Hello everyone,
I'm using skimage to correct distortions on an image, using a polynomial
transformation. Basically, my commands can be summarized to:
import skimage.transform as tf
# estimate the transformation matrix from control points before
(pts2) and after (pts1) distortion
M=tf.estimate_transform('polynomial',pts2,pts1,order=3)
# warp the initial image im
warped=tf.warp(im,M)
This does exactly what I want, except that my image is very large (35000
x 35000) and the script crashes if I try to run it for the whole image.
So I thought I could just cut my image into subimages and run warp for
each subimage. But as the matrix indexes are shifted (e.g always in the
interval [0-5000] instead of [0-350000]), I end up applying the same
correction to each subimage.
Is there a way to take into account the fact that my matrix is a subset,
for example by indicating the actual coordinates instead of using the
matrix indexes? (Like in the MatLab function imwarp using argument RA if
that helps understanding)
Thanks a lot,
Amaury
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