Spaces:
Running
Running
File size: 1,482 Bytes
90b4364 3903f4f 90b4364 3903f4f 90b4364 49e14f2 90b4364 3903f4f 90b4364 3903f4f 90b4364 3903f4f |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 |
from functools import lru_cache
from typing import List, Tuple
from huggingface_hub import hf_hub_download
from imgutils.data import ImageTyping, load_image, rgb_encode
from onnx_ import _open_onnx_model
from plot import detection_visualize
from yolo_ import _image_preprocess, _data_postprocess
_FACE_MODELS = [
'face_detect_best_n.onnx',
'face_detect_best_s.onnx',
]
_DEFAULT_FACE_MODEL = _FACE_MODELS[0]
@lru_cache()
def _open_face_detect_model(model_name):
return _open_onnx_model(hf_hub_download(
'deepghs/imgutils-models',
f'face_detect/{model_name}'
))
_LABELS = ['head']
def detect_faces(image: ImageTyping, level: str = 's', max_infer_size=1216,
conf_threshold: float = 0.25, iou_threshold: float = 0.7) \
-> List[Tuple[Tuple[int, int, int, int], str, float]]:
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_face_detect_model(level).run(['output0'], {'images': data})
return _data_postprocess(output[0], conf_threshold, iou_threshold, old_size, new_size, _LABELS)
def _gr_detect_faces(image: ImageTyping, model_name: str, max_infer_size=1216,
conf_threshold: float = 0.25, iou_threshold: float = 0.7):
ret = detect_faces(image, model_name, max_infer_size, conf_threshold, iou_threshold)
return detection_visualize(image, ret, _LABELS)
|