satellit_sam.sam3
satellit_sam.sam3
Auto-generated from
satellit_sam/src/satellit_sam/sam3.pybysatellit_sam/scripts/generate_api_docs.py.
Segmentation model wrappers and inference helpers for SAM and DINOv3.
Functions
get_sam
def get_sam(model_name: ModelVersion = "sam3") -> SamSingleton:
Return a cached SAM model wrapper for the selected model version.
Classes
SamSingleton
Singleton wrapper for loading and running a selected segmentation model.
Attributes
No public class attributes detected.
Methods
print_debug_info
def print_debug_info(self):
Print runtime versions and CUDA availability for diagnostics.
predict
def predict(
self,
image: Image,
text: str | None = None,
boxes: list[tuple[float, float, float, float]] | None = None,
box_labels: list[int] | None = None,
points: list[tuple[float, float]] | None = None,
point_labels: list[int] | None = None,
threshold: float = 0.5,
mask_threshold: float = 0.5,
confidence_threshold: float = 0.5,
allow_low_confidence_fallback: bool = False,
) -> Image:
Generate and render segmentation predictions for one image.
Arguments
image: Image to segment. text: Optional text prompt. boxes: Optional list of box prompts in ``x1,y1,x2,y2`` format. box_labels: Optional per-box labels for the SAM processor. points: Optional list of point prompts in ``x,y`` format. point_labels: Optional per-point labels for the SAM processor. threshold: Score threshold used in SAM post-processing. mask_threshold: Pixel mask threshold used in SAM post-processing. confidence_threshold: Minimum confidence required to keep detections. allow_low_confidence_fallback: Whether to keep the top-scoring mask when score filtering removes all detections.
Returns
Annotated image with segmentation overlays.
predict_detections
def predict_detections(
self,
image: Image,
text: str | None = None,
boxes: list[tuple[float, float, float, float]] | None = None,
box_labels: list[int] | None = None,
points: list[tuple[float, float]] | None = None,
point_labels: list[int] | None = None,
threshold: float = 0.5,
mask_threshold: float = 0.5,
confidence_threshold: float = 0.5,
allow_low_confidence_fallback: bool = False,
) -> sv.Detections:
Generate segmentation detections for one image.
Arguments
image: Image to segment. text: Optional text prompt. boxes: Optional list of box prompts in ``x1,y1,x2,y2`` format. box_labels: Optional per-box labels for the SAM processor. points: Optional list of point prompts in ``x,y`` format. point_labels: Optional per-point labels for the SAM processor. threshold: Score threshold used in SAM post-processing. mask_threshold: Pixel mask threshold used in SAM post-processing. confidence_threshold: Minimum confidence required to keep detections. allow_low_confidence_fallback: Whether to keep the top-scoring mask when score filtering removes all detections.
Returns
Filtered detections including masks, boxes, and confidence scores.
Exceptions
ValueError: If no prompts are provided.