Gradient.py
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import cv2
import numpy as np
from matplotlib import pyplot as plt
img = cv2.imread('couleur.png',0)
def Everything (img):
laplacian = cv2.Laplacian(img,cv2.CV_64F)
sobelx = cv2.Sobel(img,cv2.CV_64F,1,0,ksize=5)
sobely = cv2.Sobel(img,cv2.CV_64F,0,1,ksize=5)
plt.subplot(2,2,1),plt.imshow(img,cmap = 'gray')
plt.title('Original'), plt.xticks([]), plt.yticks([])
plt.subplot(2,2,2),plt.imshow(laplacian,cmap = 'gray')
plt.title('Laplacian'), plt.xticks([]), plt.yticks([])
plt.subplot(2,2,3),plt.imshow(sobelx,cmap = 'gray')
plt.title('Sobel X'), plt.xticks([]), plt.yticks([])
plt.subplot(2,2,4),plt.imshow(sobely,cmap = 'gray')
plt.title('Sobel Y'), plt.xticks([]), plt.yticks([])
plt.show()
return laplacian
def Laplacian(img):
laplacian = cv2.Laplacian(img,cv2.CV_64F)
plt.plot,plt.imshow(laplacian,cmap = 'gray')
plt.title('Laplacian'), plt.xticks([]), plt.yticks([])
plt.show()
return laplacian
def SobelX(img):
sobelx = cv2.Sobel(img,cv2.CV_64F,1,0,ksize=5)
plt.plot,plt.imshow(sobelx,cmap = 'gray')
plt.title('Sobel X'), plt.xticks([]), plt.yticks([])
plt.show()
return sobelx
def SobelY(img):
sobely = cv2.Sobel(img,cv2.CV_64F,0,1,ksize=5)
plt.plot,plt.imshow(sobely,cmap = 'gray')
plt.title('Sobel Y'), plt.xticks([]), plt.yticks([])
plt.show()
return sobely
def GradientChoiceProcessing(img, choice):
if choice == '1':
img = Laplacian(img)
return img
elif choice == '2':
img = SobelX(img)
return img
elif choice == '3':
img = SobelY(img)
return img
elif choice == '4':
img = Everything(img)
return img
else:
return
def GradientChoice() :
image = None
print('\t\tGradient Menu\n')
while (image is None):
image = str(raw_input('\tImage to use? By default couleur.png \n'))
if not image:
image = 'couleur.png'
image = cv2.imread(str(image), 0)
print ('\t1. Laplacian\n\t2. Sobel X\n\t3. Sobel Y\n\t4. Everything\n')
choice = raw_input('\n\tMultiple choices possible\n')
for i in range (0, len(choice)):
img = image.copy()
img = GradientChoiceProcessing(img, choice[i])
return