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 _HAND_MODELS = [ 'hand_detect_v0.7_s', 'hand_detect_v0.6_s', 'hand_detect_v0.5_s', 'hand_detect_v0.4_s', 'hand_detect_v0.3_s', 'hand_detect_v0.2_s', 'hand_detect_v0.1_s', 'hand_detect_v0.1_n', ] _DEFAULT_HAND_MODEL = _HAND_MODELS[0] @lru_cache() def _open_hand_detect_model(model_name): return _open_onnx_model(hf_hub_download( f'deepghs/anime_hand_detection', f'{model_name}/model.onnx' )) _LABELS = ['hand'] def detect_hands(image: ImageTyping, model_name: str, max_infer_size=640, conf_threshold: float = 0.35, 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_hand_detect_model(model_name).run(['output0'], {'images': data}) return _data_postprocess(output[0], conf_threshold, iou_threshold, old_size, new_size, _LABELS) def _gr_detect_hands(image: ImageTyping, model_name: str, max_infer_size=640, conf_threshold: float = 0.35, iou_threshold: float = 0.7): ret = detect_hands(image, model_name, max_infer_size, conf_threshold, iou_threshold) return detection_visualize(image, ret, _LABELS)