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noise.py
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noise.py
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# The implementation of digital image processing assignment 2.
# Random Noise and Spatial Filter
# Noise Generate
# author: leafy
# 2022-12-3
# last_modified: 2022-12-4
from abc import ABC
from typing import Tuple
import numpy as np
from functools import singledispatchmethod
import cv2
# from IPython import embed
# from numpy.typing import ArrayLike
__all__ = [
"NoiseGenerator",
"UniformNoiseGenerator",
"NormalNoiseGenerator",
"GaussianNoiseGenerator",
"SaltPepperNoiseGenerator",
]
class NoiseGenerator:
"""The base distribution (without uniform noise)."""
__slots__ = []
def __init__(self):
pass
@singledispatchmethod
def generate_noise(self):
"""Generating noise which should be implemented"""
raise NotImplementedError
def add_noise(self, image: np.ndarray) -> np.ndarray:
"""Adding noise to image"""
image_shape = image.shape
return image + self.generate_noise(image_shape=image_shape)
class UniformNoiseGenerator(NoiseGenerator, ABC):
"""The base distribution with uniform noise."""
__slots__ = ['min_val', 'max_val']
def __init__(self, min_val: float = 0, max_val: float = 1):
super(UniformNoiseGenerator, self).__init__()
self.min_val = min_val
self.max_val = max_val
def generate_uniform_noise(self,
min_val: float = 0,
max_val: float = 1,
image_shape: Tuple[int, int] = None) -> np.ndarray:
"""Generating noise
:param: image_shape: Image shape of generated noise
:return: output_noise: ArrayLike noise or single noise
"""
if min_val is None and max_val is None:
min_val = self.min_val
max_val = self.max_val
assert (min_val < max_val) # assertion
distribution_range = max_val - min_val # getting range of Uniform(0, b - a)
moving_factor = distribution_range - max_val # getting move of Uniform(a, b) from Uniform(0, b - a)
if image_shape is None: # just generate one noise
return np.random.rand() * distribution_range - moving_factor
return np.random.rand(*image_shape) * distribution_range - moving_factor # ArrayLike output
def add_noise(self, image: np.ndarray) -> np.ndarray:
"""Adding noise to image"""
image_shape = image.shape
return image + self.generate_uniform_noise(image_shape=image_shape)
class NormalNoiseGenerator(UniformNoiseGenerator, ABC):
"""The base distribution with additive i.i.d. uniform noise."""
def __init__(self):
super(NormalNoiseGenerator, self).__init__()
def generate_normal_noise(self, image_shape: Tuple[int, int]) -> np.ndarray:
"""Generating Normal noise"""
uniform_noise_1 = self.generate_uniform_noise(0, 1, image_shape)
uniform_noise_2 = self.generate_uniform_noise(0, 1, image_shape)
normal_noise = np.cos(2.0 * np.pi * uniform_noise_1) * np.sqrt(-2.0 * np.log(uniform_noise_2))
return normal_noise
def add_noise(self, image: np.ndarray) -> np.ndarray:
"""Adding noise to image"""
image_shape = image.shape
return image + self.generate_normal_noise(image_shape=image_shape)
class GaussianNoiseGenerator(NormalNoiseGenerator, ABC):
"""Gaussian distribution with reparameterization trick."""
