o 0j@sddlmZmZmZmZmZmZddlZddl m Z ddl m Z m Z ddlmZddlmZd d lmZd d lmZejGd d d eZe dGdddeZdS))AnyDictListOptionalTupleUnionN)pipeline_requires_extra) HPIConfigPaddlePredictorOption) DetResult) benchmark)(AutoParallelImageSimpleInferencePipeline) BasePipelinecseZdZdZddddddddedeedeedeeeefd eed e d ee eeefe fd dffd dZ    dde ee eeje ejfdee eefdee dee eeeeffdeed ef ddZZS)_ObjectDetectionPipelinezObject Detection PipelineNFdeviceengine engine_config pp_optionuse_hpip hpi_configconfigrrrrrrreturnc stjd ||||||d||dd} i} d| vr"| d| d<d| vr,| d| d<d| vr6| d| d<d| vr@| d| d<d| vrJ| d| d<|j| fi| |_d S) a/ Initializes the object detection pipeline. Args: config: Configuration dictionary containing model and other parameters. device (Optional[str], optional): Device to run the prediction on. Defaults to `None`. engine (Optional[str], optional): Inference engine. Defaults to `None`. engine_config (Optional[Dict[str, Any]], optional): Engine-specific config. Defaults to `None`. Defaults to `None`. pp_option (Optional[PaddlePredictorOption], optional): Paddle predictor options. Defaults to `None`. use_hpip (bool, optional): Whether to use HPIP. Defaults to `False`. hpi_config (Optional[Union[Dict[str, Any], HPIConfig]], optional): HPIP configuration. Defaults to `None`. r SubModulesObjectDetection thresholdZimg_size layout_nmslayout_unclip_ratiolayout_merge_bboxes_modeN)super__init__Z create_model det_model) selfrrrrrrrkwargsZ model_cfgZ model_kwargs __class__r"v/var/www/html/Deteccion_Ine/venv/lib/python3.10/site-packages/paddlex/inference/pipelines/object_detection/pipeline.pyr$s2     z!_ObjectDetectionPipeline.__init__inputrrr r!cks(|j|f||||d|EdHdS)aPredicts object detection results for the given input. Args: input (Union[str, list[str], np.ndarray, list[np.ndarray]]): The input image(s) or path(s) to the images. img_size (Optional[Union[int, Tuple[int, int]]]): The size of the input image. Default is None. threshold (Optional[float]): The threshold value to filter out low-confidence predictions. Default is None. layout_nms (Optional[bool], optional): Whether to use layout-aware NMS. Defaults to `False`. layout_unclip_ratio (Optional[Union[float, Tuple[float, float]]], optional): The ratio of unclipping the bounding box. Defaults to `None`. If it's a single number, then both width and height are used. If it's a tuple of two numbers, then they are used separately for width and height respectively. If it's None, then no unclipping will be performed. layout_merge_bboxes_mode (Optional[str], optional): The mode for merging bounding boxes. Defaults to `None`. **kwargs: Additional keyword arguments that can be passed to the function. Returns: DetResult: The predicted detection results. )rrr r!N)r%)r&r+rrr r!r'r"r"r*predictRsz _ObjectDetectionPipeline.predict)NNNN)__name__ __module__ __qualname____doc__rrstrrr boolrr r$rnpZndarrayfloatdictrr r, __classcell__r"r"r(r*rsT  6rZcvc@s$eZdZdZeddZddZdS)ObjectDetectionPipelineZobject_detectioncCstS)N)r)r&r"r"r* _pipeline_clszsz%ObjectDetectionPipeline._pipeline_clscCs|ddddS)NrrZ batch_size)get)r&rr"r"r*_get_batch_size~sz'ObjectDetectionPipeline._get_batch_sizeN)r-r.r/entitiespropertyr8r;r"r"r"r*r7vs   r7)typingrrrrrrnumpyr3Z utils.depsr modelsr r Zmodels.object_detection.resultr Zutils.benchmarkrZ _parallelrbaserZ time_methodsrr7r"r"r"r*s      Z