dev(narugo): merge from main

    types: [ published ]

jobs:

  contents:

    runs-on: ubuntu-latest

    if: ${{ !contains(github.event.head_commit.message, 'ci skip') }}

    strategy:

      matrix:

        python-version: [ 3.7 ]

    services:

      plantuml:

        image: plantuml/plantuml-server

        ports:

          - 18080:8080

    steps:

      - name: Set Swap Space

        uses: pierotofy/set-swap-space@master

        with:

          swap-size-gb: 8

      - name: Checkout code

        uses: actions/checkout@v2

        with:

          fetch-depth: 20

          submodules: 'recursive'

      - name: Set up Python ${{ matrix.python-version }}

        uses: actions/setup-python@v2

        with:

          python-version: ${{ matrix.python-version }}

      - name: Install dependencies

        if: ${{ github.event_name == 'push' }}

        run: |

          sudo apt-get update -y

          sudo apt-get install -y make wget curl cloc graphviz pandoc

          dot -V

          python -m pip install -r requirements.txt

          python -m pip install -r requirements-doc.txt

      - name: Prepare dataset

        uses: nick-fields/retry@v2

        if: ${{ github.event_name == 'push' }}

        env:

          CI: 'true'

          HF_NARUGO_USERNAME: ${{ secrets.HF_NARUGO_USERNAME }}

          HF_NARUGO_PASSWORD: ${{ secrets.HF_NARUGO_PASSWORD }}

        with:

          shell: bash

          timeout_minutes: 20

          max_attempts: 5

          retry_on: any

          command: |

            make dataset

      - name: Generate the contents

        if: ${{ github.event_name == 'push' }}

        env:

          ENV_PROD: 'true'

          PLANTUML_HOST: http://localhost:18080

        run: |

          plantumlcli -c

          make docs

      - name: Change Commit

        if: ${{ github.event_name == 'push' }}

        id: commit

        run: |

          git config user.name 'narugo1992'

          git config user.email 'narugo@126.com'

          git add -A

          git diff-index --quiet HEAD || git commit -a -m "dev(narugo): auto sync $(date -R)"

      - name: Push changes

        uses: ad-m/github-push-action@master

        if: ${{ github.event_name == 'push' }}

        with:

          github_token: ${{ secrets.GITHUB_TOKEN }}

          branch: ${{ github.ref }}

  doc:

    runs-on: ubuntu-latest

    needs:

      - contents

    if: "!contains(github.event.head_commit.message, 'ci skip')"

    strategy:

      matrix:

          - 18080:8080

    steps:

      - name: Set Swap Space

        uses: pierotofy/set-swap-space@master

        with:

          swap-size-gb: 8

      - name: Checkout code

        uses: actions/checkout@v2

        with:

      - name: Install dependencies

        run: |

          sudo apt-get update -y

          sudo apt-get install -y make wget curl cloc graphviz

          sudo apt-get install -y make wget curl cloc graphviz pandoc

          dot -V

          python -m pip install -r requirements.txt

          python -m pip install -r requirements-doc.txt

      - name: Prepare dataset

        uses: nick-fields/retry@v2

        env:

          CI: 'true'

          HF_NARUGO_USERNAME: ${{ secrets.HF_NARUGO_USERNAME }}

          HF_NARUGO_PASSWORD: ${{ secrets.HF_NARUGO_PASSWORD }}

        with:

          shell: bash

          timeout_minutes: 20

          max_attempts: 5

          retry_on: any

          command: |

            make dataset

      - name: Sync from ref

        if: ${{ github.event_name == 'push' }}

        run: |

          git pull

      - name: Generate

        env:

          ENV_PROD: 'true'

          - '3.8'

        model-name:

          #          - 'lpips'

          - 'monochrome'

          #          - 'monochrome'

          - 'person_detect'

          #          - 'face_detect'

          #          - 'manbits_detect'

    steps:

      - name: Checkout code

/docs/source/**/*.sh.err

/docs/source/**/*.sh.exitcode

/docs/source/**/*.dat.*

!/docs/source/**/*.benchmark.py.*

!/docs/source/_static/**/*

/docs/source/**/*.result.ipynb

/cartpole_dqn_*

/imgutils/**/*.json

/imgutils/**/*.yaml

/test/testfile/dataset

/zoo/testfile/dataset

/.train

*.pt

/runs

/YOLOv8

[![Contributors](https://img.shields.io/github/contributors/deepghs/imgutils)](https://github.com/deepghs/imgutils/graphs/contributors)

[![GitHub license](https://img.shields.io/github/license/deepghs/imgutils)](https://github.com/deepghs/imgutils/blob/master/LICENSE)

Utilities for Images

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

image processing models.

