It can be useful when you want to augment image dataset.
and it is also good to make image effect.
Please refer code:
import numpy as np
import cv2
from scipy.ndimage.interpolation import map_coordinates
from scipy.ndimage.filters import gaussian_filter
def elastic(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)
#print(random_state)
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[1]), np.arange(shape[0]), np.arange(shape[2]))
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='nearest') #wrap,reflect, nearest
return distored_image.reshape(image.shape)
#main
#file read
o_img = cv2.imread('izone_oy.png')
elMat = elastic(o_img, alpha=5000, sigma=8, random_state=None)
cv2.namedWindow('origin',0)
cv2.imshow('origin', o_img)
cv2.namedWindow('elastic',0)
cv2.imshow('elastic', elMat)
cv2.waitKey(0)
GitHub url :
