GeometryDetectionAnnotation

Represents a single COCO-style geometry-detection annotation for one object. Required fields include id, and optionally image_id, category_id, geometry (shape-specific dict), and geometry_shape (e.g. "circle", "ellipse", "line", "contour"). Optional fields include segmentation, bbox, area, iscrowd, and score. This datatype is used as the schema for each element in GeometryDetectionAnnotations within the Telekinesis ecosystem.

Field

Required

Field	Type	Description
id	int	Annotation identifier (int).

Optional

Field	Type	Description
image_id	int or None	Image identifier. Default None.
category_id	int or None	Category identifier. Default None.
geometry	dict or None	Shape-specific geometry (e.g. circle: center, radius; ellipse: center, axes, angle; line: rho, theta; contour: points). Default None.
geometry_shape	str or None	One of "circle", "ellipse", "line", "contour" (case-insensitive). Default None.
segmentation	list, dict, or None	Polygons or RLE. Default None.
bbox	list[float] or np.ndarray or None	Bounding box; length 4. Default [0, 0, 0, 0].
area	float or None	Area. Default 0.
iscrowd	int	Crowd flag. Default 0.
score	float or None	Confidence score. Default None.

Methods

Method	Description
to_dict()	Returns a dictionary with keys: id, image_id, category_id, geometry, geometry_shape, segmentation, bbox, area, iscrowd, score.

Example

From the datatypes examples:

from datatypes import datatypes
from loguru import logger

# ------------------------------------------------
# 1. Create GeometryDetectionAnnotation instance
# ------------------------------------------------
ann = datatypes.GeometryDetectionAnnotation(
    id=1,
    image_id=0,
    category_id=1,
    geometry={"center": [100.0, 100.0], "radius": 50.0},
    geometry_shape="circle",
)

# ------------------------------------------------
# 2. Access data via to_dict
# ------------------------------------------------
d = ann.to_dict()
logger.info("GeometryDetectionAnnotation id={}", d["id"])
logger.info("GeometryDetectionAnnotation geometry_shape={}", d["geometry_shape"])
logger.info("GeometryDetectionAnnotation geometry={}", d["geometry"])