okooooooo/supervision
1
0 Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity
Files
develop
supervision/tests/detection/utils/test_iou_and_nms.py
T

1131 lines
36 KiB
Python
Raw Permalink Blame History

from __future__ import annotations
from contextlib import ExitStack as DoesNotRaise
import numpy as np
import pytest
from supervision.detection.utils.iou_and_nms import (
OverlapMetric,
_group_overlapping_boxes,
box_iou,
box_iou_batch,
box_non_max_suppression,
mask_non_max_merge,
mask_non_max_suppression,
)
from tests.helpers import _generate_random_boxes
@pytest.mark.parametrize(
("predictions", "iou_threshold", "expected_result", "exception"),
[
(
np.empty(shape=(0, 5), dtype=float),
0.5,
[],
DoesNotRaise(),
),
(
np.array([[0, 0, 10, 10, 1.0]]),
0.5,
[[0]],
DoesNotRaise(),
),
(
np.array([[0, 0, 10, 10, 1.0], [0, 0, 9, 9, 1.0]]),
0.5,
[[1, 0]],
DoesNotRaise(),
), # High overlap, tie-break to second det
(
np.array([[0, 0, 10, 10, 1.0], [0, 0, 9, 9, 0.99]]),
0.5,
[[0, 1]],
DoesNotRaise(),
), # High overlap, merge to high confidence
(
np.array([[0, 0, 10, 10, 0.99], [0, 0, 9, 9, 1.0]]),
0.5,
[[1, 0]],
DoesNotRaise(),
), # (test symmetry) High overlap, merge to high confidence
(
np.array([[0, 0, 10, 10, 0.90], [0, 0, 9, 9, 1.0]]),
0.5,
[[1, 0]],
DoesNotRaise(),
), # (test symmetry) High overlap, merge to high confidence
(
np.array([[0, 0, 10, 10, 1.0], [0, 0, 9, 9, 1.0]]),
1.0,
[[1], [0]],
DoesNotRaise(),
), # High IOU required
(
np.array([[0, 0, 10, 10, 1.0], [0, 0, 9, 9, 1.0]]),
0.0,
[[1, 0]],
DoesNotRaise(),
), # No IOU required
(
np.array([[0, 0, 10, 10, 1.0], [0, 0, 5, 5, 0.9]]),
0.25,
[[0, 1]],
DoesNotRaise(),
), # Below IOU requirement
(
np.array([[0, 0, 10, 10, 1.0], [0, 0, 5, 5, 0.9]]),
0.26,
[[0], [1]],
DoesNotRaise(),
), # Above IOU requirement
(
np.array([[0, 0, 10, 10, 1.0], [0, 0, 9, 9, 1.0], [0, 0, 8, 8, 1.0]]),
0.5,
[[2, 1, 0]],
DoesNotRaise(),
), # 3 boxes
(
np.array(
[
[0, 0, 10, 10, 1.0],
[0, 0, 9, 9, 1.0],
[5, 5, 10, 10, 1.0],
[6, 6, 10, 10, 1.0],
[9, 9, 10, 10, 1.0],
]
),
0.5,
[[4], [3, 2], [1, 0]],
DoesNotRaise(),
), # 5 boxes, 2 merges, 1 separate
(
np.array(
[
[0, 0, 2, 1, 1.0],
[1, 0, 3, 1, 1.0],
[2, 0, 4, 1, 1.0],
[3, 0, 5, 1, 1.0],
[4, 0, 6, 1, 1.0],
]
),
0.33,
[[4, 3], [2, 1], [0]],
DoesNotRaise(),
), # sequential merge, half overlap
(
np.array(
[
[0, 0, 2, 1, 0.9],
[1, 0, 3, 1, 0.9],
[2, 0, 4, 1, 1.0],
[3, 0, 5, 1, 0.9],
[4, 0, 6, 1, 0.9],
]
),
0.33,
[[2, 3, 1], [4], [0]],
DoesNotRaise(),
), # confidence
],
)
def test_group_overlapping_boxes(
predictions: np.ndarray,
iou_threshold: float,
expected_result: list[list[int]],
exception: Exception,
) -> None:
with exception:
result = _group_overlapping_boxes(
predictions=predictions, iou_threshold=iou_threshold
)
assert result == expected_result
@pytest.mark.parametrize(
("predictions", "iou_threshold", "expected_result", "exception"),
[
(
np.empty(shape=(0, 5)),
0.5,
np.array([]),
DoesNotRaise(),
), # single box with no category
(
np.array([[10.0, 10.0, 40.0, 40.0, 0.8]]),
0.5,
np.array([True]),
DoesNotRaise(),
), # single box with no category
(
np.array([[10.0, 10.0, 40.0, 40.0, 0.8, 0]]),
0.5,
np.array([True]),
DoesNotRaise(),
), # single box with category
(
np.array(
[
[10.0, 10.0, 40.0, 40.0, 0.8],
[15.0, 15.0, 40.0, 40.0, 0.9],
]
),
0.5,
np.array([False, True]),
DoesNotRaise(),
), # two boxes with no category
(
np.array(
[
[10.0, 10.0, 40.0, 40.0, 0.8, 0],
[15.0, 15.0, 40.0, 40.0, 0.9, 1],
]
),
0.5,
np.array([True, True]),
DoesNotRaise(),
), # two boxes with different category
(
np.array(
[
[10.0, 10.0, 40.0, 40.0, 0.8, 0],
[15.0, 15.0, 40.0, 40.0, 0.9, 0],
]
),
0.5,
np.array([False, True]),
DoesNotRaise(),
), # two boxes with same category
(
np.array(
[
[0.0, 0.0, 30.0, 40.0, 0.8],
[5.0, 5.0, 35.0, 45.0, 0.9],
[10.0, 10.0, 40.0, 50.0, 0.85],
]
),
0.5,
np.array([False, True, False]),
DoesNotRaise(),
), # three boxes with no category
(
np.array(
[
[0.0, 0.0, 30.0, 40.0, 0.8, 0],
[5.0, 5.0, 35.0, 45.0, 0.9, 1],
[10.0, 10.0, 40.0, 50.0, 0.85, 2],
]
),
0.5,
np.array([True, True, True]),
DoesNotRaise(),
), # three boxes with same category
(
np.array(
[
[0.0, 0.0, 30.0, 40.0, 0.8, 0],
[5.0, 5.0, 35.0, 45.0, 0.9, 0],
[10.0, 10.0, 40.0, 50.0, 0.85, 1],
]
),
0.5,
np.array([False, True, True]),
DoesNotRaise(),
), # three boxes with different category
],
)
def test_box_non_max_suppression(
predictions: np.ndarray,
iou_threshold: float,
expected_result: np.ndarray | None,
exception: Exception,
) -> None:
with exception:
result = box_non_max_suppression(
predictions=predictions, iou_threshold=iou_threshold
)
assert np.array_equal(result, expected_result)
@pytest.mark.parametrize(
("predictions", "masks", "iou_threshold", "expected_result", "exception"),
[
(
np.empty((0, 6)),
np.empty((0, 5, 5)),
0.5,
np.array([]),
DoesNotRaise(),
), # empty predictions and masks
(
np.array([[0, 0, 0, 0, 0.8]]),
np.array(
[
[
[False, False, False, False, False],
[False, True, True, True, False],
[False, True, True, True, False],
[False, True, True, True, False],
[False, False, False, False, False],
]
]
),
0.5,
np.array([True]),
DoesNotRaise(),
), # single mask with no category
(
np.array([[0, 0, 0, 0, 0.8, 0]]),
np.array(
[
[
[False, False, False, False, False],
[False, True, True, True, False],
[False, True, True, True, False],
[False, True, True, True, False],
[False, False, False, False, False],
]
]
),
0.5,
np.array([True]),
DoesNotRaise(),
), # single mask with category
(
np.array([[0, 0, 0, 0, 0.8], [0, 0, 0, 0, 0.9]]),
np.array(
[
[
[False, False, False, False, False],
[False, True, True, False, False],
[False, True, True, False, False],
[False, False, False, False, False],
[False, False, False, False, False],
],
[
[False, False, False, False, False],
[False, False, False, False, False],
[False, False, False, True, True],
[False, False, False, True, True],
[False, False, False, False, False],
],
]
),
