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1.8 KiB
Cartoonize with CartoonGAN
author: Shiyu
Description
Convert an image into an cartoon image using CartoonGAN
.
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.cartoongan['origin', 'hayao'](model_name = 'Hayao') \
.img2img_translation.cartoongan['origin', 'hosoda'](model_name = 'Hosoda') \
.img2img_translation.cartoongan['origin', 'paprika'](model_name = 'Paprika') \
.img2img_translation.cartoongan['origin', 'shinkai'](model_name = 'Shinkai') \
.select['origin', 'hayao', 'hosoda', 'paprika', 'shinkai']() \
.show()
Factory Constructor
Create the operator via the following factory method
img2img_translation.cartoongan(model_name = 'which anime model to use')
Model options:
- Hayao
- Hosoda
- 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.
device: str
Which device being used('cpu' or 'cuda'), defaults to 'cpu'.
Returns: towhee.types.Image (a sub-class of numpy.ndarray)
The new image.
1.8 KiB
Cartoonize with CartoonGAN
author: Shiyu
Description
Convert an image into an cartoon image using CartoonGAN
.
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.cartoongan['origin', 'hayao'](model_name = 'Hayao') \
.img2img_translation.cartoongan['origin', 'hosoda'](model_name = 'Hosoda') \
.img2img_translation.cartoongan['origin', 'paprika'](model_name = 'Paprika') \
.img2img_translation.cartoongan['origin', 'shinkai'](model_name = 'Shinkai') \
.select['origin', 'hayao', 'hosoda', 'paprika', 'shinkai']() \
.show()
Factory Constructor
Create the operator via the following factory method
img2img_translation.cartoongan(model_name = 'which anime model to use')
Model options:
- Hayao
- Hosoda
- 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.
device: str
Which device being used('cpu' or 'cuda'), defaults to 'cpu'.
Returns: towhee.types.Image (a sub-class of numpy.ndarray)
The new image.