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

Building a Star feature detector


SIFT feature detector is good in many cases. However, when we build object recognition systems, we may want to use a different feature detector before we extract features using SIFT. This will give us the flexibility to cascade different blocks to get the best possible performance. So, we will use the Star feature detector in this case to 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. Define a class to handle all the functions that are related to Star feature detection:

    class StarFeatureDetector(object):
        def __init__(self):
            self.detector = cv2.xfeatures2d.StarDetector_create()
  3. Define a function to run the detector on the input image:

        def detect(self, img):
            return self.detector.detect(img)
  4. Load the input image in the main function. We will use table.jpg:

    if __name__=='__main__':
        # Load input image -- 'table.jpg'
        input_file = sys.argv[1]
        input_img...