0.5,
np.array([True, True]),
DoesNotRaise(),
), # two masks non-overlapping with no category
(
np.array([[0, 0, 0, 0, 0.8], [0, 0, 0, 0, 0.9]]),
np.array(
[
[
[False, False, False, False, False],
[False, True, True, True, False],
[False, True, True, True, False],
[False, True, True, True, False],
[False, False, False, False, False],
],
[
[False, False, False, False, False],
[False, False, True, True, True],
[False, False, True, True, True],
[False, False, True, True, True],
[False, False, False, False, False],
],
]
),
0.4,
np.array([False, True]),
DoesNotRaise(),
), # two masks partially overlapping with no category
(
np.array([[0, 0, 0, 0, 0.8, 0], [0, 0, 0, 0, 0.9, 1]]),
np.array(
[
[
[False, False, False, False, False],
[False, True, True, True, False],
[False, True, True, True, False],
[False, True, True, True, False],
[False, False, False, False, False],
],
[
[False, False, False, False, False],
[False, False, True, True, True],
[False, False, True, True, True],
[False, False, True, True, True],
[False, False, False, False, False],
],
]
),
0.5,
np.array([True, True]),
DoesNotRaise(),
), # two masks partially overlapping with different category
(
np.array(
[
[0, 0, 0, 0, 0.8],
[0, 0, 0, 0, 0.85],
[0, 0, 0, 0, 0.9],
]
),
np.array(
[
[
[False, False, False, False, False],
[False, True, True, False, False],
[False, True, True, False, False],
[False, False, False, False, False],
[False, False, False, False, False],
],
[
[False, False, False, False, False],
[False, True, True, False, False],
[False, True, True, False, False],
[False, False, False, False, False],
[False, False, False, False, False],
],
[
[False, False, False, False, False],
[False, False, False, True, True],
[False, False, False, True, True],
[False, False, False, False, False],
[False, False, False, False, False],
],
]
),
0.5,
np.array([False, True, True]),
DoesNotRaise(),
), # three masks with no category
(
np.array(
[
[0, 0, 0, 0, 0.8, 0],
[0, 0, 0, 0, 0.85, 1],
[0, 0, 0, 0, 0.9, 2],
]
),
np.array(
[
[
[False, False, False, False, False],
[False, True, True, False, False],
[False, True, True, False, False],
[False, False, False, False, False],
[False, False, False, False, False],
],
[
[False, False, False, False, False],
[False, True, True, False, False],
[False, True, True, False, False],
[False, True, True, False, False],
[False, False, False, False, False],
],
[
[False, False, False, False, False],
[False, True, True, False, False],
[False, True, True, False, False],
[False, False, False, False, False],
[False, False, False, False, False],
],
]
),
0.5,
np.array([True, True, True]),
DoesNotRaise(),
), # three masks with different category
],
)
def test_mask_non_max_suppression(
predictions: np.ndarray,
masks: np.ndarray,
iou_threshold: float,
expected_result: np.ndarray | None,
exception: Exception,
) -> None:
with exception:
result = mask_non_max_suppression(
predictions=predictions, masks=masks, iou_threshold=iou_threshold
