From a92178457325f8550a82d5ea48a4e8490672c335 Mon Sep 17 00:00:00 2001 From: shiyu22 Date: Tue, 24 May 2022 14:45:26 +0800 Subject: [PATCH] Update README Signed-off-by: shiyu22 --- README.md | 107 ++++++++++++++++++++++++++++++++++++++++++++++- cartoongan.py | 6 +-- pytorch/model.py | 3 ++ results1.png | Bin 0 -> 39366 bytes results2.png | Bin 0 -> 748230 bytes test.png | Bin 0 -> 122707 bytes 6 files changed, 111 insertions(+), 5 deletions(-) create mode 100644 results1.png create mode 100644 results2.png create mode 100644 test.png diff --git a/README.md b/README.md index f48daf0..38157f2 100644 --- a/README.md +++ b/README.md @@ -1,3 +1,106 @@ -# cartoongan +# Cartoongan + +*author: Shiyu* + + + +
+ + + +## Description + +Convert an image into an cartoon image using [`CartoonGAN`](https://github.com/Yijunmaverick/CartoonGAN-Test-Pytorch-Torch). + + + +
+ + + + +## Code Example + +Load an image from path './test.png'. + + *Write the pipeline in simplified style*: + +```python +import towhee + +towhee.glob('./test.png') \ + .image_decode() \ + .img2img_translation.animegan(model_name = 'hayao') \ + .show() +``` + + + +*Write a pipeline with explicit inputs/outputs name specifications:* + +```python +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() +``` + +results1 + + + +
+ + + +## 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. + -2 \ No newline at end of file diff --git a/cartoongan.py b/cartoongan.py index 7534f5d..625f710 100644 --- a/cartoongan.py +++ b/cartoongan.py @@ -36,10 +36,10 @@ class Cartoongan(NNOperator): transforms.ToTensor() ]) - @arg(1, to_image_color('RGB')) + @arg(1, to_image_color('BGR')) def __call__(self, image): - img = self.tfms(image).unsqueeze(0) - styled_image = self.model(img) + image = self.tfms(image).unsqueeze(0) + styled_image = self.model(image) styled_image = numpy.transpose(styled_image, (1, 2, 0)) styled_image = PImage.fromarray((styled_image * 255).astype(numpy.uint8)) diff --git a/pytorch/model.py b/pytorch/model.py index 20e274b..ff94d35 100644 --- a/pytorch/model.py +++ b/pytorch/model.py @@ -2,6 +2,7 @@ import os import torch import torch.nn as nn import torch.nn.functional as F +from torch import Tensor from pathlib import Path class Transformer(nn.Module): @@ -197,4 +198,6 @@ class Model(): # BGR -> RGB output_image = output_image[[2, 1, 0], :, :] output_image = output_image.data.cpu().float() * 0.5 + 0.5 + return output_image.numpy() + \ No newline at end of file diff --git a/results1.png b/results1.png new file mode 100644 index 0000000000000000000000000000000000000000..bdb866f7a56f768736eeb3718203b4f2a4f34632 GIT binary patch literal 39366 zcmbTdWl$Vn^es9Bmq2irAR%~ghY*5?5ZsyI?l#yU2@rw>2oO9$f(|~oyHAkd?#>K4 zgkgC6{M1%xH|K0rek^lAT z6KqUOYyw;y-2bWazg0YR0Z8%w!-R>!3V1?_fk}$-&<9|C#EJbs@E*bbFT;3(iG_`W z`v`;Zu>$njBYaHEN6^@hh#zZ*JU$0tkz$iQ7gWF@*S5rE^`sCAPx^(&_NJzrQfCs* 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