Java-拼接2张图片(OpenCV)

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我正在尝试使用OpenCV Java API将两个图像拼接在一起.但是,我得到的输出错误,因此无法解决问题.我使用以下步骤：
1.检测特征
2.提取特征
3.比赛功能.
4.找到单应性
5.找到透视变换
6.翘曲角度
7.将2幅图像“缝制”为组合图像.

但是我哪里出错了.我认为这是我整理两张图片的方式,但是我不确定.我在2张图像之间获得了214个良好的功能匹配,但是无法拼接它们？

public class ImageStitching {

static Mat image1;
static Mat image2;

static FeatureDetector fd;
static DescriptorExtractor fe;
static DescriptorMatcher fm;

public static void initialise(){
    fd = FeatureDetector.create(FeatureDetector.BRISK); 
    fe = DescriptorExtractor.create(DescriptorExtractor.SURF); 
    fm = DescriptorMatcher.create(DescriptorMatcher.BRUTEFORCE);

    //images
    image1 = Highgui.imread("room2.jpg");
    image2 = Highgui.imread("room3.jpg");

    //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(image1, keypoints1);
    fd.detect(image1, 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<keypoints1.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<DMatch>();

    //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());

    MatOfDMatch gm = new MatOfDMatch();
    gm.fromList(goodMatches);

    //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);

    //LinkedList<Point> cornerList = new LinkedList<Point>();
    Mat obj_corners = new Mat(4,1,CvType.CV_32FC2);
    Mat scene_corners = new Mat(4,1,CvType.CV_32FC2);

    obj_corners.put(0,0, new double[]{0,0});
    obj_corners.put(0,0, new double[]{image1.cols(),0});
    obj_corners.put(0,0,new double[]{image1.cols(),image1.rows()});
    obj_corners.put(0,0,new double[]{0,image1.rows()});

    Core.perspectiveTransform(obj_corners, scene_corners, H);

    //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 m = new Mat(result,new Rect(i,0,image2.cols(), image2.rows()));

    image2.copyTo(m);

    Mat img_mat = new Mat();

    Features2d.drawMatches(image1, keypoints1, image2, keypoints2, gm, img_mat, new Scalar(254,0,0),new Scalar(254,0,0) , new MatOfByte(), 2);

    //creating the output file
    boolean imageStitched = Highgui.imwrite("imageStitched.jpg",result);
    boolean imageMatched = Highgui.imwrite("imageMatched.jpg",img_mat);
}


public static void main(String args[]){
    System.loadLibrary(Core.NATIVE_LIBRARY_NAME);
    initialise();
}

由于声望高低,我不能嵌入图像也不能发布2个以上的链接？因此,我将未正确缝合的图像与显示两个图像之间匹配特征的图像进行了链接(以了解此问题)：

拼接图像不正确：http://oi61.tinypic.com/11ac01c.jpg
检测到的特征：http://oi57.tinypic.com/29m3wif.jpg

解决方法:

似乎您有很多离群值使得单应性的估计不正确.因此,您可以使用RANSAC方法来递归地拒绝这些异常值.

无需为此付出很多努力,只需在findHomography函数中使用第三个参数即可：

Mat H = Calib3d.findHomography(obj, scene, CV_RANSAC);

编辑

然后尝试确保提供给检测器的图像是8位灰度图像,如所述here

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