Elastic transformation of an image in Python

elastic_transform.py

import numpy as np
from scipy.ndimage.interpolation import map_coordinates
from scipy.ndimage.filters import gaussian_filter

def elastic_transform(image, alpha, sigma, random_state=None):
    """Elastic deformation of images as described in [Simard2003]_.
    .. [Simard2003] Simard, Steinkraus and Platt, "Best Practices for
       Convolutional Neural Networks applied to Visual Document Analysis", in
       Proc. of the International Conference on Document Analysis and
       Recognition, 2003.
    """
    if random_state is None:
        random_state = np.random.RandomState(None)

    shape = image.shape
    dx = gaussian_filter((random_state.rand(*shape) * 2 - 1), sigma, mode="constant", cval=0) * alpha
    dy = gaussian_filter((random_state.rand(*shape) * 2 - 1), sigma, mode="constant", cval=0) * alpha
    dz = np.zeros_like(dx)

    x, y, z = np.meshgrid(np.arange(shape[0]), np.arange(shape[1]), np.arange(shape[2]))
    print x.shape
    indices = np.reshape(y+dy, (-1, 1)), np.reshape(x+dx, (-1, 1)), np.reshape(z, (-1, 1))

    distored_image = map_coordinates(image, indices, order=1, mode='reflect')
    return distored_image.reshape(image.shape)

Can you elaborate a bit more @jepperaskdk - i would love to make use of this however im unsure what you mean in terms of stack the two images (i..e pull in the original from one folder X_img and the corresponding mask Y-Img and separate after processing? Literally run them all through and then manually / script separate the images that are the output?

On second thought, I'm not sure if it works.

Hi, I have load a RGB img whose shape is (400, 248, 3), but I have got an error
ValueError: operands could not be broadcast together with shapes (248,400,3) (400,248,3)
in the code here
indices = np.reshape(y + dy, (-1, 1)), np.reshape(x + dx, (-1, 1)), np.reshape(z, (-1, 1))
can anyone help ? THX!!!

you need to invert the shapes in the resolution of x,y,z :
x, y, z = np.meshgrid(np.arange(shape[1]), np.arange(shape[0]), np.arange(shape[2]))
instead of
x, y, z = np.meshgrid(np.arange(shape[0]), np.arange(shape[1]), np.arange(shape[2]))

Inverting the shapes flipped the image for me. Setting the the indexing of the meshgrid to 'ij' instead fixed this issue:
x, y, z = np.meshgrid(np.arange(shape[0]), np.arange(shape[1]), np.arange(shape[2]), indexing='ij')

Since the interpolation is also done over the channel dimension, I got this interpolation artifacts.

So I decided to do the interpolation channel-wise.
https://gist.github.com/mvoelk/0880f5de7c101c093165e1e46ce3f6e5

which augement did you apply for dataset @bigfred76