public void testMatchMatMatListOfDMatchMat() {
    Mat train = getTrainDescriptors();
    Mat query = getQueryDescriptors();
    Mat mask = getMaskImg();
    MatOfDMatch matches = new MatOfDMatch();

    matcher.match(query, train, matches, mask);

    assertListDMatchEquals(Arrays.asList(truth[0], truth[1]), matches.toList(), EPS);
  }
Esempio n. 2
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  public MatOfPoint getContourOfBestMatch() {

    if (matches == null) return null;

    Mat threshold = new Mat(imgGray.size(), imgGray.type());
    Imgproc.threshold(imgGray, threshold, 70, 255, Imgproc.THRESH_TOZERO);

    List<MatOfPoint> contours = new ArrayList<MatOfPoint>();
    Imgproc.findContours(
        threshold, contours, new Mat(), Imgproc.RETR_CCOMP, Imgproc.CHAIN_APPROX_NONE);
    // HashMap<Integer,MatOfPoint> coordinates = computeCoord(contours,)

    if (contours.size() == 0) return null;
    List<DMatch> matchList = matches.toList();
    List<KeyPoint> keyPointList = keyPointImg.toList();

    HashMap<Integer, Double> contourDensityMap = new HashMap<Integer, Double>();

    Log.i("getContourBestMatch::", "contour size::" + contours.size());

    for (int idx = 0; idx < contours.size(); idx++) {
      MatOfPoint2f ctr2f = new MatOfPoint2f(contours.get(idx).toArray());
      // double contourarea = Imgproc.contourArea(contours.get(idx));

      double contourarea = contours.get(idx).rows();
      if (contourarea < 50) continue;

      Rect r = Imgproc.boundingRect(contours.get(idx));

      double count = 0;
      // Log.i("contour area","contour area is::"+contourarea);
      for (DMatch match : matchList) {

        Point q = keyPointList.get(match.queryIdx).pt;
        if (q.x >= r.x && q.x <= (r.x + r.width) && q.y >= r.y && q.y <= (r.y + r.height)) count++;

        //
        // if(Imgproc.pointPolygonTest(ctr2f,keyPointList.get(match.queryIdx).pt,true)>0){
        //                    if(null ==contourDensityMap.get(idx))
        //                        contourDensityMap.put(idx,1.0);
        //
        //                    else{
        //                        contourDensityMap.put(idx,((Double)contourDensityMap.get(idx))+1);
        //                    }
        //
        //                }

      }
      //            if(contourDensityMap.containsKey(idx)) {
      //
      // Log.i("contourPoint","idx::"+idx+"count::"+contourDensityMap.get(idx)+"contour
      // area::"+contourarea);
      //                contourDensityMap.put(idx, contourDensityMap.get(idx) / contourarea);
      //            }
      if (count != 0) {
        contourDensityMap.put(idx, count / contourarea);
      }
    }

    Log.i("MarkerTracker", "contour density size::" + contourDensityMap.size());

    Map.Entry<Integer, Double> maxEntry = null;

    for (Map.Entry<Integer, Double> entry : contourDensityMap.entrySet()) {
      Log.i("contourDensityMap", "Entry value::" + entry.getValue());
      if (maxEntry == null || entry.getValue().compareTo(maxEntry.getValue()) > 0) {
        maxEntry = entry;
      }
    }
    Log.i("maxEntry::", "" + (maxEntry == null ? null : maxEntry.getKey()));
    // return contours;
    return contours.get(maxEntry != null ? maxEntry.getKey() : 0);
  }
Esempio n. 3
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  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;
  }
Esempio n. 4
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  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();
    }
  }