Edge detection not working as expected

Question

I was just playing around with convolution and kernels in SciPy and Python. I used the following kernel for edge detection since it was listed in this wikipedia article:



This is the image I used:


The result I got was pretty disappointing:

The code I used for the convolution:

edge = np.array([[-1, -1, -1], [-1, 8, -1], [-1, -1, -1]])
results = sg.convolve(img, edge, mode='same')
results[results > 255] = 255
results[results < 0] = 0

...and the code I used to read the image:

img = np.array(Image.open('convolution_test/1.jpg'))
img = img[:, :, 0]

Why am I getting these bad results?

TIA.

Answer 1

I think the problem is that your image works with unsigned integers. As a result if you for instance subtract one from zero, you get 0-1 = 255 for an uint8, and thus you get white where the value actually should be black.

You can however easily overcome this issue by using signed integers (preferably with more depth). For instance:

from PIL import Image
import numpy as np
import scipy.signal as sg

img = np.array(Image.open('convolution_test/1.jpg'))
img = img[:, :, 0]
img = img.astype(np.int16)

edge = np.array([[-1, -1, -1], [-1, 8, -1], [-1, -1, -1]])
results = sg.convolve(img, edge, mode='same')
results[results > 255] = 255
results[results < 0] = 0

results = results.astype(np.uint8)

For me this generates the following image: