@Martin, thanks for the ping. I don’t know about other devs but I’m easier to reach here, for sure. =) I added a comment to SO. Having said that I think Stéfan is more experienced with RANSAC. (My experience ends at having attended Stéfan’s tutorial on the topic. =P) But, can you confirm that the fundamental matrix is also varying between runs of skimage?
Generally, I’m concerned about whether the parameters are really the same. I couldn’t find an API reference for cv2 so I couldn’t check for differences. Can you point me to how you set up the cv2 ransac parameters? Thanks, Juan. On 19 Mar 2018, 1:03 PM -0400, martin sladecek <martin.slade...@gmail.com>, wrote: > Hello, > > I'm having trouble achieving robust performance with > `skimage.measure.ransac` when estimating fundamental matrix for a pair > of images. > I'm seeing highly varying results with different random seeds when > compared to OpenCV's `findFundamentalMatrix`. > > I'm running both skimage's and opencv's ransac on the same sets of > keypoints and with (what I'm assuming are) equivalent parameters. > I'm using the same image pair as OpenCV python tutorials > (https://github.com/abidrahmank/OpenCV2-Python-Tutorials/tree/master/data). > > Here's my demonstration script: > > import cv2 > import numpy as np > > from skimage import io > from skimage.measure import ransac > from skimage.feature import ORB, match_descriptors > from skimage.transform import FundamentalMatrixTransform > > orb = ORB(n_keypoints=500) > > img1 = io.imread('images/right.jpg', as_grey=True) > orb.detect_and_extract(img1) > kp1 = orb.keypoints > desc1 = orb.descriptors > > img2 = io.imread('images/left.jpg', as_grey=True) > orb.detect_and_extract(img2) > kp2 = orb.keypoints > desc2 = orb.descriptors > > matches = match_descriptors(desc1, desc2, metric='hamming', > cross_check=True) > kp1 = kp1[matches[:, 0]] > kp2 = kp2[matches[:, 1]] > > n_iter = 10 > skimage_inliers = np.empty((n_iter, len(matches))) > opencv_inliers = skimage_inliers.copy() > > for i in range(n_iter): > fmat, inliers = ransac((kp1, kp2), FundamentalMatrixTransform, > min_samples=8, residual_threshold=3, > max_trials=5000, stop_probability=0.99, > random_state=i) > skimage_inliers[i, :] = inliers > > cv2.setRNGSeed(i) > fmat, inliers = cv2.findFundamentalMat(kp1, kp2, > method=cv2.FM_RANSAC, > param1=3, param2=0.99) > opencv_inliers[i, :] = (inliers.ravel() == 1) > > skimage_sum_of_vars = np.sum(np.var(skimage_inliers, axis=0)) > opencv_sum_of_vars = np.sum(np.var(opencv_inliers, axis=0)) > > print(f'Scikit-Image sum of inlier variances: > {skimage_sum_of_vars:>8.3f}') > print(f'OpenCV sum of inlier variances: {opencv_sum_of_vars:>8.3f}') > > And the output: > > Scikit-Image sum of inlier variances: 13.240 > OpenCV sum of inlier variances: 0.000 > > I use the sum of variances of inliers obtained from different random > seeds as the metric of robustness. > > I would expect this number to be very close to zero, because truly > robust ransac should converge to the same model independently of it's > random initialization. > > How can I make skimage's `ransac` behave as robustly as opencv's? > > Any other tips on this subject would be greatly appreciated. > > Best regards, > Martin > > (I originally posted this question on stackoverflow, but I'm not getting > much traction there, so I figured I'd try the mailing list.) > > https://stackoverflow.com/questions/49342469/robust-epipolar-geometry-estimation-with-scikit-images-ransac > > _______________________________________________ > scikit-image mailing list > scikit-image@python.org > https://mail.python.org/mailman/listinfo/scikit-image
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