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- import java.util.ArrayList;
- import java.util.List;
- import org.opencv.calib3d.Calib3d;
- import org.opencv.core.Core;
- import org.opencv.core.CvType;
- import org.opencv.core.DMatch;
- import org.opencv.core.KeyPoint;
- import org.opencv.core.Mat;
- import org.opencv.core.MatOfByte;
- import org.opencv.core.MatOfDMatch;
- import org.opencv.core.MatOfKeyPoint;
- import org.opencv.core.MatOfPoint2f;
- import org.opencv.core.Point;
- import org.opencv.core.Scalar;
- import org.opencv.features2d.DescriptorMatcher;
- import org.opencv.features2d.Features2d;
- import org.opencv.highgui.HighGui;
- import org.opencv.imgcodecs.Imgcodecs;
- import org.opencv.imgproc.Imgproc;
- import org.opencv.xfeatures2d.SURF;
- class SURFFLANNMatchingHomography {
- public void run(String[] args) {
- String filenameObject = args.length > 1 ? args[0] : "../data/box.png";
- String filenameScene = args.length > 1 ? args[1] : "../data/box_in_scene.png";
- Mat imgObject = Imgcodecs.imread(filenameObject, Imgcodecs.IMREAD_GRAYSCALE);
- Mat imgScene = Imgcodecs.imread(filenameScene, Imgcodecs.IMREAD_GRAYSCALE);
- if (imgObject.empty() || imgScene.empty()) {
- System.err.println("Cannot read images!");
- System.exit(0);
- }
- //-- Step 1: Detect the keypoints using SURF Detector, compute the descriptors
- double hessianThreshold = 400;
- int nOctaves = 4, nOctaveLayers = 3;
- boolean extended = false, upright = false;
- SURF detector = SURF.create(hessianThreshold, nOctaves, nOctaveLayers, extended, upright);
- MatOfKeyPoint keypointsObject = new MatOfKeyPoint(), keypointsScene = new MatOfKeyPoint();
- Mat descriptorsObject = new Mat(), descriptorsScene = new Mat();
- detector.detectAndCompute(imgObject, new Mat(), keypointsObject, descriptorsObject);
- detector.detectAndCompute(imgScene, new Mat(), keypointsScene, descriptorsScene);
- //-- Step 2: Matching descriptor vectors with a FLANN based matcher
- // Since SURF is a floating-point descriptor NORM_L2 is used
- DescriptorMatcher matcher = DescriptorMatcher.create(DescriptorMatcher.FLANNBASED);
- List<MatOfDMatch> knnMatches = new ArrayList<>();
- matcher.knnMatch(descriptorsObject, descriptorsScene, knnMatches, 2);
- //-- Filter matches using the Lowe's ratio test
- float ratioThresh = 0.75f;
- List<DMatch> listOfGoodMatches = new ArrayList<>();
- for (int i = 0; i < knnMatches.size(); i++) {
- if (knnMatches.get(i).rows() > 1) {
- DMatch[] matches = knnMatches.get(i).toArray();
- if (matches[0].distance < ratioThresh * matches[1].distance) {
- listOfGoodMatches.add(matches[0]);
- }
- }
- }
- MatOfDMatch goodMatches = new MatOfDMatch();
- goodMatches.fromList(listOfGoodMatches);
- //-- Draw matches
- Mat imgMatches = new Mat();
- Features2d.drawMatches(imgObject, keypointsObject, imgScene, keypointsScene, goodMatches, imgMatches, Scalar.all(-1),
- Scalar.all(-1), new MatOfByte(), Features2d.DrawMatchesFlags_NOT_DRAW_SINGLE_POINTS);
- //-- Localize the object
- List<Point> obj = new ArrayList<>();
- List<Point> scene = new ArrayList<>();
- List<KeyPoint> listOfKeypointsObject = keypointsObject.toList();
- List<KeyPoint> listOfKeypointsScene = keypointsScene.toList();
- for (int i = 0; i < listOfGoodMatches.size(); i++) {
- //-- Get the keypoints from the good matches
- obj.add(listOfKeypointsObject.get(listOfGoodMatches.get(i).queryIdx).pt);
- scene.add(listOfKeypointsScene.get(listOfGoodMatches.get(i).trainIdx).pt);
- }
- MatOfPoint2f objMat = new MatOfPoint2f(), sceneMat = new MatOfPoint2f();
- objMat.fromList(obj);
- sceneMat.fromList(scene);
- double ransacReprojThreshold = 3.0;
- Mat H = Calib3d.findHomography( objMat, sceneMat, Calib3d.RANSAC, ransacReprojThreshold );
- //-- Get the corners from the image_1 ( the object to be "detected" )
- Mat objCorners = new Mat(4, 1, CvType.CV_32FC2), sceneCorners = new Mat();
- float[] objCornersData = new float[(int) (objCorners.total() * objCorners.channels())];
- objCorners.get(0, 0, objCornersData);
- objCornersData[0] = 0;
- objCornersData[1] = 0;
- objCornersData[2] = imgObject.cols();
- objCornersData[3] = 0;
- objCornersData[4] = imgObject.cols();
- objCornersData[5] = imgObject.rows();
- objCornersData[6] = 0;
- objCornersData[7] = imgObject.rows();
- objCorners.put(0, 0, objCornersData);
- Core.perspectiveTransform(objCorners, sceneCorners, H);
- float[] sceneCornersData = new float[(int) (sceneCorners.total() * sceneCorners.channels())];
- sceneCorners.get(0, 0, sceneCornersData);
- //-- Draw lines between the corners (the mapped object in the scene - image_2 )
- Imgproc.line(imgMatches, new Point(sceneCornersData[0] + imgObject.cols(), sceneCornersData[1]),
- new Point(sceneCornersData[2] + imgObject.cols(), sceneCornersData[3]), new Scalar(0, 255, 0), 4);
- Imgproc.line(imgMatches, new Point(sceneCornersData[2] + imgObject.cols(), sceneCornersData[3]),
- new Point(sceneCornersData[4] + imgObject.cols(), sceneCornersData[5]), new Scalar(0, 255, 0), 4);
- Imgproc.line(imgMatches, new Point(sceneCornersData[4] + imgObject.cols(), sceneCornersData[5]),
- new Point(sceneCornersData[6] + imgObject.cols(), sceneCornersData[7]), new Scalar(0, 255, 0), 4);
- Imgproc.line(imgMatches, new Point(sceneCornersData[6] + imgObject.cols(), sceneCornersData[7]),
- new Point(sceneCornersData[0] + imgObject.cols(), sceneCornersData[1]), new Scalar(0, 255, 0), 4);
- //-- Show detected matches
- HighGui.imshow("Good Matches & Object detection", imgMatches);
- HighGui.waitKey(0);
- System.exit(0);
- }
- }
- public class SURFFLANNMatchingHomographyDemo {
- public static void main(String[] args) {
- // Load the native OpenCV library
- System.loadLibrary(Core.NATIVE_LIBRARY_NAME);
- new SURFFLANNMatchingHomography().run(args);
- }
- }
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