Java Code Examples for org.opencv.features2d.DescriptorMatcher#create()

public static Mat FeatureSiftLannbased(Mat src, Mat dst){
	FeatureDetector fd = FeatureDetector.create(FeatureDetector.SIFT);
	DescriptorExtractor de = DescriptorExtractor.create(DescriptorExtractor.SIFT);
	DescriptorMatcher Matcher = DescriptorMatcher.create(DescriptorMatcher.FLANNBASED);
	
	MatOfKeyPoint mkp = new MatOfKeyPoint();
	fd.detect(src, mkp);
	Mat desc = new Mat();
	de.compute(src, mkp, desc);
	Features2d.drawKeypoints(src, mkp, src);
	
	MatOfKeyPoint mkp2 = new MatOfKeyPoint();
	fd.detect(dst, mkp2);
	Mat desc2 = new Mat();
	de.compute(dst, mkp2, desc2);
	Features2d.drawKeypoints(dst, mkp2, dst);
	
	
	// Matching features
	MatOfDMatch Matches = new MatOfDMatch();
	Matcher.match(desc, desc2, Matches);
	
	List<DMatch> l = Matches.toList();
	List<DMatch> goodMatch = new ArrayList<DMatch>();
	for (int i = 0; i < l.size(); i++) {
		DMatch dmatch = l.get(i);
		if (Math.abs(dmatch.queryIdx - dmatch.trainIdx) < 10f) {
			goodMatch.add(dmatch);
		}
		
	}
	
	Matches.fromList(goodMatch);
	// Show result
	Mat OutImage = new Mat();
	Features2d.drawMatches(src, mkp, dst, mkp2, Matches, OutImage);
	
	return OutImage;
}
 

private void initializeOpenCVDependencies() throws IOException {
    mOpenCvCameraView.enableView();
    detector = FeatureDetector.create(FeatureDetector.ORB);
    descriptor = DescriptorExtractor.create(DescriptorExtractor.ORB);
    matcher = DescriptorMatcher.create(DescriptorMatcher.BRUTEFORCE_HAMMING);
    img1 = new Mat();
    AssetManager assetManager = getAssets();
    InputStream istr = assetManager.open("a.jpeg");
    Bitmap bitmap = BitmapFactory.decodeStream(istr);
    Utils.bitmapToMat(bitmap, img1);
    Imgproc.cvtColor(img1, img1, Imgproc.COLOR_RGB2GRAY);
    img1.convertTo(img1, 0); //converting the image to match with the type of the cameras image
    descriptors1 = new Mat();
    keypoints1 = new MatOfKeyPoint();
    detector.detect(img1, keypoints1);
    descriptor.compute(img1, keypoints1, descriptors1);

}
 

@SuppressWarnings("rawtypes")
@Override
protected void prepareOpenCVOp(Map conf, TopologyContext context) throws Exception {
	this.connectorHolder = new ConnectorHolder(conf);
	matcher = DescriptorMatcher.create( matcherType );
	prototypes = new HashMap<Mat, String>();
	
	for(String location : protoLocations){
		FileConnector fc = connectorHolder.getConnector(location);
		fc.setExtensions(ext);
		fc.moveTo(location);
		List<String> images = fc.list();
		for(String img : images){
			fc.moveTo(img);
			File imageFile = fc.getAsFile();
			Mat proto = calculateDescriptors(ImageIO.read(imageFile));
			prototypes.put(proto, img.substring(img.lastIndexOf('/')+1));
			logger.info("Prototype "+img+" loaded and prepared for matching");
			if(!(fc instanceof LocalFileConnector)) imageFile.delete();
		}
	}
}
 

public KMeansMatcher()
{
	model = null;
	featureDetector = FeatureDetector.create(FeatureDetector.PYRAMID_ORB);
	descriptorExtractor = DescriptorExtractor.create(DescriptorExtractor.BRIEF);
	matcher = DescriptorMatcher.create(DescriptorMatcher.BRUTEFORCE_SL2);
}
 

/***
     * 匹配关键点
     * @author weishao zeng
     * @param destKeyPoints
     * @param srcKeyPoints
     * @return
     */
    private List<MatOfDMatch> matchKeyPoints(MatOfKeyPoint destKeyPoints, MatOfKeyPoint srcKeyPoints) {
    	 List<MatOfDMatch> matches = new LinkedList<>();
         DescriptorMatcher descriptorMatcher = DescriptorMatcher.create(DescriptorMatcher.BRUTEFORCE_L1);
//         System.out.println("寻找最佳匹配");
         /**
          * knnMatch方法的作用就是在给定特征描述集合中寻找最佳匹配
          * 使用KNN-matching算法，令K=2，则每个match得到两个最接近的descriptor，然后计算最接近距离和次接近距离之间的比值，当比值大于既定值时，才作为最终match。
          */
         descriptorMatcher.knnMatch(destKeyPoints, srcKeyPoints, matches, 2);
         return matches;
    }
 

@SuppressWarnings("rawtypes")
@Override
protected void prepareOpenCVOp(Map conf, TopologyContext context) throws Exception {
	this.connectorHolder = new ConnectorHolder(conf);
	matcher = DescriptorMatcher.create( matcherType );
	prototypes = new HashMap<Integer, String>();
	
	int nrTasks = context.getComponentTasks(context.getThisComponentId()).size();
	int taskIndex = context.getThisTaskIndex();
	
