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pythonimageimage-processinggradient
Ariel
Ariel

Reputation: 211

How to compute the gradients of image using Python

I wonder how to use Python to compute the gradients of the image. The gradients include x and y direction. I want to get an x gradient map of the image and a y gradient map of the image. Can anyone tell me how to do this?

Thanks~

Upvotes: 13

Views: 62612

Answers (3)

Sandipan Dey
Sandipan Dey

Reputation: 23101

We can do it with scikit-image filters module functions too, as shown below:

import matplotlib.pylab as plt
from skimage.io import imread
from skimage.color import rgb2gray
from skimage import filters
im = rgb2gray(imread('../images/cameraman.jpg')) # RGB image to gray scale
plt.gray()
plt.figure(figsize=(20,20))
plt.subplot(221)
plt.imshow(im)
plt.title('original', size=20)
plt.subplot(222)
edges_y = filters.sobel_h(im) 
plt.imshow(edges_y)
plt.title('sobel_x', size=20)
plt.subplot(223)
edges_x = filters.sobel_v(im)
plt.imshow(edges_x)
plt.title('sobel_y', size=20)
plt.subplot(224)
edges = filters.sobel(im)
plt.imshow(edges)
plt.title('sobel', size=20)
plt.show()

enter image description here

Upvotes: 6

user8190410
user8190410

Reputation: 1314

you can use opencv to compute x and y gradients as below:

import numpy as np
import cv2

img = cv2.imread('Desert.jpg')

kernely = np.array([[1,1,1],[0,0,0],[-1,-1,-1]])
kernelx = np.array([[1,0,-1],[1,0,-1],[1,0,-1]])
edges_x = cv2.filter2D(img,cv2.CV_8U,kernelx)
edges_y = cv2.filter2D(img,cv2.CV_8U,kernely)

cv2.imshow('Gradients_X',edges_x)
cv2.imshow('Gradients_Y',edges_y)
cv2.waitKey(0)

Upvotes: 14

Mark Setchell
Mark Setchell

Reputation: 207425

I think you mean this:

import numpy as np
from scipy import ndimage
import matplotlib.pyplot as plt

# Create a black image
img=np.zeros((640,480))
# ... and make a white rectangle in it
img[100:-100,80:-80]=1

# See how it looks
plt.imshow(img,cmap=plt.cm.gray)
plt.show()

enter image description here

# Rotate it for extra fun
img=ndimage.rotate(img,25,mode='constant')
# Have another look
plt.imshow(img,cmap=plt.cm.gray)
plt.show()

enter image description here

# Get x-gradient in "sx"
sx = ndimage.sobel(img,axis=0,mode='constant')
# Get y-gradient in "sy"
sy = ndimage.sobel(img,axis=1,mode='constant')
# Get square root of sum of squares
sobel=np.hypot(sx,sy)

# Hopefully see some edges
plt.imshow(sobel,cmap=plt.cm.gray)
plt.show()

enter image description here

Or you can define the x and y gradient convolution kernels yourself and call the convolve() function:

# Create a black image
img=np.zeros((640,480))
# ... and make a white rectangle in it
img[100:-100,80:-80]=1

# Define kernel for x differences
kx = np.array([[1,0,-1],[2,0,-2],[1,0,-1]])
# Define kernel for y differences
ky = np.array([[1,2,1] ,[0,0,0], [-1,-2,-1]])
# Perform x convolution
x=ndimage.convolve(img,kx)
# Perform y convolution
y=ndimage.convolve(img,ky)
sobel=np.hypot(x,y)
plt.imshow(sobel,cmap=plt.cm.gray)
plt.show()

Upvotes: 15

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