Canny edge detectorΒΆ

The Canny filter is a multi-stage edge detector. It uses a filter based on the derivative of a Gaussian in order to compute the intensity of the gradients.The Gaussian reduces the effect of noise present in the image. Then, potential edges are thinned down to 1-pixel curves by removing non-maximum pixels of the gradient magnitude. Finally, edge pixels are kept or removed using hysteresis thresholding on the gradient magnitude.

The Canny has three adjustable parameters: the width of the Gaussian (the noisier the image, the greater the width), and the low and high threshold for the hysteresis thresholding.

noisy image, Canny filter, $\sigma=1$, Canny filter, $\sigma=3$
import numpy as np
import matplotlib.pyplot as plt
from scipy import ndimage as ndi

from skimage import feature

# Generate noisy image of a square
im = np.zeros((128, 128))
im[32:-32, 32:-32] = 1

im = ndi.rotate(im, 15, mode='constant')
im = ndi.gaussian_filter(im, 4)
im += 0.2 * np.random.random(im.shape)

# Compute the Canny filter for two values of sigma
edges1 = feature.canny(im)
edges2 = feature.canny(im, sigma=3)

# display results
fig, (ax1, ax2, ax3) = plt.subplots(nrows=1, ncols=3, figsize=(8, 3),
                                    sharex=True, sharey=True)

ax1.set_title('noisy image', fontsize=20)

ax2.set_title(r'Canny filter, $\sigma=1$', fontsize=20)

ax3.set_title(r'Canny filter, $\sigma=3$', fontsize=20)


Total running time of the script: ( 0 minutes 0.336 seconds)

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