o Ďi@s<ddlmZddlmZmZddlmZGdddeZdS))Optional)INSTANCE_SEGMENTATION_MODELINSTANCE_SEGMENTATION_URL)InferenceModelc s`eZdZdZ   ddededeedeedeef fdd Zdfd d Zd dZ Z S)InstanceSegmentationModelzy Run inference on a instance segmentation model hosted on Roboflow or served through Roboflow Inference. Napi_key version_idcolors preprocessinglocalcs^t|||p t}|d|jd|j|_|durin||_|dur*i|_dS||_dS)a Create a InstanceSegmentationModel object through which you can run inference. Args: api_key (str): private roboflow api key version_id (str): the workspace/project id colors (dict): colors to use for the image preprocessing (dict): preprocessing to use for the image local (str): localhost address and port if pointing towards local inference engine /N)super__init__r dataset_idversionapi_urlr r )selfrrr r r base_url __class__a/home/jeff/fluffinator/venv/lib/python3.10/site-packages/roboflow/models/instance_segmentation.pyr s z"InstanceSegmentationModel.__init__(cstj||tdS)ac Infers detections based on image from a specified model and image path. Args: image_path (str): path to the image you'd like to perform prediction on confidence (int): confidence threshold for predictions, on a scale from 0-100 Returns: PredictionGroup Object Example: >>> import roboflow >>> rf = roboflow.Roboflow(api_key="") >>> project = rf.workspace().project("PROJECT_ID") >>> model = project.version("1").model >>> prediction = model.predict("YOUR_IMAGE.jpg") ) confidenceprediction_type)r predictr)r image_pathrrrrr&s z!InstanceSegmentationModel.predictcCs"dt|jd|jd|jdS)N)type__name__idr)rrrr__str__Bs"z!InstanceSegmentationModel.__str__)NNN)r) r __module__ __qualname____doc__strrdictrrr" __classcell__rrrrrs$ rN)typingrroboflow.configrrroboflow.models.inferencerrrrrrs