Book Image

Python Machine Learning Cookbook

By : Prateek Joshi, Vahid Mirjalili
Book Image

Python Machine Learning Cookbook

By: Prateek Joshi, Vahid Mirjalili

Overview of this book

Machine learning is becoming increasingly pervasive in the modern data-driven world. It is used extensively across many fields such as search engines, robotics, self-driving cars, and more. With this book, you will learn how to perform various machine learning tasks in different environments. We’ll start by exploring a range of real-life scenarios where machine learning can be used, and look at various building blocks. Throughout the book, you’ll use a wide variety of machine learning algorithms to solve real-world problems and use Python to implement these algorithms. You’ll discover how to deal with various types of data and explore the differences between machine learning paradigms such as supervised and unsupervised learning. We also cover a range of regression techniques, classification algorithms, predictive modeling, data visualization techniques, recommendation engines, and more with the help of real-world examples.
Table of Contents (19 chapters)
Python Machine Learning Cookbook
Credits
About the Author
About the Reviewer
www.PacktPub.com
Preface
Index

Histogram equalization


Histogram equalization is the process of modifying the intensities of the image pixels to enhance the contrast. The human eye likes contrast! This is the reason that almost all camera systems use histogram equalization to make images look nice. The interesting thing is that the histogram equalization process is different for grayscale and color images. There's a catch when dealing with color images, and we'll see it in this recipe. Let's see how to do it.

How to do it…

  1. Create a new Python file, and import the following packages:

    import sys
    
    import cv2
    import numpy as np 
  2. Load the input image. We will use the image, sunrise.jpg:

    # Load input image -- 'sunrise.jpg'
    input_file = sys.argv[1]
    img = cv2.imread(input_file)
  3. Convert the image to grayscale and display it:

    # Convert it to grayscale
    img_gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
    cv2.imshow('Input grayscale image', img_gray)
  4. Equalize the histogram of the grayscale image and display it:

    # Equalize the histogram
    img_gray_histeq...