Semantic Segmentation

SemanticSegmentationModel

Bases: InferenceModel

Run inference on a semantic segmentation model hosted on Roboflow or served through Roboflow Inference.

Source code in roboflow/models/semantic_segmentation.py

class SemanticSegmentationModel(InferenceModel):
    """
    Run inference on a semantic segmentation model hosted on Roboflow or served through Roboflow Inference.
    """

    def __init__(self, api_key: str, version_id: str):
        """
        Create a SemanticSegmentationModel object through which you can run inference.

        Args:
            api_key (str): private roboflow api key
            version_id (str): the workspace/project id
        """
        super(SemanticSegmentationModel, self).__init__(api_key, version_id)
        self.api_url = f"{SEMANTIC_SEGMENTATION_URL}/{self.dataset_id}/{self.version}"

    def predict(self, image_path: str, confidence: int = 50):
        """
        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")
        """
        return super(SemanticSegmentationModel, self).predict(
            image_path,
            confidence=confidence,
            prediction_type=SEMANTIC_SEGMENTATION_MODEL,
        )

    def __str__(self):
        return f"<{type(self).__name__} id={self.id}, api_url={self.api_url}>"

__init__(api_key, version_id)

Create a SemanticSegmentationModel object through which you can run inference.

Parameters:

Name	Type	Description	Default
api_key	str	private roboflow api key	required
version_id	str	the workspace/project id	required

Source code in roboflow/models/semantic_segmentation.py

def __init__(self, api_key: str, version_id: str):
    """
    Create a SemanticSegmentationModel object through which you can run inference.

    Args:
        api_key (str): private roboflow api key
        version_id (str): the workspace/project id
    """
    super(SemanticSegmentationModel, self).__init__(api_key, version_id)
    self.api_url = f"{SEMANTIC_SEGMENTATION_URL}/{self.dataset_id}/{self.version}"

predict(image_path, confidence=50)

Infers detections based on image from a specified model and image path.

Parameters:

Name	Type	Description	Default
image_path	str	path to the image you'd like to perform prediction on	required
confidence	int	confidence threshold for predictions, on a scale from 0-100	50

Returns:

Type	Description
	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")

Source code in roboflow/models/semantic_segmentation.py

def predict(self, image_path: str, confidence: int = 50):
    """
    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")
    """
    return super(SemanticSegmentationModel, self).predict(
        image_path,
        confidence=confidence,
        prediction_type=SEMANTIC_SEGMENTATION_MODEL,
    )