	List<String> original = new ArrayList<String>();
	original.addAll(protoLocations);
	protoLocations.clear();
	for(String dir : original){
		protoLocations.addAll(expand(dir));
	}
	
	FileConnector fc = null;
	List<Mat> training = new ArrayList<Mat>();
	for(int i=taskIndex; i<protoLocations.size(); i+=nrTasks){
		String imgFile = protoLocations.get(i);
		fc = connectorHolder.getConnector(imgFile);
		fc.moveTo(imgFile);
		File imageFile = fc.getAsFile();
		BufferedImage img = ImageIO.read(imageFile);
		if(img == null) continue;
		Mat proto = calculateDescriptors(img);
		prototypes.put(training.size(), imgFile.substring(imgFile.lastIndexOf('/')+1));
		training.add(proto);
		logger.info(this.getClass().getName()+"["+taskIndex+"] "+imgFile+" loaded and prepared for matching");
		if(!(fc instanceof LocalFileConnector)) imageFile.delete();
	}
	matcher.add(training);
	matcher.train();
}
 

/**
 * Instantiate an object detector based on the FAST, BRIEF, and BRUTEFORCE_HAMMING algorithms
 */
public ObjectDetection() {
    detector = FeatureDetector.create(FeatureDetectorType.FAST.val());
    extractor = DescriptorExtractor.create(DescriptorExtractorType.BRIEF.val());
    matcher = DescriptorMatcher.create(DescriptorMatcherType.BRUTEFORCE_HAMMING.val());
}
 

@Test
	public void imgMatching2() throws Exception {
		System.loadLibrary(Core.NATIVE_LIBRARY_NAME);
//		Mat src_base = Imgcodecs.imread("D:\\test\\test5.jpg");
//		Mat src_test = Imgcodecs.imread("D:\\test\\test3.jpg");

		Mat src_base = Imgcodecs.imread("g:/test/find-src.jpg");
		Mat src_test = Imgcodecs.imread("g:/test/find-dest2.jpg");
		
		Mat gray_base = new Mat();
		Mat gray_test = new Mat();
		// 转换为灰度
		Imgproc.cvtColor(src_base, gray_base, Imgproc.COLOR_RGB2GRAY);
		Imgproc.cvtColor(src_test, gray_test, Imgproc.COLOR_RGB2GRAY);
		// 初始化ORB检测描述子
		FeatureDetector featureDetector = FeatureDetector.create(FeatureDetector.ORB);//特别提示下这里opencv暂时不支持SIFT、SURF检测方法，这个好像是opencv(windows) java版的一个bug,本人在这里被坑了好久。
		DescriptorExtractor descriptorExtractor = DescriptorExtractor.create(DescriptorExtractor.ORB);
		// 关键点及特征描述矩阵声明
		MatOfKeyPoint keyPoint1 = new MatOfKeyPoint(), keyPoint2 = new MatOfKeyPoint();
		Mat descriptorMat1 = new Mat(), descriptorMat2 = new Mat();
		// 计算ORB特征关键点
		featureDetector.detect(gray_base, keyPoint1);
		featureDetector.detect(gray_test, keyPoint2);
		

        Mat output=new Mat();
        Features2d.drawKeypoints(gray_base, keyPoint1, output );
        Imgcodecs.imwrite("g:/test/out.jpg", output);
        
		// 计算ORB特征描述矩阵
		descriptorExtractor.compute(gray_base, keyPoint1, descriptorMat1);
		descriptorExtractor.compute(gray_test, keyPoint2, descriptorMat2);
		float result = 0;
		// 特征点匹配
		System.out.println("test5：" + keyPoint1.size());
		System.out.println("test3：" + keyPoint2.size());
		if (!keyPoint1.size().empty() && !keyPoint2.size().empty()) {
			// FlannBasedMatcher matcher = new FlannBasedMatcher();
			DescriptorMatcher matcher = DescriptorMatcher.create(DescriptorMatcher.BRUTEFORCE_L1);
			MatOfDMatch matches = new MatOfDMatch();
			matcher.match(descriptorMat1, descriptorMat2, matches);
			// 最优匹配判断
			double minDist = 100;
			DMatch[] dMatchs = matches.toArray();
			int num = 0;
			for (int i = 0; i < dMatchs.length; i++) {
				if (dMatchs[i].distance <= 2 * minDist) {
					result += dMatchs[i].distance * dMatchs[i].distance;
					num++;
				}
			}
			// 匹配度计算
			result /= num;
		}
		System.out.println(result);
	}
 

/**
 * Instantiate an object detector based on custom algorithms
 *
 * @param detector  Keypoint detection algorithm
 * @param extractor Keypoint descriptor extractor
 * @param matcher   Descriptor matcher
 */
public ObjectDetection(FeatureDetectorType detector, DescriptorExtractorType extractor, DescriptorMatcherType matcher) {
    this.detector = FeatureDetector.create(detector.val());
    this.extractor = DescriptorExtractor.create(extractor.val());
    this.matcher = DescriptorMatcher.create(matcher.val());
}