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from functools import lru_cache | |
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 plot_detection | |
from yolo_ import _image_preprocess, _data_simple_postprocess | |
_PERSON_MODELS = [ | |
'person_detect_best_s.onnx', | |
] | |
_DEFAULT_PERSON_MODEL = _PERSON_MODELS[0] | |
def _open_person_detect_model(model_name): | |
return _open_onnx_model(hf_hub_download( | |
'deepghs/imgutils-models', | |
f'person_detect/{model_name}' | |
)) | |
def detect_person(image: ImageTyping, model_name: str, max_infer_size=1216, | |
conf_threshold: float = 0.25, iou_threshold: float = 0.7): | |
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_person_detect_model(model_name).run(['output0'], {'images': data}) | |
return _data_simple_postprocess(output[0], conf_threshold, iou_threshold, old_size, new_size) | |
def _gr_detect_person(image: ImageTyping, model_name: str, max_infer_size=1216, | |
conf_threshold: float = 0.25, iou_threshold: float = 0.7): | |
ret = detect_person(image, model_name, max_infer_size, conf_threshold, iou_threshold) | |
detections = [(box, 0, score) for box, score in ret] | |
return plot_detection(image, detections, ['person']) | |