def __init__(self):
super(GaussianNoiseGenerator, self).__init__()
def generate_gaussian_noise(self, image_shape: Tuple[int, int], mean: float = 0, var: float = 1) -> np.ndarray:
"""
Generating Normal noise
:param image_shape: Shape of input image
:param mean: Mean of gaussian
:param var: Variance of gaussian
:return: gaussian_noise: Output noise with same size
"""
normal_noise = self.generate_normal_noise(image_shape)
gaussian_noise = normal_noise * np.sqrt(var) + mean
return gaussian_noise
def add_all_channel_noise(self, image: np.ndarray, mean: float = 0, var: float = 1) -> np.ndarray:
"""Adding gaussian noise to image with SAME noise in each channel
:param image: input image
:param mean: Mean of gaussian
:param var: Variance of gaussian
:return: output_img: Output image with same size adding gaussian noise
"""
image_shape = image.shape
input_img = image / 255
gaussian_noise = np.expand_dims(self.generate_gaussian_noise(image_shape[:2], mean, var), -1)
gaussian_noise = gaussian_noise.repeat(3, axis=-1)
output_img = input_img + gaussian_noise
output_img[input_img > 1] = 1
output_img[input_img < 0] = 0
return output_img * 255
def add_channel_wise_noise(self, image: np.ndarray, mean: float = 0, var: float = 1) -> np.ndarray:
"""Adding gaussian noise to image with DIFFERENT noise in each channel
:param image: input image
:param mean: Mean of gaussian
:param var: Variance of gaussian
:return: output_img: Output image with same size adding channel-wise gaussian noise
"""
image_shape = image.shape
input_img = image / 255
gaussian_noise = self.generate_gaussian_noise(image_shape, mean, var)
output_img = input_img + gaussian_noise
output_img[input_img > 1] = 1
output_img[input_img < 0] = 0
return output_img * 255
class SaltPepperNoiseGenerator(UniformNoiseGenerator, ABC):
"""Gaussian distribution with additive i.i.d. uniform noise."""
def __init__(self):
super(SaltPepperNoiseGenerator, self).__init__()
def generate_saltpepper_noise(self, image_shape: Tuple[int, int]) -> np.ndarray:
"""
:param: prob_1: Probability of "Salt" noise.
:param: prob_2: Probability of "Pepper" noise.
:param: image_shape: Shape of input image.
:return: saltpepper_noise: ArrayLike output salt pepper noise.
"""
uniform_noise = self.generate_uniform_noise(0, 1, image_shape)
saltpepper_noise = uniform_noise.copy()
return saltpepper_noise
def add_saltpepper_noise(self, prob_1: float, prob_2: float, image: np.ndarray) -> np.ndarray:
"""Adding noise to image"""
output_img = image.copy()
image_shape = image.shape
saltpepper_noise = self.generate_saltpepper_noise(image_shape[:2])
output_img[saltpepper_noise > 1 - prob_1] = 255
output_img[saltpepper_noise < prob_2] = 0
return output_img
# Press the green button in the gutter to run the script.
if __name__ == '__main__':
uniform_noise_generator = UniformNoiseGenerator()
print(uniform_noise_generator.generate_uniform_noise(2, 4, (2, 3)),
uniform_noise_generator.generate_uniform_noise())
normal_noise_generator = NormalNoiseGenerator()
print(normal_noise_generator.generate_normal_noise((2, 3)))
gaussian_noise_generator = GaussianNoiseGenerator()
print(gaussian_noise_generator.generate_gaussian_noise((2, 3)))
input_image = cv2.imread('./test_image/test3.jpg', 1)
print(type(input_image))
cv2.namedWindow('input_image', cv2.WINDOW_AUTOSIZE)
cv2.imshow('input_image', input_image)
# saltpepper_noise_generator = SaltPepperNoiseGenerator()
# tar = saltpepper_noise_generator.add_saltpepper_noise(0.12, 0.1, input_image)
# cv2.imshow('saltpepper_noise', tar)
gaussian_noise_generator = GaussianNoiseGenerator()
tar2 = gaussian_noise_generator.add_all_channel_noise(input_image, 0, 0.05)
tar2_channel_wise = gaussian_noise_generator.add_channel_wise_noise(input_image, 0, 0.05)
cv2.imshow('gaussian_noise', tar2)
cv2.imshow('gaussian_noise_channel', tar2_channel_wise)
cv2.waitKey(0)
# See PyCharm help at https://www.jetbrains.com/help/pycharm/