## Installation

## How to check truncated image

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

```shell

pip install dghs-imgutils

```

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

performance:

```shell

pip install dghs-imgutils[gpu]

```

For more information about installation, you can refer

to [Installation](https://deepghs.github.io/imgutils/main/tutorials/installation/index.html).

## Supported or Developing Features

* [Tachie(差分) Detection and Clustering](https://github.com/deepghs/imgutils#tachie%E5%B7%AE%E5%88%86-detection-and-clustering)

* [Object Detection](https://github.com/deepghs/imgutils#object-detection)

* [Edge Detection / Lineart Generation](https://github.com/deepghs/imgutils#edge-detection--lineart-generation)

* [Monochrome Image Detection](https://github.com/deepghs/imgutils#monochrome-image-detection)

* [Truncated Image Check](https://github.com/deepghs/imgutils#truncated-image-check)

* [Image Tagging](https://github.com/deepghs/imgutils#image-tagging)

* [Character Extraction](https://github.com/deepghs/imgutils#character-extraction)

### 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

![tachie](https://deepghs.github.io/imgutils/main/_images/lpips_full.dat.svg)

We can use `lpips_clustering` to cluster such situations as shown below

```python

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]

```

### Object Detection

Currently, object detection is supported for anime faces and person, as shown below

* Face Detection

![face detection](https://github.com/deepghs/imgutils/blob/gh-pages/main/_images/face_detect.dat.svg)

* Person Detection

![person detection](https://github.com/deepghs/imgutils/blob/gh-pages/main/_images/person_detect.dat.svg)

Based on practical tests, face detection currently has a very stable performance and can be used for automation tasks.

However, person detection is still being further iterated and will focus on enhancing detection capabilities for

artistic illustrations in the future.

### Edge Detection / Lineart Generation

Anime images can be converted to line drawings using the model provided

by [patrickvonplaten/controlnet_aux](https://github.com/patrickvonplaten/controlnet_aux), as shown below.

![edge example](https://github.com/deepghs/imgutils/blob/gh-pages/main/_images/edge.dat.svg)

It is worth noting that the `lineart` model may consume more computational resources, while `canny` is the fastest but

has average effect. Therefore, `lineart_anime` may be the most balanced choice in most cases.

### 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

![monochrome example](https://deepghs.github.io/imgutils/main/_images/monochrome.dat.svg)

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

```python

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](https://deepghs.github.io/imgutils/main/api_doc/validate/monochrome.html#module-imgutils.validate.monochrome)

.

### Truncated Image Check

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

process):

```python

from imgutils.validate import is_truncated_file

        print('This image is okay!')

```

### Image Tagging

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

following:

![tagging demo images](https://deepghs.github.io/imgutils/main/_images/tagging_demo.dat.svg)

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

```python

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](https://deepghs.github.io/imgutils/main/api_doc/tagging/deepdanbooru.html), but not recommended

  for production use.

* [wd14-v2 model](https://deepghs.github.io/imgutils/main/api_doc/tagging/wd14.html#), inspired

  by [SmilingWolf/wd-v1-4-tags](https://huggingface.co/spaces/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](https://deepghs.github.io/imgutils/main/api_doc/tagging/format.html#tags-to-text)) for formatting, as shown

below:

```python

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`](https://deepghs.github.io/imgutils/main/api_doc/segment/isnetis.html#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.

![isnetis](https://deepghs.github.io/imgutils/main/_images/isnetis_trans.dat.svg)

```python

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 .

 No newline at end of file

ALL_MK := ${SOURCEDIR}/all.mk

ALL    := $(MAKE) -f "${ALL_MK}" SOURCE=${SOURCEDIR}

BMS    := $(shell find ${SOURCEDIR} -name 'benchmark_*.dat.*')

.EXPORT_ALL_VARIABLES:

NO_CONTENTS_BUILD = true

	@echo $(shell readlink -f ${SOURCEDIR})

builddir:

	@echo $(shell readlink -f ${BUILDDIR}/html)

cleanbm:

	$(ALL) cleanbm