public void performMatch() {
// create feature detectors and feature extractors
FeatureDetector orbDetector = FeatureDetector.create(FeatureDetector.ORB);
DescriptorExtractor orbExtractor = DescriptorExtractor.create(DescriptorExtractor.ORB);
// set the keypoints
keyPointImg = new MatOfKeyPoint();
orbDetector.detect(imgGray, keyPointImg);
MatOfKeyPoint keyPointTempl = new MatOfKeyPoint();
orbDetector.detect(templGray, keyPointTempl);
// get the descriptions
descImg = new Mat(image.size(), image.type());
orbExtractor.compute(imgGray, keyPointImg, descImg);
Mat descTempl = new Mat(template.size(), template.type());
orbExtractor.compute(templGray, keyPointTempl, descTempl);
// perform matching
matches = new MatOfDMatch();
DescriptorMatcher matcher = DescriptorMatcher.create(DescriptorMatcher.BRUTEFORCE_HAMMING);
matcher.match(descImg, descTempl, matches);
Log.i("perform match result", matches.size().toString());
}
private Mat getTestDescriptors(Mat img) {
MatOfKeyPoint keypoints = new MatOfKeyPoint();
Mat descriptors = new Mat();
FeatureDetector detector = FeatureDetector.create(FeatureDetector.FAST);
DescriptorExtractor extractor = DescriptorExtractor.create(DescriptorExtractor.BRIEF);
detector.detect(img, keypoints);
extractor.compute(img, keypoints, descriptors);
return descriptors;
}
public Template performMatches(Map<String, Template> templates) {
// create feature detectors and feature extractors
FeatureDetector orbDetector = FeatureDetector.create(FeatureDetector.ORB);
DescriptorExtractor orbExtractor = DescriptorExtractor.create(DescriptorExtractor.ORB);
MatOfKeyPoint keyPointImgT;
Mat descImgT;
// set the keypoints
keyPointImgT = new MatOfKeyPoint();
orbDetector.detect(imgGray, keyPointImgT);
descImgT = new Mat(image.size(), image.type());
orbExtractor.compute(imgGray, keyPointImgT, descImgT);
Template best = null;
matches = null;
Map.Entry<String, Template> maxEntry = null;
// MatOfDMatch matches = new MatOfDMatch();
for (Map.Entry<String, Template> entry : templates.entrySet()) {
MatOfKeyPoint keyPointTempl = null;
Mat descTempl = null;
Mat tGray = null;
Template t = entry.getValue();
if (null == t.getTemplGray() || null == t.getDescTempl() || null == t.getKeyPointTempl()) {
// read image from stored data
Mat templ = readImgFromFile(t.getTemplName());
tGray = new Mat(templ.size(), templ.type());
Imgproc.cvtColor(templ, tGray, Imgproc.COLOR_BGRA2GRAY);
keyPointTempl = new MatOfKeyPoint();
orbDetector.detect(tGray, keyPointTempl);
descTempl = new Mat(templ.size(), templ.type());
orbExtractor.compute(tGray, keyPointTempl, descTempl);
t.setKeyPointTempl(keyPointTempl);
t.setDescTempl(descTempl);
} else {
descTempl = t.getDescTempl();
}
MatOfDMatch matchWithT = new MatOfDMatch();
DescriptorMatcher matcher = DescriptorMatcher.create(DescriptorMatcher.BRUTEFORCE_HAMMING);
// matcher.radiusMatch(descImgT, descTempl, matchWithT,200);//
matcher.match(descImgT, descTempl, matchWithT);
List<DMatch> matchList = matchWithT.toList();
// float min = Float.MAX_VALUE;
// float max = Float.MIN_VALUE;
// for(int i=0;i<matchList.size();i++){
// min = matchList.get(i).distance<min?matchList.get(i).distance:min;
// max = matchList.get(i).distance>max?matchList.get(i).distance:max;
// }
// Log.i("min distance","min distance is::"+min+"max
// distance::"+max+"size::"+matchList.size());
// Collections.sort(matchList, new Comparator<DMatch>() {
