基于openCV实现人脸检测

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";elseopts[3] = "--nested-cascade=0";if (nSmile == 1)opts[4] = "--smile-cascade=1";elseopts[4] = "--smile-cascade=0";if (nProfile == 1)opts[5] = "--profile=1";elseopts[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 );}}//smileif (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 );//eyeif( 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 );}}//smileif (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。

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华丽丽的分割线

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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。

  后更。