"""
Overview:
Detect eyes in anime images.
Trained on dataset `deepghs/anime_eye_detection <https://huggingface.co/datasets/deepghs/anime_eye_detection>`_ with YOLOv8.
.. image:: eye_detect_demo.plot.py.svg
:align: center
This is an overall benchmark of all the eye detect models:
.. image:: eye_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_eye_detection'
[docs]def detect_eyes(image: ImageTyping, level: str = 's', version: str = 'v1.0', model_name: Optional[str] = None,
conf_threshold: float = 0.3, iou_threshold: float = 0.3) \
-> List[Tuple[Tuple[int, int, int, int], str, float]]:
"""
Detect human eyes in anime images.
This function uses a YOLOv8 model to detect eyes in the given anime image. It supports
different model levels and versions, allowing for a trade-off between speed and accuracy.
:param image: The input image for eye detection. Can be various image types supported by ImageTyping.
:type image: ImageTyping
:param level: The model level to use. Can be either 's' (for higher accuracy) or 'n' (for faster processing).
Default is 's'.
:type level: str
:param version: Version of the model to use. Default is 'v1.0'.
:type version: str
:param model_name: Optional custom model name. If not provided, it's constructed using version and level.
:type model_name: Optional[str]
:param conf_threshold: Confidence threshold for detections. Only detections with confidence above this
threshold are returned. Default is 0.3.
:type conf_threshold: float
:param iou_threshold: Intersection over Union (IoU) threshold for non-maximum suppression.
Detections with IoU above this threshold are considered overlapping and merged.
Default is 0.3.
:type iou_threshold: float
:return: A list of detected eyes. Each detection is represented by a tuple containing:
- Bounding box coordinates as (x0, y0, x1, y1)
- Detection class (always 'eye' for this function)
- Confidence score of the detection
:rtype: List[Tuple[Tuple[int, int, int, int], str, float]]
:raises: May raise exceptions related to image loading or model prediction (from yolo_predict function).
Examples::
>>> from imgutils.detect import detect_eyes, detection_visualize
>>>
>>> image = 'squat.jpg'
>>> result = detect_eyes(image) # detect it
>>> result
[((297, 239, 341, 271), 'eye', 0.7760562896728516), ((230, 289, 263, 308), 'eye', 0.7682342529296875)]
>>>
>>> # 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'eye_detect_{version}_{level}',
conf_threshold=conf_threshold,
iou_threshold=iou_threshold,
)