)
assert np.array_equal(result, expected_result)
@pytest.mark.parametrize(
("predictions", "masks", "iou_threshold", "expected_result", "exception"),
[
(
np.empty((0, 6)),
np.empty((0, 5, 5)),
0.5,
[],
DoesNotRaise(),
), # empty predictions and masks
(
np.array([[0, 0, 0, 0, 0.8]]),
np.array(
[
[
[False, False, False, False, False],
[False, True, True, True, False],
[False, True, True, True, False],
[False, True, True, True, False],
[False, False, False, False, False],
]
]
),
0.5,
[[0]],
DoesNotRaise(),
), # single mask with no category
(
np.array([[0, 0, 0, 0, 0.8, 0]]),
np.array(
[
[
[False, False, False, False, False],
[False, True, True, True, False],
[False, True, True, True, False],
[False, True, True, True, False],
[False, False, False, False, False],
]
]
),
0.5,
[[0]],
DoesNotRaise(),
), # single mask with category
(
np.array([[0, 0, 0, 0, 0.8], [0, 0, 0, 0, 0.9]]),
np.array(
[
[
[False, False, False, False, False],
[False, True, True, False, False],
[False, True, True, False, False],
[False, False, False, False, False],
[False, False, False, False, False],
],
[
[False, False, False, False, False],
[False, False, False, False, False],
[False, False, False, True, True],
[False, False, False, True, True],
[False, False, False, False, False],
],
]
),
0.5,
[[0], [1]],
DoesNotRaise(),
), # two masks non-overlapping with no category
(
np.array([[0, 0, 0, 0, 0.8], [0, 0, 0, 0, 0.9]]),
np.array(
[
[
[False, False, False, False, False],
[False, True, True, True, False],
[False, True, True, True, False],
[False, True, True, True, False],
[False, False, False, False, False],
],
[
[False, False, False, False, False],
[False, False, True, True, True],
[False, False, True, True, True],
[False, False, True, True, True],
[False, False, False, False, False],
],
]
),
0.4,
[[0, 1]],
DoesNotRaise(),
), # two masks partially overlapping with no category, merge
(
np.array([[0, 0, 0, 0, 0.8], [0, 0, 0, 0, 0.9]]),
np.array(
[
[
[False, False, False, False, False],
[False, True, True, True, False],
[False, True, True, True, False],
[False, True, True, True, False],
[False, False, False, False, False],
],
[
[False, False, False, False, False],
[False, False, True, True, True],
[False, False, True, True, True],
[False, False, True, True, True],
[False, False, False, False, False],
],
]
),
0.6,
[[0, 1]],
DoesNotRaise(),
), # two masks partially overlapping with no category, no merge
(
np.array([[0, 0, 0, 0, 0.8, 0], [0, 0, 0, 0, 0.9, 1]]),
np.array(
[
[
[False, False, False, False, False],
[False, True, True, True, False],
[False, True, True, True, False],
[False, True, True, True, False],
[False, False, False, False, False],
],
[
[False, False, False, False, False],
[False, False, True, True, True],
[False, False, True, True, True],
[False, False, True, True, True],
[False, False, False, False, False],
],
]
),
0.4,
[[0], [1]],
DoesNotRaise(),
), # two masks partially overlapping with different categories
