dghs-imgutils 0.0.1

Utilities of images.

imgutils

A convenient and user-friendly anime-style image data processing library that integrates various advanced anime-style image processing models.

Installation

You can simply install it with pip command line from the official PyPI site.

pip install dghs-imgutils

If your operating environment includes a available GPU, you can use the following installation command to achieve higher performance:

pip install dghs-imgutils[gpu]

For more information about installation, you can refer to Installation.

Supported or Developing Features

Tachie(差分) Detection and Clustering

For the dataset, we need to filter the differences between the tachie(差分). As shown in the following picture

We can use lpips_clustering to cluster such situations as shown below

from imgutils.metrics import lpips_clustering

images = [f'lpips/{i}.jpg' for i in range(1, 10)]
print(images)
# ['lpips/1.jpg', 'lpips/2.jpg', 'lpips/3.jpg', 'lpips/4.jpg', 'lpips/5.jpg', 'lpips/6.jpg', 'lpips/7.jpg', 'lpips/8.jpg', 'lpips/9.jpg']
print(lpips_clustering(images))  # -1 means noises, the same as that in sklearn
# [0, 0, 0, 1, 1, -1, -1, -1, -1]

Monochrome Image Detection

When filtering the crawled images, we need to remove monochrome images. However, monochrome images are often not simply composed of grayscale colors and may still contain colors, as shown by the first two rows of six images in the figure below

We can use is_monochrome to determine whether an image is monochrome, as shown below:

from imgutils.validate import is_monochrome

print(is_monochrome('mono/1.jpg'))  # monochrome images
# True
print(is_monochrome('mono/2.jpg'))
# True
print(is_monochrome('mono/3.jpg'))
# True
print(is_monochrome('mono/4.jpg'))
# True
print(is_monochrome('mono/5.jpg'))
# True
print(is_monochrome('mono/6.jpg'))
# True
print(is_monochrome('colored/7.jpg'))  # colored images
# False
print(is_monochrome('colored/8.jpg'))
# False
print(is_monochrome('colored/9.jpg'))
# False
print(is_monochrome('colored/10.jpg'))
# False
print(is_monochrome('colored/11.jpg'))
# False
print(is_monochrome('colored/12.jpg'))
# False

For more details, please refer to the official documentation .

Truncated Image Check

The following code can be used to detect incomplete image files (such as images interrupted during the download process):

from imgutils.validate import is_truncated_file

if __name__ == '__main__':
    filename = 'test_jpg.jpg'
    if is_truncated_file(filename):
        print('This image is truncated, you\'d better '
              'remove this shit from your dataset.')
    else:
        print('This image is okay!')

Image Tagging

The imgutils library integrates various anime-style image tagging models, allowing for results similar to the following:

The ratings, features, and characters in the image can be detected, like this:

import os
from imgutils.tagging import get_wd14_tags

rating, features, chars = get_wd14_tags('skadi.jpg')
print(rating)
# {'general': 0.0011444687843322754, 'sensitive': 0.8876402974128723, 'questionable': 0.106781005859375, 'explicit': 0.000277101993560791}
print(features)
# {'1girl': 0.997527003288269, 'solo': 0.9797663688659668, 'long_hair': 0.9905703663825989, 'breasts': 0.9761719703674316,
#  'looking_at_viewer': 0.8981098532676697, 'bangs': 0.8810765743255615, 'large_breasts': 0.9498510360717773,
#  'shirt': 0.8377365469932556, 'red_eyes': 0.945058286190033, 'gloves': 0.9457170367240906, 'navel': 0.969594419002533,
#  'holding': 0.7881088852882385, 'hair_between_eyes': 0.7687551379203796, 'very_long_hair': 0.9301245212554932,
#  'standing': 0.6703325510025024, 'white_hair': 0.5292627811431885, 'short_sleeves': 0.8677047491073608,
#  'grey_hair': 0.5859264731407166, 'thighs': 0.9536856412887573, 'cowboy_shot': 0.8056888580322266,
#  'sweat': 0.8394746780395508, 'outdoors': 0.9473626613616943, 'parted_lips': 0.8986269235610962,
#  'sky': 0.9385137557983398, 'shorts': 0.8408567905426025, 'alternate_costume': 0.4245271384716034,
#  'day': 0.931140661239624, 'black_gloves': 0.8830795884132385, 'midriff': 0.7279844284057617,
#  'artist_name': 0.5333830714225769, 'cloud': 0.64717698097229, 'stomach': 0.9516432285308838,
#  'blue_sky': 0.9655293226242065, 'crop_top': 0.9485014081001282, 'black_shirt': 0.7366660833358765,
#  'short_shorts': 0.7161656618118286, 'ass_visible_through_thighs': 0.5858667492866516,
#  'black_shorts': 0.6186309456825256, 'thigh_gap': 0.41193312406539917, 'no_headwear': 0.467605859041214,
#  'low-tied_long_hair': 0.36282333731651306, 'sportswear': 0.3756745457649231, 'motion_blur': 0.5091936588287354,
#  'baseball_bat': 0.951993465423584, 'baseball': 0.5634750723838806, 'holding_baseball_bat': 0.8232709169387817}
print(chars)
# {'skadi_(arknights)': 0.9869340658187866}

