215 lines
7.2 KiB
C++
215 lines
7.2 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/videoio.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(const char** argv)
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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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argv[0] << " [--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 2.0 for example. The larger the faster the processing>]\n"
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" [--try-flip]\n"
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" [video_filename|camera_index]\n\n"
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"Example:\n" <<
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argv[0] << " --cascade=\"data/haarcascades/haarcascade_frontalface_alt.xml\" --smile-cascade=\"data/haarcascades/haarcascade_smile.xml\" --scale=2.0\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;
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string nestedCascadeName;
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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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Mat frame, image;
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string inputName;
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bool tryflip;
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help(argv);
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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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"{help h||}{scale|1|}"
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"{cascade|data/haarcascades/haarcascade_frontalface_alt.xml|}"
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"{smile-cascade|data/haarcascades/haarcascade_smile.xml|}"
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"{try-flip||}{@input||}");
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if (parser.has("help"))
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{
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help(argv);
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return 0;
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}
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cascadeName = samples::findFile(parser.get<string>("cascade"));
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nestedCascadeName = samples::findFile(parser.get<string>("smile-cascade"));
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tryflip = parser.has("try-flip");
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inputName = parser.get<string>("@input");
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scale = parser.get<int>("scale");
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if (!parser.check())
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{
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help(argv);
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return 1;
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}
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if (scale < 1)
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scale = 1;
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if( !cascade.load( cascadeName ) )
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{
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cerr << "ERROR: Could not load face cascade" << endl;
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help(argv);
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return -1;
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}
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if( !nestedCascade.load( nestedCascadeName ) )
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{
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cerr << "ERROR: Could not load smile cascade" << endl;
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help(argv);
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return -1;
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}
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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 if( inputName.size() )
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{
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inputName = samples::findFileOrKeep(inputName);
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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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if( capture.isOpened() )
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{
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cout << "Video capturing has been started ..." << endl;
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cout << endl << "NOTE: Smile intensity will only be valid after a first smile has been detected" << 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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Mat frame1 = frame.clone();
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detectAndDraw( frame1, 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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cerr << "ERROR: Could not initiate capture" << endl;
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help(argv);
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return -1;
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}
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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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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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Mat gray, smallImg;
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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, 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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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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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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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, 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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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-1;
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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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//|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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// The number of detected neighbors depends on image size (and also illumination, etc.). The
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// following steps use a floating minimum and maximum of neighbors. Intensity thus estimated will be
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//accurate only after a first smile has been displayed by the user.
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const int smile_neighbors = (int)nestedObjects.size();
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static int max_neighbors=-1;
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static int min_neighbors=-1;
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if (min_neighbors == -1) min_neighbors = smile_neighbors;
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max_neighbors = MAX(max_neighbors, smile_neighbors);
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// Draw rectangle on the left side of the image reflecting smile intensity
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float intensityZeroOne = ((float)smile_neighbors - min_neighbors) / (max_neighbors - min_neighbors + 1);
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int rect_height = cvRound((float)img.rows * intensityZeroOne);
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Scalar col = Scalar((float)255 * intensityZeroOne, 0, 0);
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rectangle(img, Point(0, img.rows), Point(img.cols/10, img.rows - rect_height), col, -1);
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}
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imshow( "result", img );
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}
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