253 lines
8.7 KiB
C++
253 lines
8.7 KiB
C++
#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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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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string cascadeName = samples::findFile(parser.get<string>("cascade"));
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string nestedCascadeName = samples::findFileOrKeep(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: " << nestedCascadeName << endl;
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if( !cascade.load( cascadeName ) )
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{
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cerr << "ERROR: Could not load classifier cascade: " << cascadeName << 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 camera = inputName.empty() ? 0 : inputName[0] - '0';
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if(!capture.open(camera))
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cout << "Capture from camera #" << camera << " didn't work" << endl;
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}
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else
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{
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inputName = samples::findFileOrKeep(inputName);
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imread(inputName, IMREAD_COLOR).copyTo(image);
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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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char c = (char)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);
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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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imread(samples::findFile(buf), IMREAD_COLOR).copyTo(image);
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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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char c = (char)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_EXACT );
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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, cv::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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