#define CV_NO_BACKWARD_COMPATIBILITY #include "cv.h" #include "highgui.h" #include #include #ifdef _EiC #define WIN32 #endif using namespace std; using namespace cv; void detectAndDraw( Mat& img, CascadeClassifier& cascade, CascadeClassifier& nestedCascade, double scale); String cascadeName = "../../data/haarcascades/haarcascade_frontalface_alt.xml"; String nestedCascadeName = "../../data/haarcascades/haarcascade_eye_tree_eyeglasses.xml"; int main( int argc, const char** argv ) { CvCapture* capture = 0; Mat frame, frameCopy, image; const String scaleOpt = "--scale="; size_t scaleOptLen = scaleOpt.length(); const String cascadeOpt = "--cascade="; size_t cascadeOptLen = cascadeOpt.length(); const String nestedCascadeOpt = "--nested-cascade"; size_t nestedCascadeOptLen = nestedCascadeOpt.length(); String inputName; CascadeClassifier cascade, nestedCascade; double scale = 1; for( int i = 1; i < argc; i++ ) { if( cascadeOpt.compare( 0, cascadeOptLen, argv[i], cascadeOptLen ) == 0 ) cascadeName.assign( argv[i] + cascadeOptLen ); else if( nestedCascadeOpt.compare( 0, nestedCascadeOptLen, argv[i], nestedCascadeOptLen ) == 0 ) { if( argv[i][nestedCascadeOpt.length()] == '=' ) nestedCascadeName.assign( argv[i] + nestedCascadeOpt.length() + 1 ); if( !nestedCascade.load( nestedCascadeName ) ) cerr << "WARNING: Could not load classifier cascade for nested objects" << endl; } else if( scaleOpt.compare( 0, scaleOptLen, argv[i], scaleOptLen ) == 0 ) { if( !sscanf( argv[i] + scaleOpt.length(), "%lf", &scale ) || scale < 1 ) scale = 1; } else if( argv[i][0] == '-' ) { cerr << "WARNING: Unknown option %s" << argv[i] << endl; } else inputName.assign( argv[i] ); } if( !cascade.load( cascadeName ) ) { cerr << "ERROR: Could not load classifier cascade" << endl; cerr << "Usage: facedetect [--cascade=\"\"]\n" " [--nested-cascade[=\"nested_cascade_path\"]]\n" " [--scale[=\n" " [filename|camera_index]\n" ; return -1; } if( inputName.empty() || (isdigit(inputName.c_str()[0]) && inputName.c_str()[1] == '\0') ) capture = cvCaptureFromCAM( inputName.empty() ? 0 : inputName.c_str()[0] - '0' ); else if( inputName.size() ) { image = imread( inputName, 1 ); if( image.empty() ) capture = cvCaptureFromAVI( inputName.c_str() ); } else image = imread( "lena.jpg", 1 ); cvNamedWindow( "result", 1 ); if( capture ) { for(;;) { IplImage* iplImg = cvQueryFrame( capture ); frame = iplImg; if( frame.empty() ) break; if( iplImg->origin == IPL_ORIGIN_TL ) frame.copyTo( frameCopy ); else flip( frame, frameCopy, 0 ); detectAndDraw( frameCopy, cascade, nestedCascade, scale ); if( waitKey( 10 ) >= 0 ) goto _cleanup_; } waitKey(0); _cleanup_: cvReleaseCapture( &capture ); } else { if( !image.empty() ) { detectAndDraw( image, cascade, nestedCascade, scale ); waitKey(0); } else if( !inputName.empty() ) { /* assume it is a text file containing the list of the image filenames to be processed - one per line */ FILE* f = fopen( inputName.c_str(), "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'; cout << "file " << buf << endl; image = imread( buf, 1 ); if( !image.empty() ) { detectAndDraw( image, cascade, nestedCascade, scale ); c = waitKey(0); if( c == 27 || c == 'q' || c == 'Q' ) break; } } fclose(f); } } } cvDestroyWindow("result"); return 0; } void detectAndDraw( Mat& img, CascadeClassifier& cascade, CascadeClassifier& nestedCascade, double scale) { int i = 0; double t = 0; vector faces; const static Scalar colors[] = { CV_RGB(0,0,255), CV_RGB(0,128,255), CV_RGB(0,255,255), CV_RGB(0,255,0), CV_RGB(255,128,0), CV_RGB(255,255,0), CV_RGB(255,0,0), CV_RGB(255,0,255)} ; Mat gray, smallImg( cvRound (img.rows/scale), cvRound(img.cols/scale), CV_8UC1 ); cvtColor( img, gray, CV_BGR2GRAY ); resize( gray, smallImg, smallImg.size(), 0, 0, INTER_LINEAR ); equalizeHist( smallImg, smallImg ); t = (double)cvGetTickCount(); cascade.detectMultiScale( smallImg, faces, 1.1, 2, 0 //|CV_HAAR_FIND_BIGGEST_OBJECT //|CV_HAAR_DO_ROUGH_SEARCH |CV_HAAR_SCALE_IMAGE , Size(30, 30) ); t = (double)cvGetTickCount() - t; printf( "detection time = %g ms\n", t/((double)cvGetTickFrequency()*1000.) ); for( vector::const_iterator r = faces.begin(); r != faces.end(); r++, i++ ) { Mat smallImgROI; vector nestedObjects; Point center; Scalar 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); circle( img, center, radius, color, 3, 8, 0 ); if( nestedCascade.empty() ) continue; smallImgROI = smallImg(*r); nestedCascade.detectMultiScale( smallImgROI, nestedObjects, 1.1, 2, 0 //|CV_HAAR_FIND_BIGGEST_OBJECT //|CV_HAAR_DO_ROUGH_SEARCH //|CV_HAAR_DO_CANNY_PRUNING |CV_HAAR_SCALE_IMAGE , Size(30, 30) ); for( vector::const_iterator nr = nestedObjects.begin(); nr != nestedObjects.end(); nr++ ) { 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); circle( img, center, radius, color, 3, 8, 0 ); } } cv::imshow( "result", img ); }