Book Image

Computer Vision Projects with Python 3 [Video]

By : Matthew Rever
Book Image

Computer Vision Projects with Python 3 [Video]

By: Matthew Rever

Overview of this book

<p>The Python programming language is an ideal platform for rapidly prototyping and developing production-grade codes for image processing and computer vision with its robust syntax and wealth of powerful libraries.</p> <p>This video course will start by showing you how to set up Anaconda Python for the major OSes with cutting-edge third-party libraries for computer vision. You’ll learn state-of-the-art techniques to classify images and find and identify humans within videos.</p> <p>Next, you’ll understand how to set up Anaconda Python 3 for the major OSes (Windows, Mac, and Linux) and augment it with the powerful vision and machine learning tools OpenCV and TensorFlow, as well as Dlib. You’ll be taken through the handwritten digits classifier and then move on to detecting facial features and finally develop a general image classifier.</p> <p>By the end of this course, you’ll know the basic tools of computer vision and be able to put it into practice.</p> <p>The code bundle for this video course is available at -&nbsp;<a href="https://github.com/PacktPublishing/Computer-Vision-Projects-with-Python-3" target="_blank">https://github.com/PacktPublishing/Computer-Vision-Projects-with-Python-3</a></p> <h1>Style and Approach</h1> <p>This video tutorial offers a project-based approach to teach you the skills required to develop computer vision solutions in Python.</p>
Table of Contents (4 chapters)
Chapter 2
Handwritten Digit Recognition with scikit-learn and TensorFlow
Content Locked
Section 1
Acquiring and Processing MNIST Digit Data
In this video, take a look at the MNIST handwritten digit dataset to see how we can use it to build a classifier. - Learn what MNIST digit dataset is - Download and extract the data - Take a look at the data and put in in usable form