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Computer Vision Projects with OpenCV and Python 3

Computer Vision Projects with OpenCV and Python 3

By : Rever
1 (1)
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Computer Vision Projects with OpenCV and Python 3

Computer Vision Projects with OpenCV and Python 3

1 (1)
By: Rever

Overview of this book

Python is the ideal programming language for rapidly prototyping and developing production-grade codes for image processing and Computer Vision with its robust syntax and wealth of powerful libraries. This book will help you design and develop production-grade Computer Vision projects tackling real-world problems. With the help of this book, you will learn how to set up Anaconda and Python for the major OSes with cutting-edge third-party libraries for Computer Vision. You'll learn state-of-the-art techniques for classifying images, finding and identifying human postures, and detecting faces within videos. You will use powerful machine learning tools such as OpenCV, Dlib, and TensorFlow to build exciting projects such as classifying handwritten digits, detecting facial features,and much more. The book also covers some advanced projects, such as reading text from license plates from real-world images using Google’s Tesseract software, and tracking human body poses using DeeperCut within TensorFlow. By the end of this book, you will have the expertise required to build your own Computer Vision projects using Python and its associated libraries.
Table of Contents (9 chapters)
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Identifying the license plate

In this project, we are going to detect and read license plates in photos of cars. We will be performing multiple steps, from locating the license plate to displaying the characters in the located license plate.

Let's refer to the code in Jupyter Notebook needed to analyze our sample images:

%pylab notebook
figure()
imshow(imread('tests/p1.jpg'))

We get the following photo when we run the code:

We have a photo of a car, with its license plate clearly visible and readable. The challenge is to locate the license plate, isolate it from the rest of the photo, and extract the characters from it.

We can now take a closer look at the license plate using the available utility functions:

There are many algorithms that can help us carry out both these tasks. For example, object detectors such as YOLO: Real-Time Object Detection can do a very...

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