anannya.py

import os
import cv2
import numpy as np
from numpy.fft import fft2, ifft2
from scipy.signal import gaussian, convolve2d
import matplotlib.pyplot as plt

def blur(img, mode = 'box', kernel_size = 3):
    # mode = 'box' or 'gaussian' or 'motion'
    dummy = np.copy(img)
    if mode == 'box':
        h = np.ones((kernel_size, kernel_size)) / kernel_size ** 2
    elif mode == 'gaussian':
        h = gaussian(kernel_size, kernel_size / 3).reshape(kernel_size, 1)
        h = np.dot(h, h.transpose())
        h /= np.sum(h)
    elif mode == 'motion':
        h = np.eye(kernel_size) / kernel_size
    dummy = convolve2d(dummy, h, mode = 'valid')
    return dummy

def add_gaussian_noise(img, sigma):
    gauss = np.random.normal(0, sigma, np.shape(img))
    noisy_img = img + gauss
    noisy_img[noisy_img < 0] = 0
    noisy_img[noisy_img > 255] = 255
    return noisy_img

def wiener_filter(img, kernel, K):
    kernel /= np.sum(kernel)
    dummy = np.copy(img)
    dummy = fft2(dummy)
    kernel = fft2(kernel, s = img.shape)
    kernel = np.conj(kernel) / (np.abs(kernel) ** 2 + K)
    dummy = dummy * kernel
    dummy = np.abs(ifft2(dummy))
    return dummy

def gaussian_kernel(kernel_size = 3):
    h = gaussian(kernel_size, kernel_size / 3).reshape(kernel_size, 1)
    h = np.dot(h, h.transpose())
    h /= np.sum(h)
    return h

def rgb2gray(rgb):
    return np.dot(rgb[...,:3], [0.2989, 0.5870, 0.1140])

#img = rgb2gray(plt.imread('original.jpg' ))
img = cv2.imread('original.jpg', cv2.IMREAD_GRAYSCALE)

cv2.waitKey(1)
cv2.imshow(img)

'''
blurred_img = blur(img, mode = 'motion', kernel_size = 3)

noisy_img = add_gaussian_noise(blurred_img, sigma = 20)

kernel = gaussian_kernel(3)

filtered_img = wiener_filter(noisy_img, kernel, K = 10)

cv2.imwrite("mb_filtered.jpg", filtered_img)

display = [img, blurred_img, noisy_img, filtered_img]
label = ['Original Image', 'Motion Blurred Image', 'Motion Blurring + Gaussian Noise', 'Wiener Filter applied']

fig = plt.figure(figsize=(12, 10))

for i in range(len(display)):
    fig.add_subplot(2, 2, i+1)
    plt.imshow(display[i], cmap = 'gray')
    plt.title(label[i])

plt.show()
'''