import sys
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):
		if sys.version_info >= (3, 0):
			image = str(input('\tImage to use ? By default couleur.png \n'))
		else:
			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')
	if sys.version_info >= (3, 0):
		choice = input('\n\tMultiple choices possible\n')
	else:
		choice = raw_input('\n\tMultiple choices possible\n')
	for i in range (0, len(choice)):
		img = image.copy()
		img = GradientChoiceProcessing(img, choice[i])
	return