Hi,
Ok, Johannes answer seemed to work, thank you! There’s a bit of brainstorming
to find out how to reshape the coordinates etc… so below is my solution if this
can help someone :
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)
# subimage
sub = im[yl:yr,xl:xr]
# get new coordinates for the subset image
coords=np.mgrid[yl:yr,xl:xr]
# coords must be shape (2,N) + order (x,y) not (y,x) for
passing to transformation matrix
coords = np.array((coords[1].ravel(),coords[0].ravel())).T
# new coordinates
coords_out = M(coords)
# now shift the coordinates to take into account image cropping
yy = coords_out[:,1].reshape(sub.shape)
xx = coords_out[:,0].reshape(sub.shape)
xx -= xl
yy -= yl
transf_coords = np.array((yy,xx))
# image warping
out_sub=tf.warp(sub,transf_coords,preserve_range=True)
Robert, I didn’t know PyTables but I’ll have a look as it can always be useful.
Amaury
Below is the
> On Oct 6, 2016, at 23:39, Johannes Schönberger <[email protected]> wrote:
>
> You can do this by iterating over windows of your image with the
> following (pseudo-)code:
>
> M = tf.estimate_transform('polynomial',pts2,pts1,order=3)
>
> for minr in range(0, image.shape[0], window_size):
> for minc in range(0, image.shape[1], window_size):
> coords = M(np.array(np.mgrid[minr:minr+window_size,
> minc:minc+window_size]))
> transformed_coords = M(coords)
> # TODO: reshape transformed_coords into the correct size, refer
> to tf.warp_coords for an example.
> total_output_image[window_bbox] = warp(image,
> transformed_coords, ...)
>
> I hope it is clear what I mean here? I don't have more time at the
> moment...
>
> On Fri, Oct 7, 2016, at 02:06 AM, Amaury Dehecq wrote:
>> 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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