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Open Source Computer Vision Library
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245 lines
8.5 KiB
245 lines
8.5 KiB
#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 <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 cascade recognizer. Now you can use Haar or LBP features.\n" |
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"This classifier can recognize many ~rigid objects, it's most known use is for faces.\n" |
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"Usage:\n" |
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"./facedetect [--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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" [filename|camera_index]\n\n" |
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"see facedetect.cmd for one call:\n" |
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"./facedetect --cascade=\"../../data/haarcascades/haarcascade_frontalface_alt.xml\" --nested-cascade=\"../../data/haarcascades/haarcascade_eye.xml\" --scale=1.3 \n" |
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"Hit any key to quit.\n" |
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"Using OpenCV version " << CV_VERSION << "\n" << endl; |
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} |
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void detectAndDraw( Mat& img, |
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CascadeClassifier& cascade, CascadeClassifier& nestedCascade, |
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double scale); |
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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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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 = "--nested-cascade"; |
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size_t nestedCascadeOptLen = nestedCascadeOpt.length(); |
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String inputName; |
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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( argv[i][0] == '-' ) |
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{ |
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cerr << "WARNING: Unknown option %s" << 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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cerr << "Usage: facedetect [--cascade=<cascade_path>]\n" |
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" [--nested-cascade[=nested_cascade_path]]\n" |
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" [--scale[=<image scale>\n" |
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" [filename|camera_index]\n" << endl ; |
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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 ); |
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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 ); |
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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 ); |
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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, |
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CascadeClassifier& cascade, CascadeClassifier& nestedCascade, |
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double scale) |
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{ |
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int i = 0; |
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double t = 0; |
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vector<Rect> faces; |
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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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t = (double)cvGetTickCount(); |
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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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t = (double)cvGetTickCount() - t; |
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printf( "detection time = %g ms\n", t/((double)cvGetTickFrequency()*1000.) ); |
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for( vector<Rect>::const_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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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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if( nestedCascade.empty() ) |
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continue; |
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smallImgROI = smallImg(*r); |
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nestedCascade.detectMultiScale( smallImgROI, nestedObjects, |
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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_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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for( vector<Rect>::const_iterator nr = nestedObjects.begin(); nr != nestedObjects.end(); nr++ ) |
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{ |
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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( img, center, radius, color, 3, 8, 0 ); |
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} |
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} |
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cv::imshow( "result", img ); |
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}
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