mirror of https://github.com/opencv/opencv.git
Open Source Computer Vision Library
https://opencv.org/
You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
213 lines
6.8 KiB
213 lines
6.8 KiB
15 years ago
|
#define CV_NO_BACKWARD_COMPATIBILITY
|
||
|
|
||
|
#include "cv.h"
|
||
|
#include "highgui.h"
|
||
|
|
||
|
#include <iostream>
|
||
|
#include <cstdio>
|
||
|
|
||
|
#ifdef _EiC
|
||
|
#define WIN32
|
||
|
#endif
|
||
|
|
||
|
using namespace std;
|
||
|
using namespace cv;
|
||
|
|
||
|
void detectAndDraw( Mat& img,
|
||
|
CascadeClassifier& cascade, CascadeClassifier& nestedCascade,
|
||
|
double scale);
|
||
|
|
||
|
String cascadeName =
|
||
|
"../../data/haarcascades/haarcascade_frontalface_alt.xml";
|
||
|
String nestedCascadeName =
|
||
|
"../../data/haarcascades/haarcascade_eye_tree_eyeglasses.xml";
|
||
|
|
||
|
int main( int argc, const char** argv )
|
||
|
{
|
||
|
CvCapture* capture = 0;
|
||
|
Mat frame, frameCopy, image;
|
||
|
const String scaleOpt = "--scale=";
|
||
|
size_t scaleOptLen = scaleOpt.length();
|
||
|
const String cascadeOpt = "--cascade=";
|
||
|
size_t cascadeOptLen = cascadeOpt.length();
|
||
|
const String nestedCascadeOpt = "--nested-cascade";
|
||
|
size_t nestedCascadeOptLen = nestedCascadeOpt.length();
|
||
|
String inputName;
|
||
|
|
||
|
CascadeClassifier cascade, nestedCascade;
|
||
|
double scale = 1;
|
||
|
|
||
|
for( int i = 1; i < argc; i++ )
|
||
|
{
|
||
|
if( cascadeOpt.compare( 0, cascadeOptLen, argv[i], cascadeOptLen ) == 0 )
|
||
|
cascadeName.assign( argv[i] + cascadeOptLen );
|
||
|
else if( nestedCascadeOpt.compare( 0, nestedCascadeOptLen, argv[i], nestedCascadeOptLen ) == 0 )
|
||
|
{
|
||
|
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( !sscanf( argv[i] + scaleOpt.length(), "%lf", &scale ) || scale < 1 )
|
||
|
scale = 1;
|
||
|
}
|
||
|
else if( argv[i][0] == '-' )
|
||
|
{
|
||
|
cerr << "WARNING: Unknown option %s" << argv[i] << endl;
|
||
|
}
|
||
|
else
|
||
|
inputName.assign( argv[i] );
|
||
|
}
|
||
|
|
||
|
if( !cascade.load( cascadeName ) )
|
||
|
{
|
||
|
cerr << "ERROR: Could not load classifier cascade" << endl;
|
||
|
cerr << "Usage: facedetect [--cascade=\"<cascade_path>\"]\n"
|
||
|
" [--nested-cascade[=\"nested_cascade_path\"]]\n"
|
||
|
" [--scale[=<image scale>\n"
|
||
|
" [filename|camera_index]\n" ;
|
||
|
return -1;
|
||
|
}
|
||
|
|
||
|
if( inputName.empty() || (isdigit(inputName.c_str()[0]) && inputName.c_str()[1] == '\0') )
|
||
|
capture = cvCaptureFromCAM( inputName.empty() ? 0 : inputName.c_str()[0] - '0' );
|
||
|
else if( inputName.size() )
|
||
|
{
|
||
|
image = imread( inputName, 1 );
|
||
|
if( image.empty() )
|
||
|
capture = cvCaptureFromAVI( inputName.c_str() );
|
||
|
}
|
||
|
else
|
||
|
image = imread( "lena.jpg", 1 );
|
||
|
|
||
|
cvNamedWindow( "result", 1 );
|
||
|
|
||
|
if( capture )
