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Practical Data Analysis

You're reading from  Practical Data Analysis

Product type Book
Published in Oct 2013
Publisher Packt
ISBN-13 9781783280995
Pages 360 pages
Edition 1st Edition
Languages
Author (1):
Hector Cuesta Hector Cuesta
Profile icon Hector Cuesta
Toc

Table of Contents (24) Chapters close

Practical Data Analysis
Credits
Foreword
About the Author
Acknowledgments
About the Reviewers
www.PacktPub.com
Preface
1. Getting Started 2. Working with Data 3. Data Visualization 4. Text Classification 5. Similarity-based Image Retrieval 6. Simulation of Stock Prices 7. Predicting Gold Prices 8. Working with Support Vector Machines 9. Modeling Infectious Disease with Cellular Automata 10. Working with Social Graphs 11. Sentiment Analysis of Twitter Data 12. Data Processing and Aggregation with MongoDB 13. Working with MapReduce 14. Online Data Analysis with IPython and Wakari Setting Up the Infrastructure Index

Introduction to image processing with PIL


The goal of this chapter is to present some of the preinstalled capabilities of Wakari. In this section, we will explore some of the basic functions of the PIL (Python Image Library) such as histogram, filters, operations, and transformations. We have already installed and used PIL in Chapter 5, Similarity-based Image Retrieval.

First, we will upload the images 412.jpg (Dinosaur) and 826.jpg (Land) to the path (see the arrow in the following screenshot). The images came from the Caltech-256 images-dataset used in the Chapter 5, Similarity-based Image Retrieval.

Opening an image

The first thing we need to start working on is importing the PIL and pylab modules. Next, we will use the open method of the Image object. Finally, we will visualize the image with the imshow method of pylab. In the following screenshot, we may see the output of the code:

Tip

You can find more information about PIL from http://www.pythonware.com/products/pil/.

Image histogram

A histogram...

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