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()); }
public void testClear() { matcher.add(Arrays.asList(new Mat())); matcher.clear(); assertTrue(matcher.empty()); }
public void testCloneBoolean() { matcher.add(Arrays.asList(new Mat())); DescriptorMatcher cloned = matcher.clone(true); assertNotNull(cloned); assertTrue(cloned.empty()); }
public void testMatchMatListOfDMatch() { Mat train = getTrainDescriptors(); Mat query = getQueryDescriptors(); MatOfDMatch matches = new MatOfDMatch(); matcher.add(Arrays.asList(train)); matcher.match(query, matches); assertArrayDMatchEquals(truth, matches.toArray(), EPS); }
public void testGetTrainDescriptors() { Mat train = new Mat(1, 1, CvType.CV_8U, new Scalar(123)); Mat truth = train.clone(); matcher.add(Arrays.asList(train)); List<Mat> descriptors = matcher.getTrainDescriptors(); assertEquals(1, descriptors.size()); assertMatEqual(truth, descriptors.get(0)); }
public void testMatchMatListOfDMatchListOfMat() { Mat train = getTrainDescriptors(); Mat query = getQueryDescriptors(); Mat mask = getMaskImg(); MatOfDMatch matches = new MatOfDMatch(); matcher.add(Arrays.asList(train)); matcher.match(query, matches, Arrays.asList(mask)); assertListDMatchEquals(Arrays.asList(truth[0], truth[1]), matches.toList(), EPS); }
public void testRead() { String filename = OpenCVTestRunner.getTempFileName("yml"); writeFile(filename, "%YAML:1.0\n"); matcher.read(filename); assertTrue(true); // BruteforceMatcher has no settings }
public void testWrite() { String filename = OpenCVTestRunner.getTempFileName("yml"); matcher.write(filename); String truth = "%YAML:1.0\n"; assertEquals(truth, readFile(filename)); }
public void onCameraViewStarted(int width, int height) { try { detectThread = null; if (detectThread == null) { detectThread = new DetectThread(); thread = new Thread(detectThread); thread.setPriority(Thread.MIN_PRIORITY); thread.start(); } // -- Step 1: Detect the keypoints using SURF Detector featureDetector = FeatureDetector.create(FeatureDetector.FAST); // -- Step 2: Calculate descriptors (feature vectors) extractor = DescriptorExtractor.create(DescriptorExtractor.FREAK); // -- Step 3: Matching descriptor vectors using FLANN matcher matcher = DescriptorMatcher.create(DescriptorMatcher.BRUTEFORCE_L1); img_object = Utils.loadResource(this, R.drawable.cardobj, Highgui.CV_LOAD_IMAGE_GRAYSCALE); img_scene = new Mat(); // img_scene = Utils.loadResource(this, R.drawable.cardscene, // Highgui.CV_LOAD_IMAGE_GRAYSCALE); matches = new MatOfDMatch(); listGoodMatches = new ArrayList<>(); keypoints_object = new MatOfKeyPoint(); keypoints_scene = new MatOfKeyPoint(); descriptors_object = new Mat(); descriptors_scene = new Mat(); obj = new MatOfPoint2f(); scene = new MatOfPoint2f(); H = new Mat(); obj_corners = new MatOfPoint2f(); scene_corners = new MatOfPoint2f(); min_dist = 9999999; p0 = new Point(0, 0); p1 = new Point(0, 0); p2 = new Point(0, 0); p3 = new Point(0, 0); good_matches = new MatOfDMatch(); listPointScene = new ArrayList<>(); } catch (Exception ex) { } }
protected void setUp() throws Exception { super.setUp(); matcher = DescriptorMatcher.create(DescriptorMatcher.BRUTEFORCE_HAMMINGLUT); matSize = 100; truth = new DMatch[] { new DMatch(0, 0, 0, 51), new DMatch(1, 2, 0, 42), new DMatch(2, 1, 0, 40), new DMatch(3, 3, 0, 53) }; }
public void testMatchMatMatListOfDMatch() { Mat train = getTrainDescriptors(); Mat query = getQueryDescriptors(); MatOfDMatch matches = new MatOfDMatch(); matcher.match(query, train, matches); /* OpenCVTestRunner.Log("matches found: " + matches.size()); for (DMatch m : matches.toArray()) OpenCVTestRunner.Log(m.toString()); */ assertArrayDMatchEquals(truth, matches.toArray(), EPS); }
