Added smile detector
This commit is contained in:
Oscar Deniz Suarez
8353
data/haarcascades/haarcascade_smile.xml
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8353
data/haarcascades/haarcascade_smile.xml
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Load Diff
282
samples/c/smiledetect.cpp
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282
samples/c/smiledetect.cpp
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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 <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 1.3 for example. The larger the faster the processing>]\n"
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" [--try-flip]\n"
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" [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=1.3\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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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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// The number of detected neighbors depends on image size, these are for performing an approximate mapping to a maximum number of neighbors
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const float coef1 = 0.3190;
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const float coef2 = -48.7187;
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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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if( !nestedCascade.load( nestedCascadeName ) )
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cerr << "WARNING: Could not load classifier cascade for nested objects" << endl;
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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 classifier 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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image = imread( inputName, 1 );
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if( image.empty() )
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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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}
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else
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{
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image = imread( "lena.jpg", 1 );
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if(image.empty()) cout << "Couldn't read lena.jpg" << 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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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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cout << "In image read" << endl;
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if( !image.empty() )
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{
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detectAndDraw( image, cascade, nestedCascade, scale, tryflip );
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waitKey(0);
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}
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else if( !inputName.empty() )
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{
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/* assume it is a text file containing the
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list of the image filenames to be processed - one per line */
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FILE* f = fopen( inputName.c_str(), "rt" );
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if( f )
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{
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char buf[1000+1];
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while( fgets( buf, 1000, f ) )
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{
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int len = (int)strlen(buf), c;
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while( len > 0 && isspace(buf[len-1]) )
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len--;
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buf[len] = '\0';
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cout << "file " << buf << endl;
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image = imread( buf, 1 );
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if( !image.empty() )
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{
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detectAndDraw( image, cascade, nestedCascade, scale, tryflip );
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c = waitKey(0);
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if( c == 27 || c == 'q' || c == 'Q' )
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break;
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}
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else
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{
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cerr << "Aw snap, couldn't read image " << buf << endl;
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}
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}
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fclose(f);
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}
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}
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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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const int max_neighbors = MAX(0, cvRound((float)coef1*smallImg.cols + coef2));
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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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if( nestedCascade.empty() )
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continue;
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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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// Draw rectangle reflecting confidence
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const int smile_neighbors = nestedObjects.size();
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cout << "Detected " << smile_neighbors << " smile neighbors" << endl;
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const int rect_height = cvRound((float)img.rows * smile_neighbors / max_neighbors);
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CvScalar col = CV_RGB((float)255 * smile_neighbors / max_neighbors, 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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