Java Code Examples for org.opencv.features2d.FeatureDetector#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);

}
 

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

public void init() {
	featureDetector = FeatureDetector.create(detectorType);
	descriptorExtractor = DescriptorExtractor.create(extractorType);
	
	if (this.writeDebugImage) {
		this.writeDestImageKeyPoints = true;
		this.writeCutMatchedImageFromSrc = true;
		this.writeMatchingImage = true;
		this.writeDrawMatchedLineImage = true;
	}
}
 

/**
 * Calculates descriptors as defined by detectorType and 
 * descriptorType provided at construction for the provided image
 * @param input
 * @return
 * @throws IOException
 */
private Mat calculateDescriptors(byte[] buffer) throws IOException{
	MatOfByte mob = new MatOfByte(buffer);
	Mat image = Highgui.imdecode(mob, Highgui.CV_LOAD_IMAGE_ANYCOLOR);
	
	FeatureDetector siftDetector = FeatureDetector.create(detectorType);
	MatOfKeyPoint mokp = new MatOfKeyPoint();
	siftDetector.detect(image, mokp);
	
	Mat descriptors = new Mat();
	DescriptorExtractor extractor = DescriptorExtractor.create(descriptorType);
	extractor.compute(image, mokp, descriptors);
	return descriptors;
}
 

/**
 * Calculates descriptors as defined by detectorType and 
 * descriptorType provided at construction for the provided image
 * @param input
 * @return
 * @throws IOException
 */
private Mat calculateDescriptors(byte[] buffer) throws IOException{
	MatOfByte mob = new MatOfByte(buffer);
	Mat image = Highgui.imdecode(mob, Highgui.CV_LOAD_IMAGE_ANYCOLOR);
	
	FeatureDetector siftDetector = FeatureDetector.create(detectorType);
	MatOfKeyPoint mokp = new MatOfKeyPoint();
	siftDetector.detect(image, mokp);
	
	Mat descriptors = new Mat();
	DescriptorExtractor extractor = DescriptorExtractor.create(descriptorType);
	extractor.compute(image, mokp, descriptors);
	return descriptors;
}
 

/**
 * Detects groups of pixels in an image.
 * @param input The image on which to perform the find blobs.
 * @param minArea The minimum size of a blob that will be found
 * @param circularity The minimum and maximum circularity of blobs that will be found
 * @param darkBlobs The boolean that determines if light or dark blobs are found.
 * @param blobList The output where the MatOfKeyPoint is stored.
 */
private void findBlobs(Mat input, double minArea, double[] circularity,
	Boolean darkBlobs, MatOfKeyPoint blobList) {
	FeatureDetector blobDet = FeatureDetector.create(FeatureDetector.SIMPLEBLOB);
	try {
		File tempFile = File.createTempFile("config", ".xml");

		StringBuilder config = new StringBuilder();

		config.append("<?xml version=\"1.0\"?>\n");
		config.append("<opencv_storage>\n");
		config.append("<thresholdStep>10.</thresholdStep>\n");
		config.append("<minThreshold>50.</minThreshold>\n");
		config.append("<maxThreshold>220.</maxThreshold>\n");
		config.append("<minRepeatability>2</minRepeatability>\n");
		config.append("<minDistBetweenBlobs>10.</minDistBetweenBlobs>\n");
		config.append("<filterByColor>1</filterByColor>\n");
		config.append("<blobColor>");
		config.append((darkBlobs ? 0 : 255));
		config.append("</blobColor>\n");
		config.append("<filterByArea>1</filterByArea>\n");
		config.append("<minArea>");
		config.append(minArea);
		config.append("</minArea>\n");
		config.append("<maxArea>");
		config.append(Integer.MAX_VALUE);
		config.append("</maxArea>\n");
		config.append("<filterByCircularity>1</filterByCircularity>\n");
		config.append("<minCircularity>");
		config.append(circularity[0]);
		config.append("</minCircularity>\n");
		config.append("<maxCircularity>");
		config.append(circularity[1]);
		config.append("</maxCircularity>\n");
		config.append("<filterByInertia>1</filterByInertia>\n");
		config.append("<minInertiaRatio>0.1</minInertiaRatio>\n");
		config.append("<maxInertiaRatio>" + Integer.MAX_VALUE + "</maxInertiaRatio>\n");
		config.append("<filterByConvexity>1</filterByConvexity>\n");
		config.append("<minConvexity>0.95</minConvexity>\n");
		config.append("<maxConvexity>" + Integer.MAX_VALUE + "</maxConvexity>\n");
		config.append("</opencv_storage>\n");
		FileWriter writer;
		writer = new FileWriter(tempFile, false);
		writer.write(config.toString());
		writer.close();
		blobDet.read(tempFile.getPath());
	} catch (IOException e) {
		e.printStackTrace();
	}

