#include "opencv2/objdetect.hpp" #include "opencv2/highgui.hpp" #include "opencv2/imgproc.hpp" #include "opencv2/videoio.hpp" #include <iostream> using namespace std; using namespace cv; static void help(const char** argv) { cout << "\nThis program demonstrates the use of cv::CascadeClassifier class to detect objects (Face + eyes). You can use Haar or LBP features.\n" "This classifier can recognize many kinds of rigid objects, once the appropriate classifier is trained.\n" "It's most known use is for faces.\n" "Usage:\n" << argv[0] << " [--cascade=<cascade_path> this is the primary trained classifier such as frontal face]\n" " [--nested-cascade[=nested_cascade_path this an optional secondary classifier such as eyes]]\n" " [--scale=<image scale greater or equal to 1, try 1.3 for example>]\n" " [--try-flip]\n" " [filename|camera_index]\n\n" "example:\n" << argv[0] << " --cascade=\"data/haarcascades/haarcascade_frontalface_alt.xml\" --nested-cascade=\"data/haarcascades/haarcascade_eye_tree_eyeglasses.xml\" --scale=1.3\n\n" "During execution:\n\tHit any key to quit.\n" "\tUsing OpenCV version " << CV_VERSION << "\n" << endl; } void detectAndDraw( Mat& img, CascadeClassifier& cascade, CascadeClassifier& nestedCascade, double scale, bool tryflip ); string cascadeName; string nestedCascadeName; int main( int argc, const char** argv ) { VideoCapture capture; Mat frame, image; string inputName; bool tryflip; CascadeClassifier cascade, nestedCascade; double scale; cv::CommandLineParser parser(argc, argv, "{help h||}" "{cascade|data/haarcascades/haarcascade_frontalface_alt.xml|}" "{nested-cascade|data/haarcascades/haarcascade_eye_tree_eyeglasses.xml|}" "{scale|1|}{try-flip||}{@filename||}" ); if (parser.has("help")) { help(argv); return 0; } cascadeName = parser.get<string>("cascade"); nestedCascadeName = parser.get<string>("nested-cascade"); scale = parser.get<double>("scale"); if (scale < 1) scale = 1; tryflip = parser.has("try-flip"); inputName = parser.get<string>("@filename"); if (!parser.check()) { parser.printErrors(); return 0; } if (!nestedCascade.load(samples::findFileOrKeep(nestedCascadeName))) cerr << "WARNING: Could not load classifier cascade for nested objects" << endl; if (!cascade.load(samples::findFile(cascadeName))) { cerr << "ERROR: Could not load classifier cascade" << endl; help(argv); return -1; } if( inputName.empty() || (isdigit(inputName[0]) && inputName.size() == 1) ) { int camera = inputName.empty() ? 0 : inputName[0] - '0'; if(!capture.open(camera)) { cout << "Capture from camera #" << camera << " didn't work" << endl; return 1; } } else if (!inputName.empty()) { image = imread(samples::findFileOrKeep(inputName), IMREAD_COLOR); if (image.empty()) { if (!capture.open(samples::findFileOrKeep(inputName))) { cout << "Could not read " << inputName << endl; return 1; } } } else { image = imread(samples::findFile("lena.jpg"), IMREAD_COLOR); if (image.empty()) { cout << "Couldn't read lena.jpg" << endl; return 1; } } if( capture.isOpened() ) { cout << "Video capturing has been started ..." << endl; for(;;) { capture >> frame; if( frame.empty() ) break; Mat frame1 = frame.clone(); detectAndDraw( frame1, cascade, nestedCascade, scale, tryflip ); char c = (char)waitKey(10); if( c == 27 || c == 'q' || c == 'Q' ) break; } } else { cout << "Detecting face(s) in " << inputName << endl; if( !image.empty() ) { detectAndDraw( image, cascade, nestedCascade, scale, tryflip ); 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); 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, tryflip ); char c = (char)waitKey(0); if( c == 27 || c == 'q' || c == 'Q' ) break; } else { cerr << "Aw snap, couldn't read image " << buf << endl; } } fclose(f); } } } return 0; } void detectAndDraw( Mat& img, CascadeClassifier& cascade, CascadeClassifier& nestedCascade, double scale, bool tryflip ) { double t = 0; vector<Rect> faces, faces2; const static Scalar colors[] = { Scalar(255,0,0), Scalar(255,128,0), Scalar(255,255,0), Scalar(0,255,0), Scalar(0,128,255), Scalar(0,255,255), Scalar(0,0,255), Scalar(255,0,255) }; Mat gray, smallImg; cvtColor( img, gray, COLOR_BGR2GRAY ); double fx = 1 / scale; resize( gray, smallImg, Size(), fx, fx, INTER_LINEAR_EXACT ); equalizeHist( smallImg, smallImg ); t = (double)getTickCount(); cascade.detectMultiScale( smallImg, faces, 1.1, 2, 0 //|CASCADE_FIND_BIGGEST_OBJECT //|CASCADE_DO_ROUGH_SEARCH |CASCADE_SCALE_IMAGE, Size(30, 30) ); if( tryflip ) { flip(smallImg, smallImg, 1); cascade.detectMultiScale( smallImg, faces2, 1.1, 2, 0 //|CASCADE_FIND_BIGGEST_OBJECT //|CASCADE_DO_ROUGH_SEARCH |CASCADE_SCALE_IMAGE, Size(30, 30) ); for( vector<Rect>::const_iterator r = faces2.begin(); r != faces2.end(); ++r ) { faces.push_back(Rect(smallImg.cols - r->x - r->width, r->y, r->width, r->height)); } } t = (double)getTickCount() - t; printf( "detection time = %g ms\n", t*1000/getTickFrequency()); for ( size_t i = 0; i < faces.size(); i++ ) { Rect r = faces[i]; Mat smallImgROI; vector<Rect> nestedObjects; Point center; Scalar color = colors[i%8]; int radius; double aspect_ratio = (double)r.width/r.height; if( 0.75 < aspect_ratio && aspect_ratio < 1.3 ) { 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 ); } else rectangle( img, Point(cvRound(r.x*scale), cvRound(r.y*scale)), Point(cvRound((r.x + r.width-1)*scale), cvRound((r.y + r.height-1)*scale)), color, 3, 8, 0); if( nestedCascade.empty() ) continue; smallImgROI = smallImg( r ); nestedCascade.detectMultiScale( smallImgROI, nestedObjects, 1.1, 2, 0 //|CASCADE_FIND_BIGGEST_OBJECT //|CASCADE_DO_ROUGH_SEARCH //|CASCADE_DO_CANNY_PRUNING |CASCADE_SCALE_IMAGE, Size(30, 30) ); for ( size_t j = 0; j < nestedObjects.size(); j++ ) { Rect nr = nestedObjects[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); circle( img, center, radius, color, 3, 8, 0 ); } } imshow( "result", img ); }