--- /dev/null
+<!--\r
+ This is 20x34 detector of profile faces using LBP features.\r
+ It was created by Attila Novak during GSoC 2012.\r
+ Note that the detector only detects faces rotated to the right,\r
+ so you may want to run it on the original and on\r
+ the flipped image to detect different profile faces.\r
+-->\r
+<?xml version="1.0"?>\r
+<opencv_storage>\r
+<cascade>\r
+ <stageType>BOOST</stageType>\r
+ <featureType>LBP</featureType>\r
+ <height>34</height>\r
+ <width>20</width>\r
+ <stageParams>\r
+ <boostType>GAB</boostType>\r
+ <minHitRate>9.9500000476837158e-001</minHitRate>\r
+ <maxFalseAlarm>3.0000001192092896e-001</maxFalseAlarm>\r
+ <weightTrimRate>9.4999999999999996e-001</weightTrimRate>\r
+ <maxDepth>1</maxDepth>\r
+ <maxWeakCount>100</maxWeakCount></stageParams>\r
+ <featureParams>\r
+ <maxCatCount>256</maxCatCount>\r
+ <featSize>1</featSize></featureParams>\r
+ <stageNum>16</stageNum>\r
+ <stages>\r
+ <!-- stage 0 -->\r
+ <_>\r
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+ <!-- stage 1 -->\r
+ <_>\r
+ <maxWeakCount>6</maxWeakCount>\r
+ <stageThreshold>-5.4879629611968994e-001</stageThreshold>\r
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+ <!-- stage 2 -->\r
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+ <!-- stage 3 -->\r
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+ <!-- stage 4 -->\r
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+ <!-- stage 5 -->\r
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+ <!-- stage 6 -->\r
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--- /dev/null
+<!--\r
+ This is 12x80 detector of the silverware (forks, spoons, knives) using LBP features.\r
+ It was created by Attila Novak during GSoC 2012.\r
+ Note that the detector only detects vertically oriented silverware,\r
+ so you should care of the proper image orientation\r
+ (probably should run detector several times).\r
+ It also assumes the "top view" when the camera optical axis is orthogonal to the table plane.\r
+-->\r
+<?xml version="1.0"?>\r
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+ <maxDepth>1</maxDepth>\r
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+ <featureParams>\r
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+ <stages>\r
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+</opencv_storage>\r
#include "opencv2/imgproc/imgproc.hpp"
#include <iostream>
+#include <iterator>
#include <stdio.h>
using namespace std;
static void help()
{
cout << "\nThis program demonstrates the cascade recognizer. Now you can use Haar or LBP features.\n"
- "This classifier can recognize many ~rigid objects, it's most known use is for faces.\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"
+ " [--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.xml\" --scale=1.3 \n"
- "Hit any key to quit.\n"
- "Using OpenCV version " << CV_VERSION << "\n" << endl;
+ "./facedetect --cascade=\"../../data/haarcascades/haarcascade_frontalface_alt.xml\" --nested-cascade=\"../../data/haarcascades/haarcascade_eye.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);
+void detectAndDraw( Mat& img, 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";
+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=";
+ const string scaleOpt = "--scale=";
size_t scaleOptLen = scaleOpt.length();
- const String cascadeOpt = "--cascade=";
+ const string cascadeOpt = "--cascade=";
size_t cascadeOptLen = cascadeOpt.length();
- const String nestedCascadeOpt = "--nested-cascade";
+ const string nestedCascadeOpt = "--nested-cascade";
size_t nestedCascadeOptLen = nestedCascadeOpt.length();
- String inputName;
+ const string tryFlipOpt = "--try-flip";
+ size_t tryFlipOptLen = tryFlipOpt.length();
+ string inputName;
+ bool tryflip = false;
help();
scale = 1;
cout << " from which we read scale = " << scale << endl;
}
+ else if( tryFlipOpt.compare( 0, tryFlipOptLen, argv[i], tryFlipOptLen ) == 0 )
+ {
+ tryflip = true;
+ cout << " will try to flip image horizontally to detect assymetric objects\n";
+ }
else if( argv[i][0] == '-' )
{
cerr << "WARNING: Unknown option %s" << argv[i] << endl;
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" << endl ;
+ help();
return -1;
}
else
flip( frame, frameCopy, 0 );
- detectAndDraw( frameCopy, cascade, nestedCascade, scale );
+ detectAndDraw( frameCopy, cascade, nestedCascade, scale, tryflip );
if( waitKey( 10 ) >= 0 )
goto _cleanup_;
cout << "In image read" << endl;
if( !image.empty() )
{
- detectAndDraw( image, cascade, nestedCascade, scale );
+ detectAndDraw( image, cascade, nestedCascade, scale, tryflip );
waitKey(0);
}
else if( !inputName.empty() )
image = imread( buf, 1 );
if( !image.empty() )
{
- detectAndDraw( image, cascade, nestedCascade, scale );
+ detectAndDraw( image, cascade, nestedCascade, scale, tryflip );
c = waitKey(0);
if( c == 27 || c == 'q' || c == 'Q' )
break;
return 0;
}
-void detectAndDraw( Mat& img,
- CascadeClassifier& cascade, CascadeClassifier& nestedCascade,
- double scale)
+void detectAndDraw( Mat& img, CascadeClassifier& cascade,
+ CascadeClassifier& nestedCascade,
+ double scale, bool tryflip )
{
int i = 0;
double t = 0;
- vector<Rect> faces;
+ vector<Rect> faces, faces2;
const static Scalar colors[] = { CV_RGB(0,0,255),
CV_RGB(0,128,255),
CV_RGB(0,255,255),
|CV_HAAR_SCALE_IMAGE
,
Size(30, 30) );
+ if( tryflip )
+ {
+ flip(smallImg, smallImg, 1);
+ cascade.detectMultiScale( smallImg, faces2,
+ 1.1, 2, 0
+ //|CV_HAAR_FIND_BIGGEST_OBJECT
+ //|CV_HAAR_DO_ROUGH_SEARCH
+ |CV_HAAR_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)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++ )
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 );
+
+ 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, 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;
smallImgROI = smallImg(*r);
projects / profile / ivi / opencv.git / commitdiff