#include <stdio.h>
#include <iostream>
#include "opencv2/imgproc/imgproc.hpp"
#include "opencv2/core/core.hpp"
#include "opencv2/features2d/features2d.hpp"
#include "opencv2/highgui/highgui.hpp"
#include "opencv2/calib3d/calib3d.hpp"
#include "opencv2/nonfree/nonfree.hpp"
using namespace cv; int main( int argc, char** argv )
{ CvCapture* capture = cvCreateFileCapture( "sign3.mp4" );
Mat img_object = imread( "pic3.jpg", CV_LOAD_IMAGE_GRAYSCALE );
Mat frame = cvQueryFrame( capture ); Mat img_scene;
cvtColor(frame, img_scene, CV_BGR2GRAY); int minHessian = ;
SurfFeatureDetector detector( minHessian );
std::vector<KeyPoint> keypoints_object, keypoints_scene;
detector.detect( img_object, keypoints_object ); SurfDescriptorExtractor extractor;
Mat descriptors_object, descriptors_scene;
extractor.compute( img_object, keypoints_object, descriptors_object ); FlannBasedMatcher matcher;
std::vector< DMatch > matches; std::vector<Point2f> obj;
std::vector<Point2f> scene; while()
{
frame = cvQueryFrame( capture );
cvtColor(frame, img_scene, CV_BGR2GRAY);
if( !img_object.data || !img_scene.data )
{ std::cout<< " --(!) Error reading images " << std::endl; return -; } //-- Step 1: Detect the keypoints using SURF Detector detector.detect( img_scene, keypoints_scene ); //-- Step 2: Calculate descriptors (feature vectors) extractor.compute( img_scene, keypoints_scene, descriptors_scene ); //-- Step 3: Matching descriptor vectors using FLANN matcher matcher.match( descriptors_object, descriptors_scene, matches );
double max_dist = ; double min_dist = ; //-- Quick calculation of max and min distances between keypoints
for( int i = ; i < descriptors_object.rows; i++ )
{
double dist = matches[i].distance;
if( dist < min_dist )
min_dist = dist;
if( dist > max_dist )
max_dist = dist;
}
// printf("-- Max dist : %f \n", max_dist );
// printf("-- Min dist : %f \n", min_dist ); //-- Draw only "good" matches (i.e. whose distance is less than 3*min_dist )
std::vector< DMatch > good_matches;
for( int i = ; i < descriptors_object.rows; i++ )
{
if( matches[i].distance < *min_dist )
{
good_matches.push_back( matches[i]);
}
}
Mat img_matches;
drawMatches( img_object, keypoints_object, img_scene, keypoints_scene,
good_matches, img_matches, Scalar::all(-), Scalar::all(-),
vector<char>(), DrawMatchesFlags::NOT_DRAW_SINGLE_POINTS ); //-- Localize the object for( int i = ; i < good_matches.size(); i++ )
{
//-- Get the keypoints from the good matches
obj.push_back( keypoints_object[ good_matches[i].queryIdx ].pt );
scene.push_back( keypoints_scene[ good_matches[i].trainIdx ].pt );
}
Mat H = findHomography( obj, scene, CV_RANSAC,5.0 ); //-- Get the corners from the image_1 ( the object to be "detected" )
std::vector<Point2f> obj_corners();
obj_corners[] = cvPoint(,); obj_corners[] = cvPoint( img_object.cols, );
obj_corners[] = cvPoint( img_object.cols, img_object.rows ); obj_corners[] = cvPoint( , img_object.rows );
std::vector<Point2f> scene_corners();
perspectiveTransform( obj_corners, scene_corners, H); //-- Draw lines between the corners (the mapped object in the scene - image_2 )
line( img_matches, scene_corners[] + Point2f( img_object.cols, ), scene_corners[] + Point2f( img_object.cols, ), Scalar(, , ), );
line( img_matches, scene_corners[] + Point2f( img_object.cols, ), scene_corners[] + Point2f( img_object.cols, ), Scalar( , , ), );
line( img_matches, scene_corners[] + Point2f( img_object.cols, ), scene_corners[] + Point2f( img_object.cols, ), Scalar( , , ), );
line( img_matches, scene_corners[] + Point2f( img_object.cols, ), scene_corners[] + Point2f( img_object.cols, ), Scalar( , , ), ); //-- Show detected matches
namedWindow( "Good Matches & Object detection", WINDOW_NORMAL );
imshow( "Good Matches & Object detection", img_matches );
char c = cvWaitKey();
if( c == ) break; }
return ;
}

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