	blobDet.detect(input, blobList);
}
 

/**
 * Detects groups of pixels in an image.
 * @param input The image on which to perform the find blobs.
 * @param minArea The minimum size of a blob that will be found
 * @param circularity The minimum and maximum circularity of blobs that will be found
 * @param darkBlobs The boolean that determines if light or dark blobs are found.
 * @param blobList The output where the MatOfKeyPoint is stored.
 */
private void findBlobs(Mat input, double minArea, double[] circularity,
	Boolean darkBlobs, MatOfKeyPoint blobList) {
	FeatureDetector blobDet = FeatureDetector.create(FeatureDetector.SIMPLEBLOB);
	try {
		File tempFile = File.createTempFile("config", ".xml");

		StringBuilder config = new StringBuilder();

		config.append("<?xml version=\"1.0\"?>\n");
		config.append("<opencv_storage>\n");
		config.append("<thresholdStep>10.</thresholdStep>\n");
		config.append("<minThreshold>50.</minThreshold>\n");
		config.append("<maxThreshold>220.</maxThreshold>\n");
		config.append("<minRepeatability>2</minRepeatability>\n");
		config.append("<minDistBetweenBlobs>10.</minDistBetweenBlobs>\n");
		config.append("<filterByColor>1</filterByColor>\n");
		config.append("<blobColor>");
		config.append((darkBlobs ? 0 : 255));
		config.append("</blobColor>\n");
		config.append("<filterByArea>1</filterByArea>\n");
		config.append("<minArea>");
		config.append(minArea);
		config.append("</minArea>\n");
		config.append("<maxArea>");
		config.append(Integer.MAX_VALUE);
		config.append("</maxArea>\n");
		config.append("<filterByCircularity>1</filterByCircularity>\n");
		config.append("<minCircularity>");
		config.append(circularity[0]);
		config.append("</minCircularity>\n");
		config.append("<maxCircularity>");
		config.append(circularity[1]);
		config.append("</maxCircularity>\n");
		config.append("<filterByInertia>1</filterByInertia>\n");
		config.append("<minInertiaRatio>0.1</minInertiaRatio>\n");
		config.append("<maxInertiaRatio>" + Integer.MAX_VALUE + "</maxInertiaRatio>\n");
		config.append("<filterByConvexity>1</filterByConvexity>\n");
		config.append("<minConvexity>0.95</minConvexity>\n");
		config.append("<maxConvexity>" + Integer.MAX_VALUE + "</maxConvexity>\n");
		config.append("</opencv_storage>\n");
		FileWriter writer;
		writer = new FileWriter(tempFile, false);
		writer.write(config.toString());
		writer.close();
		blobDet.read(tempFile.getPath());
	} catch (IOException e) {
		e.printStackTrace();
	}

	blobDet.detect(input, blobList);
}
 

/**
 * Detects groups of pixels in an image.
 * @param input The image on which to perform the find blobs.
 * @param minArea The minimum size of a blob that will be found
 * @param circularity The minimum and maximum circularity of blobs that will be found
 * @param darkBlobs The boolean that determines if light or dark blobs are found.
 * @param blobList The output where the MatOfKeyPoint is stored.
 */
private void findBlobs(Mat input, double minArea, double[] circularity,
	Boolean darkBlobs, MatOfKeyPoint blobList) {
	FeatureDetector blobDet = FeatureDetector.create(FeatureDetector.SIMPLEBLOB);
	try {
		File tempFile = File.createTempFile("config", ".xml");

