c++,opencv,transformation,homography,ransac

I've done it this way in the past: cv::Mat R = cv::estimateRigidTransform(p1,p2,false); if(R.cols == 0) { continue; } cv::Mat H = cv::Mat(3,3,R.type()); H.at<double>(0,0) = R.at<double>(0,0); H.at<double>(0,1) = R.at<double>(0,1); H.at<double>(0,2) = R.at<double>(0,2); H.at<double>(1,0) = R.at<double>(1,0); H.at<double>(1,1) = R.at<double>(1,1); H.at<double>(1,2) = R.at<double>(1,2); H.at<double>(2,0) = 0.0; H.at<double>(2,1) = 0.0; H.at<double>(2,2) =...

opencv,image-processing,computer-vision,feature-detection,ransac

What is your definition of a good result? RANSAC is about a tradeoff between the number of points and their precision, so there is no uniform definition of good: you have more inliers if their accuracy is worse and vice versa. The parameter you are talking about is probably an...

python,opencv,numpy,matplotlib,ransac

OpenCV use NumPy ndarray to represent image, the axis 0 of the array is vertical, corresponding to Y axis of the image. So, to plot the points you need: plt.plot(points[:,1],points[:,0],'wo')...