetectObjects( &small_img_roi, nested_cascade, storage, 1.1, 2, 0 //|CV_HAAR_FIND_BIGGEST_OBJECT //|CV_HAAR_DO_ROUGH_SEARCH //|CV_HAAR_DO_CANNY_PRUNING //|CV_HAAR_SCALE_IMAGE , cvSize(0, 0) ); for( j = 0; j < (nested_objects ? nested_objects->total : 0); j++ ) { CvRect* nr = (CvRect*)cvGetSeqElem( nested_objects, j ); center.x = cvRound((r->x + nr->x + nr->width*0.5)*scale); center.y = cvRound((r->y + nr->y + nr->height*0.5)*scale); radius = cvRound((nr->width + nr->height)*0.25*scale); cvCircle( img, center, radius, color, 3, 8, 0 ); } } } cvShowImage( "result", img ); cvReleaseImage( &gray ); cvReleaseImage( &small_img );}void cvText(IplImage* img, const char* text, int x, int y) { CvFont font; double hscale = 1.0; double vscale = 1.0; int linewidth = 2; cvInitFont(&font,CV_FONT_HERSHEY_SIMPLEX | CV_FONT_ITALIC,hscale,vscale,0,linewidth); CvScalar textColor =cvScalar(0,255,255); CvPoint textPos =cvPoint(x, y); cvPutText(img, text, textPos, &font,textColor); }Mat norm_0_255(cv::InputArray _src){Mat src = _src.getMat();Mat dst;switch(src.channels()){case 1:cv::normalize(_src, dst, 0, 255, cv::NORM_MINMAX, CV_8UC1);break;case 3:cv::normalize(_src, dst, 0, 255, cv::NORM_MINMAX, CV_8UC3);break;default:src.copyTo(dst);break;}return dst;}//读取文件中的图像数据和类别，存入images和labels这两个容器void read_csv(const string &filename, vector<Mat> &images, vector<int> &labels, char separator){std::ifstream file(filename.c_str(), ifstream::in);if(!file){string error_message = "No valid input file was given.";CV_Error(CV_StsBadArg, error_message);}string line, path, classlabel;while(getline(file, line)){stringstream liness(line);getline(liness, path, separator); //遇到分号就结束getline(liness, classlabel); //继续从分号后面开始，遇到换行结束if(!path.empty() && !classlabel.empty()){images.push_back(imread(path, 0));labels.push_back(atoi(classlabel.c_str()));}}}bool read_img(vector<Mat> &images, vector<int> &labels){long file; struct _finddata_t find; _chdir("./einfacedata/trainingdata/"); if((file=_findfirst("*.*", &find))==-1L) { //printf("空白!/n"); return false; } //fileNum = 0; //strcpy(fileName[fileNum], find.name);int i = 0; while(_findnext(file, &find)==0) { if(i == 0){i++;continue;} images.push_back(imread(find.name, 0));labels.push_back(0); cout << find.name << endl; } _findclose(file);return true;}// 只是检测人脸，并将人脸圈出 void detect_and_draw( IplImage* img ) { static CvScalar colors[] = { {{0,0,255}}, {{0,128,255}}, {{0,255,255}}, {{0,255,0}}, {{255,128,0}}, {{255,255,0}}, {{255,0,0}}, {{255,0,255}} }; IplImage *gray, *small_img; int i, j; gray = cvCreateImage( cvSize(img->width,img->height), 8, 1 ); small_img = cvCreateImage( cvSize( cvRound (img->width/scale), cvRound (img->height/scale)), 8, 1 ); cvCvtColor( img, gray, CV_BGR2GRAY ); // 彩色RGB图像转为灰度图像 cvResize( gray, small_img, CV_INTER_LINEAR ); cvEqualizeHist( small_img, small_img ); // 直方图均衡化 cvClearMemStorage( storage ); if( cascade ) { double t = (double)cvGetTickCount(); CvSeq* faces = cvHaarDetectObjects( small_img, cascade, storage, 1.1, 2, 0 //|CV_HAAR_FIND_BIGGEST_OBJECT //|CV_HAAR_DO_ROUGH_SEARCH |CV_HAAR_DO_CANNY_PRUNING //|CV_HAAR_SCALE_IMAGE , cvSize(30, 30) ); t = (double)cvGetTickCount() - t; // 统计检测使用时间 printf( "detection time = %gms\n", t/((double)cvGetTickFrequency()*1000.) ); for( i = 0; i < (faces ? faces->total : 0); i++ ) { CvRect* r = (CvRect*)cvGetSeqElem( faces, i ); // 将faces数据从CvSeq转为CvRect CvMat small_img_roi; CvSeq* nested_objects; CvPoint center; CvScalar color = colors[i%8]; // 使用不同颜色绘制各个face，共八种色 int radius; center.x = cvRound((r->x + r->width*0.5)*scale); // 找出faces中心 center.y = cvRound((r->y + r->height*0.5)*scale); radius = cvRound((r->width + r->height)*0.25*scale); cvCircle( img, center, radius, color, 3, 8, 0 ); // 从中心位置画圆，圈出脸部区域 if( !nested_cascade ) continue; cvGetSubRect( small_img, &small_img_roi, *r ); nested_objects = cvHaarDetectObjects( &small_img_roi, nested_cascade, storage, 1.1, 2, 0 //|CV_HAAR_FIND_BIGGEST_OBJECT //|CV_HAAR_DO_ROUGH_SEARCH //|CV_HAAR_DO_CANNY_PRUNING //|CV_HAAR_SCALE_IMAGE ,cvSize(0, 0) ); for( j = 0; j < (nested_objects ? nested_objects->total : 0); j++ ) { CvRect* nr = (CvRect*)cvGetSeqElem( nested_objects, j ); center.x = cvRound((r->x + nr->x + nr->width*0.5)*scale); center.y = cvRound((r->y + nr->y + nr->height*0.5)*scale); radius = cvRound((nr->width + nr->height)*0.25*scale); cvCircle( img, center, radius, color, 3, 8, 0 ); } } } cvShowImage( "result", img ); cvReleaseImage( &gray ); cvReleaseImage( &small_img );}detect_recog.h:
#include "stdafx.h"#include "cv.h"#include "highgui.h"#include <stdio.h>#include <stdlib.h>#include <string.h>#include <assert.h>#include <math.h>#include <float.h>#include <limits.h>#include <time.h>#include <ctype.h>//////////////////////////////////s///////////////////////////////////#include <opencv2\contrib\contrib.hpp> #include <opencv2\core\core.hpp> #include <opencv2\highgui\highgui.hpp> #include <iostream>#include <fstream>#include <sstream>using namespace std;using namespace cv;#ifndef DETECT_RECOG_H#define DETECT_RECOG_Hextern CvMemStorage* storage;extern CvHaarClassifierCascade* cascade;extern CvHaarClassifierCascade* nested_cascade;extern int use_nested_cascade;extern const char* cascade_name;extern const char* nested_cascade_name;extern double scale;extern cv::Ptr<cv::FaceRecognizer> model;extern vector<Mat> images;extern vector<int> labels;void detect_and_draw( IplImage* img ); // 检测和绘画 void recog_and_draw( IplImage* img ); // 检测和绘画 void read_csv(const string &filename, vector<Mat> &images, vector<int> &labels, char separator = ';');bool read_img(vector<Mat> &images, vector<int> &labels);Mat norm_0_255(cv::InputArray _src);void cvText(IplImage* img, const char* text, int x, int y);#endif
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- 基于OpenCV读取摄像头进行人脸检测和人脸识别
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