mirror of https://github.com/opencv/opencv.git
Andrey Kamaev
12 years ago
commit
9e12b7c3c2
64 changed files with 11513 additions and 3107 deletions
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#include "opencv2/objdetect/objdetect.hpp" |
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#include "opencv2/highgui/highgui.hpp" |
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#include "opencv2/imgproc/imgproc.hpp" |
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#include <cctype> |
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#include <iostream> |
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#include <iterator> |
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#include <stdio.h> |
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using namespace std; |
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using namespace cv; |
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static void help() |
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{ |
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cout << "\nThis program demonstrates the smile detector.\n" |
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"Usage:\n" |
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"./smiledetect [--cascade=<cascade_path> this is the frontal face classifier]\n" |
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" [--smile-cascade=[<smile_cascade_path>]]\n" |
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" [--scale=<image scale greater or equal to 1, try 2.0 for example. The larger the faster the processing>]\n" |
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" [--try-flip]\n" |
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" [video_filename|camera_index]\n\n" |
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"Example:\n" |
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"./smiledetect --cascade=\"../../data/haarcascades/haarcascade_frontalface_alt.xml\" --smile-cascade=\"../../data/haarcascades/haarcascade_smile.xml\" --scale=2.0\n\n" |
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"During execution:\n\tHit any key to quit.\n" |
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"\tUsing OpenCV version " << CV_VERSION << "\n" << endl; |
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} |
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|
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void detectAndDraw( Mat& img, CascadeClassifier& cascade, |
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CascadeClassifier& nestedCascade, |
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double scale, bool tryflip ); |
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string cascadeName = "../../data/haarcascades/haarcascade_frontalface_alt.xml"; |
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string nestedCascadeName = "../../data/haarcascades/haarcascade_smile.xml"; |
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int main( int argc, const char** argv ) |
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{ |
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CvCapture* capture = 0; |
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Mat frame, frameCopy, image; |
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const string scaleOpt = "--scale="; |
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size_t scaleOptLen = scaleOpt.length(); |
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const string cascadeOpt = "--cascade="; |
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size_t cascadeOptLen = cascadeOpt.length(); |
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const string nestedCascadeOpt = "--smile-cascade"; |
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size_t nestedCascadeOptLen = nestedCascadeOpt.length(); |
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const string tryFlipOpt = "--try-flip"; |
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size_t tryFlipOptLen = tryFlipOpt.length(); |
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string inputName; |
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bool tryflip = false; |
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help(); |
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CascadeClassifier cascade, nestedCascade; |
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double scale = 1; |
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for( int i = 1; i < argc; i++ ) |
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{ |
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cout << "Processing " << i << " " << argv[i] << endl; |
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if( cascadeOpt.compare( 0, cascadeOptLen, argv[i], cascadeOptLen ) == 0 ) |
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{ |
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cascadeName.assign( argv[i] + cascadeOptLen ); |
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cout << " from which we have cascadeName= " << cascadeName << endl; |
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} |
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else if( nestedCascadeOpt.compare( 0, nestedCascadeOptLen, argv[i], nestedCascadeOptLen ) == 0 ) |
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{ |
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if( argv[i][nestedCascadeOpt.length()] == '=' ) |
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nestedCascadeName.assign( argv[i] + nestedCascadeOpt.length() + 1 ); |
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} |
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else if( scaleOpt.compare( 0, scaleOptLen, argv[i], scaleOptLen ) == 0 ) |
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{ |
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if( !sscanf( argv[i] + scaleOpt.length(), "%lf", &scale ) || scale < 1 ) |
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scale = 1; |
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cout << " from which we read scale = " << scale << endl; |
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} |
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else if( tryFlipOpt.compare( 0, tryFlipOptLen, argv[i], tryFlipOptLen ) == 0 ) |
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{ |
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tryflip = true; |
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cout << " will try to flip image horizontally to detect assymetric objects\n"; |
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} |
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else if( argv[i][0] == '-' ) |
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{ |
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cerr << "WARNING: Unknown option " << argv[i] << endl; |
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} |
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else |
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inputName.assign( argv[i] ); |
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} |
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if( !cascade.load( cascadeName ) ) |
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{ |
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cerr << "ERROR: Could not load face cascade" << endl; |
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help(); |
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return -1; |
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} |
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if( !nestedCascade.load( nestedCascadeName ) ) |
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{ |
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cerr << "ERROR: Could not load smile cascade" << endl; |
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help(); |
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return -1; |
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} |
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if( inputName.empty() || (isdigit(inputName.c_str()[0]) && inputName.c_str()[1] == '\0') ) |
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{ |
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capture = cvCaptureFromCAM( inputName.empty() ? 0 : inputName.c_str()[0] - '0' ); |
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int c = inputName.empty() ? 0 : inputName.c_str()[0] - '0' ; |
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if(!capture) cout << "Capture from CAM " << c << " didn't work" << endl; |
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} |
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else if( inputName.size() ) |
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{ |
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capture = cvCaptureFromAVI( inputName.c_str() ); |
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if(!capture) cout << "Capture from AVI didn't work" << endl; |
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} |
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cvNamedWindow( "result", 1 ); |
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if( capture ) |
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{ |
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cout << "In capture ..." << endl; |
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cout << endl << "NOTE: Smile intensity will only be valid after a first smile has been detected" << endl; |
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for(;;) |
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{ |