(
np.array([[0, 0, 0, 0, 0.8, 0], [0, 0, 0, 0, 0.9, 0]]),
np.array(
[
[
[False, False, False, False, False],
[False, True, True, True, False],
[False, True, True, True, False],
[False, True, True, True, False],
[False, False, False, False, False],
],
[
[False, False, False, False, False],
[False, False, True, True, True],
[False, False, True, True, True],
[False, False, True, True, True],
[False, False, False, False, False],
],
]
),
0.4,
[[0, 1]],
DoesNotRaise(),
), # two masks partially overlapping with same category
],
)
def test_mask_non_max_merge(
predictions: np.ndarray,
masks: np.ndarray,
iou_threshold: float,
expected_result: list[list[int]],
exception: Exception,
) -> None:
with exception:
result = mask_non_max_merge(
predictions=predictions, masks=masks, iou_threshold=iou_threshold
)
sorted_result = sorted([sorted(group) for group in result])
sorted_expected_result = sorted([sorted(group) for group in expected_result])
assert sorted_result == sorted_expected_result
@pytest.mark.parametrize(
("box_true", "box_detection", "overlap_metric", "expected_overlap", "exception"),
[
(
[100.0, 100.0, 200.0, 200.0],
[150.0, 150.0, 250.0, 250.0],
OverlapMetric.IOU,
0.14285714285714285,
DoesNotRaise(),
), # partial overlap, IOU
(
[100.0, 100.0, 200.0, 200.0],
[150.0, 150.0, 250.0, 250.0],
OverlapMetric.IOS,
0.25,
DoesNotRaise(),
), # partial overlap, IOS
(
np.array([0.0, 0.0, 10.0, 10.0], dtype=np.float32),
np.array([0.0, 0.0, 10.0, 10.0], dtype=np.float32),
OverlapMetric.IOU,
1.0,
DoesNotRaise(),
), # identical boxes, both boxes are arrays, IOU
(
np.array([0.0, 0.0, 10.0, 10.0], dtype=np.float32),
np.array([0.0, 0.0, 10.0, 10.0], dtype=np.float32),
OverlapMetric.IOS,
1.0,
DoesNotRaise(),
), # identical boxes, both boxes are arrays, IOS
(
[0.0, 0.0, 10.0, 10.0],
[0.0, 0.0, 10.0, 10.0],
"iou",
1.0,
DoesNotRaise(),
), # identical boxes, both boxes are arrays, IOU as lowercase string
(
[0.0, 0.0, 10.0, 10.0],
[0.0, 0.0, 10.0, 10.0],
"ios",
1.0,
DoesNotRaise(),
), # identical boxes, both boxes are arrays, IOS as lowercase string
(
[0.0, 0.0, 10.0, 10.0],
[0.0, 0.0, 10.0, 10.0],
"IOU",
1.0,
DoesNotRaise(),
), # identical boxes, both boxes are arrays, IOU as uppercase string
(
[0.0, 0.0, 10.0, 10.0],
[20.0, 20.0, 30.0, 30.0],
OverlapMetric.IOU,
0.0,
DoesNotRaise(),
), # no overlap, IOU
(
[0.0, 0.0, 10.0, 10.0],
[20.0, 20.0, 30.0, 30.0],
OverlapMetric.IOS,
0.0,
DoesNotRaise(),
), # no overlap, IOS
(
[0.0, 0.0, 10.0, 10.0],
[10.0, 0.0, 20.0, 10.0],
OverlapMetric.IOU,
0.0,
DoesNotRaise(),
), # boxes touch at edge, zero intersection, IOU
(
[0.0, 0.0, 10.0, 10.0],
[10.0, 0.0, 20.0, 10.0],
OverlapMetric.IOS,
0.0,
DoesNotRaise(),
), # boxes touch at edge, zero intersection, IOU
(
[0.0, 0.0, 10.0, 10.0],
[2.0, 2.0, 8.0, 8.0],
OverlapMetric.IOU,
0.36,
DoesNotRaise(),
), # one box inside another, IOU
(
[0.0, 0.0, 10.0, 10.0],
[2.0, 2.0, 8.0, 8.0],
OverlapMetric.IOS,
1.0,
DoesNotRaise(),
