Median filter

gistfile1.txt

from __future__ import division
import matplotlib.pyplot as plt
import numpy as np
from skimage import io, img_as_float

def salt_and_pepper_noise_gray(A, d):
    rows = np.shape(A)[0]
    columns = np.shape(A)[1]
    number_of_pixels = int(d*rows*columns)

    x = columns*np.random.random((number_of_pixels))   
    y = rows*np.random.random((number_of_pixels))
    
    coordinates = np.column_stack((x, y))

    for i in coordinates[:len(coordinates)/2]:
        A[int(i[1]), int(i[0])] = 0
    for i in coordinates[len(coordinates)/2:]:
        A[int(i[1]), int(i[0])] = 1

    return A


def salt_and_pepper_noise_colour(A, d):
    A[:, :, 0] = salt_and_pepper_noise_gray(A[:, :, 0], d)
    A[:, :, 1] = salt_and_pepper_noise_gray(A[:, :, 1], d)    
    A[:, :, 2] = salt_and_pepper_noise_gray(A[:, :, 2], d)

    return A


def median_filter(A, filter_size):
    rows = np.shape(A)[0]
    columns = np.shape(A)[1]
    half_length = int((filter_size-1)/2)
    A = salt_and_pepper_noise_colour(A, d)  

    pad_A = np.concatenate([np.zeros((rows, half_length, 3)), A, np.zeros((rows, half_length, 3))], axis=1)
    pad_A = np.concatenate([np.zeros((half_length, columns+(2*half_length), 3)), pad_A, np.zeros((half_length, columns+(2*half_length), 3))],  axis=0)
    
    B = np.zeros_like(A)
    c2 = 0
    c1 = 0
    k = 0
    for i in xrange(0, rows):
        for j in xrange(0, columns):
            B[i, j, k] = np.median(pad_A[c1:filter_size + c1, c2:filter_size + c2, k])
            c2 += 1  
        c2 = 0 
        c1 += 1
    k += 1

    return B
    

A = io.imread('lugbalonne.jpg')
A = (0.3*A[:, :, 0] + 0.59*A[:, :, 1] + 0.11*A[:, :, 2])/256  
G = np.empty_like(A)
G[:] = A  
d = 0.2
out = salt_and_pepper_noise_gray(A, d)


B = io.imread('meisie.jpg')
B = img_as_float(B)
C = np.empty_like(B)
C[:] = B

d = 0.2
out1 = salt_and_pepper_noise_colour(B, d)

filter_size = 5
out2 = median_filter(B, filter_size)

f, (ax0, ax1) = plt.subplots(1, 2)
f.tight_layout()

ax0.imshow(G, cmap=plt.cm.gray)
ax0.set_title('Input image')

ax1.imshow(out, cmap=plt.cm.gray)
ax1.set_title('Salt and pepper noise, d = 0.2')

f, (ax0, ax1) = plt.subplots(1, 2)
f.tight_layout()
   
ax0.imshow(C)
ax0.set_title('Input image')

ax1.imshow(out1)
ax1.set_title('Salt and pepper noise, d = 0.2')

f, (ax0, ax1) = plt.subplots(1, 2)
f.tight_layout()

ax0.imshow(out1)
ax0.set_title('Input image, d = 0.2')

ax1.imshow(out2)
ax1.set_title('Median filter, filter size = 5')


plt.show()