#!/usr/bin/env python
# coding: utf-8

# > This is one of the 100 recipes of the [IPython Cookbook](http://ipython-books.github.io/), the definitive guide to high-performance scientific computing and data science in Python.
# 

# # 11.2. Applying filters on an image

# 1. Let's import the packages.

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import numpy as np
import matplotlib.pyplot as plt
import skimage
import skimage.filter as skif
import skimage.data as skid
import matplotlib as mpl
get_ipython().run_line_magic('matplotlib', 'inline')


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

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def show(img):
    plt.figure(figsize=(4,2));
    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.

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img = skimage.img_as_float(skid.lena())[...,0]


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show(img)


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

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show(skif.gaussian_filter(img, 5.))


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

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sobimg = skif.sobel(img)
show(sobimg)


# 6. We can threshold the filtered image to get a *sketch effect*. We obtain a binary image that only contains the edges. We use a notebook widget to find an adequate thresholding value.

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from IPython.html import widgets
@widgets.interact(x=(0.01, .4, .005))
def edge(x):
    show(sobimg<x)


# 7. Finally, we add some noise to the image to illustrate the effect of a denoising filter.

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img = skimage.img_as_float(skid.lena())
# We take a portion of the image to show the details.
img = img[200:-100, 200:-150]
# We add Gaussian noise.
img = np.clip(img + .3 * np.random.rand(*img.shape), 0, 1)


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show(img)


# 8. The `denoise_tv_bregman` function implements total-variation denoising using split-Bregman optimization.

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show(skimage.restoration.denoise_tv_bregman(img, 5.))


# > You'll find all the explanations, figures, references, and much more in the book (to be released later this summer).
# 
# > [IPython Cookbook](http://ipython-books.github.io/), by [Cyrille Rossant](http://cyrille.rossant.net), Packt Publishing, 2014 (500 pages).