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# 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.

```
import numpy as np
import matplotlib.pyplot as plt
from scipy import ndimage as ndi
from skimage.util import random_noise
from skimage import feature
# Generate noisy image of a square
image = np.zeros((128, 128), dtype=float)
image[32:-32, 32:-32] = 1
image = ndi.rotate(image, 15, mode='constant')
image = ndi.gaussian_filter(image, 4)
image = random_noise(image, mode='speckle', mean=0.1)
# Compute the Canny filter for two values of sigma
edges1 = feature.canny(image)
edges2 = feature.canny(image, sigma=3)
# display results
fig, ax = plt.subplots(nrows=1, ncols=3, figsize=(8, 3))
ax[0].imshow(image, cmap='gray')
ax[0].set_title('noisy image', fontsize=20)
ax[1].imshow(edges1, cmap='gray')
ax[1].set_title(r'Canny filter, $\sigma=1$', fontsize=20)
ax[2].imshow(edges2, cmap='gray')
ax[2].set_title(r'Canny filter, $\sigma=3$', fontsize=20)
for a in ax:
a.axis('off')
fig.tight_layout()
plt.show()
```

**Total running time of the script:** ( 0 minutes 0.089 seconds)