Edge detection is another popular image processing technique (http://en.wikipedia.org/wiki/Edge_detection). scikit-image has a Canny filter implementation based on the standard deviation of the Gaussian distribution, which can perform edge detection out of the box. Besides the image data as a 2D array, this filter accepts the following parameters:
Standard deviation of the Gaussian distribution
Lower bound threshold
Upper bound threshold
We will use the same image as in the previous recipe. The code is almost the same (see edge_detection.py). Pay extra attention to the line where we call the Canny filter function:
from sklearn.datasets import load_sample_images
import matplotlib.pyplot as plt
import skimage.feature
dataset = load_sample_images()
img = dataset.images[0]
edges = skimage.feature.canny(img[..., 0])
plt.axis('off')
plt.imshow(edges)
plt.show()The code produces an image of the edges within the original image, as shown in the following screenshot:
