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:

   In [1]: import numpy as np
           import matplotlib.pyplot as plt
           import skimage
           import skimage.filter as skif
           import skimage.data as skid
           %matplotlib inline

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

   In [2]: def show(img):
               plt.imshow(img, cmap=plt.cm.gray)
               plt.axis('off')
               plt.show()

3. Now, we load the Lena image (bundled in scikit-image). We select a single RGB component to get a grayscale image:

   In [3]: img = skimage.img_as_float(skid.lena())[...,0]
   In [4]: show(img)

   

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

   In [5]: show(skif.gaussian_filter(img, 5.))

   

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

   In [6]: sobimg = skif.sobel(img)
           show(sobimg)

   

6. We can threshold the filtered image to get a sketch effect. We obtain...