Applying filters on an image

In this recipe, we apply filters on an image for various purposes: blurring, denoising, and edge detection.

How it works...

1. Let's import the packages:

   >>> import numpy as np
       import matplotlib.pyplot as plt
       import skimage
       import skimage.color as skic
       import skimage.filters as skif
       import skimage.data as skid
       import skimage.util as sku
       %matplotlib inline

2. We create a function that displays a grayscale image:

   >>> def show(img):
           fig, ax = plt.subplots(1, 1, figsize=(8, 8))
           ax.imshow(img, cmap=plt.cm.gray)
           ax.set_axis_off()
           plt.show()

3. Now, we load the Astronaut image (bundled in scikit-image). We convert it to a grayscale image with the rgb2gray() function:

   >>> img = skic.rgb2gray(skid.astronaut())
   >>> show(img)

   

4. Let's apply a blurring Gaussian filter to the image:

   >>> show(skif.gaussian(img, 5.))

   

5. We now apply a Sobel filter that enhances the edges in the image:

   >>&gt...