by Matthew Rever | Released June 2018 | ISBN: 9781788835565

Explore Python's powerful tools for extracting data from images and videosAbout This VideoBuild powerful computer vision tools in Python with clear and concise codeDiscover deep learning methods that can be applied to a wide variety of problems in computer visionCrisp videos that take you directly to a practical approach to solving real-world examplesIn DetailThe 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.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.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.By the end of this course, you'll know the basic tools of computer vision and be able to put it into practice.The code bundle for this video course is available at - https://github.com/PacktPublishing/Computer-Vision-Projects-with-Python-3 Show and hide more

1. Chapter 1 : Introduction and Tool Setup
   • The Course Overview 00:03:37
   • Downloading and Installing Python 3/Anaconda 00:05:38
   • Installing Additional Libraries 00:09:25
   • Exploring the Jupyter Notebook 00:11:30
2. Chapter 2 : Handwritten Digit Recognition with scikit-learn and TensorFlow
   • Acquiring and Processing MNIST Digit Data 00:10:33
   • Creating and Training a Support Vector Machine 00:03:22
   • Applying the Support Vector Machine to New Data 00:09:13
   • Introducing TensorFlow with Digit Classification 00:10:13
   • Evaluating the Results 00:02:49
3. Chapter 3 : Facial Feature Tracking and Classification with dlib
   • Introducing dlib 00:03:05
   • What Are Facial Landmarks? 00:04:13
   • Example One – Finding 68 Facial Landmarks in Images 00:11:26
   • Example Two – Faces in Videos 00:05:47
   • Example Three – Facial Recognition 00:09:54
4. Chapter 4 : Deep Learning Image Classification with TensorFlow
   • A Deeper Introduction to TensorFlow 00:08:28
   • Using a Pre-Trained Model (Inception) for Image Classification 00:12:10
   • Retraining with Our Own Images 00:13:13
   • Speeding Up Computations with GPUs 00:04:29
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