pythonnumpyimage-processinginteger-overflowsobel

python - Implementing Sobel operators with python without opencv

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: enter image description here

enter image description here

And then aplies the given formula:

enter image description here

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

enter image description here

I get this when applying the function:

enter image description here

but should get this:

enter image description here

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

Solution

  • 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.