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)

This version also supports color images (3 RGB channels).

Nice! What are good values for alpha and sigma? I assume the alpha from the original paper (a=8) cannot be directly translated to this implementation?

Thanks!
A note: dz defined in line L18 is actually never used (in L22).
For RGB images it makes sense not to mix channels. In that case, you can just delete the line with dz = np.zeros_like(dx).

Thank you. But should Input Image be square?

How can I save this distored image ?
I tried with PIL and scipy but the output is entirely black , actually nothing.
I can't even show the image with matplot. (TypeError: Invalid dimensions for image data)
The error is clear but how can I create the appropriate shape for images?

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!!!

Nice! What are good values for alpha and sigma?

Input	Example output (with alpha=991, sigma=8)

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]))

with the correction I gave in the previous post, the algo works perfectly ! Thanks
Results on the dataset of OCR digits recognition I'm currently building :

For the question of sigma alpha values, I build the dataset with 3 pairs of values as follows :
ELASTIC_ALPHA_SIGMA = ((1201, 10), (1501, 12), (991, 8))