使用opencv裁剪汽車檔案中的框

我是 opencv 的新手，想用它來裁剪影像的一部分，然后使用 tesseract 來讀取它們。我不確定裁剪我需要的所有必要框的最佳方法是什么。

這是我需要轉換的檔案的一個簡單示例：

關于什么是最好的任何建議？

我嘗試使用 ORB 和以下影像作為模板：

但沒有成功。

在模板上，一些線被選為關鍵點，但在影像上我想處理它主要是文本而不是線。這是一個糟糕的模板嗎？我需要先處理影像嗎？

和我的代碼：

Feature2D f2d = ORB.create(5000); // SIFT.create(1000);

MatOfKeyPoint keypoints1 = new MatOfKeyPoint();
Mat descriptors1 = new Mat();
Mat mask1 = new Mat();
f2d.detectAndCompute(img1, mask1, keypoints1, descriptors1);

MatOfKeyPoint keypoints2 = new MatOfKeyPoint();
Mat descriptors2 = new Mat();
Mat mask2 = new Mat();
f2d.detectAndCompute(img2, mask2, keypoints2, descriptors2);

DescriptorMatcher matcher = DescriptorMatcher.create(DescriptorMatcher.BRUTEFORCE_HAMMING); 
MatOfDMatch matches = new MatOfDMatch();
matcher.match(descriptors1, descriptors2, matches);


Mat outputImg = new Mat();
MatOfByte drawnMatches = new MatOfByte();
Features2d.drawMatches(img1, keypoints1, img2, keypoints2, matches, outputImg, new Scalar(0, 255, 0), new Scalar(255, 0, 0), drawnMatches, Features2d.DrawMatchesFlags_NOT_DRAW_SINGLE_POINTS);

uj5u.com熱心網友回復：

通過使用包含表單中永不更改的所有文本的模板，我可以獲得良好的結果。此外，創建 2 個模板（每頁 1 個）并使用 SIFT 代替 ORB 也有很大幫助。

這是我的解決方案：

public static Mat matchTEmplateSIFT(Mat img1, Mat template, boolean showKeypoints, boolean drawMatchs) {
  Feature2D f2d = SIFT.create(15000);
  DescriptorMatcher matcher = DescriptorMatcher.create(DescriptorMatcher.BRUTEFORCE_SL2); // or FLANNBASED for better performance
  return matchTEmplate(img1, template, f2d, matcher);
}

public static Mat matchTEmplate(Mat baseImage, Mat template, Feature2D f2d, DescriptorMatcher matcher) {
  int dilateSize = 5;
  Mat scene = dilateBitwise(dilateSize, baseImage.clone());
  template = dilateBitwise(dilateSize, template.clone());

  MatOfKeyPoint keypoints1 = new MatOfKeyPoint();
  Mat descriptors1 = new Mat();
  f2d.detectAndCompute(scene, new Mat(), keypoints1, descriptors1);

  MatOfKeyPoint keypoints2 = new MatOfKeyPoint();
  Mat descriptors2 = new Mat();
  f2d.detectAndCompute(template, new Mat(), keypoints2, descriptors2);

  List<MatOfDMatch> matches = new ArrayList<>();
  matcher.knnMatch(descriptors1, descriptors2, matches, 2);

  MatOfDMatch goodMatches = getBestMatches(matches);
  Mat result = transformAndWarp(baseImage, template, keypoints1, keypoints2, goodMatches);

  return result;
}

private static Mat transformAndWarp(Mat baseImage, Mat template, MatOfKeyPoint keypoints1, MatOfKeyPoint keypoints2, MatOfDMatch goodMatches) {
  Mat H = findHomographyMatrix(keypoints1, keypoints2, goodMatches);
  perspectiveTransform(template, H);
  Mat result = new Mat();
  Imgproc.warpPerspective(baseImage, result, H, new Size(template.cols(), template.rows()));
  return result;
}

private static void perspectiveTransform(Mat template, Mat H) {
  Mat obj_corners = new Mat(4, 1, CvType.CV_32FC2);
  obj_corners.put(0, 0, new double[]{0, 0});
  obj_corners.put(0, 0, new double[]{template.cols(), 0});
  obj_corners.put(0, 0, new double[]{template.cols(), template.rows()});
  obj_corners.put(0, 0, new double[]{0, template.rows()});

  Mat scene_corners = new Mat(4, 1, CvType.CV_32FC2);

  Core.perspectiveTransform(obj_corners, scene_corners, H);
}

private static Mat findHomographyMatrix(MatOfKeyPoint keypoints1, MatOfKeyPoint keypoints2, MatOfDMatch goodMatches) {
  LinkedList<Point> templateList = new LinkedList<>();
  LinkedList<Point> sceneList = new LinkedList<>();

  List<KeyPoint> templateKeyPoints = keypoints1.toList();
  List<KeyPoint> sceneKeypoints = keypoints2.toList();

  for (int i = 0; i < goodMatches.toList().size(); i  ) {
     templateList.addLast(templateKeyPoints.get(goodMatches.toList().get(i).queryIdx).pt);
     sceneList.addLast(sceneKeypoints.get(goodMatches.toList().get(i).trainIdx).pt);
  }

  MatOfPoint2f templateMat = new MatOfPoint2f(); 
  templateMat.fromList(templateList);

  MatOfPoint2f sceneMat = new MatOfPoint2f();
  sceneMat.fromList(sceneList);

  return Calib3d.findHomography(templateMat, sceneMat, Calib3d.RANSAC);
}

// https://docs.opencv.org/3.4/d5/d6f/tutorial_feature_flann_matcher.html
private static MatOfDMatch getBestMatches(List<MatOfDMatch> knnMatches) {
  //-- Filter matches using the Lowe's ratio test
  float ratioThresh = 0.5f;
  List<DMatch> listOfGoodMatches = new ArrayList<>();
  for (int i = 0; i < knnMatches.size(); i  ) {
     if (knnMatches.get(i).rows() > 1) {
        DMatch[] matches = knnMatches.get(i).toArray();
        if (matches[0].distance < ratioThresh * matches[1].distance) {
           listOfGoodMatches.add(matches[0]);
        }
     }
  }
  MatOfDMatch matOfDMatch = new MatOfDMatch();
  matOfDMatch.fromList(listOfGoodMatches);
  return matOfDMatch;

}

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