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Practical Computer Vision

You're reading from   Practical Computer Vision Extract insightful information from images using TensorFlow, Keras, and OpenCV

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Product type Paperback
Published in Feb 2018
Publisher Packt
ISBN-13 9781788297684
Length 234 pages
Edition 1st Edition
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Author (1):
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Abhinav Dadhich Abhinav Dadhich
Author Profile Icon Abhinav Dadhich
Abhinav Dadhich
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Toc

Table of Contents (12) Chapters Close

Preface 1. A Fast Introduction to Computer Vision FREE CHAPTER 2. Libraries, Development Platform, and Datasets 3. Image Filtering and Transformations in OpenCV 4. What is a Feature? 5. Convolutional Neural Networks 6. Feature-Based Object Detection 7. Segmentation and Tracking 8. 3D Computer Vision 9. Mathematics for Computer Vision 10. Machine Learning for Computer Vision 11. Other Books You May Enjoy

Datasets and libraries used

In this chapter, we will be using Keras to write neural nets with TensorFlow as backend. A detailed installation procedure is explained in Chapter 2, Libraries, Development Platforms and Datasets. To check if you have Keras installed, in shell run:

python -c "import keras;print(keras.__version__)"

This will print the Keras version as well as which backend you are using. If you have TensorFlow installed and Keras is using TensorFlow, it will print using Tensorflow backend. If you have an older version of Keras and TensorFlow, there might be some issues, so please install or upgrade to the latest versions. We will also be using other libraries like NumPy and OpenCV.

We will be using the Fashion–MNIST dataset by Zalando SE which is available at https://github.com/zalandoresearch/fashion-mnist. This can be downloaded directly with Keras...
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