imgutils.detect.censor

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 with YOLOv8.

Overview of Censor Detect (NSFW Warning!!!)../../_images/censor_detect_demo.plot.py.svg

This is an overall benchmark of all the censor detect models:

../../_images/censor_detect_benchmark.plot.py.svg

detect_censors

imgutils.detect.censor.detect_censors(image: str | PathLike | bytes | bytearray | BinaryIO | Image, level: str = 's', version: str = 'v1.0', model_name: str | None = None, conf_threshold: float = 0.3, iou_threshold: float = 0.7) → List[Tuple[Tuple[int, int, int, int], str, float]][source]

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.

Parameters:

  • image (ImageTyping) – The input image to be analyzed. Can be a file path, URL, or image data.

  • level (str) – 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.

  • version (str) – The version of the model to use. Default is ‘v1.0’.

  • model_name (Optional[str]) – Optional custom model name. If not provided, it will be constructed from the version and level.

  • conf_threshold (float) – The confidence threshold for detections. Only detections with confidence above this value will be returned. Default is 0.3.

  • iou_threshold (float) – The Intersection over Union (IoU) threshold for non-maximum suppression. Detections with IoU above this value will be merged. Default is 0.7.

Returns:

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

Return type:

List[Tuple[Tuple[int, int, int, int], str, float]]

Raises:

  • ValueError – If an invalid level is provided.

  • 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()