cout << "\nThis program demonstrates the smile detector.\n"
"Usage:\n"
"./smiledetect [--cascade=<cascade_path> this is the frontal face classifier]\n"
- " [--smile-cascade[=smile_cascade_path]]\n"
- " [--scale=<image scale greater or equal to 1, try 1.3 for example. The larger the faster the processing>]\n"
+ " [--smile-cascade=[<smile_cascade_path>]]\n"
+ " [--scale=<image scale greater or equal to 1, try 2.0 for example. The larger the faster the processing>]\n"
" [--try-flip]\n"
- " [filename|camera_index]\n\n"
+ " [video_filename|camera_index]\n\n"
"Example:\n"
- "./smiledetect --cascade=\"../../data/haarcascades/haarcascade_frontalface_alt.xml\" --smile-cascade=\"../../data/haarcascades/haarcascade_smile.xml\" --scale=1.3\n\n"
+ "./smiledetect --cascade=\"../../data/haarcascades/haarcascade_frontalface_alt.xml\" --smile-cascade=\"../../data/haarcascades/haarcascade_smile.xml\" --scale=2.0\n\n"
"During execution:\n\tHit any key to quit.\n"
"\tUsing OpenCV version " << CV_VERSION << "\n" << endl;
}
string cascadeName = "../../data/haarcascades/haarcascade_frontalface_alt.xml";
string nestedCascadeName = "../../data/haarcascades/haarcascade_smile.xml";
-// The number of detected neighbors depends on image size, these are for performing an approximate mapping to a maximum number of neighbors
-const float coef1 = 0.3190;
-const float coef2 = -48.7187;
-
int main( int argc, const char** argv )
{
{
if( argv[i][nestedCascadeOpt.length()] == '=' )
nestedCascadeName.assign( argv[i] + nestedCascadeOpt.length() + 1 );
- if( !nestedCascade.load( nestedCascadeName ) )
- cerr << "WARNING: Could not load classifier cascade for nested objects" << endl;
}
else if( scaleOpt.compare( 0, scaleOptLen, argv[i], scaleOptLen ) == 0 )
{
if( !cascade.load( cascadeName ) )
{
- cerr << "ERROR: Could not load classifier cascade" << endl;
+ cerr << "ERROR: Could not load face cascade" << endl;
+ help();
+ return -1;
+ }
+ if( !nestedCascade.load( nestedCascadeName ) )
+ {
+ cerr << "ERROR: Could not load smile cascade" << endl;
help();
return -1;
}
}
else if( inputName.size() )
{
- image = imread( inputName, 1 );
- if( image.empty() )
- {
- capture = cvCaptureFromAVI( inputName.c_str() );
- if(!capture) cout << "Capture from AVI didn't work" << endl;
- }
- }
- else
- {
- image = imread( "lena.jpg", 1 );
- if(image.empty()) cout << "Couldn't read lena.jpg" << endl;
+ capture = cvCaptureFromAVI( inputName.c_str() );
+ if(!capture) cout << "Capture from AVI didn't work" << endl;
}
cvNamedWindow( "result", 1 );
if( capture )
{
cout << "In capture ..." << endl;
+ cout << endl << "NOTE: Smile intensity will only be valid after a first smile has been detected" << endl;
+
for(;;)
{
IplImage* iplImg = cvQueryFrame( capture );
}
else
{
- cout << "In image read" << 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), c;
- 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 );
- c = waitKey(0);
- if( c == 27 || c == 'q' || c == 'Q' )
- break;
- }
- else
- {
- cerr << "Aw snap, couldn't read image " << buf << endl;
- }
- }
- fclose(f);
- }
- }
+ cerr << "ERROR: Could not initiate capture" << endl;
+ help();
+ return -1;
}
cvDestroyWindow("result");
CV_RGB(255,0,255)} ;
Mat gray, smallImg( cvRound (img.rows/scale), cvRound(img.cols/scale), CV_8UC1 );
- const int max_neighbors = MAX(0, cvRound((float)coef1*smallImg.cols + coef2));
-
cvtColor( img, gray, CV_BGR2GRAY );
resize( gray, smallImg, smallImg.size(), 0, 0, INTER_LINEAR );
equalizeHist( smallImg, smallImg );
faces.push_back(Rect(smallImg.cols - r->x - r->width, r->y, r->width, r->height));
}
}
+
for( vector<Rect>::iterator r = faces.begin(); r != faces.end(); r++, i++ )
{
Mat smallImgROI;
rectangle( img, cvPoint(cvRound(r->x*scale), cvRound(r->y*scale)),
cvPoint(cvRound((r->x + r->width-1)*scale), cvRound((r->y + r->height-1)*scale)),
color, 3, 8, 0);
- if( nestedCascade.empty() )
- continue;
const int half_height=cvRound((float)r->height/2);
r->y=r->y + half_height;
,
Size(30, 30) );
- // Draw rectangle reflecting confidence
+ // The number of detected neighbors depends on image size (and also illumination, etc.). The
+ // following steps use a floating minimum and maximum of neighbors. Intensity thus estimated will be
+ //accurate only after a first smile has been displayed by the user.
const int smile_neighbors = nestedObjects.size();
- cout << "Detected " << smile_neighbors << " smile neighbors" << endl;
- const int rect_height = cvRound((float)img.rows * smile_neighbors / max_neighbors);
- CvScalar col = CV_RGB((float)255 * smile_neighbors / max_neighbors, 0, 0);
+ static int max_neighbors=-1;
+ static int min_neighbors=-1;
+ if (min_neighbors == -1) min_neighbors = smile_neighbors;
+ max_neighbors = MAX(max_neighbors, smile_neighbors);
+
+ // Draw rectangle on the left side of the image reflecting smile intensity
+ float intensityZeroOne = ((float)smile_neighbors - min_neighbors) / (max_neighbors - min_neighbors + 1);
+ int rect_height = cvRound((float)img.rows * intensityZeroOne);
+ CvScalar col = CV_RGB((float)255 * intensityZeroOne, 0, 0);
rectangle(img, cvPoint(0, img.rows), cvPoint(img.cols/10, img.rows - rect_height), col, -1);
- }
+ }
- cv::imshow( "result", img );
+ cv::imshow( "result", img );
}