我有一个非常奇怪的问题。我正在使用此代码检测另一个图像（java opencv）中的图像：
更新代码：
public void startRecognition() {
//load images, I want to find img_object in img_scene
Mat img_scene = Highgui.imread("D:/opencvws/ImageRecognition/src/main/resources/ascene.jpg");
Mat img_object = Highgui.imread("D:/opencvws/ImageRecognition/src/main/resources/aobj1.jpg");
run++;
System.out.println("RUN NO: " + run);
//init detector
FeatureDetector detector = FeatureDetector.create(FeatureDetector.SURF);
//keypoint detection for both images (keyponts_scene for img_scene, keypoint_object for img_object)
MatOfKeyPoint keypoints_object = new MatOfKeyPoint();
MatOfKeyPoint keypoints_scene = new MatOfKeyPoint();
detector.detect(img_object, keypoints_object);
detector.detect(img_scene, keypoints_scene);
System.out.println("OK: " + keypoints_object.total());
System.out.println("SK: " + keypoints_scene.total());
//extractor init
DescriptorExtractor extractor = DescriptorExtractor.create(2); //2 = SURF;
Mat descriptor_object = new Mat();
Mat descriptor_scene = new Mat() ;
//Compute descriptors
extractor.compute(img_object, keypoints_object, descriptor_object);
extractor.compute(img_scene, keypoints_scene, descriptor_scene);
//init matcher
DescriptorMatcher matcher = DescriptorMatcher.create(DescriptorMatcher.FLANNBASED); // 1 = FLANNBASED
matcher.clear();
MatOfDMatch matches = new MatOfDMatch();
//match both descriptors
matcher.match(descriptor_object, descriptor_scene, matches);
List<DMatch> matchesList = matches.toList();
//calc min/max dist
Double max_dist = 0.0;
Double min_dist = 100.0;
for(int i = 0; i < descriptor_object.rows(); i++){
Double dist = (double) matchesList.get(i).distance;
if(dist < min_dist) min_dist = dist;
if(dist > max_dist) max_dist = dist;
}
//filter good matches
LinkedList<DMatch> good_matches = new LinkedList<DMatch>();
MatOfDMatch gm = new MatOfDMatch();
//good match = distance > 2*min_distance ==> put them in a list
for(int i = 0; i < descriptor_object.rows(); i++){
if(matchesList.get(i).distance < 2*min_dist){
good_matches.addLast(matchesList.get(i));
}
}
//List -> Mat
gm.fromList(good_matches);
//mat for resulting image
Mat img_matches = new Mat();
//filter keypoints (use only good matches); First in a List, iterate, afterwards ==> Mat
LinkedList<Point> objList = new LinkedList<Point>();
LinkedList<Point> sceneList = new LinkedList<Point>();
List<KeyPoint> keypoints_objectList = keypoints_object.toList();
List<KeyPoint> keypoints_sceneList = keypoints_scene.toList();
for(int i = 0; i<good_matches.size(); i++){
objList.addLast(keypoints_objectList.get(good_matches.get(i).queryIdx).pt);
sceneList.addLast(keypoints_sceneList.get(good_matches.get(i).trainIdx).pt);
}
MatOfPoint2f obj = new MatOfPoint2f();
obj.fromList(objList);
MatOfPoint2f scene = new MatOfPoint2f();
scene.fromList(sceneList);
//calc transformation matrix; method = 8 (RANSAC) ransacReprojThreshold=3
Mat hg = Calib3d.findHomography(obj, scene, 8,3);
//init corners
Mat obj_corners = new Mat(4,1,CvType.CV_32FC2);
Mat scene_corners = new Mat(4,1,CvType.CV_32FC2);
//obj
obj_corners.put(0, 0, new double[] {0,0});
obj_corners.put(1, 0, new double[] {img_object.cols(),0});
obj_corners.put(2, 0, new double[] {img_object.cols(),img_object.rows()});
obj_corners.put(3, 0, new double[] {0,img_object.rows()});
//transform obj corners to scene_img (stored in scene_corners)
Core.perspectiveTransform(obj_corners,scene_corners, hg);
//move points for img_obg width to the right to fit the matching image
Point p1 = new Point(scene_corners.get(0,0)[0]+img_object.cols(), scene_corners.get(0,0)[1]);
Point p2 = new Point(scene_corners.get(1,0)[0]+img_object.cols(), scene_corners.get(1,0)[1]);
Point p3 = new Point(scene_corners.get(2,0)[0]+img_object.cols(), scene_corners.get(2,0)[1]);
Point p4 = new Point(scene_corners.get(3,0)[0]+img_object.cols(), scene_corners.get(3,0)[1]);
//create the matching image
Features2d.drawMatches(
img_object,
keypoints_object,
img_scene,
keypoints_scene,
gm,
img_matches);
//draw lines to the matching image
Core.line(img_matches, p1 , p2, new Scalar(0, 255, 0),4);
Core.line(img_matches, p2, p3, new Scalar(0, 255, 0),4);
Core.line(img_matches, p3, p4, new Scalar(0, 255, 0),4);
Core.line(img_matches, p4, p1, new Scalar(0, 255, 0),4);
// resizing...
Mat resizeimage = new Mat();
Size sz = new Size(1200, 1000);
Imgproc.resize(img_matches, img_matches, sz);
panel1.setimagewithMat(img_matches);
frame1.repaint();
//tried to prevent any old references to mix up new calculation
matcher.clear();
img_matches = new Mat();
img_object = new Mat();
img_scene = new Mat();
keypoints_object = new MatOfKeyPoint();
keypoints_scene = new MatOfKeyPoint();
hg = new Mat();
}
如果我在我正在运行的应用程序中运行 startRecognition 方法两次（opencv 库在启动时加载），我会得到相同的识别结果。对于第三次尝试，它检测其他关键点并计算另一个变换矩阵 (hg)。例子：
第二次尝试后：
第三次之后：
谁能解释为什么？或者告诉我如何预防？当我重新启动整个程序时，它会再次检测到 2 次正确，然后发生变化。经过几次尝试后，它将再次计算正确的 hg（从第一次和第二次尝试）。我不明白为什么会这样。
提前致谢
杰莫拉