TP5

# Import numpy
import numpy as np
# Import skimage and matplotlib
from skimage import io,color,util
import matplotlib.pyplot as plt
import math

# Image loading
img = io.imread('lena.png')
img = color.rgb2gray(img)

#Generate noisy image
noise_img = util.random_noise(img,mode='gaussian',mean=0,var=0.001)*255

#User Input
size=3
k=size//2
C=2

new_image1=np.zeros([c,r])
new_image2=np.zeros([c,r])
new_image3=np.zeros([c,r])

#Gradient method
for i in range(0,r-1):
    for j in range(0,c-1):
        dx=noise_img[i,j]-noise_img[i,j+1]
        dy=noise_img[i,j]-noise_img[i+1,j]
        new_image1[i,j]=math.sqrt(dx**2+dy**2)

#Compass method
for i in range(0,r-1):
    for j in range(0,c-1):
        d1=noise_img[i,j-1]-noise_img[i,j+1]
        d2=noise_img[i-1,j]-noise_img[i+1,j]
        d3=noise_img[i-1,j-1]-noise_img[i+1,j+1]
        d4=noise_img[i+1,j-1]-noise_img[i-1,j+1]
        new_image2[i,j]=np.max(np.abs([d1,d2,d3,d4]))

#Sobel method
F1=[[1,C,1],[0,0,0],[-1,-C,-1]]
F2=[[0,1,C],[-1,0,1],[-C,-1,0]]
F3=[[-1,0,1],[-C,0,C],[-1,0,1]]
F4=[[-C,-1,0],[-1,0,1],[0,1,C]]

for i in range(k,r-k):
    for j in range(k,c-k):
        V=noise_img[i-k:i+k+1,j-k:j+k+1]
        V1=np.sum((V*F1).flatten())
        V2=np.sum((V*F2).flatten())
        V3=np.sum((V*F3).flatten())
        V4=np.sum((V*F4).flatten())
        new_image3[i,j]=max(np.abs([V1,V2,V3,V4]))

#Plots
plt.figure(figsize=(5,5)),plt.imshow(noise_img,cmap='gray')
plt.figure(figsize=(5,5)),plt.imshow(new_image1,cmap='gray')
plt.figure(figsize=(5,5)),plt.imshow(new_image2,cmap='gray')
plt.figure(figsize=(5,5)),plt.imshow(new_image3,cmap='gray')