openCV的人脸识别主要通过Haar分类器实现，当然，这是在已有训练数据的基础上。openCV安装在 opencv/opencv/sources/data/haarcascades_cuda（或haarcascades）中存在预先训练好的物体检测器（xml格式），包括正脸、侧脸、眼睛、微笑、上半身、下半身、全身等。
openCV的的Haar分类器是一个监督分类器，首先对图像进行直方图均衡化并归一化到同样大小，然后标记里面是否包含要监测的物体。它首先由Paul Viola和Michael Jones设计，称为Viola Jones检测器。Viola Jones分类器在级联的每个节点中使用AdaBoost来学习一个高检测率低拒绝率的多层树分类器。它使用了以下一些新的特征：
1. 使用类Haar输入特征：对矩形图像区域的和或者差进行阈值化。
2. 积分图像技术加速了矩形区域的45°旋转的值的计算，用来加速类Haar输入特征的计算。
3. 使用统计boosting来创建两类问题（人脸和非人脸）的分类器节点（高通过率，低拒绝率）
4. 把弱分类器节点组成筛选式级联。即，第一组分类器最优，能通过包含物体的图像区域，同时允许一些不包含物体通过的图像通过；第二组分
(资料图片仅供参考)
类器次优分类器，也是有较低的拒绝率；以此类推。也就是说，对于每个boosting分类器，只要有人脸都能检测到，同时拒绝一小部分非人脸，并将其传给下一个分类器，是为低拒绝率。以此类推，最后一个分类器将几乎所有的非人脸都拒绝掉，只剩下人脸区域。只要图像区域通过了整个级联，则认为里面有物体。
此技术虽然适用于人脸检测，但不限于人脸检测，还可用于其他物体的检测，如汽车、飞机等的正面、侧面、后面检测。在检测时，先导入训练好的参数文件，其中haarcascade_frontalface_alt2.xml对正面脸的识别效果较好haarcascade_profileface.xml对侧脸的检测效果较好。当然，如果要达到更高的分类精度，可以收集更多的数据进行训练，这是后话。
以下代码基本实现了正脸、眼睛、微笑、侧脸的识别，若要添加其他功能，可以自行调整。
// faceDetector.h // This is just the face, eye, smile, profile detector from OpenCV"s samples/c directory // /* *************** License:************************** Jul. 18, 2016 Author: Liuph Right to use this code in any way you want without warranty, support or any guarantee of it working. OTHER OPENCV SITES: * The source code is on sourceforge at: http://sourceforge.net/projects/opencvlibrary/ * The OpenCV wiki page (As of Oct 1, 2008 this is down for changing over servers, but should come back): http://opencvlibrary.sourceforge.net/ * An active user group is at: http://tech.groups.yahoo.com/group/OpenCV/ * The minutes of weekly OpenCV development meetings are at: http://pr.willowgarage.com/wiki/OpenCV ************************************************** */ #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 <iostream> using namespace std; static CvMemStorage* storage = 0; static CvHaarClassifierCascade* cascade = 0; static CvHaarClassifierCascade* nested_cascade = 0; static CvHaarClassifierCascade* smile_cascade = 0; static CvHaarClassifierCascade* profile = 0; int use_nested_cascade = 0; void detect_and_draw( IplImage* image ); /* The path that stores the trained parameter files. After openCv is installed, the file path is "opencv/opencv/sources/data/haarcascades_cuda" or "opencv/opencv/sources/data/haarcascades" */ const char* cascade_name = "../faceDetect/haarcascade_frontalface_alt2.xml"; const char* nested_cascade_name = "../faceDetect/haarcascade_eye_tree_eyeglasses.xml"; const char* smile_cascade_name = "../faceDetect/haarcascade_smile.xml"; const char* profile_name = "../faceDetect/haarcascade_profileface.xml"; double scale = 1; int faceDetector(const char* imageName, int nNested, int nSmile, int nProfile) { CvCapture* capture = 0; IplImage *frame, *frame_copy = 0; IplImage *image = 0; const char* scale_opt = "--scale="; 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); const char* smile_cascade_opt = "--smile-cascade"; int smile_cascade_opt_len = (int)strlen(smile_cascade_opt); const char* profile_opt = "--profile"; int profile_opt_len = (int)strlen(profile_opt); int i; const char* input_name = 0; int opt_num = 7; char** opts = new char*[7]; opts[0] = "compile_opencv.exe"; opts[1] = "--scale=1"; opts[2] = "--cascade=1"; if (nNested == 1) opts[3] = "--nested-cascade=1"; else opts[3] = "--nested-cascade=0"; if (nSmile == 1) opts[4] = "--smile-cascade=1"; else opts[4] = "--smile-cascade=0"; if (nProfile == 1) opts[5] = "--profile=1"; else opts[5] = "--profile=0"; opts[6] = (char*)imageName; for( i = 1; i < opt_num; i++ ) { if( strncmp( opts[i], cascade_opt, cascade_opt_len) == 0) { cout<<"cascade: "<<cascade_name<<endl; } else if( strncmp( opts[i], nested_cascade_opt, nested_cascade_opt_len ) == 0) { if( opts[i][nested_cascade_opt_len + 1] == "1") { cout<<"nested: "<<nested_cascade_name<<endl; nested_cascade = (CvHaarClassifierCascade*)cvLoad( nested_cascade_name, 0, 0, 0 ); } if( !nested_cascade ) fprintf( stderr, "WARNING: Could not load classifier cascade for nested objects\n" ); } else if( strncmp( opts[i], scale_opt, scale_opt_len ) == 0 ) { cout<< "scale: "<< scale<<endl; if( !sscanf( opts[i] + scale_opt_len, "%lf", &scale ) || scale < 1 ) scale = 1; } else if (strncmp( opts[i], smile_cascade_opt, smile_cascade_opt_len ) == 0) { if( opts[i][smile_cascade_opt_len + 1] == "1") { cout<<"smile: "<<smile_cascade_name<<endl; smile_cascade = (CvHaarClassifierCascade*)cvLoad( smile_cascade_name, 0, 0, 0 ); } if( !smile_cascade ) fprintf( stderr, "WARNING: Could not load classifier cascade for smile objects\n" ); } else if (strncmp( opts[i], profile_opt, profile_opt_len ) == 0) { if( opts[i][profile_opt_len + 1] == "1") { cout<<"profile: "<<profile_name<<endl; profile = (CvHaarClassifierCascade*)cvLoad( profile_name, 0, 0, 0 ); } if( !profile ) fprintf( stderr, "WARNING: Could not load classifier cascade for profile objects\n" ); } else if( opts[i][0] == "-" ) { fprintf( stderr, "WARNING: Unknown option %s\n", opts[i] ); } else { input_name = imageName; printf("input_name: %s\n", imageName); } } cascade = (CvHaarClassifierCascade*)cvLoad( cascade_name, 0, 0, 0 ); if( !cascade ) { fprintf( stderr, "ERROR: Could not load classifier cascade\n" ); fprintf( stderr, "Usage: facedetect [--cascade=\"<cascade_path>\"]\n" " [--nested-cascade[=\"nested_cascade_path\"]]\n" " [--scale[=<image scale>\n" " [filename|camera_index]\n" ); return -1; } storage = cvCreateMemStorage(0); if( !input_name || (isdigit(input_name[0]) && input_name[1] == "\0") ) capture = cvCaptureFromCAM( !input_name ? 0 : input_name[0] - "0" ); else if( input_name ) { image = cvLoadImage( input_name, 1 ); if( !image ) capture = cvCaptureFromAVI( input_name ); } else image = cvLoadImage( "../lena.jpg", 1 ); cvNamedWindow( "result", 1 ); if( capture ) { for(;;) { if( !cvGrabFrame( capture )) break; frame = cvRetrieveFrame( capture ); if( !frame ) break; if( !frame_copy ) frame_copy = cvCreateImage( cvSize(frame->width,frame->height), 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 ); if( cvWaitKey( 10 ) >= 0 ) goto _cleanup_; } cvWaitKey(0); _cleanup_: cvReleaseImage( &frame_copy ); cvReleaseCapture( &capture ); } else { if( image ) { detect_and_draw( image ); cvWaitKey(0); cvReleaseImage( &image ); } else if( input_name ) { /* assume it is a text file containing the list of the image filenames to be processed - one per line */ FILE* f = fopen( input_name, "rt" ); if( f ) { char buf[1000+1]; while( fgets( buf, 1000, f ) ) { int len = (int)strlen(buf), c; while( len > 0 && isspace(buf[len-1]) ) len--; buf[len] = "\0"; printf( "file %s\n", buf ); image = cvLoadImage( buf, 1 ); if( image ) { detect_and_draw( image ); c = cvWaitKey(0); if( c == 27 || c == "q" || c == "Q" ) break; cvReleaseImage( &image ); } } fclose(f); } } } cvDestroyWindow("result"); return 0; } 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 ); cvResize( gray, small_img, CV_INTER_LINEAR ); cvEqualizeHist( small_img, small_img ); 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; printf( "faces detection time = %gms\n", t/((double)cvGetTickFrequency()*1000.) ); for( i = 0; i < (faces ? faces->total : 0); i++ ) { CvRect* r = (CvRect*)cvGetSeqElem( faces, i ); CvMat small_img_roi; CvSeq* nested_objects; CvSeq* smile_objects; CvPoint center; CvScalar color = colors[i%8]; int radius; center.x = cvRound((r->x + r->width*0.5)*scale); 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 ); //eye if( nested_cascade != 0) { 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 ); } } //smile if (smile_cascade != 0) { cvGetSubRect( small_img, &small_img_roi, *r ); smile_objects = cvHaarDetectObjects( &small_img_roi, smile_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 < (smile_objects ? smile_objects->total : 0); j++ ) { CvRect* nr = (CvRect*)cvGetSeqElem( smile_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 ); } } } } if( profile ) { double t = (double)cvGetTickCount(); CvSeq* faces = cvHaarDetectObjects( small_img, profile, 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; printf( "profile faces detection time = %gms\n", t/((double)cvGetTickFrequency()*1000.) ); for( i = 0; i < (faces ? faces->total : 0); i++ ) { CvRect* r = (CvRect*)cvGetSeqElem( faces, i ); CvMat small_img_roi; CvSeq* nested_objects; CvSeq* smile_objects; CvPoint center; CvScalar color = colors[(7-i)%8]; int radius; center.x = cvRound((r->x + r->width*0.5)*scale); 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 ); //eye if( nested_cascade != 0) { 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 ); } } //smile if (smile_cascade != 0) { cvGetSubRect( small_img, &small_img_roi, *r ); smile_objects = cvHaarDetectObjects( &small_img_roi, smile_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 < (smile_objects ? smile_objects->total : 0); j++ ) { CvRect* nr = (CvRect*)cvGetSeqElem( smile_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 ); } //main.cpp //openCV配置 //附加包含目录: include, include/opencv, include/opencv2 //附加库目录: lib //附加依赖项: debug:--> opencv_calib3d243d.lib;...; // release:--> opencv_calib3d243.lib;...; #include<string> #include <opencv2\opencv.hpp> #include "CV2_compile.h" #include "CV_compile.h" #include "face_detector.h" using namespace cv; using namespace std; int main(int argc, char** argv) { const char* imagename = "../lena.jpg"; faceDetector(imagename,1,0,0); return 0; } 调整主函数中faceDetect(const char* imageName, int nNested, int nSmile, int nProfile)函数中的参数，分别表示图像文件名，是否检测眼睛，是否检测微笑，是否检测侧脸。以检测正脸、眼睛为例：
再来看一张合影。
========华丽丽的分割线==========
如果对分类器的参数不满意，或者说想识别其他的物体例如车、人、飞机、苹果等等等等，只需要选择适当的样本训练，获取该物体的各个方面的参数，训练过程可以通过openCV的haartraining实现（参考haartraining参考文档，opencv/apps/traincascade），主要包括个步骤：
1. 收集打算学习的物体数据集（如正面人脸图，侧面汽车图等， 1000~10000个正样本为宜），把它们存储在一个或多个目录下面。
2. 使用createsamples来建立正样本的向量输出文件，通过这个文件可以重复训练过程，使用同一个向量输出文件尝试各种参数。
3. 获取负样本，即不包含该物体的图像。
4. 训练。命令行实现。
以上就是本文的全部内容，希望对大家的学习有所帮助，。