		StringBuilder config = new StringBuilder();

		config.append("<?xml version=\"1.0\"?>\n");
		config.append("<opencv_storage>\n");
		config.append("<thresholdStep>10.</thresholdStep>\n");
		config.append("<minThreshold>50.</minThreshold>\n");
		config.append("<maxThreshold>220.</maxThreshold>\n");
		config.append("<minRepeatability>2</minRepeatability>\n");
		config.append("<minDistBetweenBlobs>10.</minDistBetweenBlobs>\n");
		config.append("<filterByColor>1</filterByColor>\n");
		config.append("<blobColor>");
		config.append((darkBlobs ? 0 : 255));
		config.append("</blobColor>\n");
		config.append("<filterByArea>1</filterByArea>\n");
		config.append("<minArea>");
		config.append(minArea);
		config.append("</minArea>\n");
		config.append("<maxArea>");
		config.append(Integer.MAX_VALUE);
		config.append("</maxArea>\n");
		config.append("<filterByCircularity>1</filterByCircularity>\n");
		config.append("<minCircularity>");
		config.append(circularity[0]);
		config.append("</minCircularity>\n");
		config.append("<maxCircularity>");
		config.append(circularity[1]);
		config.append("</maxCircularity>\n");
		config.append("<filterByInertia>1</filterByInertia>\n");
		config.append("<minInertiaRatio>0.1</minInertiaRatio>\n");
		config.append("<maxInertiaRatio>" + Integer.MAX_VALUE + "</maxInertiaRatio>\n");
		config.append("<filterByConvexity>1</filterByConvexity>\n");
		config.append("<minConvexity>0.95</minConvexity>\n");
		config.append("<maxConvexity>" + Integer.MAX_VALUE + "</maxConvexity>\n");
		config.append("</opencv_storage>\n");
		FileWriter writer;
		writer = new FileWriter(tempFile, false);
		writer.write(config.toString());
		writer.close();
		blobDet.read(tempFile.getPath());
	} catch (IOException e) {
		e.printStackTrace();
	}

	blobDet.detect(input, blobList);
}
 

/**
 * 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);
	}
 

@Override
public List<CVParticle> execute(CVParticle particle) throws Exception {
	List<CVParticle> result = new ArrayList<CVParticle>();
	if(!(particle instanceof Frame)) return result;
	
	Frame frame = (Frame)particle;
	if(frame.getImageType().equals(Frame.NO_IMAGE)) return result;
	try{
		MatOfByte mob = new MatOfByte(frame.getImageBytes());
		Mat image = Highgui.imdecode(mob, Highgui.CV_LOAD_IMAGE_ANYCOLOR);
		
		FeatureDetector siftDetector = FeatureDetector.create(detectorType);
		MatOfKeyPoint mokp = new MatOfKeyPoint();
		siftDetector.detect(image, mokp);
		List<KeyPoint> keypoints = mokp.toList();
		
		Mat descriptors = new Mat();
		DescriptorExtractor extractor = DescriptorExtractor.create(descriptorType);
		extractor.compute(image, mokp, descriptors);
		List<Descriptor> descrList = new ArrayList<Descriptor>();
		float[] tmp = new float[1];
		for(int r=0; r<descriptors.rows(); r++){
			float[] values = new float[descriptors.cols()];
			for(int c=0; c<descriptors.cols(); c++){
				descriptors.get(r, c, tmp);
				values[c] = tmp[0];
			}
			descrList.add(new Descriptor(frame.getStreamId(), frame.getSequenceNr(), new Rectangle((int)keypoints.get(r).pt.x, (int)keypoints.get(r).pt.y, 0, 0), 0, values));
		}
		
		Feature feature = new Feature(frame.getStreamId(), frame.getSequenceNr(), featureName, 0, descrList, null);
		if(outputFrame){
			frame.getFeatures().add(feature);
			result.add(frame);
		}else{
			result.add(feature);
		}		
	}catch(Exception e){
		// catching exception at this point will prevent the sent of a fail! 
		logger.warn("Unable to extract features for frame!", e);
	}
	return 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());
}