), # one box inside another, IOS
(
[0.0, 0.0, 0.0, 0.0],
[0.0, 0.0, 10.0, 10.0],
OverlapMetric.IOU,
0.0,
DoesNotRaise(),
), # degenerate true box with zero area, IOU
(
[0.0, 0.0, 0.0, 0.0],
[0.0, 0.0, 10.0, 10.0],
OverlapMetric.IOS,
0.0,
DoesNotRaise(),
), # degenerate true box with zero area, IOS
(
[0.0, 0.0, 0.0, 0.0],
[0.0, 0.0, 0.0, 0.0],
OverlapMetric.IOU,
0.0,
DoesNotRaise(),
), # both boxes fully degenerate, IOU
(
[0.0, 0.0, 0.0, 0.0],
[0.0, 0.0, 0.0, 0.0],
OverlapMetric.IOS,
0.0,
DoesNotRaise(),
), # both boxes fully degenerate, IOS
(
[-5.0, 0.0, 5.0, 10.0],
[0.0, 0.0, 10.0, 10.0],
OverlapMetric.IOU,
1.0 / 3.0,
DoesNotRaise(),
), # negative x_min, overlapping boxes, IOU is 1/3
(
[-5.0, 0.0, 5.0, 10.0],
[0.0, 0.0, 10.0, 10.0],
OverlapMetric.IOS,
0.5,
DoesNotRaise(),
), # negative x_min, overlapping boxes, IOS is 0.5
(
[0.0, 0.0, 1.0, 1.0],
[0.5, 0.5, 1.5, 1.5],
OverlapMetric.IOU,
0.14285714285714285,
DoesNotRaise(),
), # partial overlap with fractional coordinates, IOU
(
[0.0, 0.0, 1.0, 1.0],
[0.5, 0.5, 1.5, 1.5],
OverlapMetric.IOS,
0.25,
DoesNotRaise(),
), # partial overlap with fractional coordinates, IOS
],
)
def test_box_iou(
box_true: list[float] | np.ndarray,
box_detection: list[float] | np.ndarray,
overlap_metric: str | OverlapMetric,
expected_overlap: float,
exception: Exception,
) -> None:
with exception:
result = box_iou(
box_true=box_true,
box_detection=box_detection,
overlap_metric=overlap_metric,
)
assert result == pytest.approx(expected_overlap, rel=1e-6, abs=1e-12)
@pytest.mark.parametrize(
(
"boxes_true",
"boxes_detection",
"overlap_metric",
"expected_overlap",
"exception",
),
[
# both inputs empty
(
np.empty((0, 4), dtype=np.float32),
np.empty((0, 4), dtype=np.float32),
OverlapMetric.IOU,
np.empty((0, 0), dtype=np.float32),
DoesNotRaise(),
),
# one true box, no detections
(
np.array([[0.0, 0.0, 10.0, 10.0]], dtype=np.float32),
np.empty((0, 4), dtype=np.float32),
OverlapMetric.IOU,
np.empty((1, 0), dtype=np.float32),
DoesNotRaise(),
),
# no true boxes, one detection
(
np.empty((0, 4), dtype=np.float32),
np.array([[0.0, 0.0, 10.0, 10.0]], dtype=np.float32),
OverlapMetric.IOU,
np.empty((0, 1), dtype=np.float32),
DoesNotRaise(),
),
# 1x1 partial overlap, IOU
(
np.array([[100.0, 100.0, 200.0, 200.0]], dtype=np.float32),
np.array([[150.0, 150.0, 250.0, 250.0]], dtype=np.float32),
OverlapMetric.IOU,
np.array([[0.14285715]], dtype=np.float32),
DoesNotRaise(),
),
# 1x1 partial overlap, IOS
(
np.array([[100.0, 100.0, 200.0, 200.0]], dtype=np.float32),
np.array([[150.0, 150.0, 250.0, 250.0]], dtype=np.float32),
OverlapMetric.IOS,
np.array([[0.25]], dtype=np.float32),
DoesNotRaise(),
),
# 1x1 identical boxes, IOU as lowercase string
(
np.array([[0.0, 0.0, 10.0, 10.0]], dtype=np.float32),
np.array([[0.0, 0.0, 10.0, 10.0]], dtype=np.float32),
"iou",
np.array([[1.0]], dtype=np.float32),
DoesNotRaise(),
),
# 1x1 identical boxes, IOS as lowercase string
(
