In my opencv project, I want to detect copy-move forgery in an image. I know how to use the opencv FLANN for feature matching in 2 different image, but I am become so confused on how to use FLANN for detection copy-move forgery in an image.
P.S1: I get the sift keypoints and descriptors of image and stuck in using the feature matching class.
P.S2: the type of feature matching is not important for me.
Thanks in advance.
Update :
These pictures is an example of what I need
And There is a code which matches features of two images and do something like it on two images (not a single one), the code in android native opencv format is like below:
vector<KeyPoint> keypoints; Mat descriptors; // Create a SIFT keypoint detector. SiftFeatureDetector detector; detector.detect(image_gray, keypoints); LOGI("Detected %d Keypoints ...", (int) keypoints.size()); // Compute feature description. detector.compute(image, keypoints, descriptors); LOGI("Compute Feature ..."); FlannBasedMatcher matcher; std::vector< DMatch > matches; matcher.match( descriptors, descriptors, matches ); double max_dist = 0; double min_dist = 100; //-- Quick calculation of max and min distances between keypoints for( int i = 0; i < descriptors.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 2*min_dist, //-- or a small arbitary value ( 0.02 ) in the event that min_dist is very //-- small) //-- PS.- radiusMatch can also be used here. std::vector< DMatch > good_matches; for( int i = 0; i < descriptors.rows; i++ ) { if( matches[i].distance <= max(2*min_dist, 0.02) ) { good_matches.push_back( matches[i]); } } //-- Draw only "good" matches Mat img_matches; drawMatches( image, keypoints, image, keypoints, good_matches, img_matches, Scalar::all(-1), Scalar::all(-1), vector<char>(), DrawMatchesFlags::NOT_DRAW_SINGLE_POINTS ); //-- Show detected matches // imshow( "Good Matches", img_matches ); imwrite(imgOutFile, img_matches); 1 Answers
Answers 1
I don't know if it's a good idea to use keypoints for this problem. I'd rather test template matching (using a sliding window on your image as patch). Compared to keypoints, this method has the disadvantage of being sensible to rotation and scale.
If you want to use keypoints, you can :
- find a set of keypoints (SURF, SIFT, or whatever you want),
- compute the matching score with every other keypoints, with the
knnMatchfunction of the Brute Force Matcher (cv::BFMatcher), keep matches between distincts points, i.e. points whose distance is greater than zero (or a threshold).
int nknn = 10; // max number of matches for each keypoint double minDist = 0.5; // distance threshold // Match each keypoint with every other keypoints cv::BFMatcher matcher(cv::NORM_L2, false); std::vector< std::vector< cv::DMatch > > matches; matcher.knnMatch(descriptors, descriptors, matches, nknn); // Compute distance and store distant matches std::vector< cv::DMatch > good_matches; for (int i = 0; i < matches.size(); i++) { for (int j = 0; j < matches[i].size(); j++) { double dist = matches[i][j].distance; if (dist > minDist) good_matches.push_back(matches[i][j]); } } Mat img_matches; drawMatches(image_gray, keypoints, image_gray, keypoints, good_matches, img_matches);
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