|
||
|
{
|
||
|
for(;;)
|
||
|
{
|
||
|
IplImage* iplImg = cvQueryFrame( capture );
|
||
|
frame = iplImg;
|
||
|
if( frame.empty() )
|
||
|
break;
|
||
|
if( iplImg->origin == IPL_ORIGIN_TL )
|
||
|
frame.copyTo( frameCopy );
|
||
|
else
|
||
|
flip( frame, frameCopy, 0 );
|
||
|
|
||
|
detectAndDraw( frameCopy, cascade, nestedCascade, scale );
|
||
|
|
||
|
if( waitKey( 10 ) >= 0 )
|
||
|
goto _cleanup_;
|
||
|
}
|
||
|
|
||
|
waitKey(0);
|
||
|
_cleanup_:
|
||
|
cvReleaseCapture( &capture );
|
||
|
}
|
||
|
else
|
||
|
{
|
||
|
if( !image.empty() )
|
||
|
{
|
||
|
detectAndDraw( image, cascade, nestedCascade, scale );
|
||
|
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 );
|
||
|
c = waitKey(0);
|
||
|
if( c == 27 || c == 'q' || c == 'Q' )
|
||
|
break;
|
||
|
}
|
||
|
}
|
||
|
fclose(f);
|
||
|
}
|
||
|
}
|
||
|
}
|
||
|
|
||
|
cvDestroyWindow("result");
|
||
|
|
||
|
return 0;
|
||
|
}
|
||
|
|
||
|
void detectAndDraw( Mat& img,
|
||
|
CascadeClassifier& cascade, CascadeClassifier& nestedCascade,
|
||
|
double scale)
|
||
|
{
|
||
|
int i = 0;
|
||
|
double t = 0;
|
||
|
vector<Rect> faces;
|
||
|
const static Scalar colors[] = { CV_RGB(0,0,255),
|
||
|
CV_RGB(0,128,255),
|
||
|
CV_RGB(0,255,255),
|
||
|
CV_RGB(0,255,0),
|
||
|
CV_RGB(255,128,0),
|
||
|
CV_RGB(255,255,0),
|
||
|
CV_RGB(255,0,0),
|
||
|
CV_RGB(255,0,255)} ;
|
||
|
Mat gray, smallImg( cvRound (img.rows/scale), cvRound(img.cols/scale), CV_8UC1 );
|
||
|
|
||
|
cvtColor( img, gray, CV_BGR2GRAY );
|
||
|
resize( gray, smallImg, smallImg.size(), 0, 0, INTER_LINEAR );
|
||
|
equalizeHist( smallImg, smallImg );
|
||
|
|
||
|
t = (double)cvGetTickCount();
|
||
|
cascade.detectMultiScale( smallImg, faces,
|
||
|
1.1, 2, 0
|
||
|
//|CV_HAAR_FIND_BIGGEST_OBJECT
|
||
|
//|CV_HAAR_DO_ROUGH_SEARCH
|
||
|
|CV_HAAR_SCALE_IMAGE
|
||
|
,
|
||
|
Size(30, 30) );
|
||
|
t = (double)cvGetTickCount() - t;
|
||
|
printf( "detection time = %g ms\n", t/((double)cvGetTickFrequency()*1000.) );
|
||
|
for( vector<Rect>::const_iterator r = faces.begin(); r != faces.end(); r++, i++ )
|
||
|
{
|
||
|
Mat smallImgROI;
|
||
|
vector<Rect> nestedObjects;
|
||
|
Point center;
|
||
|
Scalar color = colors[i%8];
|
||
|
int radius;
|
||
|
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 );
|
||
|
if( nestedCascade.empty() )
|
||
|
continue;
|
||
|
smallImgROI = smallImg(*r);
|
||
|
nestedCascade.detectMultiScale( smallImgROI, nestedObjects,
|
||
|
1.1, 2, 0
|
||
|
//|CV_HAAR_FIND_BIGGEST_OBJECT
|
||
|
//|CV_HAAR_DO_ROUGH_SEARCH
|
||
|
//|CV_HAAR_DO_CANNY_PRUNING
|
||
|
|CV_HAAR_SCALE_IMAGE
|
||
|
,
|
||
|
Size(30, 30) );
|
||
|
for( vector<Rect>::const_iterator nr = nestedObjects.begin(); nr != nestedObjects.end(); nr++ )
|
||
|
{
|
||
|
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 );
|
||
|
}
|
||
|
}
|
||
|
cv::imshow( "result", img );
|
||
|
}
|