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OpenCV 4 with Python Blueprints

You're reading from   OpenCV 4 with Python Blueprints Build creative computer vision projects with the latest version of OpenCV 4 and Python 3

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Product type Paperback
Published in Mar 2020
Publisher Packt
ISBN-13 9781789801811
Length 366 pages
Edition 2nd Edition
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Authors (4):
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Michael Beyeler (USD) Michael Beyeler (USD)
Author Profile Icon Michael Beyeler (USD)
Michael Beyeler (USD)
Dr. Menua Gevorgyan Dr. Menua Gevorgyan
Author Profile Icon Dr. Menua Gevorgyan
Dr. Menua Gevorgyan
Michael Beyeler Michael Beyeler
Author Profile Icon Michael Beyeler
Michael Beyeler
Arsen Mamikonyan Arsen Mamikonyan
Author Profile Icon Arsen Mamikonyan
Arsen Mamikonyan
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Toc

Table of Contents (14) Chapters Close

Preface 1. Fun with Filters 2. Hand Gesture Recognition Using a Kinect Depth Sensor FREE CHAPTER 3. Finding Objects via Feature Matching and Perspective Transforms 4. 3D Scene Reconstruction Using Structure from Motion 5. Using Computational Photography with OpenCV 6. Tracking Visually Salient Objects 7. Learning to Recognize Traffic Signs 8. Learning to Recognize Facial Emotions 9. Learning to Classify and Localize Objects 10. Learning to Detect and Track Objects 11. Profiling and Accelerating Your Apps 12. Setting Up a Docker Container 13. Other Books You May Enjoy

Preparing the dataset

As mentioned previously, in this chapter, we are going to use the Oxford-IIIT-Pet dataset. It will be a good idea to encapsulate the preparation of the dataset in a separate data.py script, which can then be used throughout the chapter. As with any other script, first of all, we have to import all the required modules, as shown in the following code snippet:

import glob
import os

from itertools import count
from collections import defaultdict, namedtuple

import cv2
import numpy as np
import tensorflow as tf
import xml.etree.ElementTree as ET

In order to prepare our dataset for use, we will first download and parse the dataset into memory. Then, out of the parsed data, we will create a TensorFlow dataset, which allows us to work with a dataset in a convenient manner as well as prepare the data in the background so that the preparation of the data does not interrupt...

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