Animating using AnimeGanV2

author: Filip Haltmayer

Description

Convert an image into an animated image using AnimeganV2.

Code Example

Load an image from path './test.png'.

Write the pipeline in simplified style:

import towhee

towhee.glob('./test.png') \
      .image_decode() \
      .img2img_translation.animegan(model_name = 'hayao') \
      .show()

Write a pipeline with explicit inputs/outputs name specifications:

import towhee
      
towhee.glob['path']('./test.png') \
      .image_decode['path', 'origin']() \
      .img2img_translation.animegan['origin', 'facepaintv2'](model_name = 'facepaintv2') \
      .img2img_translation.animegan['origin', 'hayao'](model_name = 'hayao') \
      .img2img_translation.animegan['origin', 'paprika'](model_name = 'paprika') \
      .img2img_translation.animegan['origin', 'shinkai'](model_name = 'shinkai') \
      .select['origin', 'facepaintv2', 'hayao', 'paprika', 'shinkai']() \
      .show()

Factory Constructor

Create the operator via the following factory method

img2img_translation.animegan(model_name = 'which anime model to use')

Model options:

• celeba
• facepaintv1
• facepaintv2
• hayao
• paprika
• shinkai

Interface

Takes in a numpy rgb image in channels first. It transforms input into animated image in numpy form.

Parameters:

​ model_name: str

​ Which model to use for transfer.

framework: str

​ Which ML framework being used, for now only supports PyTorch.

Returns: towhee.types.Image (a sub-class of dumpy.ndarray)

​ The new image.

Reference

Jie Chen, Gang Liu, Xin Chen "AnimeGAN: A Novel Lightweight GAN for Photo Animation." ISICA 2019: Artificial Intelligence Algorithms and Applications pp 242-256, 2019.