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)
Section 1: Fundamentals of Image Generation with TensorFlow
Section 2: Applications of Deep Generative Models
Section 3: Advanced Deep Generative Techniques

Swapping faces

Here comes the last step of the deepfake pipeline, but let's first recap the pipeline. The deepfake production pipeline involves three main stages:

  1. Extract a face from an image using dlib and OpenCV.
  2. Translate the face using the trained encoder and decoders.
  3. Swap the new face back into the original image.

The new face generated by the autoencoder is an aligned face of size 64×64, so we will need to warp it to the position, size, and angle of the face in the original image. We'll use the affine matrix obtained from step 1 in the face extraction stage. We'll use cv2.warpAffine like before, but this time, the cv2.WARP_INVERSE_MAP flag is used to reverse the direction of image transformation as follows:

h, w, _ = image.shape
size = 64
new_image = np.zeros_like(image, dtype=np.uint8)
new_image = cv2.warpAffine(np.array(new_face, 						   dtype=np.uint8)