我正在尝试在 Java 中使用 OpenCV。我想把两张照片拼接在一起。OpenCV 是一个 C++ 库，它有一个 Java 包装器。
我从官方网站下载了带有预构建 Windows .dll 的 OpenCV Java： https ://sourceforge.net/projects/opencvlibrary/files/opencv-win/3.4.1/opencv-3.4.1-vc14_vc15.exe/download
我正在使用 IntelliJ 2016.1.4
我设置了我的项目并将其指向相关的 .jar
我在网上找到了如下所示的代码。
它没有开箱即用，所以我修复了一些问题，例如： private static final int CV_RANSAC = 8; // 这只是一个猜测！
我跑了。它失败并出现错误：“错误：(-5) 不支持指定的描述符提取器类型”：fe = DescriptorExtractor.create(DescriptorExtractor.SURF); 我
我尝试了一堆替代算法（ORB、SIFT、BRIEF）并得到了同样的错误。
我想让这段代码工作。理想情况下，我会得到不使用一堆不推荐使用的函数的工作代码......这些函数已被弃用，但没有评论说我应该使用什么......这总是让我烦恼。
（更一般地说，我想要任何可以将照片拼接在一起形成全景图的工作 Java 示例代码。）
任何人都可以帮忙吗？
import org.opencv.calib3d.Calib3d;
import org.opencv.core.*;
import org.opencv.features2d.DescriptorExtractor;
import org.opencv.features2d.DescriptorMatcher;
import org.opencv.features2d.FeatureDetector;
import org.opencv.features2d.Features2d;
import org.opencv.imgproc.Imgproc;
import java.util.LinkedList;
import java.util.List;
import static org.opencv.imgcodecs.Imgcodecs.imread;
import static org.opencv.imgcodecs.Imgcodecs.imwrite;
public class ImageStitching {
static Mat image1;
static Mat image2;
static FeatureDetector fd;
static DescriptorExtractor fe;
static DescriptorMatcher fm;
// Compulsory
static{
try {
System.loadLibrary(Core.NATIVE_LIBRARY_NAME);
}
catch (UnsatisfiedLinkError e) {
throw new RuntimeException("Couldn't find \"" + Core.NATIVE_LIBRARY_NAME + ".dll .\n"
+"You need to add something like this to the run configuration \"VM options\":\n"
+"-Djava.library.path=C:\\OpenCvPreBuilt\\opencv\\build\\java\\x64");
}
}
public static void go()
{
//new CvException("hello");
fd = FeatureDetector.create(FeatureDetector.BRISK);
fe = DescriptorExtractor.create(DescriptorExtractor.SURF);
fm = DescriptorMatcher.create(DescriptorMatcher.BRUTEFORCE);
//images
image1 = imread("A.jpg");
image2 = imread("B.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, CV_RANSAC, 3);
//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 = imwrite("imageStitched.jpg",result);
boolean imageMatched = imwrite("imageMatched.jpg",img_mat);
}
public static void main(String args[])
{
go();
}
}