o ‹ÄŽiº ã @s`ddlmZddlmZddlmZddlmZddddd d d d d œZgd¢Z Gdd„dƒZ dS)é)Ú annotations)ÚAny)ÚDetectionModel)Úimport_model_classÚUltralyticsDetectionModelÚRTDetrDetectionModelÚMmdetDetectionModelÚYolov5DetectionModelÚDetectron2DetectionModelÚHuggingfaceDetectionModelÚTorchVisionDetectionModelÚRoboflowDetectionModel)Ú ultralyticsÚrtdetrÚmmdetÚyolov5Ú detectron2Ú huggingfaceÚ torchvisionÚroboflow)Úyolov8Úyolov11Úyolo11rc@s0eZdZe          dddd„ƒZdS)ÚAutoDetectionModelNçà?ç333333Ó?TÚ model_typeÚstrÚ model_pathú str | NoneÚmodelú Any | NoneÚ config_pathÚdeviceÚmask_thresholdÚfloatÚconfidence_thresholdÚcategory_mappingú dict | NoneÚcategory_remappingÚ load_at_initÚboolÚ image_sizeú int | NoneÚreturnrc  KsB|tvrd}t|} t|| ƒ} | d||||||||| | dœ | ¤ŽS)a‚Loads a DetectionModel from given path. Args: model_type: str Name of the detection framework (example: "ultralytics", "huggingface", "torchvision") model_path: str Path of the detection model (ex. 'model.pt') model: Any A pre-initialized model instance, if available config_path: str Path of the config file (ex. 'mmdet/configs/cascade_rcnn_r50_fpn_1x.py') device: str Device, "cpu" or "cuda:0" mask_threshold: float Value to threshold mask pixels, should be between 0 and 1 confidence_threshold: float All predictions with score < confidence_threshold will be discarded category_mapping: dict: str to str Mapping from category id (str) to category name (str) e.g. {"1": "pedestrian"} category_remapping: dict: str to int Remap category ids based on category names, after performing inference e.g. {"car": 3} load_at_init: bool If True, automatically loads the model at initialization image_size: int Inference input size. Returns: Returns an instance of a DetectionModel Raises: ImportError: If given {model_type} framework is not installed r) rr r"r#r$r&r'r)r*r,N©)ÚULTRALYTICS_MODEL_NAMESÚMODEL_TYPE_TO_MODEL_CLASS_NAMEr)rrr r"r#r$r&r'r)r*r,ÚkwargsÚmodel_class_namerr/r/úK/home/jeff/fluffinator/venv/lib/python3.10/site-packages/sahi/auto_model.pyÚfrom_pretraineds$/ ö õz"AutoDetectionModel.from_pretrained) NNNNrrNNTN)rrrrr r!r"rr#rr$r%r&r%r'r(r)r(r*r+r,r-r.r)Ú__name__Ú __module__Ú __qualname__Ú staticmethodr5r/r/r/r4rsõrN) Ú __future__rÚtypingrÚsahi.models.baserÚsahi.utils.filerr1r0rr/r/r/r4Ús   ø