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...

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) =...

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')...