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OpenCV 4 with Python Blueprints

OpenCV 4 with Python Blueprints

By : Dr. Menua Gevorgyan , Michael Beyeler (USD), Mamikonyan, Michael Beyeler
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OpenCV 4 with Python Blueprints

OpenCV 4 with Python Blueprints

5 (4)
By: Dr. Menua Gevorgyan , Michael Beyeler (USD), Mamikonyan, Michael Beyeler

Overview of this book

OpenCV is a native cross-platform C++ library for computer vision, machine learning, and image processing. It is increasingly being adopted in Python for development. This book will get you hands-on with a wide range of intermediate to advanced projects using the latest version of the framework and language, OpenCV 4 and Python 3.8, instead of only covering the core concepts of OpenCV in theoretical lessons. This updated second edition will guide you through working on independent hands-on projects that focus on essential OpenCV concepts such as image processing, object detection, image manipulation, object tracking, and 3D scene reconstruction, in addition to statistical learning and neural networks. You’ll begin with concepts such as image filters, Kinect depth sensor, and feature matching. As you advance, you’ll not only get hands-on with reconstructing and visualizing a scene in 3D but also learn to track visually salient objects. The book will help you further build on your skills by demonstrating how to recognize traffic signs and emotions on faces. Later, you’ll understand how to align images, and detect and track objects using neural networks. By the end of this OpenCV Python book, you’ll have gained hands-on experience and become proficient at developing advanced computer vision apps according to specific business needs.
Table of Contents (14 chapters)
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11
Profiling and Accelerating Your Apps
12
Setting Up a Docker Container

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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