"""
Overview:
Detect human faces in both real photo and anime images.
Trained with `deepghs/anime_face_detection <https://huggingface.co/datasets/deepghs/anime_face_detection>`_ \
and open-sourced real photos datasets.
.. image:: face_detect_demo.plot.py.svg
:align: center
This is an overall benchmark of all the face detect models:
.. image:: face_detect_benchmark.plot.py.svg
:align: center
The models are hosted on
`huggingface - deepghs/real_face_detection <https://huggingface.co/deepghs/real_face_detection>`_.
"""
from typing import List, Tuple
from imgutils.data import ImageTyping
from imgutils.generic import yolo_predict
_REPO_ID = 'deepghs/real_face_detection'
[docs]def detect_faces(image: ImageTyping, model_name: str = 'face_detect_v0_s_yv11',
conf_threshold: float = 0.25, iou_threshold: float = 0.7, **kwargs) \
-> List[Tuple[Tuple[int, int, int, int], str, float]]:
"""
Detect human faces in both real photo and anime images using YOLO models.
This function applies a pre-trained YOLO 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.
:param image: The input image for face detection. Can be various image types supported by ImageTyping.
:type image: ImageTyping
:param model_name: Optional custom model name. If provided, it overrides the auto-generated model name.
:type model_name: str
:param conf_threshold: The confidence threshold for detections. Only detections with confidence
scores above this threshold will be returned. Default is 0.25.
:type conf_threshold: float
:param iou_threshold: The Intersection over Union (IoU) threshold for non-maximum suppression.
Detections with IoU above this threshold will be merged. Default is 0.7.
:type iou_threshold: float
:return: 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
:rtype: List[Tuple[Tuple[int, int, int, int], str, float]]
:example:
>>> from realutils.detect import detect_faces
>>>
>>> detect_faces('yolo/solo.jpg')
[((157, 94, 252, 208), 'face', 0.8836570382118225)]
>>> detect_faces('yolo/2girls.jpg')
[((718, 154, 1110, 728), 'face', 0.8841166496276855), ((157, 275, 519, 715), 'face', 0.8668240904808044)]
>>> detect_faces('yolo/3+cosplay.jpg')
[((349, 227, 413, 305), 'face', 0.8543888330459595), ((383, 61, 432, 117), 'face', 0.8080574870109558), ((194, 107, 245, 162), 'face', 0.8035706877708435)]
>>> detect_faces('yolo/multiple.jpg')
[((1070, 728, 1259, 985), 'face', 0.8765808939933777), ((548, 286, 760, 558), 'face', 0.8693087697029114), ((896, 315, 1067, 520), 'face', 0.8671919107437134), ((1198, 220, 1342, 406), 'face', 0.8485829830169678), ((1376, 526, 1546, 719), 'face', 0.8469308018684387)]
>>> from imgutils.detect import detection_visualize
>>> from matplotlib import pyplot as plt
>>>
>>> image = 'yolo/solo.jpg'
>>> result = detect_faces(image)
>>>
>>> # visualize it
>>> plt.imshow(detection_visualize(image, result))
>>> plt.show()
"""
return yolo_predict(
image=image,
repo_id=_REPO_ID,
model_name=model_name,
conf_threshold=conf_threshold,
iou_threshold=iou_threshold,
**kwargs,
)