samples/cpp/facedetect.cpp


This program demonstrates usage of the Cascade classifier class

Cascade_Classifier_Tutorial_Result_Haar.jpg
Sample screenshot
#include <iostream>
using namespace std ;
using namespace cv ;
static void help()
{
cout << "\nThis program demonstrates the use of cv::CascadeClassifier class to detect objects (Face + eyes). You can use Haar or LBP features.\n"
"This classifier can recognize many kinds of rigid objects, once the appropriate classifier is trained.\n"
"It's most known use is for faces.\n"
"Usage:\n"
"./facedetect [--cascade=<cascade_path> this is the primary trained classifier such as frontal face]\n"
" [--nested-cascade[=nested_cascade_path this an optional secondary classifier such as eyes]]\n"
" [--scale=<image scale greater or equal to 1, try 1.3 for example>]\n"
" [--try-flip]\n"
" [filename|camera_index]\n\n"
"see facedetect.cmd for one call:\n"
"./facedetect --cascade=\"data/haarcascades/haarcascade_frontalface_alt.xml\" --nested-cascade=\"data/haarcascades/haarcascade_eye_tree_eyeglasses.xml\" --scale=1.3\n\n"
"During execution:\n\tHit any key to quit.\n"
"\tUsing OpenCV version " << CV_VERSION << "\n" << endl;
}
void detectAndDraw( Mat & img, CascadeClassifier & cascade,
CascadeClassifier & nestedCascade,
double scale , bool tryflip );
string cascadeName;
string nestedCascadeName;
int main( int argc, const char ** argv )
{
Mat frame, image;
string inputName;
bool tryflip;
CascadeClassifier cascade, nestedCascade;
double scale ;
cv::CommandLineParser parser(argc, argv,
"{help h||}"
"{cascade|data/haarcascades/haarcascade_frontalface_alt.xml|}"
"{nested-cascade|data/haarcascades/haarcascade_eye_tree_eyeglasses.xml|}"
"{scale|1|}{try-flip||}{@filename||}"
);
if (parser. has ( "help" ))
{
help();
return 0;
}
cascadeName = parser. get < string >( "cascade" );
nestedCascadeName = parser. get < string >( "nested-cascade" );
scale = parser. get < double >( "scale" );
if (scale < 1)
scale = 1;
tryflip = parser. has ( "try-flip" );
inputName = parser. get < string >( "@filename" );
if (!parser. check ())
{
parser. printErrors ();
return 0;
}
if (!nestedCascade. load ( samples::findFileOrKeep (nestedCascadeName)))
cerr << "WARNING: Could not load classifier cascade for nested objects" << endl;
if (!cascade. load ( samples::findFile (cascadeName)))
{
cerr << "ERROR: Could not load classifier cascade" << endl;
help();
return -1;
}
if ( inputName.empty() || (isdigit(inputName[0]) && inputName.size() == 1) )
{
int camera = inputName.empty() ? 0 : inputName[0] - '0' ;
if (!capture. open (camera))
{
cout << "Capture from camera #" << camera << " didn't work" << endl;
return 1;
}
}
else if (!inputName.empty())
{
if (image. empty ())
{
if (!capture. open ( samples::findFileOrKeep (inputName)))
{
cout << "Could not read " << inputName << endl;
return 1;
}
}
}
else
{
if (image. empty ())
{
cout << "Couldn't read lena.jpg" << endl;
return 1;
}
}
if ( capture. isOpened () )
{
cout << "Video capturing has been started ..." << endl;
for (;;)
{
capture >> frame;
if ( frame.empty() )
break ;
Mat frame1 = frame. clone ();
detectAndDraw( frame1, cascade, nestedCascade, scale, tryflip );
char c = (char) waitKey (10);
if ( c == 27 || c == 'q' || c == 'Q' )
break ;
}
}
else
{
cout << "Detecting face(s) in " << inputName << endl;
if ( !image. empty () )
{
detectAndDraw( image, cascade, nestedCascade, scale, tryflip );
}
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);
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 );
char c = (char) waitKey (0);
if ( c == 27 || c == 'q' || c == 'Q' )
break ;
}
else
{
cerr << "Aw snap, couldn't read image " << buf << endl;
}
}
fclose(f);
}
}
}
return 0;
}
void detectAndDraw( Mat & img, CascadeClassifier & cascade,
CascadeClassifier & nestedCascade,
double scale , bool tryflip )
{
double t = 0;
vector<Rect> faces, faces2;
const static Scalar colors[] =
{
Scalar (255,0,0),
Scalar (255,128,0),
Scalar (255,255,0),
Scalar (0,255,0),
Scalar (0,128,255),
Scalar (0,255,255),
Scalar (0,0,255),
Scalar (255,0,255)
};
Mat gray, smallImg;
double fx = 1 / scale ;
resize ( gray, smallImg, Size (), fx, fx, INTER_LINEAR_EXACT );
equalizeHist ( smallImg, smallImg );
t = (double) getTickCount ();
cascade. detectMultiScale ( smallImg, faces,
1.1, 2, 0
//|CASCADE_FIND_BIGGEST_OBJECT
//|CASCADE_DO_ROUGH_SEARCH
Size (30, 30) );
if ( tryflip )
{
flip (smallImg, smallImg, 1);
cascade. detectMultiScale ( smallImg, faces2,
1.1, 2, 0
//|CASCADE_FIND_BIGGEST_OBJECT
//|CASCADE_DO_ROUGH_SEARCH
Size (30, 30) );
for ( vector<Rect>::const_iterator r = faces2.begin(); r != faces2.end(); ++r )
{
faces.push_back( Rect (smallImg. cols - r->x - r->width, r->y, r->width, r->height));
}
}
t = (double) getTickCount () - t;
printf( "detection time = %g ms\n" , t*1000/ getTickFrequency ());
for ( size_t i = 0; i < faces.size(); i++ )
{
Rect r = faces[i];
Mat smallImgROI;
vector<Rect> nestedObjects;
Point center;
Scalar color = colors[i%8];
int radius;
double aspect_ratio = (double)r. width /r. height ;
if ( 0.75 < aspect_ratio && aspect_ratio < 1.3 )
{
center. x = cvRound ((r. x + r. width *0.5)* scale );
center. y = cvRound ((r. y + r. height *0.5)* scale );
radius = cvRound ((r. width + r. height )*0.25* scale );
circle ( img, center, radius, color, 3, 8, 0 );
}
else
color, 3, 8, 0);
if ( nestedCascade. empty () )
continue ;
smallImgROI = smallImg( r );
nestedCascade. detectMultiScale ( smallImgROI, nestedObjects,
1.1, 2, 0
//|CASCADE_FIND_BIGGEST_OBJECT
//|CASCADE_DO_ROUGH_SEARCH
//|CASCADE_DO_CANNY_PRUNING
Size (30, 30) );
for ( size_t j = 0; j < nestedObjects.size(); j++ )
{
Rect nr = nestedObjects[j];
center. x = cvRound ((r. x + nr. x + nr. width *0.5)* scale );
center. y = cvRound ((r. y + nr. y + nr. height *0.5)* scale );
radius = cvRound ((nr. width + nr. height )*0.25* scale );
circle ( img, center, radius, color, 3, 8, 0 );
}
}
imshow ( "result" , img );
}