拼接2张图片(OpenCV) [英] Stitching 2 images (OpenCV)
问题描述
我正在尝试使用OpenCV Java API将两个图像拼接在一起。但是,我得到错误的输出,我无法解决问题。我使用以下步骤:
1.检测功能
2.提取功能
3.匹配功能。
4.查找单应性
5.查找透视变换
6.变形透视
7.将2张图像'拼接'成合成图像。
I'm trying to stitch two images together, using the OpenCV Java API. However, I get the wrong output and I cannot work out the problem. I use the following steps: 1. detect features 2. extract features 3. match features. 4. find homography 5. find perspective transform 6. warp perspective 7. 'stitch' the 2 images, into a combined image.
但某处我错了。我认为这是我梳理2张图片的方式,但我不确定。我在2张图片之间获得了214个好的功能匹配,但无法拼接它们?
but somewhere I'm going wrong. I think it's the way I'm combing the 2 images, but I'm not sure. I get 214 good feature matches between the 2 images, but cannot stitch them?
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();
}
由于声望点,我无法嵌入图片或发布超过2个链接?所以我已经链接了错误拼接的图像和显示2张图像之间匹配功能的图像(以便了解问题):
I cannot embed images nor post more than 2 links, because of reputation points? so I've linked the incorrectly stitched images and an image showing the matched features between the 2 images (to get an understanding of the issue):
错误的拼接图像:< a href =http://oi61.tinypic.com/11ac01c.jpg =nofollow> http://oi61.tinypic.com/11ac01c.jpg
检测到的功能: http://oi57.tinypic.com/29m3wif.jpg
推荐答案
似乎你有很多异常值会使单应性的估计不正确。所以你可以使用递归拒绝这些异常值的RANSAC方法。
It seems that you have a lot of outliers that make the estimation of homography is incorrect. SO you can use RANSAC method that recursively reject those outliers.
不需要太多努力,只需使用中的第三个参数findHomography
作为:
No need much efforts for that, just use a third parameter in findHomography
function as:
Mat H = Calib3d.findHomography(obj, scene, CV_RANSAC);
修改
然后尝试确保给检测器的图像是8位灰度图像,如上所述这里
Then try to be sure that your images given to detector are 8-bit grayscale image, as mentioned here
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