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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 main script

The main script will be responsible for the complete logic of the app. It will process a video stream and use an object-detection deep convolutional neural network combined with the tracking algorithm that we will prepare later in this chapter.

The algorithm is used to track objects from frame to frame. It will also be responsible for illustrating results. The script will accept arguments and have some intrinsic constants, which are defined in the following initialization steps of the script:

  1. As with any other script, we start by importing all the required modules:
import argparse

import cv2
import numpy as np

from classes import CLASSES_90
from sort import Sort

We will use argparse as we want our script to accept arguments. We store the object classes in a separate file in order not to contaminate our script. Finally, we import our Sort tracker, which...

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