Book Image

Hands-On Image Generation with TensorFlow

By : Soon Yau Cheong
Book Image

Hands-On Image Generation with TensorFlow

By: Soon Yau Cheong

Overview of this book

The emerging field of Generative Adversarial Networks (GANs) has made it possible to generate indistinguishable images from existing datasets. With this hands-on book, you’ll not only develop image generation skills but also gain a solid understanding of the underlying principles. Starting with an introduction to the fundamentals of image generation using TensorFlow, this book covers Variational Autoencoders (VAEs) and GANs. You’ll discover how to build models for different applications as you get to grips with performing face swaps using deepfakes, neural style transfer, image-to-image translation, turning simple images into photorealistic images, and much more. You’ll also understand how and why to construct state-of-the-art deep neural networks using advanced techniques such as spectral normalization and self-attention layer before working with advanced models for face generation and editing. You'll also be introduced to photo restoration, text-to-image synthesis, video retargeting, and neural rendering. Throughout the book, you’ll learn to implement models from scratch in TensorFlow 2.x, including PixelCNN, VAE, DCGAN, WGAN, pix2pix, CycleGAN, StyleGAN, GauGAN, and BigGAN. By the end of this book, you'll be well versed in TensorFlow and be able to implement image generative technologies confidently.
Table of Contents (15 chapters)
1
Section 1: Fundamentals of Image Generation with TensorFlow
5
Section 2: Applications of Deep Generative Models
9
Section 3: Advanced Deep Generative Techniques

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...