Revision history [back]

I had the same weird error but in Java. In my case, It seemed that estimateRigidTransform couldn't recognize that the two Mat images I was giving where actually 2D Point Sets. So I applied a workaround in order to convert my match points from MatOfKeyPoint to MatOfPoint2f type.

Here is the complete Java code (It's not Python, but maybe it will help you):

    System.load("opencv.dll");

    FeatureDetector detector = FeatureDetector.create(FeatureDetector.ORB);
    DescriptorExtractor descriptor = DescriptorExtractor.create(DescriptorExtractor.ORB);
    DescriptorMatcher matcher = DescriptorMatcher.create(DescriptorMatcher.BRUTEFORCE_HAMMING);

    // Load First Image
    Mat img1 = Imgcodecs.imread("path", Imgcodecs.IMREAD_GRAYSCALE);
    Mat img1_descriptors = new Mat();
    MatOfKeyPoint img1_keypoints_mat = new MatOfKeyPoint();

    // Detect KeyPoints
    detector.detect(img1, img1_keypoints_mat);
    descriptor.compute(img1, img1_keypoints_mat, img1_descriptors);

    // Load Second Image
    Mat img2 = Imgcodecs.imread("path", Imgcodecs.IMREAD_GRAYSCALE);
    Mat img2_descriptors = new Mat();
    MatOfKeyPoint img2_keypoints_mat = new MatOfKeyPoint();

    // Detect KeyPoints
    detector.detect(img2, img2_keypoints_mat);
    descriptor.compute(img2, img2_keypoints_mat, img2_descriptors);

    // Match KeyPoints
    MatOfDMatch matOfDMatch = new MatOfDMatch();
    matcher.match(img1_descriptors, img2_descriptors, matOfDMatch);

    // Converting matches in matOfDMatch to a MatOfPoint2f format
    List<DMatch> dMatchList;
    dMatchList = matOfDMatch.toList();

    LinkedList<Point> img1_points_list = new LinkedList<>();
    LinkedList<Point> img2_points_list = new LinkedList<>();

    List<KeyPoint> img1_keyPoints_list = img1_keypoints_mat.toList();
    List<KeyPoint> img2_keyPoints_list = img2_keypoints_mat.toList();

    for(int i = 0; i < dMatchList.size(); i++){
        img1_points_list.addLast(img1_keyPoints_list.get(dMatchList.get(i).queryIdx).pt);
        img2_points_list.addLast(img2_keyPoints_list.get(dMatchList.get(i).trainIdx).pt);
    }

    MatOfPoint2f img1_point2f_mat = new MatOfPoint2f();
    img1_point2f_mat.fromList(img1_points_list);

    MatOfPoint2f img2_point2f_mat = new MatOfPoint2f();
    img2_point2f_mat.fromList(img2_points_list);

    // Draw match points
    Mat output = new Mat();
    Features2d.drawMatches(img1, img1_keypoints_mat, img2, img2_keypoints_mat, matOfDMatch, output);
    Imgcodecs.imwrite("output.png", output);


    Mat result = Video.estimateRigidTransform(img1_point2f_mat, img2_point2f_mat, true);
    printMat(result); // Printing the optimal affine transformation 2x3 array

    // The following variables correspond to the estimateRigidTransform result as shown here: https://stackoverflow.com/a/29511091/5165833
    double a = result.get(0,0)[0];
    double b = result.get(0,1)[0];
    double d = result.get(1,1)[0];
    double c = result.get(1,0)[0];

    // Solving for scale as shown in the link above
    double scale_x = Math.signum(a) * Math.sqrt( (a*a) + (b*b) );
    double scale_y = Math.signum(d) * Math.sqrt( (c*c) + (d*d) );

    System.out.println("a = "+a);
    System.out.println("b = "+b);
    System.out.println("scale_x = "+scale_x);
    System.out.println("scale_y = "+scale_y);

}

public static void printMat(Mat m)
{
    for (int x=0; x < m.height(); x++)
    {
        for (int y=0; y < m.width(); y++)
        {
            System.out.printf("%f",m.get(x,y)[0]);
            System.out.printf("%s"," ");
        }
        System.out.printf("\n");
    }
}

UPDATE: Filtering your matches is important, cause if you don't you may get an empty array as a result of the transform.

