noise_utils.py

# CREDIT: https://stackoverflow.com/questions/22937589/how-to-add-noise-gaussian-salt-and-pepper-etc-to-image-in-python-with-opencv

'''
Parameters
----------
image : ndarray
Input image data. Will be converted to float.
mode : str
One of the following strings, selecting the type of noise to add:
'gauss'     Gaussian-distributed additive noise.
'poisson'   Poisson-distributed noise generated from the data.
's&p'       Replaces random pixels with 0 or 1.
'speckle'   Multiplicative noise using out = image + n*image,where
n is uniform noise with specified mean & variance.
'''

import numpy as np
import os
import cv2
def noisy(noise_typ, images, level):
	if noise_typ == "gauss":
		no_images,row,col,ch= images.shape
		mean = 0
		var = level
		sigma = var
		gauss = np.random.normal(mean,sigma,(no_images,row,col,ch))
		gauss = gauss.reshape(no_images,row,col,ch)
		noisy_images = images + gauss
		noisy_images[noisy_images < 0.] = 0.
		noisy_images[noisy_images > 255.] = 255.
		return noisy_images
	elif noise_typ == "s&p":
		row,col,ch = image.shape
		s_vs_p = 0.5
		amount = 0.004
		out = np.copy(image)
		# Salt mode
		num_salt = np.ceil(amount * image.size * s_vs_p)
		coords = [np.random.randint(0, i - 1, int(num_salt))
									for i in image.shape]
		out[coords] = 1
		# Pepper mode
		num_pepper = np.ceil(amount* image.size * (1. - s_vs_p))
		coords = [np.random.randint(0, i - 1, int(num_pepper))
		for i in image.shape]
		out[coords] = 0
		return out
	elif noise_typ == "poisson":
		vals = len(np.unique(image))
		vals = 2 ** np.ceil(np.log2(vals))
		noisy = np.random.poisson(image * vals) / float(vals)
		return noisy
	elif noise_typ =="speckle":
		row,col,ch = image.shape
		gauss = np.random.randn(row,col,ch)
		gauss = gauss.reshape(row,col,ch)        
		noisy = image + image * gauss
		return noisy