python - Implementing Sobel operators with python without opencv

Question

Given a greyscale 8 bit image (2D array with values from 0 - 255 for pixel intensity), I want to implement the Sobel operators (mask) on an image. The Sobel function below basically loops around a given pixel,applies the following weight to the pixels:

And then aplies the given formula:

Im trying to implement the formulas from this link: http://homepages.inf.ed.ac.uk/rbf/HIPR2/sobel.htm

import numpy as np
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
import Image


def Sobel(arr,rstart, cstart,masksize, divisor):
  sum = 0;
  x = 0
  y = 0

  for i in range(rstart, rstart+masksize, 1):
    x = 0
    for j in range(cstart, cstart+masksize, 1):
        if x == 0 and y == 0:
            p1 = arr[i][j]
        if x == 0 and y == 1:
            p2 = arr[i][j]
        if x == 0 and y == 2:
            p3 = arr[i][j]
        if x == 1 and y == 0:
            p4 = arr[i][j]
        if x == 1 and y == 1:
            p5 = arr[i][j]           
        if x == 1 and y == 2:
            p6 = arr[i][j]
        if x == 2 and y == 0:
            p7 = arr[i][j]
        if x == 2 and y == 1:
            p8 = arr[i][j]
        if x == 2 and y == 2:
            p9 = arr[i][j]
        x +=1
    y +=1
  return np.abs((p1 + 2*p2 + p3) - (p7 + 2*p8+p9)) + np.abs((p3 + 2*p6 + p9) - (p1 + 2*p4 +p7)) 


def padwithzeros(vector, pad_width, iaxis, kwargs):
   vector[:pad_width[0]] = 0
   vector[-pad_width[1]:] = 0
   return vector

im = Image.open('charlie.jpg')
im.show()
img = np.asarray(im)
img.flags.writeable = True
p = 1
k = 2
m = img.shape[0]
n = img.shape[1]
masksize = 3
img = np.lib.pad(img, p, padwithzeros) #this function padds image with zeros to cater for pixels on the border.
x = 0
y = 0
for row in img:
  y = 0
  for col in row:
    if not (x < p or y < p or y > (n-k) or x > (m-k)):
        img[x][y] = Sobel(img, x-p,y-p,masksize,masksize*masksize)
    y = y + 1
  x = x + 1

img2 = Image.fromarray(img)
img2.show()

Given this greyscale 8 bit image

I get this when applying the function:

but should get this:

I have implemented other gaussian filters with python, I'm not sure where I'm going wrong here?

There are (at least) two bugs in the code: (2) You cannot do this filter in-place. You overwrite a value in the input matrix, and for the next pixel you read this new value instead of the original one. You must have a separate output array. (2) The equation is likely to produce values that don’t fit in the 8-bit range, when assigning the value into the array, larger values wrap around. You might want to clip or scale the values before writing them into the output array. — Cris Luengo
– Cris Luengo, Commented Aug 31, 2024 at 19:59
here too I should point out that I've been grave-digging in the [vision] tag and this question is 8 years old — Christoph Rackwitz
– Christoph Rackwitz, Commented Aug 31, 2024 at 23:10

Martin Valgur · Accepted Answer · 2016-08-19 11:56:41Z

4

Sticking close to what your code is doing, one elegant solution is to use the scipy.ndimage.filters.generic_filter() with the formula provided above.

import numpy as np
from scipy.ndimage.filters import generic_filter
from scipy.ndimage import imread

# Load sample data
with np.DataSource().open("https://i.sstatic.net/8zINU.gif", "rb") as f:
    img = imread(f, mode="I")

# Apply the Sobel operator
def sobel_filter(P):
    return (np.abs((P[0] + 2 * P[1] + P[2]) - (P[6] + 2 * P[7] + P[8])) +
            np.abs((P[2] + 2 * P[6] + P[7]) - (P[0] + 2 * P[3] + P[6])))
G = generic_filter(img, sobel_filter, (3, 3))

Running this on the sample image takes about 400 ms. For comparison, the convolve2d's performance is about 6.5 ms.

edited Aug 19, 2016 at 11:56

answered Aug 19, 2016 at 11:50

Martin Valgur

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Martin Valgur · Accepted Answer · 2016-08-19 11:50:53Z

3

If using NumPy ans SciPy is not a problem, then a simple solution is to use the SciPy's convolve2d().

import numpy as np
from scipy.signal import convolve2d
from scipy.ndimage import imread

# Load sample data
with np.DataSource().open("https://i.sstatic.net/8zINU.gif", "rb") as f:
    img = imread(f, mode="I")

# Prepare the kernels
a1 = np.matrix([1, 2, 1])
a2 = np.matrix([-1, 0, 1])
Kx = a1.T * a2
Ky = a2.T * a1

# Apply the Sobel operator
Gx = convolve2d(img, Kx, "same", "symm")
Gy = convolve2d(img, Ky, "same", "symm")
G = np.sqrt(Gx**2 + Gy**2)
# or using the absolute values
G = np.abs(Gx) + np.abs(Gy)

edited Aug 19, 2016 at 11:50

answered Aug 19, 2016 at 11:17

Martin Valgur

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Comments

Claire_Xu · Accepted Answer · 2019-10-16 08:54:08Z

0

I met the same problem as you. I fix it by reading the image of format 'gray', you could see below

import PIL.Image
img = PIL.Image.open('image.gif').convert('L')

answered Oct 16, 2019 at 8:54

Claire_Xu

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