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Open Source Computer Vision Library
https://opencv.org/
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257 lines
8.7 KiB
257 lines
8.7 KiB
#include "opencv2/objdetect.hpp" |
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#include "opencv2/highgui.hpp" |
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#include "opencv2/imgproc.hpp" |
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#include "opencv2/core/ocl.hpp" |
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#include <iostream> |
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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 cascade recognizer. Now you can use Haar or LBP features.\n" |
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"This classifier can recognize many kinds of rigid objects, once the appropriate classifier is trained.\n" |
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"It's most known use is for faces.\n" |
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"Usage:\n" |
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"./ufacedetect [--cascade=<cascade_path> this is the primary trained classifier such as frontal face]\n" |
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" [--nested-cascade[=nested_cascade_path this an optional secondary classifier such as eyes]]\n" |
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" [--scale=<image scale greater or equal to 1, try 1.3 for example>]\n" |
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" [--try-flip]\n" |
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" [filename|camera_index]\n\n" |
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"see facedetect.cmd for one call:\n" |
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"./ufacedetect --cascade=\"../../data/haarcascades/haarcascade_frontalface_alt.xml\" --nested-cascade=\"../../data/haarcascades/haarcascade_eye_tree_eyeglasses.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( UMat& img, Mat& canvas, 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_eye_tree_eyeglasses.xml"; |
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int main( int argc, const char** argv ) |
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{ |
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VideoCapture capture; |
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UMat frame, image; |
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Mat canvas; |
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string inputName; |
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bool tryflip; |
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CascadeClassifier cascade, nestedCascade; |
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double scale; |
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cv::CommandLineParser parser(argc, argv, |
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"{cascade|../../data/haarcascades/haarcascade_frontalface_alt.xml|}" |
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"{nested-cascade|../../data/haarcascades/haarcascade_eye_tree_eyeglasses.xml|}" |
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"{help h ||}{scale|1|}{try-flip||}{@filename||}" |
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); |
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if (parser.has("help")) |
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{ |
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help(); |
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return 0; |
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} |
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cascadeName = parser.get<string>("cascade"); |
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nestedCascadeName = parser.get<string>("nested-cascade"); |
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scale = parser.get<double>("scale"); |
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tryflip = parser.has("try-flip"); |
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inputName = parser.get<string>("@filename"); |
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if ( !parser.check()) |
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{ |
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parser.printErrors(); |
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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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cerr << "WARNING: Could not load classifier cascade for nested objects" << endl; |
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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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cout << "old cascade: " << (cascade.isOldFormatCascade() ? "TRUE" : "FALSE") << endl; |
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if( inputName.empty() || (isdigit(inputName[0]) && inputName.size() == 1) ) |
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{ |
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int c = inputName.empty() ? 0 : inputName[0] - '0'; |
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if(!capture.open(c)) |
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cout << "Capture from camera #" << c << " didn't work" << endl; |
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} |
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else |
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{ |
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if( inputName.empty() ) |
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inputName = "../data/lena.jpg"; |
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image = imread( inputName, 1 ).getUMat(ACCESS_READ); |
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if( image.empty() ) |
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{ |
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if(!capture.open( inputName )) |
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cout << "Could not read " << inputName << endl; |
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} |
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} |
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if( capture.isOpened() ) |
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{ |
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cout << "Video capturing has been started ..." << endl; |
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for(;;) |
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{ |
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capture >> frame; |
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if( frame.empty() ) |
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break; |
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detectAndDraw( frame, canvas, cascade, nestedCascade, scale, tryflip ); |
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int c = waitKey(10); |
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if( c == 27 || c == 'q' || c == 'Q' ) |
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break; |
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} |
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} |
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else |
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{ |
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cout << "Detecting face(s) in " << inputName << endl; |
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if( !image.empty() ) |
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{ |
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detectAndDraw( image, canvas, 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 ).getUMat(ACCESS_READ); |
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if( !image.empty() ) |
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{ |
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detectAndDraw( image, canvas, 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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return 0; |
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} |
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void detectAndDraw( UMat& img, Mat& canvas, CascadeClassifier& cascade, |
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CascadeClassifier& nestedCascade, |
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double scale, bool tryflip ) |
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{ |
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double t = 0; |
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vector<Rect> faces, faces2; |
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const static Scalar colors[] = |
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{ |
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Scalar(255,0,0), |
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Scalar(255,128,0), |
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Scalar(255,255,0), |
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Scalar(0,255,0), |
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Scalar(0,128,255), |
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Scalar(0,255,255), |
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Scalar(0,0,255), |
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Scalar(255,0,255) |
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}; |
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static UMat gray, smallImg; |
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t = (double)getTickCount(); |
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cvtColor( img, gray, COLOR_BGR2GRAY ); |
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double fx = 1 / scale; |
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resize( gray, smallImg, Size(), fx, fx, INTER_LINEAR ); |
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equalizeHist( smallImg, smallImg ); |
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cascade.detectMultiScale( smallImg, faces, |
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1.1, 3, 0 |
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//|CASCADE_FIND_BIGGEST_OBJECT |
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//|CASCADE_DO_ROUGH_SEARCH |
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|CASCADE_SCALE_IMAGE, |
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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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//|CASCADE_FIND_BIGGEST_OBJECT |
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//|CASCADE_DO_ROUGH_SEARCH |
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|CASCADE_SCALE_IMAGE, |
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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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t = (double)getTickCount() - t; |
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img.copyTo(canvas); |
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double fps = getTickFrequency()/t; |
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static double avgfps = 0; |
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static int nframes = 0; |
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nframes++; |
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double alpha = nframes > 50 ? 0.01 : 1./nframes; |
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avgfps = avgfps*(1-alpha) + fps*alpha; |
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putText(canvas, format("OpenCL: %s, fps: %.1f", ocl::useOpenCL() ? "ON" : "OFF", avgfps), Point(50, 30), |
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FONT_HERSHEY_SIMPLEX, 0.8, Scalar(0,255,0), 2); |
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for ( size_t i = 0; i < faces.size(); i++ ) |
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{ |
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Rect r = faces[i]; |
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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( canvas, center, radius, color, 3, 8, 0 ); |
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} |
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else |
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rectangle( canvas, Point(cvRound(r.x*scale), cvRound(r.y*scale)), |
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Point(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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UMat smallImgROI = smallImg(r); |
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nestedCascade.detectMultiScale( smallImgROI, nestedObjects, |
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1.1, 2, 0 |
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//|CASCADE_FIND_BIGGEST_OBJECT |
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//|CASCADE_DO_ROUGH_SEARCH |
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//|CASCADE_DO_CANNY_PRUNING |
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|CASCADE_SCALE_IMAGE, |
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Size(30, 30) ); |
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for ( size_t j = 0; j < nestedObjects.size(); j++ ) |
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{ |
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Rect nr = nestedObjects[j]; |
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center.x = cvRound((r.x + nr.x + nr.width*0.5)*scale); |
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center.y = cvRound((r.y + nr.y + nr.height*0.5)*scale); |
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radius = cvRound((nr.width + nr.height)*0.25*scale); |
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circle( canvas, center, radius, color, 3, 8, 0 ); |
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} |
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} |
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imshow( "result", canvas ); |
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}
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