Learning opencv中的一个基于级联的Hear分类器的人脸检测
2011-08-11 16:43
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这是opencv中的一个源程序,基于级联的Hear分类器的
检测效果图:
如果是很多人的话,检测的就不是特别好,会漏检,误检
稍微修改注释后的源代码:
检测效果图:
如果是很多人的话,检测的就不是特别好,会漏检,误检
稍微修改注释后的源代码:
#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> static CvMemStorage* storage = 0; static CvHaarClassifierCascade* cascade = 0; static CvHaarClassifierCascade* nested_cascade = 0; int use_nested_cascade = 0; void detect_and_draw( IplImage* image ); const char* cascade_name = "haarcascade_frontalface_alt.xml"; const char* nested_cascade_name = "haarcascade_eye_tree_eyeglasses.xml"; double scale = 1; int main( int argc, char** argv ) { 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); int i; const char* input_name = 0; /* for( i = 1; i < argc; i++ ) { if( strncmp( argv[i], cascade_opt, cascade_opt_len) == 0 ) cascade_name = argv[i] + cascade_opt_len; else if( strncmp( argv[i], nested_cascade_opt, nested_cascade_opt_len ) == 0 ) { if( argv[i][nested_cascade_opt_len] == '=' ) nested_cascade_name = argv[i] + nested_cascade_opt_len + 1; 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( argv[i], scale_opt, scale_opt_len ) == 0 ) { if( !sscanf( argv[i] + scale_opt_len, "%lf", &scale ) || scale < 1 ) scale = 1; } else if( argv[i][0] == '-' ) { fprintf( stderr, "WARNING: Unknown option %s\n", argv[i] ); } else input_name = argv[i]; }*/ 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); image = cvLoadImage( "lena.jpg", 1 ); cvNamedWindow( "result", 1 ); { if( image ) { detect_and_draw( image ); cvSaveImage("1.jpg" , image); cvWaitKey(0); cvReleaseImage( &image ); } } 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();//返回时钟计数 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( "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; 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 ); 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 ); }
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