Oreilly - Advanced Computer Vision Projects
by Matthew Rever | Released August 2018 | ISBN: 9781788620772
Gain a working knowledge of advanced machine learningAbout This VideoWrite concise Python scripts to generate captions from imagesDevelop an application that estimates human poses from 2D imagesSee deep learning methods that can be applied to a wide variety of problems in computer visionIn DetailPython's wealth of powerful packages along with its clear syntax make state-of-the art computer vision and machine learning accessible to developers with a variety of backgrounds. This video course will equip you with the tools and skills to utilize the latest and greatest algorithms in computer vision, making applications that weren't possible until recent years.In this course, you'll continue to use TensorFlow and extend it to generate full captions from images. Later, you'll see how to read text from license plates from real-world images using Google's Tesseract Software. Finally, you'll see how to track human body poses using “DeeperCut” within TensorFlow.At the end of this course, you'll develop an application that can estimate human poses within images and will be able to take on the world with best practices in computer vision with machine learning.The code bundle for this video course is available at - https://github.com/PacktPublishing/Advanced-Computer-Vision-Projects Show and hide more Publisher Resources Download Example Code
- Chapter 1 : Image Captioning with TensorFlow
- The Course Overview 00:02:21
- Image Captioning Introduction 00:05:06
- Google Brain im2txt Captioning Model 00:04:28
- Running Our Captioning Code in Jupyter 00:11:48
- Retraining the Model 00:07:49
- Chapter 2 : Reading License Plates with OpenCV
- Steps to Read License Plates 00:02:58
- Plate Utility Functions 00:06:42
- Finding Plate Characters 00:09:05
- Finding and Reading License Plates 00:11:47
- Chapter 3 : Human Pose Estimation with TensorFlow
- Pose Estimation with DeeperCut and ArtTrack 00:04:30
- Single-Person Pose Detection 00:15:58
- Multi-Person Pose Detection 00:10:37
- Videos and Retraining 00:03:29
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