@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-estimation-with-scikit-images-ransac

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