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IplImage* iplImg = cvQueryFrame( capture ); |
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frame = iplImg; |
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if( frame.empty() ) |
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break; |
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if( iplImg->origin == IPL_ORIGIN_TL ) |
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frame.copyTo( frameCopy ); |
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else |
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flip( frame, frameCopy, 0 ); |
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detectAndDraw( frameCopy, cascade, nestedCascade, scale, tryflip ); |
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if( waitKey( 10 ) >= 0 ) |
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goto _cleanup_; |
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} |
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waitKey(0); |
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_cleanup_: |
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cvReleaseCapture( &capture ); |
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} |
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else |
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{ |
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cerr << "ERROR: Could not initiate capture" << endl; |
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help(); |
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return -1; |
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} |
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cvDestroyWindow("result"); |
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return 0; |
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} |
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void detectAndDraw( Mat& img, CascadeClassifier& cascade, |
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CascadeClassifier& nestedCascade, |
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double scale, bool tryflip) |
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{ |
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int i = 0; |
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vector<Rect> faces, faces2; |
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const static Scalar colors[] = { CV_RGB(0,0,255), |
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CV_RGB(0,128,255), |
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CV_RGB(0,255,255), |
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CV_RGB(0,255,0), |
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CV_RGB(255,128,0), |
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CV_RGB(255,255,0), |
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CV_RGB(255,0,0), |
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CV_RGB(255,0,255)} ; |
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Mat gray, smallImg( cvRound (img.rows/scale), cvRound(img.cols/scale), CV_8UC1 ); |
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cvtColor( img, gray, CV_BGR2GRAY ); |
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resize( gray, smallImg, smallImg.size(), 0, 0, INTER_LINEAR ); |
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equalizeHist( smallImg, smallImg ); |
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cascade.detectMultiScale( smallImg, faces, |
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1.1, 2, 0 |
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//|CV_HAAR_FIND_BIGGEST_OBJECT
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//|CV_HAAR_DO_ROUGH_SEARCH
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|CV_HAAR_SCALE_IMAGE |
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, |
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Size(30, 30) ); |
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if( tryflip ) |
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{ |
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flip(smallImg, smallImg, 1); |
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cascade.detectMultiScale( smallImg, faces2, |
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1.1, 2, 0 |
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//|CV_HAAR_FIND_BIGGEST_OBJECT
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//|CV_HAAR_DO_ROUGH_SEARCH
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|CV_HAAR_SCALE_IMAGE |
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, |
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Size(30, 30) ); |
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for( vector<Rect>::const_iterator r = faces2.begin(); r != faces2.end(); r++ ) |
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{ |
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faces.push_back(Rect(smallImg.cols - r->x - r->width, r->y, r->width, r->height)); |
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} |
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} |
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for( vector<Rect>::iterator r = faces.begin(); r != faces.end(); r++, i++ ) |
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{ |
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Mat smallImgROI; |
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vector<Rect> nestedObjects; |
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Point center; |
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Scalar color = colors[i%8]; |
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int radius; |
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double aspect_ratio = (double)r->width/r->height; |
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if( 0.75 < aspect_ratio && aspect_ratio < 1.3 ) |
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{ |
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center.x = cvRound((r->x + r->width*0.5)*scale); |
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center.y = cvRound((r->y + r->height*0.5)*scale); |
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radius = cvRound((r->width + r->height)*0.25*scale); |
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circle( img, center, radius, color, 3, 8, 0 ); |
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} |
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else |
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rectangle( img, cvPoint(cvRound(r->x*scale), cvRound(r->y*scale)), |
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cvPoint(cvRound((r->x + r->width-1)*scale), cvRound((r->y + r->height-1)*scale)), |
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color, 3, 8, 0); |
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const int half_height=cvRound((float)r->height/2); |
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r->y=r->y + half_height; |
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r->height = half_height; |
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smallImgROI = smallImg(*r); |
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nestedCascade.detectMultiScale( smallImgROI, nestedObjects, |
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1.1, 0, 0 |
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//|CV_HAAR_FIND_BIGGEST_OBJECT
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//|CV_HAAR_DO_ROUGH_SEARCH
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//|CV_HAAR_DO_CANNY_PRUNING
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|CV_HAAR_SCALE_IMAGE |
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, |
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Size(30, 30) ); |
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// The number of detected neighbors depends on image size (and also illumination, etc.). The
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// following steps use a floating minimum and maximum of neighbors. Intensity thus estimated will be
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//accurate only after a first smile has been displayed by the user.
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const int smile_neighbors = (int)nestedObjects.size(); |
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static int max_neighbors=-1; |
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static int min_neighbors=-1; |
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if (min_neighbors == -1) min_neighbors = smile_neighbors; |
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max_neighbors = MAX(max_neighbors, smile_neighbors); |
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// Draw rectangle on the left side of the image reflecting smile intensity
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float intensityZeroOne = ((float)smile_neighbors - min_neighbors) / (max_neighbors - min_neighbors + 1); |
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int rect_height = cvRound((float)img.rows * intensityZeroOne); |
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CvScalar col = CV_RGB((float)255 * intensityZeroOne, 0, 0); |
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rectangle(img, cvPoint(0, img.rows), cvPoint(img.cols/10, img.rows - rect_height), col, -1); |
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} |
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cv::imshow( "result", img ); |
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} |
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