realutils.detect.face

Overview:

Detect human faces in both real photo and anime images.

Trained with deepghs/anime_face_detection and open-sourced real photos datasets.

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

The models are hosted on huggingface - deepghs/real_face_detection.

detect_faces

realutils.detect.face.detect_faces(image: str | PathLike | bytes | bytearray | BinaryIO | Image, 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.

Parameters:

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

• model_name (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]]

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()