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https://github.com/deepghs/waifuc
Efficient Train Data Collector for Anime Waifu
https://github.com/deepghs/waifuc
Last synced: 24 days ago
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Efficient Train Data Collector for Anime Waifu
- Host: GitHub
- URL: https://github.com/deepghs/waifuc
- Owner: deepghs
- License: mit
- Created: 2023-06-27T04:14:39.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2024-08-24T08:05:00.000Z (5 months ago)
- Last Synced: 2024-08-29T01:09:46.303Z (5 months ago)
- Language: Python
- Homepage: https://deepghs.github.io/waifuc/
- Size: 72.2 MB
- Stars: 243
- Watchers: 2
- Forks: 19
- Open Issues: 11
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
- awesome-acg - waifuc - Anime image data collector for training models. [English] (Downloaders)
README
# waifuc
[](https://pypi.org/project/waifuc/)
[](https://github.com/deepghs/waifuc/actions?query=workflow%3A%22Code+Test%22)
[](https://github.com/deepghs/waifuc/actions?query=workflow%3A%22Package+Release%22)
[](https://codecov.io/gh/deepghs/waifuc)
[](https://discord.gg/TwdHJ42N72)
[](https://github.com/deepghs/waifuc/stargazers)
[](https://github.com/deepghs/waifuc/network)
[](https://github.com/deepghs/waifuc/issues)
[](https://github.com/deepghs/waifuc/pulls)
[](https://github.com/deepghs/waifuc/graphs/contributors)
[](https://github.com/deepghs/waifuc/blob/master/LICENSE)
Efficient Train Data Collector for Anime Waifu.
**This project is still under development, official version will be released soon afterwards.**
If you need to use it immediately, just clone it and run `pip install .`.
## Installation
PyPI version is not ready now, please install waifuc with source code.
```shell
pip install git+https://github.com/deepghs/waifuc.git@main#egg=waifuc
```
If your operating environment includes available CUDA, you can use the following installation command to achieve higher
```shell
pip install git+https://github.com/deepghs/waifuc.git@main#egg=waifuc[gpu]
```
If you need to process with videos, you can install waifuc with
```shell
pip install git+https://github.com/deepghs/waifuc.git@main#egg=waifuc[video]
```
For more information about installation, you can refer
to [Installation](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html).
## An Example
### Quickly Get Character's Dataset
Grab surtr (arknights)'s dataset for LoRA Training
```python
from waifuc.action import NoMonochromeAction, FilterSimilarAction, \
TaggingAction, PaddingAlignAction, PersonSplitAction, FaceCountAction, FirstNSelectAction, \
CCIPAction, ModeConvertAction, ClassFilterAction, RandomFilenameAction, AlignMinSizeAction
from waifuc.export import TextualInversionExporter
from waifuc.source import GcharAutoSource
if __name__ == '__main__':
# data source for surtr in arknights, images from many sites will be crawled
# all supported games and sites can be found at
# https://narugo1992.github.io/gchar/main/best_practice/supported/index.html#supported-games-and-sites
# ATTENTION: GcharAutoSource required `git+https://github.com/deepghs/waifuc.git@main#egg=waifuc[gchar]`
s = GcharAutoSource('surtr')
# crawl images, process them, and then save them to directory with given format
s.attach(
# preprocess images with white background RGB
ModeConvertAction('RGB', 'white'),
# pre-filtering for images
NoMonochromeAction(), # no monochrome, greyscale or sketch
ClassFilterAction(['illustration', 'bangumi']), # no comic or 3d
# RatingFilterAction(['safe', 'r15']), # filter images with rating, like safe, r15, r18
FilterSimilarAction('all'), # filter duplicated images
# human processing
FaceCountAction(1), # drop images with 0 or >1 faces
PersonSplitAction(), # crop for each person
FaceCountAction(1),
# CCIP, filter the character you may not want to see in dataset
CCIPAction(min_val_count=15),
# if min(height, weight) > 800, resize it to 800
AlignMinSizeAction(800),
# tagging with wd14 v2, if you don't need character tag, set character_threshold=1.01
TaggingAction(force=True),
PaddingAlignAction((512, 512)), # align to 512x512
FilterSimilarAction('all'), # filter again
FirstNSelectAction(200), # first 200 images
# MirrorAction(), # mirror image for data augmentation
RandomFilenameAction(ext='.png'), # random rename files
).export(
# save to surtr_dataset directory
TextualInversionExporter('surtr_dataset')
)
```
### Quick Crawl Images from Websites
The following code will give you 10 images of surtr (arknights) with metadata saved.
