68 lines
2.2 KiB
Python
68 lines
2.2 KiB
Python
from typing import Optional
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from roboflow.config import INSTANCE_SEGMENTATION_MODEL, INSTANCE_SEGMENTATION_URL
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from roboflow.models.inference import InferenceModel
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class InstanceSegmentationModel(InferenceModel):
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"""
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Run inference on a instance segmentation model hosted on
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Roboflow or served through Roboflow Inference.
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"""
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def __init__(
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self,
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api_key: str,
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version_id: str,
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colors: Optional[dict] = None,
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preprocessing: Optional[dict] = None,
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local: Optional[str] = None,
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):
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"""
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Create a InstanceSegmentationModel object through which you can run inference.
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Args:
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api_key (str): private roboflow api key
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version_id (str): the workspace/project id
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colors (dict): colors to use for the image
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preprocessing (dict): preprocessing to use for the image
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local (str): localhost address and port if pointing towards local inference engine
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"""
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super().__init__(api_key, version_id)
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base_url = local or INSTANCE_SEGMENTATION_URL
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self.api_url = f"{base_url}/{self.dataset_id}/{self.version}"
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self.colors = {} if colors is None else colors
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self.preprocessing = {} if preprocessing is None else preprocessing
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def predict(self, image_path, confidence=40): # type: ignore[override]
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"""
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Infers detections based on image from a specified model and image path.
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Args:
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image_path (str): path to the image you'd like to perform prediction on
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confidence (int): confidence threshold for predictions, on a scale from 0-100
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Returns:
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PredictionGroup Object
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Example:
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>>> import roboflow
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>>> rf = roboflow.Roboflow(api_key="")
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>>> project = rf.workspace().project("PROJECT_ID")
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>>> model = project.version("1").model
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>>> prediction = model.predict("YOUR_IMAGE.jpg")
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""" # noqa: E501
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return super().predict(
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image_path,
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confidence=confidence,
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prediction_type=INSTANCE_SEGMENTATION_MODEL,
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)
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def __str__(self):
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return f"<{type(self).__name__} id={self.id}, api_url={self.api_url}>"
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