@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.sladecek@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-estima...
_______________________________________________ scikit-image mailing list scikit-image@python.org https://mail.python.org/mailman/listinfo/scikit-image