imgutils.detect.face

Overview:

Detect human faces in anime images.

Trained on dataset Anime Face CreateML with YOLOv8.

../../_images/face_detect_demo.plot.py.svg

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

../../_images/face_detect_benchmark.plot.py.svg

The models are hosted on huggingface - deepghs/anime_face_detection.

detect_faces

imgutils.detect.face.detect_faces(image: str | PathLike | bytes | bytearray | BinaryIO | Image, level: str = 's', version: str = 'v1.4', model_name: str | None = None, conf_threshold: float = 0.25, iou_threshold: float = 0.7) → List[Tuple[Tuple[int, int, int, int], str, float]][source]

Detect human faces in anime images using YOLOv8 models.

This function applies a pre-trained YOLOv8 model to detect faces in the given anime image. It supports different model levels and versions, allowing users to balance between detection speed and accuracy.

Parameters:

  • image (ImageTyping) – The input image for face detection. Can be various image types supported by ImageTyping.

  • level (str) – The model level to use. Can be either ‘s’ (standard) or ‘n’ (nano). The ‘n’ model is faster with less system overhead, while ‘s’ offers higher accuracy. Default is ‘s’.

  • version (str) – The version of the model to use. Available versions are ‘v0’, ‘v1’, ‘v1.3’, and ‘v1.4’. Default is ‘v1.4’.

  • model_name (Optional[str]) – Optional custom model name. If provided, it overrides the auto-generated model name.

  • conf_threshold (float) – The confidence threshold for detections. Only detections with confidence scores above this threshold will be returned. Default is 0.25.

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

Returns:

A list of detected faces. Each face is represented by a tuple containing: - Bounding box coordinates as (x0, y0, x1, y1) - The string ‘face’ (as this function only detects faces) - The confidence score of the detection

Return type:

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

Examples::
>>> from imgutils.detect import detect_faces, detection_visualize
>>>
>>> image = 'mostima_post.jpg'
>>> result = detect_faces(image)  # detect it
>>> result
[
    ((29, 441, 204, 584), 'face', 0.7874319553375244),
    ((346, 59, 529, 275), 'face', 0.7510495185852051),
    ((606, 51, 895, 336), 'face', 0.6986488103866577)
]
>>>
>>> # visualize it
>>> from matplotlib import pyplot as plt
>>> plt.imshow(detection_visualize(image, result))
>>> plt.show()