// @Override
// public int compare(DMatch o1, DMatch o2) {
// if (o1.distance < o2.distance)
// return -1;
// if (o1.distance > o2.distance)
// return 1;
// return 0;
// }
// });
float ratio = -1;
if (matchList.size() > 0) ratio = findMinTwoRatio(matchList);
if (ratio > 0.8 || ratio == -1) continue;
Log.i("match", "ratio::" + ratio);
// Todo:revisit logic
if (matches == null || (matchWithT.size().height > matches.size().height)) {
matches = matchWithT;
keyPointImg = keyPointImgT;
descImg = descImgT;
best = t;
}
}
// Log.i("perform match result", matches.size().toString());
return best;
}
private void detectObject() {
readLock.lock();
featureDetector.detect(img_scene, keypoints_scene);
featureDetector.detect(img_object, keypoints_object);
extractor.compute(img_object, keypoints_object, descriptors_object);
extractor.compute(img_scene, keypoints_scene, descriptors_scene);
readLock.unlock();
if (!descriptors_scene.empty()) {
matcher.match(descriptors_object, descriptors_scene, matches);
// readLock.unlock();
//
listMatches = matches.toList();
int size = descriptors_object.rows();
// -- Quick calculation of max and min distances between keypoints
for (int i = 0; i < size; i++) {
double dist = listMatches.get(i).distance;
if (dist < min_dist) {
min_dist = dist;
}
}
Log.e("Min", min_dist + "");
threeMinDist = 3 * min_dist;
listGoodMatches.removeAll(listGoodMatches);
for (int i = 0; i < size; i++) {
DMatch dMatch = listMatches.get(i);
float distance = dMatch.distance;
if (distance < threeMinDist) {
listGoodMatches.add(dMatch);
}
}
// good_matches.fromList(listGoodMatches);
Log.e("Matches", listMatches.size() + "");
Log.e("Good Matches", listGoodMatches.size() + "");
//
if (listGoodMatches.size() > 4) {
Point pointObj[] = new Point[listGoodMatches.size()];
Point pointScene[] = new Point[listGoodMatches.size()];
listKeyPointObject = keypoints_object.toList();
listKeyPointScene = keypoints_scene.toList();
// listPointScene.removeAll(listPointScene);
for (int i = 0; i < listGoodMatches.size(); i++) {
// -- Get the keypoints from the good matches
pointObj[i] = listKeyPointObject.get(listGoodMatches.get(i).queryIdx).pt;
pointScene[i] = listKeyPointScene.get(listGoodMatches.get(i).trainIdx).pt;
// listPointScene.add(listKeyPointScene.get(listGoodMatches.get(i).trainIdx).pt);
}
obj.fromArray(pointObj);
scene.fromArray(pointScene);
Log.e("Before findHomography", "");
H = Calib3d.findHomography(obj, scene, Calib3d.RANSAC, 9);
Log.e("AFTERRR findHomography", "");
pointObjConners[0] = new Point(0, 0);
pointObjConners[1] = new Point(img_object.cols(), 0);
pointObjConners[2] = new Point(img_object.cols(), img_object.rows());
pointObjConners[3] = new Point(0, img_object.rows());
obj_corners.fromArray(pointObjConners);
Core.perspectiveTransform(obj_corners, scene_corners, H);
p0 = new Point(scene_corners.toList().get(0).x, scene_corners.toList().get(0).y + 0);
p1 = new Point(scene_corners.toList().get(1).x, scene_corners.toList().get(1).y + 0);
p2 = new Point(scene_corners.toList().get(2).x, scene_corners.toList().get(2).y + 0);
p3 = new Point(scene_corners.toList().get(3).x, scene_corners.toList().get(3).y + 0);
Log.e("POINT THREAD", p0.toString() + p1.toString() + p2.toString() + p3.toString());
Log.e("detect ok", "detect ok");
}
} else {
Log.e("No descritor", "No descritor");
// readLock.unlock();
}
}