When I run test.py, I get a ValueError: Unknown resampling filter (InterpolationMode.BICUBIC) error in data.py. To avoid errors data.py line 96 image_pil = image_pil.resize (output_resize, bic) is image_pil = image_pil.resize (output_resize, Image.BICUBIC) or Shouldn't image_pil = image_pil.resize (output_resize, 3)? This way the error no longer occurs.

Hi

Thanks for your great work.

When I test on my own dataset, sometimes it will show some artifacts in darkness place. Do you have any good suggestions to fix it?

Hi, thanks to great works.

tho found this project has been stalled for a year

can it do line extraction but with colored lines (mark) , train model on multi-colour lines pairs ?

In fact, in animation production, called 色トレス, need work that tracing the scanned paper drawing into digital

the tracing easy to coloring (compare to illustration making), tracing following coloring reference to standardize lines colour, like solid line, shadow, highlight mark

你好，可以使用这个模型用来训练普通图片到草图吗？我使用AODA进行了一些尝试，但是效果并不理想，我猜可能是我哪里做错了。 这是我的训练参数，希望能得到你的帮助，谢谢！

python train.py --dataroot ./dataset/sketch/ --name scribble_aoda  --model aoda_gan  --gan_mode vanilla --no_dropout --n_classes 9 --direction BtoA --save_epoch_freq 1 --load_size 260 --continue_train --gpu_ids 0 

Summary

Add Dockerfile and example Makefile. You can run demo just by customizing mount volumes for input/output images directory on Makefile. #3

Build docker image

make docker-build

Run

Customize mount volumes for input/output directory on Makefile docker run -it --rm --gpus all -v `pwd`:/workspace -v <input images dir>:/input -v <output images dir>:/output anime2sketch Run container make docker-run

if you run cpu only, you have to fix Dockerfile CMD to CMD [ "python", "test.py", "--dataroot", "/input", "--load_size", "512", "--output_dir", "/output" ] and Makefile docker-run to docker run -it --rm -v `pwd`:/workspace -v `pwd`/images/input:/input -v `pwd`/images/output:/output anime2sketch

I'm using Windows 10 and python 3.9

PS C:\Users\Tamal\Desktop\Anime2Sketch> python3 test.py --dataroot image --load_size 512 --output_dir results Traceback (most recent call last): File "C:\Users\Tamal\Desktop\Anime2Sketch\test.py", line 46, in save_image(aus_img, aus_path, aus_resize) File "C:\Users\Tamal\Desktop\Anime2Sketch\data.py", line 96, in save_image image_pil = image_pil.resize(output_resize, bic) File "C:\Users\Tamal\AppData\Local\Programs\Python\Python39\lib\site-packages\PIL\Image.py", line 1957, in resize raise ValueError( ValueError: Unknown resampling filter (InterpolationMode.BICUBIC). Use Image.NEAREST (0), Image.LANCZOS (1), Image.BILINEAR (2), Image.BICUBIC (3), Image.BOX (4) or Image.HAMMING (5) PS C:\Users\Tamal\Desktop\Anime2Sketch>

Hi， thanks for your sharing , I want to train my own dataset using this network

I see in readme for training in AODA, but AODA repo also has nothing about how to train 。。。

Hello, just like the title said. I can't perform Anime2Sketch using the Gradio demo website and I got an error.

And I can't use the demo image from the website.

I don't know it is a bug or just my internet because I already try to delete the cookies and I still have that trouble.

Thank you

Hello, I love your research and output is so great

I'm just wondering how mach size of dataset are used for pretrained model of this Anime2sketch, through the original code https://github.com/Mukosame/AODA

waiting for reply

Thank you

I am getting the following error while trying the test code.

UserWarning: Argument interpolation should be of type InterpolationMode instead of int. Please, use InterpolationMode enum. warnings.warn(

Hi, I would like to convert normal face images to sketch. Would a model trained on ffhq dataset perform well with normal images? Have you tried something like this before?

This fix prevent the following error happening when resizing input image(s) before generating sketches: ValueError: Unknown resampling filter (InterpolationMode.BICUBIC). Use Image.Resampling.NEAREST (0), Image.Resampling.LANCZOS (1), Image.Resampling.BILINEAR (2), Image.Resampling.BICUBIC (3), Image.Resampling.BOX (4) or Image.Resampling.HAMMING (5)

ValueError: Unknown resampling filter (InterpolationMode.BICUBIC). Use Image.Resampling.NEAREST (0), Image.Resampling.LANCZOS (1), Image.Resampling.BILINEAR (2), Image.Resampling.BICUBIC (3), Image.Resampling.BOX (4) or Image.Resampling.HAMMING (5)

raise ValueError("Expected more than 1 spatial element when training, got input size {}".format(size)) ValueError: Expected more than 1 spatial element when training, got input size torch.Size([1, 512, 1, 1])

analytics-python 1.4.0 backoff 1.10.0 bcrypt 3.2.0 certifi 2021.10.8 cffi 1.15.0 charset-normalizer 2.0.8 click 8.0.3 colorama 0.4.4 cryptography 36.0.0 cycler 0.11.0 ffmpy 0.3.0 Flask 2.0.2 Flask-CacheBuster 1.0.0 Flask-Cors 3.0.10 Flask-Login 0.5.0 fonttools 4.28.2 gradio 2.4.6 idna 3.3 itsdangerous 2.0.1 Jinja2 3.0.3 kiwisolver 1.3.2 markdown2 2.4.1 MarkupSafe 2.0.1 matplotlib 3.5.0 monotonic 1.6 numpy 1.21.4 packaging 21.3 pandas 1.3.4 paramiko 2.8.1 pep517 0.12.0 Pillow 8.4.0 pip 21.3.1 pycparser 2.21 pycryptodome 3.11.0 pydub 0.25.1 PyNaCl 1.4.0 pyparsing 3.0.6 python-dateutil 2.8.2 pytz 2021.3 requests 2.26.0 setuptools 41.2.0 setuptools-scm 6.3.2 six 1.16.0 tomli 1.2.2 torch 1.10.0 torchtext 0.11.0 torchvision 0.11.1 tqdm 4.62.3 typing_extensions 4.0.0 urllib3 1.26.7 Werkzeug 2.0.2

heres what works for me and converts all files from in folder to out folder with 2048 tex quality :

@echo off for %%F in (in*.*) do test.py --dataroot in --load_size 2048 --output_dir out/