Box2D

SUMMARY

An axis-aligned bounding box in 2D space.

from telekinesis import datatypes
box2d = datatypes.Box2D([1, 2.5, 3, 3])

API Reference

Complete API documentation for Box2D, including parameters, attributes, and methods.

View Reference →

Parameters

Parameter	Type	Default	Description
data	np.ndarray | list | tuple	Required	Box coordinates [cx, cy, width, height] (center point + size, the native CXCYWH format).

Raises

Exception	Condition
TypeError	data can't be converted into a uniform float32 array (e.g. ragged nested lists)
ValueError	data is not rank-1 (e.g. a (N, 4) batch — use Boxes2D instead)
ValueError	data does not have exactly 4 elements
ValueError	Any element is non-finite (NaN/Inf)
ValueError	width or height (data[2], data[3]) is negative

A zero-sized width/height is allowed but logs a warning (area will be 0).

Attributes

Attribute	Type	Description
data	np.ndarray	Defensive copy of the underlying (4,) array [cx, cy, width, height]. Reading it returns a copy; writing re-validates the same way as construction.
shape	tuple[int, ...]	Always (4,).
ndim	int	Always 1.
dtype	np.dtype	Always float32.
size	int	Always 4.
shape_spec	tuple[int, ...]	Class-level shape spec (4,).
center	np.ndarray	[cx, cy] — data[:2] directly. This is the native storage format, so no offset computation is needed to get the center.
dimensions	np.ndarray	[width, height], data[2:4], non-negative.
area	np.ndarray (scalar)	width * height.

Methods

Method	Type	Description
Box2D.coerce(value)	Box2D	Converts array-like data into a Box2D. If value is already a Box2D, it is returned unchanged; otherwise it goes through the same checks as constructing one directly.
Box2D.from_xywh(data)	Box2D	Builds a box from [x_min, y_min, width, height], converting it to the native center-based format.
Box2D.from_xyxy(data)	Box2D	Builds a box from [x_min, y_min, x_max, y_max], converting it to the native center-based format.
as_xywh()	np.ndarray	Returns this box as [x_min, y_min, width, height] (min corner + size) instead of the native center-based format.
as_xyxy()	np.ndarray	Returns this box as [x_min, y_min, x_max, y_max] (corner-to-corner) instead of the native center-based format.
to_numpy(copy=True)	np.ndarray	Returns the box as a plain array. Pass copy=False for a zero-copy view instead — mutating it mutates the Box2D.
copy()	Box2D	Returns a new, independent Box2D with the same data.

Operators

Operation	Behavior
box == other	True only if other is also a Box2D with element-equal data. False for anything else.
len(box)	Returns 4 — the length of the coordinate vector, not a box count (a single box is conceptually one item).
box[i]	Raises TypeError — Box2D defines no __getitem__, so a single box isn't indexable like a batch. Use .data[i] for raw coordinate access instead.
for x in box	Raises TypeError — with no __getitem__/__iter__, a single Box2D isn't iterable.
np.asarray(box)	Returns a copy of data as an np.ndarray; NumPy functions accept a Box2D directly.

Visualization

import rerun as rr

# Your code block
# ....

rr.init("box2d_example", spawn=True)
datatypes.visualize(box2d, entity_path="/box2d", label="Box2D")

Example

"""Demonstrates the Telekinesis Box2D datatype."""

import time

import numpy as np
import rerun as rr
from loguru import logger

from telekinesis import datatypes

def box2d_example():
    """Demonstrate creation, inspection, operations, visualization, and serialization."""

    # ======================= Create ============================================
    # Box2D format is CXCYWH = [cx, cy, width, height]
    coords = [1, 2.5, 3, 3]
    box2d = datatypes.Box2D(coords)
    logger.info(f"Original Box2D: {box2d}")

    xyxy_coords = [1.0, 1.5, 4.0, 4.5]
    box2d_from_xyxy = datatypes.Box2D.from_xyxy(xyxy_coords)
    logger.info(f"Box2D created from xyxy format: {box2d_from_xyxy}")

    xywh_coords = [1.0, 1.5, 3.0, 3.0]
    box2d_from_xywh = datatypes.Box2D.from_xywh(xywh_coords)
    logger.info(f"Box2D created from xywh format: {box2d_from_xywh}")

    # ======================= Inspect ===========================================
    logger.info(f"data={box2d.data}")
    logger.info(f"dtype={box2d.dtype}")
    logger.info(f"ndim={box2d.ndim}")
    logger.info(f"shape={box2d.shape}")
    logger.info(f"size={box2d.size}")
    logger.info(f"dimensions={box2d.dimensions}")
    logger.info(f"area={box2d.area}")
    logger.info(f"center={box2d.center}")

    # ======================= Operations =========================================
    updated_coords = [3, 4, 3, 5]
    box2d.data = updated_coords
    logger.info(f"Updated Box2D: {box2d}")

    xyxy_view = box2d.as_xyxy()
    logger.info(f"Box2D converted to xyxy format: {xyxy_view}")

    xywh_view = box2d.as_xywh()
    logger.info(f"Box2D converted to xywh format: {xywh_view}")

    box2d_copy = box2d.copy()
    logger.info(f"Copied Box2D: {box2d_copy}")

    # Returns the internal data as a NumPy array. If copy=True, returns a copy; otherwise, returns a view.
    box2d_numpy = box2d.to_numpy(copy=False)
    logger.info(f"NumPy Box2D:\n{box2d_numpy}")

    # __array__ is implemented, so Box2D works directly with NumPy functions.
    logger.info(f"np.asarray(box2d)={np.asarray(box2d)}")
    logger.info(f"np.sum(box2d)={np.sum(box2d)}")

    # Translate and scale by operating on the underlying NumPy array directly.
    translation = [2, 3]
    translated_data = box2d.data.copy()
    translated_data[:2] += translation
    translated_box2d = datatypes.Box2D(translated_data)
    logger.info(f"Translated Box2D: {translated_box2d}")

    scale_factors = [2, 0.5]
    scaled_data = box2d.data.copy()
    scaled_data[2:] *= np.asarray(scale_factors, dtype=np.float32)
    scaled_box2d = datatypes.Box2D(scaled_data)
    logger.info(f"Scaled Box2D: {scaled_box2d}")

    # ======================= Visualize =========================================
    rr.init("box2d_example", spawn=True)
    datatypes.visualize(box2d, entity_path="/box2d/updated", label="Updated Box2D")
    datatypes.visualize(
        translated_box2d, entity_path="/box2d/translated", label="Translated Box2D"
    )
    datatypes.visualize(scaled_box2d, entity_path="/box2d/scaled", label="Scaled Box2D")

    # ======================= Serialize / Deserialize ===========================
    start = time.perf_counter()
    serialized = datatypes.serialize(box2d)
    serialization_ms = (time.perf_counter() - start) * 1000

    start = time.perf_counter()
    deserialized = datatypes.deserialize(serialized)["param_0"]
    deserialization_ms = (time.perf_counter() - start) * 1000

    logger.info(f"Deserialized Box2D: {deserialized}")
    logger.info(f"Round-trip successful: {box2d == deserialized}")
    logger.info(f"Serialization time: {serialization_ms:.3f} ms")
    logger.info(f"Deserialization time: {deserialization_ms:.3f} ms")


if __name__ == "__main__":
    box2d_example()