image augmentation by python

pip install imgaug

pip install imagecorruptions

github : https://github.com/aleju/imgaug

import numpy as np

import imgaug as ia

import imgaug.augmenters as iaa

import cv2

def agument_rewrite(file_list):

sometimes = lambda aug: iaa.Sometimes(0.1, aug)

seq = iaa.Sequential(

[

# apply the following augmenters to most images

sometimes(iaa.CropAndPad(percent=(-0.02, 0.02), pad_mode=ia.ALL, pad_cval=(0, 255))),

sometimes(iaa.AdditiveGaussianNoise(loc=0, scale=(0.0, 0.05*255), per_channel=0.5)), # add gaussian noise to images

sometimes(iaa.Dropout(p=(0, 0.2))),

sometimes(iaa.CoarseDropout(0.02, size_percent=0.15, per_channel=0.5)),

sometimes(iaa.Solarize(0.5, threshold=(32, 128))),

sometimes(iaa.Cartoon()),

sometimes(iaa.MotionBlur(k=15)),

sometimes(iaa.AllChannelsCLAHE()),

sometimes(iaa.Emboss(alpha=(0.0, 1.0), strength=(0.5, 1.5))),

sometimes(iaa.ElasticTransformation(alpha=(0, 5.0), sigma=0.25)),

sometimes(iaa.PiecewiseAffine(scale=(0.01, 0.05))),

sometimes(iaa.imgcorruptlike.Snow(severity=2)),

sometimes(iaa.Superpixels(p_replace=0.3, n_segments=500)),

sometimes(iaa.Rain(speed=(0.1, 0.3))),

sometimes(iaa.Snowflakes(flake_size=(0.1, 0.4), speed=(0.01, 0.05))),

sometimes(iaa.Fog()),

],

random_order=True

)

for i, v in enumerate(file_list):

img = cv2.imread(v)

images_aug = seq(images=[img])[0] # done by the library

cv2.imwrite(v, images_aug)

print('{}/{} aug : {}'.format(i, len(file_list), v))

Elastic image effect, python opencv example source code.

Elastic effect source code.
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 :

https://github.com/MareArts/Elastic_Effect.git

Test it here:
http://www.marearts.com/webapp/elastic/