public void map(Text key, Text value, Context context) throws InterruptedException, IOException { String filename = key.toString(); String json = value.toString(); // Make sure the input is valid if (!(filename.isEmpty() || json.isEmpty())) { // Change the json-type feature to Mat-type feature Mat descriptor = json2mat(json); if (descriptor != null) { // Read the query feature from the cache in Hadoop Mat query_features; String pathStr = context.getConfiguration().get("featureFilePath"); FileSystem fs = FileSystem.get(context.getConfiguration()); FSDataInputStream fsDataInputStream = fs.open(new Path(pathStr)); StringBuilder sb = new StringBuilder(); // Use a buffer to read the query_feature int remain = fsDataInputStream.available(); while (remain > 0) { int read; byte[] buf = new byte[BUF_SIZE]; read = fsDataInputStream.read(buf, fsDataInputStream.available() - remain, BUF_SIZE); sb.append(new String(buf, 0, read, StandardCharsets.UTF_8)); remain = remain - read; System.out.println("remain:" + remain + "\tread:" + read + "\tsb.size:" + sb.length()); } // Read the query_feature line by line // Scanner sc = new Scanner(fsDataInputStream, "UTF-8"); // StringBuilder sb = new StringBuilder(); // while (sc.hasNextLine()) { // sb.append(sc.nextLine()); // } // String query_json = sb.toString(); // String query_json = new String(buf, StandardCharsets.UTF_8); String query_json = sb.toString(); fsDataInputStream.close(); query_features = json2mat(query_json); // Get the similarity of the current database image against the query image DescriptorMatcher matcher = DescriptorMatcher.create(DescriptorMatcher.FLANNBASED); MatOfDMatch matches = new MatOfDMatch(); // Ensure the two features have same length of cols (the feature extracted are all 128 // cols(at least in this case)) if (query_features.cols() == descriptor.cols()) { matcher.match(query_features, descriptor, matches); DMatch[] dMatches = matches.toArray(); // Calculate the max/min distances // double max_dist = Double.MAX_VALUE; // double min_dist = Double.MIN_VALUE; double max_dist = 0; double min_dist = 100; for (int i = 0; i < dMatches.length; i++) { double dist = dMatches[i].distance; if (min_dist > dist) min_dist = dist; if (max_dist < dist) max_dist = dist; } // Only distances ≤ threshold are good matches double threshold = max_dist * THRESHOLD_FACTOR; // double threshold = min_dist * 2; LinkedList<DMatch> goodMatches = new LinkedList<DMatch>(); for (int i = 0; i < dMatches.length; i++) { if (dMatches[i].distance <= threshold) { goodMatches.addLast(dMatches[i]); } } // Get the ratio of good_matches to all_matches double ratio = (double) goodMatches.size() / (double) dMatches.length; System.out.println("*** current_record_filename:" + filename + " ***"); System.out.println("feature:" + descriptor + "\nquery_feature:" + query_features); System.out.println( "min_dist of keypoints:" + min_dist + " max_dist of keypoints:" + max_dist); System.out.println( "total_matches:" + dMatches.length + "\tgood_matches:" + goodMatches.size()); // System.out.println("type:" + descriptor.type() + " channels:" + // descriptor.channels() + " rows:" + descriptor.rows() + " cols:" + descriptor.cols()); // System.out.println("qtype:" + query_features.type() + " // qchannels:" + query_features.channels() + " qrows:" + query_features.rows() + " // qcols:" + query_features.cols()); System.out.println(); if (ratio > PERCENTAGE_THRESHOLD) { // Key:1 Value:filename|ratio context.write(ONE, new Text(filename + "|" + ratio)); // context.write(ONE, new Text(filename + "|" + // String.valueOf(goodMatches.size()))); } } else { System.out.println("The size of the features are not equal"); } } else { // a null pointer, do nothing System.out.println("A broken/null feature:" + filename); System.out.println(); } } }
public void testIsMaskSupported() { assertTrue(matcher.isMaskSupported()); }
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(); } }
public void testAdd() { matcher.add(Arrays.asList(new Mat())); assertFalse(matcher.empty()); }
public void testEmpty() { assertTrue(matcher.empty()); }
public void testTrain() { matcher.train(); // BruteforceMatcher does not need to train }