np.array([[0.0, 0.0, 10.0, 10.0]], dtype=np.float32),
np.array([[0.0, 0.0, 10.0, 10.0]], dtype=np.float32),
"ios",
np.array([[1.0]], dtype=np.float32),
DoesNotRaise(),
),
# 1x1 identical boxes, IOU as uppercase string
(
np.array([[0.0, 0.0, 10.0, 10.0]], dtype=np.float32),
np.array([[0.0, 0.0, 10.0, 10.0]], dtype=np.float32),
"IOU",
np.array([[1.0]], dtype=np.float32),
DoesNotRaise(),
),
# 1x1 identical boxes, IOS as uppercase string
(
np.array([[0.0, 0.0, 10.0, 10.0]], dtype=np.float32),
np.array([[0.0, 0.0, 10.0, 10.0]], dtype=np.float32),
"IOS",
np.array([[1.0]], dtype=np.float32),
DoesNotRaise(),
),
# 1x1 no overlap, IOU
(
np.array([[0.0, 0.0, 10.0, 10.0]], dtype=np.float32),
np.array([[20.0, 20.0, 30.0, 30.0]], dtype=np.float32),
OverlapMetric.IOU,
np.array([[0.0]], dtype=np.float32),
DoesNotRaise(),
),
# 1x1 no overlap, IOS
(
np.array([[0.0, 0.0, 10.0, 10.0]], dtype=np.float32),
np.array([[20.0, 20.0, 30.0, 30.0]], dtype=np.float32),
OverlapMetric.IOS,
np.array([[0.0]], dtype=np.float32),
DoesNotRaise(),
),
# 1x1 touching at edge, zero intersection, IOU
(
np.array([[0.0, 0.0, 10.0, 10.0]], dtype=np.float32),
np.array([[10.0, 0.0, 20.0, 10.0]], dtype=np.float32),
OverlapMetric.IOU,
np.array([[0.0]], dtype=np.float32),
DoesNotRaise(),
),
# 1x1 touching at edge, zero intersection, IOS
(
np.array([[0.0, 0.0, 10.0, 10.0]], dtype=np.float32),
np.array([[10.0, 0.0, 20.0, 10.0]], dtype=np.float32),
OverlapMetric.IOS,
np.array([[0.0]], dtype=np.float32),
DoesNotRaise(),
),
# 1x1 box inside another, IOU
(
np.array([[0.0, 0.0, 10.0, 10.0]], dtype=np.float32),
np.array([[2.0, 2.0, 8.0, 8.0]], dtype=np.float32),
OverlapMetric.IOU,
np.array([[0.36]], dtype=np.float32),
DoesNotRaise(),
),
# 1x1 box inside another, IOS
(
np.array([[0.0, 0.0, 10.0, 10.0]], dtype=np.float32),
np.array([[2.0, 2.0, 8.0, 8.0]], dtype=np.float32),
OverlapMetric.IOS,
np.array([[1.0]], dtype=np.float32),
DoesNotRaise(),
),
# 1x1 degenerate true box, IOU
(
np.array([[0.0, 0.0, 0.0, 0.0]], dtype=np.float32),
np.array([[0.0, 0.0, 10.0, 10.0]], dtype=np.float32),
OverlapMetric.IOU,
np.array([[0.0]], dtype=np.float32),
DoesNotRaise(),
),
# 1x1 degenerate true box, IOS
(
np.array([[0.0, 0.0, 0.0, 0.0]], dtype=np.float32),
np.array([[0.0, 0.0, 10.0, 10.0]], dtype=np.float32),
OverlapMetric.IOS,
np.array([[0.0]], dtype=np.float32),
DoesNotRaise(),
),
# 1x1 both boxes degenerate, IOU
(
np.array([[0.0, 0.0, 0.0, 0.0]], dtype=np.float32),
np.array([[0.0, 0.0, 0.0, 0.0]], dtype=np.float32),
OverlapMetric.IOU,
np.array([[0.0]], dtype=np.float32),
DoesNotRaise(),
),
# 1x1 both boxes degenerate, IOS
(
np.array([[0.0, 0.0, 0.0, 0.0]], dtype=np.float32),
np.array([[0.0, 0.0, 0.0, 0.0]], dtype=np.float32),
OverlapMetric.IOS,
np.array([[0.0]], dtype=np.float32),
DoesNotRaise(),
),
# 1x1 negative coordinate, partial overlap, IOU
(
np.array([[-5.0, 0.0, 5.0, 10.0]], dtype=np.float32),
np.array([[0.0, 0.0, 10.0, 10.0]], dtype=np.float32),
