import numpy as np
import cv2
# https://stackoverflow.com/questions/22937589/how-to-add-noise-gaussian-salt-and-pepper-etc-to-image-in-python-with-opencv
class Noiser(object):
def __init__(self, cfg):
self.cfg = cfg
def apply(self, img):
"""
:param img: word image with big background
"""
p = []
funcs = []
if self.cfg.noise.gauss.enable:
p.append(self.cfg.noise.gauss.fraction)
funcs.append(self.apply_gauss_noise)
if self.cfg.noise.uniform.enable:
p.append(self.cfg.noise.uniform.fraction)
funcs.append(self.apply_uniform_noise)
if self.cfg.noise.salt_pepper.enable:
p.append(self.cfg.noise.salt_pepper.fraction)
funcs.append(self.apply_sp_noise)
if self.cfg.noise.poisson.enable:
p.append(self.cfg.noise.poisson.fraction)
funcs.append(self.apply_poisson_noise)
if len(p) == 0:
return img
noise_func = np.random.choice(funcs, p=p)
return noise_func(img)
def apply_gauss_noise(self, img):
"""
Gaussian-distributed additive noise.
"""
mean = 0
stddev = np.sqrt(15)
gauss_noise = np.zeros(img.shape)
cv2.randn(gauss_noise, mean, stddev)
out = img + gauss_noise
return out
def apply_uniform_noise(self, img):
"""
Apply zero-mean uniform noise
"""
imshape = img.shape
alpha = 0.05
gauss = np.random.uniform(0 - alpha, alpha, imshape)
gauss = gauss.reshape(*imshape)
out = img + img * gauss
return out
def apply_sp_noise(self, img):
"""
Salt and pepper noise. Replaces random pixels with 0 or 255.
"""
s_vs_p = 0.5
amount = np.random.uniform(0.004, 0.01)
out = np.copy(img)
# Salt mode
num_salt = np.ceil(amount * img.size * s_vs_p)
coords = [np.random.randint(0, i - 1, int(num_salt))
for i in img.shape]
out[coords] = 255.
# Pepper mode
num_pepper = np.ceil(amount * img.size * (1. - s_vs_p))
coords = [np.random.randint(0, i - 1, int(num_pepper))
for i in img.shape]
out[coords] = 0
return out
def apply_poisson_noise(self, img):
"""
Poisson-distributed noise generated from the data.
"""
vals = len(np.unique(img))
vals = 2 ** np.ceil(np.log2(vals))
if vals < 0:
return img
noisy = np.random.poisson(img * vals) / float(vals)
return noisy
