Last time I've tried on skimage I think that I didn't do really understand how it works. As you seem pretty confident, I take a little time to study it. As I often switch between OpenCV and skimage I've performed the coordinate interpolation using both methods. I've also used the scipy.interpolation.griddata which is the only one that I figure out to make work. As usual OpenCV is 6x faster but skimage perform good even with 4096x4096 images.

## Advertising

I just create a disk and transform it in ellipse using warping function. For Ndimage, you can find an example of geometric_transform, but it doesn't work for me, map_coordinate is not suited for a full image mapping. I'm pretty sure both ndimage and scikit-image can handle larger images than > that. Did you see these fail in practice? > > StÃ©fan > > > -- You received this message because you are subscribed to the Google Groups "scikit-image" group. To unsubscribe from this group and stop receiving emails from it, send an email to scikit-image+unsubscr...@googlegroups.com. To post to this group, send an email to scikit-image@googlegroups.com. To view this discussion on the web, visit https://groups.google.com/d/msgid/scikit-image/c59117bc-4415-41db-be69-7ac3ce52a424%40googlegroups.com. For more options, visit https://groups.google.com/d/optout.

import numpy as np import scipy.interpolate as ip import scipy.ndimage as nd import time from skimage.transform import warp import cv2 Size_x=1024 Size_y=1024 radius=100 Y,X = np.meshgrid(range(0,Size_x), range(0,Size_y)) Img=np.zeros(Y.shape) # Creation of a dummy image Img[((X-X.max()/2)**2+(Y-Y.max()/2)**2)**0.5<radius]=255 # Displacement and transformation creation U=-0.2*(X-X.max()/2) V=0.1*(Y-Y.max()/2) Xi=X+U # compression Yi=Y+V # elongation # griddata from interpolate ## Need delaunay triangulation that consume a lot of memory don't go over 1024x1024 ... points=(X.reshape(X.shape[0]*X.shape[1],1)[:,0],Y.reshape(X.shape[0]*X.shape[1],1)[:,0]) value=Img.reshape(X.shape[0]*X.shape[1],1)[:,0] t0=time.time() Imgi_griddata = ip.griddata(points,value , (Xi,Yi), method='linear') print("Griddata time : %f"%(time.time()-t0)) Trans=np.zeros((2,Img.shape[0],Img.shape[1])) Trans[0,::,::]=Xi Trans[1,::,::]=Yi t0=time.time() Imgi_skimage=warp(Img,Trans) print("Skimage warp time : %f"%(time.time()-t0)) t0=time.time() Imgi_cv2=cv2.remap(Img.astype('float32'),Yi.astype('float32'),Xi.astype('float32'),cv2.INTER_LINEAR) print("OpenCV remap time : %f"%(time.time()-t0)) ## ndimage interpolation tools # Does not work on my PC #def shift_func(output_coordinates, s0, s1): #return (output_coordinates[0] + s0, output_coordinates[1] + s1) #t0=time.time() #imgi_nd=nd.geometric_transform(Img,shift_func,extra_arguments = (U, V)) #print("Ndimage time : %d"%(time.time()-t0))