mirror of
https://github.com/opencv/opencv.git
synced 2024-12-13 16:09:23 +08:00
258 lines
8.7 KiB
C++
258 lines
8.7 KiB
C++
#include "opencv2/objdetect.hpp"
|
|
#include "opencv2/highgui.hpp"
|
|
#include "opencv2/imgproc.hpp"
|
|
#include "opencv2/core/ocl.hpp"
|
|
#include <iostream>
|
|
|
|
using namespace std;
|
|
using namespace cv;
|
|
|
|
static void help()
|
|
{
|
|
cout << "\nThis program demonstrates the cascade recognizer. Now 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"
|
|
"./ufacedetect [--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"
|
|
"./ufacedetect --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( UMat& img, Mat& canvas, CascadeClassifier& cascade,
|
|
CascadeClassifier& nestedCascade,
|
|
double scale, bool tryflip );
|
|
|
|
string cascadeName = "../../data/haarcascades/haarcascade_frontalface_alt.xml";
|
|
string nestedCascadeName = "../../data/haarcascades/haarcascade_eye_tree_eyeglasses.xml";
|
|
|
|
int main( int argc, const char** argv )
|
|
{
|
|
VideoCapture capture;
|
|
UMat frame, image;
|
|
Mat canvas;
|
|
|
|
string inputName;
|
|
bool tryflip;
|
|
|
|
CascadeClassifier cascade, nestedCascade;
|
|
double scale;
|
|
|
|
cv::CommandLineParser parser(argc, argv,
|
|
"{cascade|../../data/haarcascades/haarcascade_frontalface_alt.xml|}"
|
|
"{nested-cascade|../../data/haarcascades/haarcascade_eye_tree_eyeglasses.xml|}"
|
|
"{help h ||}{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");
|
|
tryflip = parser.has("try-flip");
|
|
inputName = parser.get<string>("@filename");
|
|
if ( !parser.check())
|
|
{
|
|
parser.printErrors();
|
|
help();
|
|
return -1;
|
|
}
|
|
|
|
if ( !nestedCascade.load( nestedCascadeName ) )
|
|
cerr << "WARNING: Could not load classifier cascade for nested objects" << endl;
|
|
if( !cascade.load( cascadeName ) )
|
|
{
|
|
cerr << "ERROR: Could not load classifier cascade" << endl;
|
|
help();
|
|
return -1;
|
|
}
|
|
|
|
cout << "old cascade: " << (cascade.isOldFormatCascade() ? "TRUE" : "FALSE") << endl;
|
|
|
|
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;
|
|
}
|
|
else
|
|
{
|
|
if( inputName.empty() )
|
|
inputName = "../data/lena.jpg";
|
|
image = imread( inputName, 1 ).getUMat(ACCESS_READ);
|
|
if( image.empty() )
|
|
{
|
|
if(!capture.open( inputName ))
|
|
cout << "Could not read " << inputName << endl;
|
|
}
|
|
}
|
|
|
|
if( capture.isOpened() )
|
|
{
|
|
cout << "Video capturing has been started ..." << endl;
|
|
for(;;)
|
|
{
|
|
capture >> frame;
|
|
if( frame.empty() )
|
|
break;
|
|
|
|
detectAndDraw( frame, canvas, 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, canvas, 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 ).getUMat(ACCESS_READ);
|
|
if( !image.empty() )
|
|
{
|
|
detectAndDraw( image, canvas, 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( UMat& img, Mat& canvas, 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)
|
|
};
|
|
static UMat gray, smallImg;
|
|
|
|
t = (double)getTickCount();
|
|
|
|
cvtColor( img, gray, COLOR_BGR2GRAY );
|
|
double fx = 1 / scale;
|
|
resize( gray, smallImg, Size(), fx, fx, INTER_LINEAR_EXACT );
|
|
equalizeHist( smallImg, smallImg );
|
|
|
|
cascade.detectMultiScale( smallImg, faces,
|
|
1.1, 3, 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;
|
|
img.copyTo(canvas);
|
|
|
|
double fps = getTickFrequency()/t;
|
|
static double avgfps = 0;
|
|
static int nframes = 0;
|
|
nframes++;
|
|
double alpha = nframes > 50 ? 0.01 : 1./nframes;
|
|
avgfps = avgfps*(1-alpha) + fps*alpha;
|
|
|
|
putText(canvas, format("OpenCL: %s, fps: %.1f", ocl::useOpenCL() ? "ON" : "OFF", avgfps), Point(50, 30),
|
|
FONT_HERSHEY_SIMPLEX, 0.8, Scalar(0,255,0), 2);
|
|
|
|
for ( size_t i = 0; i < faces.size(); i++ )
|
|
{
|
|
Rect r = faces[i];
|
|
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( canvas, center, radius, color, 3, 8, 0 );
|
|
}
|
|
else
|
|
rectangle( canvas, 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;
|
|
UMat 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( canvas, center, radius, color, 3, 8, 0 );
|
|
}
|
|
}
|
|
imshow( "result", canvas );
|
|
}
|