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supervision/tests/detection/utils/test_converters.py

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from __future__ import annotations
import numpy as np
import pytest
from supervision.detection.utils.converters import (
xcycwh_to_xyxy,
xywh_to_xyxy,
xyxy_to_mask,
xyxy_to_xcycarh,
xyxy_to_xywh,
)
@pytest.mark.parametrize(
("xywh", "expected_result"),
[
(np.array([[10, 20, 30, 40]]), np.array([[10, 20, 40, 60]])), # standard case
(np.array([[0, 0, 0, 0]]), np.array([[0, 0, 0, 0]])), # zero size bounding box
(
np.array([[50, 50, 100, 100]]),
np.array([[50, 50, 150, 150]]),
), # large bounding box
(
np.array([[-10, -20, 30, 40]]),
np.array([[-10, -20, 20, 20]]),
), # negative coordinates
(np.array([[50, 50, 0, 30]]), np.array([[50, 50, 50, 80]])), # zero width
(np.array([[50, 50, 20, 0]]), np.array([[50, 50, 70, 50]])), # zero height
(np.array([]).reshape(0, 4), np.array([]).reshape(0, 4)), # empty array
],
)
def test_xywh_to_xyxy(xywh: np.ndarray, expected_result: np.ndarray) -> None:
result = xywh_to_xyxy(xywh)
np.testing.assert_array_equal(result, expected_result)
@pytest.mark.parametrize(
("xyxy", "expected_result"),
[
(np.array([[10, 20, 40, 60]]), np.array([[10, 20, 30, 40]])), # standard case
(np.array([[0, 0, 0, 0]]), np.array([[0, 0, 0, 0]])), # zero size bounding box
(
np.array([[50, 50, 150, 150]]),
np.array([[50, 50, 100, 100]]),
), # large bounding box
(
np.array([[-10, -20, 20, 20]]),
np.array([[-10, -20, 30, 40]]),
), # negative coordinates
(np.array([[50, 50, 50, 80]]), np.array([[50, 50, 0, 30]])), # zero width
(np.array([[50, 50, 70, 50]]), np.array([[50, 50, 20, 0]])), # zero height
(np.array([]).reshape(0, 4), np.array([]).reshape(0, 4)), # empty array
],
)
def test_xyxy_to_xywh(xyxy: np.ndarray, expected_result: np.ndarray) -> None:
result = xyxy_to_xywh(xyxy)
np.testing.assert_array_equal(result, expected_result)
@pytest.mark.parametrize(
("xyxy", "expected_result"),
[
# Empty and zero cases
(np.array([]).reshape(0, 4), np.array([]).reshape(0, 4)), # empty array
(
np.array([[0, 0, 0, 0]]),
np.array([[0, 0, 0.0, 0]]),
), # zero size bounding box
(
np.array([[10, 10, 10, 10]]),
np.array([[10, 10, 0.0, 0]]),
), # point (x1=x2, y1=y2)
# Zero width/height cases
(np.array([[50, 50, 80, 50]]), np.array([[65, 50, 0.0, 0]])), # zero height
(np.array([[50, 50, 50, 80]]), np.array([[50, 65, 0.0, 30]])), # zero width
# Standard cases
(np.array([[10, 20, 40, 60]]), np.array([[25, 40, 0.75, 40]])), # standard case
(
np.array([[-30, -40, -10, -20]]),
np.array([[-20, -30, 1.0, 20]]),
), # all negative values
(
np.array([[0.1, 0.2, 0.4, 0.6]]),
np.array([[0.25, 0.4, 0.75, 0.4]]),
), # values between 0-1
# Different aspect ratios
(
np.array([[10, 20, 50, 100]]),
np.array([[30, 60, 0.5, 80]]),
), # tall rectangle (height > width)
(
np.array([[20, 10, 100, 50]]),
np.array([[60, 30, 2.0, 40]]),
), # wide rectangle (width > height)
(
np.array([[50, 50, 150, 150]]),
np.array([[100, 100, 1.0, 100]]),
), # height == width
# Multiple boxes in one array
(
np.array([[0, 0, 0, 0], [10, 20, 40, 60]]),
np.array([[0, 0, 0.0, 0], [25, 40, 0.75, 40]]),
), # one zero-sized box and one normal box
],
)
def test_xyxy_to_xcycarh(xyxy: np.ndarray, expected_result: np.ndarray) -> None:
result = xyxy_to_xcycarh(xyxy)
np.testing.assert_allclose(result, expected_result)
@pytest.mark.parametrize(
("xcycwh", "expected_result"),
[
(np.array([[50, 50, 20, 30]]), np.array([[40, 35, 60, 65]])), # standard case
(np.array([[0, 0, 0, 0]]), np.array([[0, 0, 0, 0]])), # zero size bounding box
(
np.array([[50, 50, 100, 100]]),
np.array([[0, 0, 100, 100]]),
), # large bounding box centered at (50, 50)
(
