This is the code repository for OpenCV 3.x with Python By Example - Second Edition, published by Packt. It contains all the supporting project files necessary to work through the book from start to finish.
Computer vision is found everywhere in modern technology. OpenCV for Python enables us to run computer vision algorithms in real time. With the advent of powerful machines, we are getting more processing power to work with. Using this technology, we can seamlessly integrate our computer vision applications into the cloud. This book will walk you through all the building blocks needed to build amazing computer vision applications with ease.
We start off by applying geometric transformations to images. We then discuss affine and projective transformations and see how we can use them to apply cool geometric effects to photos. We will then cover techniques used for object recognition, 3D reconstruction, stereo imaging, and other computer vision applications. The book also covers popular OpenCV libraries with the help of examples. You will also learn about camera calibration and how to apply machine learning with artificial neural networks.
The book is a practical tutorial that covers various examples at different levels, teaching you about the different functions of OpenCV and their actual implementation. By the end of this book, you will have acquired the skills to use OpenCV and Python to develop real-world computer vision applications.
All of the code is organized into folders. Each folder starts with a number followed by the application name. For example, Chapter02.
The code will look like the following:
import cv2
img = cv2.imread('images/input.jpg')
cv2.imwrite('images/output.png', img, [cv2.IMWRITE_PNG_COMPRESSION])
You'll need the following software:
- OpenCV 3.1 or higher
- NumPy 1.13 or higher
- SciPy 1.0 or higher
- scikit-learn 0.19 or higher
- pickleshare 0.7 or higher
The hardware specifications requirement is any computer with at least 8 GB DDR3 RAM.
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