dev(narugo): add more intro to new features

## 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)

* [Contrastive Character Image Pretraining](https://github.com/deepghs/imgutils#contrastive-character-image-pretraining)

* [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)

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

* [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)

`imgutils` also includes many other features besides that. For detailed descriptions and examples, please refer to

the [official documentation](https://deepghs.github.io/imgutils/main/index.html). Here, we won't go into each of them

individually.

### 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

# [0, 0, 0, 1, 1, -1, -1, -1, -1]

```

### Contrastive Character Image Pretraining

We can use `imgutils` to extract features from anime character images (containing only a single character), calculate

the visual dissimilarity between two characters, and determine whether two images depict the same character. We can also

perform clustering operations based on this metric, as shown below

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

```python

from imgutils.metrics import ccip_difference, ccip_clustering

# same character

print(ccip_difference('ccip/1.jpg', 'ccip/2.jpg'))  # 0.16583099961280823

# different characters

print(ccip_difference('ccip/1.jpg', 'ccip/6.jpg'))  # 0.42947039008140564

print(ccip_difference('ccip/1.jpg', 'ccip/7.jpg'))  # 0.4037521779537201

print(ccip_difference('ccip/2.jpg', 'ccip/6.jpg'))  # 0.4371533691883087

print(ccip_difference('ccip/2.jpg', 'ccip/7.jpg'))  # 0.40748104453086853

print(ccip_difference('ccip/6.jpg', 'ccip/7.jpg'))  # 0.392294704914093

images = [f'ccip/{i}.jpg' for i in range(1, 13)]

print(images)

# ['ccip/1.jpg', 'ccip/2.jpg', 'ccip/3.jpg', 'ccip/4.jpg', 'ccip/5.jpg', 'ccip/6.jpg', 'ccip/7.jpg', 'ccip/8.jpg', 'ccip/9.jpg', 'ccip/10.jpg', 'ccip/11.jpg', 'ccip/12.jpg']

print(ccip_clustering(images, min_samples=2))  # few images, min_sample should not be too large

# [0, 0, 0, 3, 3, 3, 1, 1, 1, 1, 2, 2]

```

For more usage, please refer

to [official documentation of CCIP](https://deepghs.github.io/imgutils/main/api_doc/metrics/ccip.html).

### Object Detection

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

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

.

### Image Rating

We can use `imgutils` to perform a rough and quick determination of the rating (`safe`, `r15`, or `r18`) of anime

images, as shown below.

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

```python

from imgutils.validate import anime_rating

print(anime_rating('rating/safe/1.jpg'))  # ('safe', 0.9999998807907104)

print(anime_rating('rating/safe/2.jpg'))  # ('safe', 0.9924363493919373)

print(anime_rating('rating/safe/3.jpg'))  # ('safe', 0.996969997882843)

print(anime_rating('rating/safe/4.jpg'))  # ('safe', 0.9966891407966614)

print(anime_rating('rating/r15/5.jpg'))  # ('r15', 0.9996721744537354)

print(anime_rating('rating/r15/6.jpg'))  # ('r15', 0.9998563528060913)

print(anime_rating('rating/r15/7.jpg'))  # ('r15', 0.9827859997749329)

print(anime_rating('rating/r15/8.jpg'))  # ('r15', 0.8339558839797974)

print(anime_rating('rating/r18/9.jpg'))  # ('r18', 0.9997004270553589)

print(anime_rating('rating/r18/10.jpg'))  # ('r18', 0.9997699856758118)

print(anime_rating('rating/r18/11.jpg'))  # ('r18', 0.9999485015869141)

print(anime_rating('rating/r18/12.jpg'))  # ('r18', 0.9994290471076965)

```

### Truncated Image Check

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