OverlapMetric.IOU,
np.array([[1.0 / 3.0]], dtype=np.float32),
DoesNotRaise(),
),
# 1x1 negative coordinate, partial overlap, IOS
(
np.array([[-5.0, 0.0, 5.0, 10.0]], dtype=np.float32),
np.array([[0.0, 0.0, 10.0, 10.0]], dtype=np.float32),
OverlapMetric.IOS,
np.array([[0.5]], dtype=np.float32),
DoesNotRaise(),
),
# 1x1 fractional coordinates, partial overlap, IOU
(
np.array([[0.0, 0.0, 1.0, 1.0]], dtype=np.float32),
np.array([[0.5, 0.5, 1.5, 1.5]], dtype=np.float32),
OverlapMetric.IOU,
np.array([[0.14285715]], dtype=np.float32),
DoesNotRaise(),
),
# 1x1 fractional coordinates, partial overlap, IOS
(
np.array([[0.0, 0.0, 1.0, 1.0]], dtype=np.float32),
np.array([[0.5, 0.5, 1.5, 1.5]], dtype=np.float32),
OverlapMetric.IOS,
np.array([[0.25]], dtype=np.float32),
DoesNotRaise(),
),
# true batch case, 2x2, IOU
(
np.array(
[
[0.0, 0.0, 10.0, 10.0],
[10.0, 10.0, 20.0, 20.0],
],
dtype=np.float32,
),
np.array(
[
[0.0, 0.0, 10.0, 10.0],
[5.0, 5.0, 15.0, 15.0],
],
dtype=np.float32,
),
OverlapMetric.IOU,
np.array(
[
[1.0, 0.14285715],
[0.0, 0.14285715],
],
dtype=np.float32,
),
DoesNotRaise(),
),
# true batch case, 2x2, IOS
(
np.array(
[
[0.0, 0.0, 10.0, 10.0],
[10.0, 10.0, 20.0, 20.0],
],
dtype=np.float32,
),
np.array(
[
[0.0, 0.0, 10.0, 10.0],
[5.0, 5.0, 15.0, 15.0],
],
dtype=np.float32,
),
OverlapMetric.IOS,
np.array(
[
[1.0, 0.25],
[0.0, 0.25],
],
dtype=np.float32,
),
DoesNotRaise(),
),
# invalid overlap_metric
(
np.array([[0.0, 0.0, 10.0, 10.0]], dtype=np.float32),
np.array([[0.0, 0.0, 10.0, 10.0]], dtype=np.float32),
"invalid",
None,
pytest.raises(ValueError, match="Invalid value: INVALID"),
),
],
)
def test_box_iou_batch(
boxes_true: np.ndarray,
boxes_detection: np.ndarray,
overlap_metric: str | OverlapMetric,
expected_overlap: np.ndarray | None,
exception: Exception,
) -> None:
with exception:
result = box_iou_batch(
boxes_true=boxes_true,
boxes_detection=boxes_detection,
overlap_metric=overlap_metric,
)
assert isinstance(result, np.ndarray)
assert result.shape == expected_overlap.shape
assert np.allclose(
result,
expected_overlap,
rtol=1e-6,
atol=1e-12,
)
@pytest.mark.parametrize(
("num_true", "num_det"),
[
(5, 5),
(5, 10),
(10, 5),
(10, 10),
(20, 30),
(30, 20),
(50, 50),
(100, 100),
],
)
@pytest.mark.parametrize(
"overlap_metric",
[OverlapMetric.IOU, OverlapMetric.IOS],
)
def test_box_iou_batch_symmetric_large(
num_true: int,
num_det: int,
overlap_metric: OverlapMetric,
) -> None:
boxes_true = _generate_random_boxes(num_true)
boxes_det = _generate_random_boxes(num_det)
result_ab = box_iou_batch(
boxes_true=boxes_true,
boxes_detection=boxes_det,
overlap_metric=overlap_metric,
)
result_ba = box_iou_batch(
boxes_true=boxes_det,
boxes_detection=boxes_true,
overlap_metric=overlap_metric,
)
assert result_ab.shape == (num_true, num_det)
assert result_ba.shape == (num_det, num_true)
assert np.allclose(
result_ab,
result_ba.T,
rtol=1e-6,
atol=1e-12,
)
Reference in new issue View Git Blame Copy Permalink