```python
from waifuc.action import HeadCountAction, AlignMinSizeAction
from waifuc.export import SaveExporter
from waifuc.source import DanbooruSource
if __name__ == '__main__':
source = DanbooruSource(['surtr_(arknights)', 'solo'])
source.attach(
# only 1 head,
HeadCountAction(1),
# if shorter side is over 640, just resize it to 640
AlignMinSizeAction(640),
)[:10].export( # only first 10 images
# save images (with meta information from danbooru site)
SaveExporter('/data/surtr_arknights')
)
```
And this is what's in `/data/surtr_arknights` afterwards
Similarly, you can crawl from pixiv with similar code, just by changing the source
```python
from waifuc.action import HeadCountAction, AlignMinSizeAction, CCIPAction
from waifuc.export import SaveExporter
from waifuc.source import PixivSearchSource
if __name__ == '__main__':
source = PixivSearchSource(
'アークナイツ (surtr OR スルト OR 史尔特尔)',
refresh_token='use_your_own_refresh_token'
)
source.attach(
# only 1 head,
HeadCountAction(1),
# pixiv often have some irrelevant character mixed in
# so CCIPAction is necessary here to drop these images
CCIPAction(),
# if shorter side is over 640, just resize it to 640
AlignMinSizeAction(640),
)[:10].export( # only first 10 images
# save images (with meta information from danbooru site)
SaveExporter('/data/surtr_arknights_pixiv')
)
```
This is what you can get at `/data/surtr_arknights_pixiv`
Here is a list of website source we currently supported
| Name | Import Statement |
|:--------------------------------------------------|:----------------------------------------------|
| [ATFBooruSource](https://booru.allthefallen.moe) | from waifuc.source import ATFBooruSource |
| [AnimePicturesSource](https://anime-pictures.net) | from waifuc.source import AnimePicturesSource |
| [DanbooruSource](https://danbooru.donmai.us) | from waifuc.source import DanbooruSource |
| [DerpibooruSource](https://derpibooru.org) | from waifuc.source import DerpibooruSource |
| [DuitangSource](https://www.duitang.com) | from waifuc.source import DuitangSource |
| [E621Source](https://e621.net) | from waifuc.source import E621Source |
| [E926Source](https://e926.net) | from waifuc.source import E926Source |
| [FurbooruSource](https://furbooru.com) | from waifuc.source import FurbooruSource |
| [GelbooruSource](https://gelbooru.com) | from waifuc.source import GelbooruSource |
| [Huashi6Source](https://www.huashi6.com) | from waifuc.source import Huashi6Source |
| [HypnoHubSource](https://hypnohub.net) | from waifuc.source import HypnoHubSource |
| [KonachanNetSource](https://konachan.net) | from waifuc.source import KonachanNetSource |
| [KonachanSource](https://konachan.com) | from waifuc.source import KonachanSource |
| [LolibooruSource](https://lolibooru.moe) | from waifuc.source import LolibooruSource |
| [PahealSource](https://rule34.paheal.net) | from waifuc.source import PahealSource |
| [PixivRankingSource](https://pixiv.net) | from waifuc.source import PixivRankingSource |
| [PixivSearchSource](https://pixiv.net) | from waifuc.source import PixivSearchSource |
| [PixivUserSource](https://pixiv.net) | from waifuc.source import PixivUserSource |
| [Rule34Source](https://rule34.xxx) | from waifuc.source import Rule34Source |
| [SafebooruOrgSource](https://safebooru.org) | from waifuc.source import SafebooruOrgSource |
| [SafebooruSource](https://safebooru.donmai.us) | from waifuc.source import SafebooruSource |
| [SankakuSource](https://chan.sankakucomplex.com) | from waifuc.source import SankakuSource |
| [TBIBSource](https://tbib.org) | from waifuc.source import TBIBSource |
| [WallHavenSource](https://wallhaven.cc) | from waifuc.source import WallHavenSource |
| [XbooruSource](https://xbooru.com) | from waifuc.source import XbooruSource |
| [YandeSource](https://yande.re) | from waifuc.source import YandeSource |
| [ZerochanSource](https://www.zerochan.net) | from waifuc.source import ZerochanSource |
### 3-Stage-Cropping of Local Dataset
This code is loading images from local directory, and crop the images with 3-stage-cropping method (head, halfbody, full
person), and then save it to another local directory.
```python
from waifuc.action import ThreeStageSplitAction
from waifuc.export import SaveExporter
from waifuc.source import LocalSource
source = LocalSource('/your/path/contains/images')
source.attach(
ThreeStageSplitAction(),
).export(SaveExporter('/your/output/path'))
```