| OpenCV 3.4.7 Open Source Computer Vision

samples/cpp/facedetect.cpp

This program demonstrates usage of the Cascade classifier class

Sample screenshot
#include "opencv2/objdetect.hpp"
#include "opencv2/highgui.hpp"
#include "opencv2/imgproc.hpp"
#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 )
{
VideoCapture capture;
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())
{
image = imread(samples::findFileOrKeep(inputName), IMREAD_COLOR);
if (image.empty())
{
if (!capture.open(samples::findFileOrKeep(inputName)))
{
cout << "Could not read " << inputName << endl;
return 1;
}
}
}
else
{
image = imread(samples::findFile("lena.jpg"), IMREAD_COLOR);
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 );
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);
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;
cvtColor( img, gray, COLOR_BGR2GRAY );
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
|CASCADE_SCALE_IMAGE,
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
|CASCADE_SCALE_IMAGE,
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
rectangle( img, Point(cvRound(r.x*scale), cvRound(r.y*scale)),
Point(cvRound((r.x + r.width-1)*scale), cvRound((r.y + r.height-1)*scale)),
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
|CASCADE_SCALE_IMAGE,
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 );
}