dev(narugo): refactor doc system

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

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

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

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

!/docs/source/**/*.plot.py.svg

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

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

/cartpole_dqn_*

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)

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

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

* Head Detection

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

![head detection](https://github.com/deepghs/imgutils/blob/gh-pages/main/_images/head_detect.plot.py.svg)

* Person Detection

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

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

Based on practical tests, head 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

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)

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

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)

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

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

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)

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

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

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)

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

```python

from imgutils.segment import segment_rgba_with_isnetis

builddir:

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

cleanbm:

	$(ALL) cleanbm

cleanplt:

	$(ALL) cleanplt

from functools import wraps

import click

def _wrap_func_as_cli(func):

    @wraps(func)

    def _new_func(*args, **kwargs):

        @click.command()

        @click.option('--output', '-o', 'save_as', type=click.Path(dir_okay=False), required=True,

                      help='Output path of image file.', show_default=True)

        def _execute(save_as):

            func(*args, save_as=save_as, **kwargs)

        _execute()

    return _new_func

import matplotlib.pyplot as plt

from PIL import Image

from cli import _wrap_func_as_cli

from imgutils.data import load_image, grid_transparent, ImageTyping

from imgutils.validate.truncate import _mock_load_truncated_images

    return grid_transparent(image), label

@_wrap_func_as_cli

@_mock_load_truncated_images(True)

def image_plot(*images, save_as: str, columns=2, keep_axis: bool = False, figsize=(6, 6)):

    plt.cla()

    plt.savefig(save_as, bbox_inches='tight', pad_inches=0.1, dpi=300, transparent=True)

@_wrap_func_as_cli

@_mock_load_truncated_images(True)

def image_table(images: List[List[ImageTyping]], columns: List[str], rows: List[str], save_as: str,

                keep_axis: bool = False, figsize=(720, 600), dpi: int = 300, fontsize: int = 18, padsize: int = 5):