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| import org.opencv.calib3d.Calib3d;
import org.opencv.core.*;
import org.opencv.features2d.*;
import org.opencv.highgui.Highgui;
import org.opencv.imgproc.Imgproc;
import java.util.LinkedList;
import java.util.List;
public class Main {
static Mat image1;
static Mat image2;
static FeatureDetector fd;
static DescriptorExtractor fe;
static DescriptorMatcher fm;
public static void initialise() {
fd = FeatureDetector.create(FeatureDetector.SURF);
fe = DescriptorExtractor.create(DescriptorExtractor.SURF);
fm = DescriptorMatcher.create(DescriptorMatcher.FLANNBASED);
//images
image1 = Highgui.imread("panorama_image1.jpg");
image2 = Highgui.imread("panorama_image2.jpg");
Mat imageGray1 = new Mat();
Mat imageGray2 = new Mat();
Imgproc.cvtColor(image1, imageGray1, Imgproc.COLOR_RGB2GRAY);
Imgproc.cvtColor(image2, imageGray2, Imgproc.COLOR_RGB2GRAY);
//structures for the keypoints from the 2 images
MatOfKeyPoint keypoints1 = new MatOfKeyPoint();
MatOfKeyPoint keypoints2 = new MatOfKeyPoint();
//structures for the computed descriptors
Mat descriptors1 = new Mat();
Mat descriptors2 = new Mat();
//structure for the matches
MatOfDMatch matches = new MatOfDMatch();
//getting the keypoints
fd.detect(imageGray1, keypoints1);
fd.detect(imageGray2, keypoints2);
//getting the descriptors from the keypoints
fe.compute(image1, keypoints1, descriptors1);
fe.compute(image2, keypoints2, descriptors2);
//getting the matches the 2 sets of descriptors
fm.match(descriptors2, descriptors1, matches);
//turn the matches to a list
List<DMatch> matchesList = matches.toList();
Double maxDist = 0.0; //keep track of max distance from the matches
Double minDist = 100.0; //keep track of min distance from the matches
//calculate max & min distances between keypoints
for (int i = 0; i < descriptors1.rows(); i++) {
Double dist = (double) matchesList.get(i).distance;
if (dist < minDist) minDist = dist;
if (dist > maxDist) maxDist = dist;
}
System.out.println("max dist: " + maxDist);
System.out.println("min dist: " + minDist);
//structure for the good matches
LinkedList<DMatch> goodMatches = new LinkedList<>();
//use only the good matches (i.e. whose distance is less than 3*min_dist)
for (int i = 0; i < descriptors1.rows(); i++) {
if (matchesList.get(i).distance < 3 * minDist) {
goodMatches.addLast(matchesList.get(i));
}
}
//structures to hold points of the good matches (coordinates)
LinkedList<Point> objList = new LinkedList<Point>(); // image1
LinkedList<Point> sceneList = new LinkedList<Point>(); //image 2
List<KeyPoint> keypoints_objectList = keypoints1.toList();
List<KeyPoint> keypoints_sceneList = keypoints2.toList();
//putting the points of the good matches into above structures
for (int i = 0; i < goodMatches.size(); i++) {
objList.addLast(keypoints_objectList.get(goodMatches.get(i).queryIdx).pt);
sceneList.addLast(keypoints_sceneList.get(goodMatches.get(i).trainIdx).pt);
}
System.out.println("\nNum. of good matches" + goodMatches.size());
//converting the points into the appropriate data structure
MatOfPoint2f obj = new MatOfPoint2f();
obj.fromList(objList);
MatOfPoint2f scene = new MatOfPoint2f();
scene.fromList(sceneList);
//finding the homography matrix
Mat H = Calib3d.findHomography(obj, scene, Calib3d.RANSAC,3);
//structure to hold the result of the homography matrix
Mat result = new Mat();
//size of the new image - i.e. image 1 + image 2
Size s = new Size(image1.cols() + image2.cols(), image1.rows());
//using the homography matrix to warp the two images
Imgproc.warpPerspective(image1, result, H, s);
int i = image1.cols();
Mat half = new Mat(result, new Rect(0, 0, image2.cols(), image2.rows()));
image2.copyTo(half);
Highgui.imwrite("imageStitched.jpg", result);
}
public static void main(String args[]) {
System.loadLibrary(Core.NATIVE_LIBRARY_NAME);
initialise();
}
} |
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