Object detection with OpenCV Feature Matching with a threshold/similarity score - Java/C++
Your Edited code is working fine for me, and working perfectly,
Following are changes that i have done in your code for detecting objects(small image) in the large image :
using SURF method for feature detection as well as feature extraction.(SURF is available in opencv 4.1.1 for Android and earlier, after that it have been removed from that, so here i have used opencv 4.1.1)
change threshold of image matched or not from 1 to 4, in following line
if(matchesFiltered.rows() >= 1)
to
if(matchesFiltered.rows() >= 4)
only this changes have worked perfectly for me, make sure that object/small image have rich texture(atleast should have keypoints that can be matched)
Emily Webb
Updated on August 26, 2020Comments
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Emily Webb over 3 years
I am in the process of creating a small program which detects objects(small image) in the large image and I am using OpenCV java. As I have to consider rotation and scaling I have used FeatureDetector.BRISK and DescriptorExtractor.BRISK.
Following approach is used to filter the match results to get the best matches only.
I have two questions
- Is there a way to find the below min_dist and max_dist with the loop I have used?
- Most important question - Now the problem is I need to use these matches to determine whether the object(template) found or not. Would be great if some one help me here.
Thanks in advance.
FeatureDetector fd = FeatureDetector.create(FeatureDetector.BRISK); final MatOfKeyPoint keyPointsLarge = new MatOfKeyPoint(); final MatOfKeyPoint keyPointsSmall = new MatOfKeyPoint(); fd.detect(largeImage, keyPointsLarge); fd.detect(smallImage, keyPointsSmall); System.out.println("keyPoints.size() : "+keyPointsLarge.size()); System.out.println("keyPoints2.size() : "+keyPointsSmall.size()); Mat descriptorsLarge = new Mat(); Mat descriptorsSmall = new Mat(); DescriptorExtractor extractor = DescriptorExtractor.create(DescriptorExtractor.BRISK); extractor.compute(largeImage, keyPointsLarge, descriptorsLarge); extractor.compute(smallImage, keyPointsSmall, descriptorsSmall); System.out.println("descriptorsA.size() : "+descriptorsLarge.size()); System.out.println("descriptorsB.size() : "+descriptorsSmall.size()); MatOfDMatch matches = new MatOfDMatch(); DescriptorMatcher matcher = DescriptorMatcher.create(DescriptorMatcher.BRUTEFORCE_HAMMINGLUT); matcher.match(descriptorsLarge, descriptorsSmall, matches); System.out.println("matches.size() : "+matches.size()); MatOfDMatch matchesFiltered = new MatOfDMatch(); List<DMatch> matchesList = matches.toList(); List<DMatch> bestMatches= new ArrayList<DMatch>(); Double max_dist = 0.0; Double min_dist = 100.0; for (int i = 0; i < matchesList.size(); i++) { Double dist = (double) matchesList.get(i).distance; if (dist < min_dist && dist != 0) { min_dist = dist; } if (dist > max_dist) { max_dist = dist; } } System.out.println("max_dist : "+max_dist); System.out.println("min_dist : "+min_dist); double threshold = 3 * min_dist; double threshold2 = 2 * min_dist; if (threshold2 >= max_dist) { threshold = min_dist * 1.1; } else if (threshold >= max_dist) { threshold = threshold2 * 1.4; } System.out.println("Threshold : "+threshold); for (int i = 0; i < matchesList.size(); i++) { Double dist = (double) matchesList.get(i).distance; System.out.println(String.format(i + " match distance best : %s", dist)); if (dist < threshold) { bestMatches.add(matches.toList().get(i)); System.out.println(String.format(i + " best match added : %s", dist)); } } matchesFiltered.fromList(bestMatches); System.out.println("matchesFiltered.size() : " + matchesFiltered.size());
Edit
Edited my code as follows.I know still it's not the best way to come to a conclusion whether the object found or not based on no of best matches. So please share your views.
System.out.println("max_dist : "+max_dist); System.out.println("min_dist : "+min_dist); if(min_dist > 50 ) { System.out.println("No match found"); System.out.println("Just return "); return false; } double threshold = 3 * min_dist; double threshold2 = 2 * min_dist; if (threshold > 75) { threshold = 75; } else if (threshold2 >= max_dist) { threshold = min_dist * 1.1; } else if (threshold >= max_dist) { threshold = threshold2 * 1.4; } System.out.println("Threshold : "+threshold); for (int i = 0; i < matchesList.size(); i++) { Double dist = (double) matchesList.get(i).distance; if (dist < threshold) { bestMatches.add(matches.toList().get(i)); //System.out.println(String.format(i + " best match added : %s", dist)); } } matchesFiltered.fromList(bestMatches); System.out.println("matchesFiltered.size() : " + matchesFiltered.size()); if(matchesFiltered.rows() >= 1) { System.out.println("match found"); return true; } else { return false; }