np.array([[-10, -10, 20, 30]]),
np.array([[-20, -25, 0, 5]]),
), # negative coordinates
(np.array([[50, 50, 0, 30]]), np.array([[50, 35, 50, 65]])), # zero width
(np.array([[50, 50, 20, 0]]), np.array([[40, 50, 60, 50]])), # zero height
(np.array([]).reshape(0, 4), np.array([]).reshape(0, 4)), # empty array
],
)
def test_xcycwh_to_xyxy(xcycwh: np.ndarray, expected_result: np.ndarray) -> None:
result = xcycwh_to_xyxy(xcycwh)
np.testing.assert_array_equal(result, expected_result)
@pytest.mark.parametrize(
("boxes", "resolution_wh", "expected"),
[
# 0) Empty input
(
np.array([], dtype=float).reshape(0, 4),
(5, 4),
np.array([], dtype=bool).reshape(0, 4, 5),
),
# 1) Single pixel box
(
np.array([[2, 1, 2, 1]], dtype=float),
(5, 4),
np.array(
[
[
[False, False, False, False, False],
[False, False, True, False, False],
[False, False, False, False, False],
[False, False, False, False, False],
]
],
dtype=bool,
),
),
# 2) Horizontal line, inclusive bounds
(
np.array([[1, 2, 3, 2]], dtype=float),
(5, 4),
np.array(
[
[
[False, False, False, False, False],
[False, False, False, False, False],
[False, True, True, True, False],
[False, False, False, False, False],
]
],
dtype=bool,
),
),
# 3) Vertical line, inclusive bounds
(
np.array([[3, 0, 3, 2]], dtype=float),
(5, 4),
np.array(
[
[
[False, False, False, True, False],
[False, False, False, True, False],
[False, False, False, True, False],
[False, False, False, False, False],
]
],
dtype=bool,
),
),
# 4) Proper rectangle fill
(
np.array([[1, 1, 3, 2]], dtype=float),
(5, 4),
np.array(
[
[
[False, False, False, False, False],
[False, True, True, True, False],
[False, True, True, True, False],
[False, False, False, False, False],
]
],
dtype=bool,
),
),
# 5) Negative coordinates clipped to [0, 0]
(
np.array([[-2, -1, 1, 1]], dtype=float),
(5, 4),
np.array(
[
[
[True, True, False, False, False],
[True, True, False, False, False],
[False, False, False, False, False],
[False, False, False, False, False],
]
],
dtype=bool,
),
),
# 6) Overflow coordinates clipped to width-1 and height-1
(
np.array([[3, 2, 10, 10]], dtype=float),
(5, 4),
np.array(
[
[
[False, False, False, False, False],
[False, False, False, False, False],
[False, False, False, True, True],
[False, False, False, True, True],
]
],
dtype=bool,
),
),
# 7) Invalid box where max < min after ints, mask stays empty
(
np.array([[3, 2, 1, 4]], dtype=float),
(5, 4),
np.array(
[
[
[False, False, False, False, False],
[False, False, False, False, False],
[False, False, False, False, False],
[False, False, False, False, False],
]
],
dtype=bool,
),
),
# 8) Fractional coordinates are floored by int conversion
# (0.2,0.2)-(2.8,1.9) -> (0,0)-(2,1)
(
np.array([[0.2, 0.2, 2.8, 1.9]], dtype=float),
(5, 4),
np.array(
[
[
[True, True, True, False, False],
[True, True, True, False, False],
[False, False, False, False, False],
[False, False, False, False, False],
]
],
dtype=bool,
),
),
# 9) Multiple boxes, separate masks
(
np.array([[0, 0, 1, 0], [2, 1, 4, 3]], dtype=float),
(5, 4),
np.array(
[
# Box 0: row 0, cols 0..1
[
[True, True, False, False, False],
[False, False, False, False, False],
[False, False, False, False, False],
[False, False, False, False, False],
],
# Box 1: rows 1..3, cols 2..4
[
[False, False, False, False, False],
[False, False, True, True, True],
[False, False, True, True, True],
[False, False, True, True, True],
],
],
dtype=bool,
),
),
],
)
def test_xyxy_to_mask(boxes: np.ndarray, resolution_wh, expected: np.ndarray) -> None:
result = xyxy_to_mask(boxes, resolution_wh)
assert result.dtype == np.bool_
assert result.shape == expected.shape
np.testing.assert_array_equal(result, expected)
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