#!/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.
# 

# # 11.1. Manipulating the exposure of an image

# You need scikit-image for this recipe. You will find the installation instructions [here](http://scikit-image.org/download.html).
# 
# You also need to download the *Beach* dataset. (http://ipython-books.github.io)

# 1. Let's import the packages.

# In[ ]:


import numpy as np
import matplotlib as mpl
import matplotlib.pyplot as plt
import skimage.exposure as skie
get_ipython().run_line_magic('matplotlib', 'inline')


# 2. We open an image with matplotlib. We only take a single RGB component to have a grayscale image.

# In[ ]:


img = plt.imread('data/pic1.jpg')[...,0]


# 3. We create a function that displays the image along with its **histogram**.

# In[ ]:


def show(img):
    # Display the image.
    plt.figure(figsize=(8,2));
    plt.subplot(121);
    plt.imshow(img, cmap=plt.cm.gray);
    plt.axis('off');
    # Display the histogram.
    plt.subplot(122);
    plt.hist(img.ravel(), lw=0, bins=256);
    plt.xlim(0, img.max());
    plt.yticks([]);
    plt.show()


# 4. Let's display the image along with its histogram.

# In[ ]:


show(img)


# The histogram is unbalanced and the image appears slightly over-exposed.

# 5. Now, we rescale the intensity of the image using scikit-image's `rescale_intensity` function. The `in_range` and `out_range` define a linear mapping from the original image to the modified image. The pixels that are outside `in_range` are clipped to the extremal values of `out_range`. Here, the darkest pixels (intensity less than 100) become completely black (0), whereas the brightest pixels (>240) become completely white (255).

# In[ ]:


show(skie.rescale_intensity(img,
     in_range=(100, 240), out_range=(0, 255)))


# Many intensity values seem to be missing in the histogram, which reflects the poor quality of this exposure correction technique.

# 6. We now use a more advanced exposure correction technique called **Contrast Limited Adaptive Histogram Equalization**.

# In[ ]:


show(skie.equalize_adapthist(img))


# The histogram seems more balanced, and the image now appears more contrasted.

# > 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).