rating, features, chars = get_wd14_tags('hutao.jpg')
print(rating)
# {'general': 0.49491602182388306, 'sensitive': 0.5193622708320618, 'questionable': 0.003406703472137451,
#  'explicit': 0.0007208287715911865}
print(features)
# {'1girl': 0.9798132181167603, 'solo': 0.8046203851699829, 'long_hair': 0.7596215009689331,
#  'looking_at_viewer': 0.7620116472244263, 'blush': 0.46084529161453247, 'smile': 0.48454540967941284,
#  'bangs': 0.5152207016944885, 'skirt': 0.8023070096969604, 'brown_hair': 0.8653596639633179,
#  'hair_ornament': 0.7201820611953735, 'red_eyes': 0.7816740870475769, 'long_sleeves': 0.697688639163971,
#  'twintails': 0.8974947333335876, 'school_uniform': 0.7491052746772766, 'jacket': 0.5015512704849243,
#  'flower': 0.6401398181915283, 'ahoge': 0.43420469760894775, 'pleated_skirt': 0.4528769850730896,
#  'outdoors': 0.5730487704277039, 'tongue': 0.6739872694015503, 'hair_flower': 0.5545973181724548,
#  'tongue_out': 0.6946243047714233, 'bag': 0.5487751364707947, 'symbol-shaped_pupils': 0.7439308166503906,
#  'blazer': 0.4186026453971863, 'backpack': 0.47378358244895935, ':p': 0.4690653085708618, 'ghost': 0.7565015554428101}
print(chars)
# {'hu_tao_(genshin_impact)': 0.9262397289276123, 'boo_tao_(genshin_impact)': 0.942080020904541}

We currently integrate the following tagging models:

• Deepdanbooru model, but not recommended for production use.
• wd14-v2 model, inspired by SmilingWolf/wd-v1-4-tags.

In addition, if you need to convert the dict-formatted data mentioned above into the text format required for image training and tagging, you can also use the tags_to_text function (see the link here) for formatting, as shown below:

from imgutils.tagging import tags_to_text

# a group of tags
tags = {
    'panty_pull': 0.6826801300048828,
    'panties': 0.958938717842102,
    'drinking_glass': 0.9340789318084717,
    'areola_slip': 0.41196826100349426,
    '1girl': 0.9988248348236084
}

print(tags_to_text(tags))
# '1girl, panties, drinking_glass, panty_pull, areola_slip'
print(tags_to_text(tags, use_spaces=True))
# '1girl, panties, drinking glass, panty pull, areola slip'
print(tags_to_text(tags, include_score=True))
# '(1girl:0.999), (panties:0.959), (drinking_glass:0.934), (panty_pull:0.683), (areola_slip:0.412)'

Character Extraction

When we need to extract the character parts from anime images, we can use the segment-rgba-with-isnetis function for extraction and obtain an RGBA format image (with the background part being transparent), just like the example shown below.

from imgutils.segment import segment_rgba_with_isnetis

mask_, image_ = segment_rgba_with_isnetis('hutao.png')
image_.save('hutao_seg.png')

mask_, image_ = segment_rgba_with_isnetis('skadi.jpg')
image_.save('skadi_seg.png')

This model can be found at https://huggingface.co/skytnt/anime-seg .