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
About the Author
About the Reviewer

Detecting edges

Edge detection is one of the most popular techniques in Computer Vision. It is used as a preprocessing step in many applications. Let's look at how to use different edge detectors to detect edges in the input image.

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 chair.jpg:

    # Load the input image -- 'chair.jpg'
    # Convert it to grayscale 
    input_file = sys.argv[1]
    img = cv2.imread(input_file, cv2.IMREAD_GRAYSCALE)
  3. Extract the height and width of the image:

    h, w = img.shape
  4. Sobel filter is a type of edge detector that uses a 3x3 kernel to detect horizontal and vertical edges separately. You can learn more about it at Let's start with the horizontal detector:

    sobel_horizontal = cv2.Sobel(img, cv2.CV_64F, 1, 0, ksize=5)
  5. Run the vertical Sobel detector:

    sobel_vertical = cv2.Sobel(img, cv2.CV_64F, 0, 1, ksize=5)
  6. Laplacian edge detector...