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Python Machine Learning Cookbook

You're reading from   Python Machine Learning Cookbook 100 recipes that teach you how to perform various machine learning tasks in the real world

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Product type Paperback
Published in Jun 2016
Publisher Packt
ISBN-13 9781786464477
Length 304 pages
Edition 1st Edition
Languages
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Authors (2):
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Vahid Mirjalili Vahid Mirjalili
Author Profile Icon Vahid Mirjalili
Vahid Mirjalili
Prateek Joshi Prateek Joshi
Author Profile Icon Prateek Joshi
Prateek Joshi
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Toc

Table of Contents (14) Chapters Close

Preface 1. The Realm of Supervised Learning FREE CHAPTER 2. Constructing a Classifier 3. Predictive Modeling 4. Clustering with Unsupervised Learning 5. Building Recommendation Engines 6. Analyzing Text Data 7. Speech Recognition 8. Dissecting Time Series and Sequential Data 9. Image Content Analysis 10. Biometric Face Recognition 11. Deep Neural Networks 12. Visualizing Data Index

Operating on images using OpenCV-Python


Let's take a look at how to operate on images using OpenCV-Python. In this recipe, we will see how to load and display an image. We will also look at how to crop, resize, and save an image to an output file.

How to do it…

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

    import sys
    
    import cv2
    import numpy as np
  2. Specify the input image as the first argument to the file, and read it using the image read function. We will use forest.jpg, as follows:

    # Load and display an image -- 'forest.jpg'
    input_file = sys.argv[1]
    img = cv2.imread(input_file)
  3. Display the input image, as follows:

    cv2.imshow('Original', img)
  4. We will now crop this image. Extract the height and width of the input image, and then specify the boundaries:

    # Cropping an image
    h, w = img.shape[:2]
    start_row, end_row = int(0.21*h), int(0.73*h)
    start_col, end_col= int(0.37*w), int(0.92*w)
  5. Crop the image using NumPy style slicing and display it:

    img_cropped = img[start_row:end_row, start_col...
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