You're reading from Hands-On Image Generation with TensorFlow

Product typeBook

Published inDec 2020

Reading LevelIntermediate

PublisherPackt

ISBN-139781838826789

Edition1st Edition

Languages

Python

Tools

TensorFlow

Concepts

Computer Vision

Author (1)

Soon Yau Cheong

Implementing face image processing

We will use mainly two Python libraries – dlib and OpenCV – to implement most of the face processing tasks. OpenCV is good for general-purpose computer vision tasks and includes low-level functions and algorithms. While dlib was originally a C++ toolkit for machine learning, it also has a Python interface, and it is the go-to machine learning Python library for facial landmark detection. Most of the image processing code used in this chapter is adapted from https://github.com/deepfakes/faceswap.

Extracting image from video

The first thing in the production pipeline is to extract images from video. A video is made up of a series of images separated by a fixed time interval. If you check a video file's properties, you may find something that says frame rate = 25 fps. FPS indicates the number of image frames per second in a video, and 25 fps is the standard video frame rate. That means 25 images are played within a 1-second...

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Hands-On Image Generation with TensorFlow

Published in: Dec 2020Publisher: PacktISBN-13: 9781838826789

Author (1)

Soon Yau Cheong

Soon Yau Cheong is an AI consultant and the founder of Sooner.ai Ltd. With a history of being associated with industry giants such as NVIDIA and Qualcomm, he provides consultation in the various domains of AI, such as deep learning, computer vision, natural language processing, and big data analytics. He was awarded a full scholarship to study for his PhD at the University of Bristol while working as a teaching assistant. He is also a mentor for AI courses with Udacity.
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You're reading from Hands-On Image Generation with TensorFlow

Implementing face image processing

Extracting image from video

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