"""
Overview:
Detect human censor points (including female's nipples and genitals of both male and female) in anime images.
Trained on dataset `deepghs/anime_censor_detection <https://huggingface.co/datasets/deepghs/anime_censor_detection>`_ with YOLOv8.
.. collapse:: Overview of Censor Detect (NSFW Warning!!!)
.. image:: censor_detect_demo.plot.py.svg
:align: center
This is an overall benchmark of all the censor detect models:
.. image:: censor_detect_benchmark.plot.py.svg
:align: center
"""
from typing import List, Tuple, Optional
from ..data import ImageTyping
from ..generic import yolo_predict
_REPO_ID = 'deepghs/anime_censor_detection'
[docs]def detect_censors(image: ImageTyping, level: str = 's', version: str = 'v1.0', model_name: Optional[str] = None,
conf_threshold: float = 0.3, iou_threshold: float = 0.7, **kwargs) \
-> List[Tuple[Tuple[int, int, int, int], str, float]]:
"""
Detect human censor points in anime images.
This function uses pre-trained YOLOv8 models to identify and locate specific
anatomical features that are typically censored in anime images. It can detect
female nipples, male genitals, and female genitals.
:param image: The input image to be analyzed. Can be a file path, URL, or image data.
:type image: ImageTyping
:param level: The model level to use, either 's' (standard) or 'n' (nano).
The 'n' model is faster but less accurate, while 's' is more accurate but slower.
:type level: str
:param version: The version of the model to use. Default is 'v1.0'.
:type version: str
:param model_name: Optional custom model name. If not provided, it will be constructed
from the version and level.
:type model_name: Optional[str]
:param conf_threshold: The confidence threshold for detections. Only detections with
confidence above this value will be returned. Default is 0.3.
:type conf_threshold: float
:param iou_threshold: The Intersection over Union (IoU) threshold for non-maximum
suppression. Detections with IoU above this value will be merged.
Default is 0.7.
:type iou_threshold: float
:return: A list of tuples, each containing:
- A tuple of four integers (x0, y0, x1, y1) representing the bounding box
- A string indicating the type of detection ('nipple_f', 'penis', or 'pussy')
- A float representing the confidence score of the detection
:rtype: List[Tuple[Tuple[int, int, int, int], str, float]]
:raises ValueError: If an invalid level is provided.
:raises RuntimeError: If the model fails to load or process the image.
Examples::
>>> from imgutils.detect import detect_censors, detection_visualize
>>>
>>> image = 'nude_girl.png'
>>> result = detect_censors(image) # detect it
>>> result
[
((365, 264, 399, 289), 'nipple_f', 0.7473511695861816),
((224, 260, 252, 285), 'nipple_f', 0.6830288171768188),
((206, 523, 240, 608), 'pussy', 0.6799028515815735)
]
>>>
>>> # visualize it
>>> from matplotlib import pyplot as plt
>>> plt.imshow(detection_visualize(image, result))
>>> plt.show()
"""
return yolo_predict(
image=image,
repo_id=_REPO_ID,
model_name=model_name or f'censor_detect_{version}_{level}',
conf_threshold=conf_threshold,
iou_threshold=iou_threshold,
**kwargs,
)