基于openCV实现人脸检测

时间:2022-10-22 09:02:06       来源:互联网

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,。

关键词: openCV 人脸检测