supervision/test/detection/test_core.py

from contextlib import ExitStack as DoesNotRaise

import pytest

from supervision import Detections

from typing import Optional, Union, List

import numpy as np

from test.utils import mock_detections

PREDICTIONS = np.array([
        [       2254,         906,        2447,        1353,     0.90538,           0],
        [       2049,        1133,        2226,        1371,     0.59002,          56],
        [        727,        1224,         838,        1601,     0.51119,          39],
        [        808,        1214,         910,        1564,     0.45287,          39],
        [          6,          52,        1131,        2133,     0.45057,          72],
        [        299,        1225,         512,        1663,     0.45029,          39],
        [        529,         874,         645,         945,     0.31101,          39],
        [          8,          47,        1935,        2135,     0.28192,          72],
        [       2265,         813,        2328,         901,      0.2714,          62]
    ], dtype=np.float32)

DETECTIONS = Detections(
    xyxy=PREDICTIONS[:, :4],
    confidence=PREDICTIONS[:, 4],
    class_id=PREDICTIONS[:, 5].astype(int)
)


@pytest.mark.parametrize(
    'detections, index, expected_result, exception',
    [
        (
            DETECTIONS,
            DETECTIONS.class_id == 0,
            mock_detections(
                xyxy=[[2254, 906, 2447, 1353]],
                confidence=[0.90538],
                class_id=[0]
            ),
            DoesNotRaise()
        ),  # take only detections with class_id = 0
        (
            DETECTIONS,
            DETECTIONS.confidence > 0.5,
            mock_detections(
                xyxy=[
                    [2254, 906, 2447, 1353],
                    [2049, 1133, 2226, 1371],
                    [727, 1224, 838, 1601]
                ],
                confidence=[0.90538, 0.59002, 0.51119],
                class_id=[0, 56, 39]
            ),
            DoesNotRaise()
        ),  # take only detections with confidence > 0.5
        (
            DETECTIONS,
            np.array([True, True, True, True, True, True, True, True, True], dtype=bool),
            DETECTIONS,
            DoesNotRaise()
        ),  # take all detections
        (
            DETECTIONS,
            np.array([False, False, False, False, False, False, False, False, False], dtype=bool),
            Detections(
                xyxy=np.empty((0, 4), dtype=np.float32),
                confidence=np.array([], dtype=np.float32),
                class_id=np.array([], dtype=int)
            ),
            DoesNotRaise()
        ),  # take no detections
        (
            DETECTIONS,
            [0, 2],
            mock_detections(
                xyxy=[
                    [2254, 906, 2447, 1353],
                    [727, 1224, 838, 1601]
                ],
                confidence=[0.90538, 0.51119],
                class_id=[0, 39]
            ),
            DoesNotRaise()
        ),  # take only first and third detection using List[int] index
        (
            DETECTIONS,
            np.array([0, 2]),
            mock_detections(
                xyxy=[
                    [2254, 906, 2447, 1353],
                    [727, 1224, 838, 1601]
                ],
                confidence=[0.90538, 0.51119],
                class_id=[0, 39]
            ),
            DoesNotRaise()
        ),  # take only first and third detection using np.ndarray index
        (
            DETECTIONS,
            0,
            mock_detections(
                xyxy=[[2254, 906, 2447, 1353]],
                confidence=[0.90538],
                class_id=[0]
            ),
            DoesNotRaise()
        ),  # take only first detection by index
        (
            DETECTIONS,
            slice(1, 3),
            mock_detections(
                xyxy=[
                    [2049, 1133, 2226, 1371],
                    [727, 1224, 838, 1601]
                ],
                confidence=[0.59002, 0.51119],
                class_id=[56, 39]
            ),
            DoesNotRaise()
        ),  # take only first detection by index slice (1, 3)
        (
            DETECTIONS,
            10,
            None,
            pytest.raises(IndexError)
        ),  # index out of range
        (
            DETECTIONS,
            [0, 2, 10],
            None,
            pytest.raises(IndexError)
        ),  # index out of range
        (
            DETECTIONS,
            np.array([0, 2, 10]),
            None,
            pytest.raises(IndexError)
        ),
        (
            DETECTIONS,
            np.array([True, True, True, True, True, True, True, True, True, True, True]),
            None,
            pytest.raises(IndexError)
        )
    ]
)
def test_getitem(
        detections: Detections,
        index:  Union[int, slice, List[int], np.ndarray],
        expected_result: Optional[Detections],
        exception: Exception
) -> None:
    with exception:
        result = detections[index]
        assert result == expected_result


@pytest.mark.parametrize(
    'detections_list, expected_result, exception',
    [
        (
            [],
            Detections.empty(),
            DoesNotRaise()
        ),  # empty detections list
        (
            [
                Detections.empty()
            ],
            Detections.empty(),
            DoesNotRaise()
        ),  # single empty detections
        (
            [
                mock_detections(xyxy=[[10, 10, 20, 20]])
            ],
            mock_detections(xyxy=[[10, 10, 20, 20]]),
            DoesNotRaise()
        ),  # single detection with xyxy field
        (
            [
                mock_detections(xyxy=[[10, 10, 20, 20]]),
                Detections.empty()
            ],
            mock_detections(xyxy=[[10, 10, 20, 20]]),
            DoesNotRaise()
        ),  # single detection with xyxy field + empty detection
        (
            [
                mock_detections(xyxy=[[10, 10, 20, 20]]),
                mock_detections(xyxy=[[20, 20, 30, 30]])
            ],
            mock_detections(
                xyxy=[
                    [10, 10, 20, 20],
                    [20, 20, 30, 30]
                ]),
            DoesNotRaise()
        ),  # two detections with xyxy field
        (
            [
                mock_detections(
                    xyxy=[[10, 10, 20, 20]],
                    class_id=[0]),
                mock_detections(
                    xyxy=[[20, 20, 30, 30]])
            ],
            mock_detections(
                xyxy=[
                    [10, 10, 20, 20],
                    [20, 20, 30, 30]
                ]),
            DoesNotRaise()
        ),  # detection with xyxy, class_id fields + detection with xyxy field
(
            [
                mock_detections(
                    xyxy=[[10, 10, 20, 20]],
                    class_id=[0]),
                mock_detections(
                    xyxy=[[20, 20, 30, 30]],
                    class_id=[1]),
            ],
            mock_detections(
                xyxy=[
                    [10, 10, 20, 20],
                    [20, 20, 30, 30]
                ],
                class_id=[0, 1]
            ),
            DoesNotRaise()
        ),  # two detections with xyxy, class_id fields
    ]
)
def test_merge(
        detections_list: List[Detections],
        expected_result: Optional[Detections],
        exception: Exception
) -> None:
    with exception:
        result = Detections.merge(detections_list=detections_list)
        assert result == expected_result