"""
Overview:
Detect human hands in anime images.
Trained on dataset `deepghs/anime_hand_detection <https://huggingface.co/datasets/deepghs/anime_hand_detection>`_ with YOLOv8.
.. image:: hand_detect_demo.plot.py.svg
:align: center
This is an overall benchmark of all the hand detect models:
.. image:: hand_detect_benchmark.plot.py.svg
:align: center
"""
from functools import lru_cache
from typing import List, Tuple
from huggingface_hub import hf_hub_download
from ._yolo import _image_preprocess, _data_postprocess
from ..data import ImageTyping, load_image, rgb_encode
from ..utils import open_onnx_model
@lru_cache()
def _open_hand_detect_model(level: str = 's', version: str = 'v1.0'):
return open_onnx_model(hf_hub_download(
f'deepghs/anime_hand_detection',
f'hand_detect_{version}_{level}/model.onnx'
))
_LABELS = ["hand"]
[docs]def detect_hands(image: ImageTyping, level: str = 's', version: str = 'v1.0', max_infer_size=640,
conf_threshold: float = 0.35, iou_threshold: float = 0.7) \
-> List[Tuple[Tuple[int, int, int, int], str, float]]:
"""
Overview:
Detect human hand points in anime images.
:param image: Image to detect.
:param level: The model level being used can be either `s` or `n`.
The `n` model runs faster with smaller system overhead, while the `s` model achieves higher accuracy.
The default value is `s`.
:param version: Version of model, default is ``v1.0``.
:param max_infer_size: The maximum image size used for model inference, if the image size exceeds this limit,
the image will be resized and used for inference. The default value is `640` pixels.
:param conf_threshold: The confidence threshold, only detection results with confidence scores above
this threshold will be returned. The default value is `0.3`.
:param iou_threshold: The detection area coverage overlap threshold, areas with overlaps above this threshold
will be discarded. The default value is `0.7`.
:return: The detection results list, each item includes the detected area `(x0, y0, x1, y1)`,
the target type (always `hand`) and the target confidence score.
"""
image = load_image(image, mode='RGB')
new_image, old_size, new_size = _image_preprocess(image, max_infer_size)
data = rgb_encode(new_image)[None, ...]
output, = _open_hand_detect_model(level).run(['output0'], {'images': data})
return _data_postprocess(output[0], conf_threshold, iou_threshold, old_size, new_size, _LABELS)