Function get_prediction

sahi/predict.py:77–173

Function for performing prediction for given image using given detection_model. Args: image: str or np.ndarray Location of image or numpy image matrix to slice detection_model: model.DetectionMode shift_amount: List To shift the box and mask predi

(
    image: str | np.ndarray | Image.Image,
    detection_model: DetectionModel,
    shift_amount: list[int] | None = None,
    full_shape: list[int] | None = None,
    postprocess: PostprocessPredictions | None = None,
    verbose: int = 0,
    exclude_classes_by_name: list[str] | None = None,
    exclude_classes_by_id: list[int] | None = None,
    confidence_threshold: float | None = None,
)

def get_prediction(
    image: str | np.ndarray | Image.Image,
    detection_model: DetectionModel,
    shift_amount: list[int] | None = None,
    full_shape: list[int] | None = None,
    postprocess: PostprocessPredictions | None = None,
    verbose: int = 0,
    exclude_classes_by_name: list[str] | None = None,
    exclude_classes_by_id: list[int] | None = None,
    confidence_threshold: float | None = None,
) -> PredictionResult:
    """Function for performing prediction for given image using given detection_model.

    Args:
        image: str or np.ndarray
            Location of image or numpy image matrix to slice
        detection_model: model.DetectionMode
        shift_amount: List
            To shift the box and mask predictions from sliced image to full
            sized image, should be in the form of [shift_x, shift_y]
        full_shape: List
            Size of the full image, should be in the form of [height, width]
        postprocess: sahi.postprocess.combine.PostprocessPredictions
        verbose: int
            0: no print (default)
            1: print prediction duration
        exclude_classes_by_name: Optional[List[str]]
            None: if no classes are excluded
            List[str]: set of classes to exclude using its/their class label name/s
        exclude_classes_by_id: Optional[List[int]]
            None: if no classes are excluded
            List[int]: set of classes to exclude using one or more IDs
        confidence_threshold: float, optional
            Override the model's confidence threshold for this call only.
            The model's original threshold is restored after the call.

    Example:
        >>> from sahi import AutoDetectionModel
        >>> from sahi.predict import get_prediction
        >>> model = AutoDetectionModel.from_pretrained(
        ...     model_type="ultralytics",
        ...     model_path="yolo11n.pt",
        ...     confidence_threshold=0.3,
        ... )
        >>> # run with a different threshold without changing the model
        >>> result = get_prediction("image.jpg", model, confidence_threshold=0.7)
        >>> print(model.confidence_threshold)  # still 0.3

    Returns:
        A dict with fields:
            object_prediction_list: a list of ObjectPrediction
            durations_in_seconds: a dict containing elapsed times for profiling
    """
    original_confidence_threshold = detection_model.confidence_threshold
    if confidence_threshold is not None:
        detection_model.confidence_threshold = confidence_threshold

    try: