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View Code? Open in Web Editor NEWPure python implementation of Gunnar Farneback's optical flow algorithm
License: MIT License
Pure python implementation of Gunnar Farneback's optical flow algorithm
License: MIT License
Since the previous displacement result is calculated in a downscaled image, the value of displacement will also be shrunk to a smaller scale. I guess we should restore it to the same scale as the current pyramid layer.
Propose to change it to:
d = d[: pyr1.shape[0], : pyr2.shape[1]] * 2
Hi,
First of all, I would like to thank you for creating this code. It helps me a lot to understand Farneback algorithm. I have one question about calculating G
in polynomial expansion. Here is the code:
G[..., i, j] = scipy.ndimage.correlate1d(
c, abb[..., i, j], axis=0, mode="constant", cval=0
)
Why do we have to perform convolution over certainty c
and abb
? Could you please explain it to me or point me to the reference in the paper?
Thank you.
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