Here is the complete Java code (It's not Python, but maybe it will help you):

    System.load("opencv.dll");

    FeatureDetector detector = FeatureDetector.create(FeatureDetector.ORB);
 DescriptorExtractor descriptor = DescriptorExtractor.create(DescriptorExtractor.ORB);
 DescriptorMatcher matcher = DescriptorMatcher.create(DescriptorMatcher.BRUTEFORCE_HAMMING);

 // Load First Image
 Mat img1 = Imgcodecs.imread("path", Imgcodecs.imread("img1_path", Imgcodecs.IMREAD_GRAYSCALE);
 Mat img1_descriptors = new Mat();
 MatOfKeyPoint img1_keypoints_mat = new MatOfKeyPoint();

 // Detect KeyPoints
 detector.detect(img1, img1_keypoints_mat);
 descriptor.compute(img1, img1_keypoints_mat, img1_descriptors);

 // Load Second Image
 Mat img2 = Imgcodecs.imread("path", Imgcodecs.imread("img2_path", Imgcodecs.IMREAD_GRAYSCALE);
 Mat img2_descriptors = new Mat();
 MatOfKeyPoint img2_keypoints_mat = new MatOfKeyPoint();

 // Detect KeyPoints
 detector.detect(img2, img2_keypoints_mat);
 descriptor.compute(img2, img2_keypoints_mat, img2_descriptors);

 // Match KeyPoints
 MatOfDMatch matOfDMatch = new MatOfDMatch();
 matcher.match(img1_descriptors, img2_descriptors, matOfDMatch);

 // Filtering the matches
List<DMatch> dMatchList = matOfDMatch.toList();
Double max_dist = 0.0;
Double min_dist = 100.0;

for(int i = 0; i < img1_descriptors.rows(); i++){
    Double dist = (double) dMatchList.get(i).distance;
    if(dist < min_dist) min_dist = dist;
    if(dist > max_dist) max_dist = dist;
}
LinkedList<DMatch> good_matches = new LinkedList<>();
for(int i = 0; i < img1_descriptors.rows(); i++){
    if(dMatchList.get(i).distance < 3*min_dist){
        good_matches.addLast(dMatchList.get(i));
    }
}

// Converting matches in matOfDMatch to a MatOfPoint2f format
    List<DMatch> dMatchList;
    dMatchList = matOfDMatch.toList();

    LinkedList<Point> img1_points_list = new LinkedList<>();
 LinkedList<Point> img2_points_list = new LinkedList<>();

 List<KeyPoint> img1_keyPoints_list = img1_keypoints_mat.toList();
 List<KeyPoint> img2_keyPoints_list = img2_keypoints_mat.toList();

 int limit = good_matches.size();
for(int i = 0; i < dMatchList.size(); limit; i++){
        img1_points_list.addLast(img1_keyPoints_list.get(dMatchList.get(i).queryIdx).pt);
        img2_points_list.addLast(img2_keyPoints_list.get(dMatchList.get(i).trainIdx).pt);
    img1_points_list.addLast(img1_keyPoints_list.get(good_matches.get(i).queryIdx).pt);
    img2_points_list.addLast(img2_keyPoints_list.get(good_matches.get(i).trainIdx).pt);
}

 MatOfPoint2f img1_point2f_mat = new MatOfPoint2f();
 img1_point2f_mat.fromList(img1_points_list);

 MatOfPoint2f img2_point2f_mat = new MatOfPoint2f();
 img2_point2f_mat.fromList(img2_points_list);

 // Draw match points
 Mat output = new Mat();
 Features2d.drawMatches(img1, img1_keypoints_mat, img2, img2_keypoints_mat, matOfDMatch, output);
 Imgcodecs.imwrite("output.png", output);

 Mat result = Video.estimateRigidTransform(img1_point2f_mat, img2_point2f_mat, true);
 printMat(result); // Printing the optimal affine transformation 2x3 array

 // The following variables correspond to the estimateRigidTransform result as shown here: https://stackoverflow.com/a/29511091/5165833
 double a = result.get(0,0)[0];
 double b = result.get(0,1)[0];
 double d = result.get(1,1)[0];
 double c = result.get(1,0)[0];

 // Solving for scale as shown in the link above
 double scale_x = Math.signum(a) * Math.sqrt( (a*a) + (b*b) );
 double scale_y = Math.signum(d) * Math.sqrt( (c*c) + (d*d) );

 System.out.println("a = "+a);
 System.out.println("b = "+b);
 System.out.println("scale_x = "+scale_x);
 System.out.println("scale_y = "+scale_y);

}

}

public static void printMat(Mat m) { for (int x=0; x < m.height(); x++) { for (int y=0; y < m.width(); y++) { System.out.printf("%f",m.get(x,y)[0]); System.out.printf("%s"," "); } System.out.printf("\n"); } } }