818 lines
27 KiB
Python
818 lines
27 KiB
Python
from __future__ import annotations
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from contextlib import ExitStack as DoesNotRaise
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import numpy as np
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import pytest
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from supervision.detection.core import Detections, merge_inner_detection_object_pair
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from supervision.geometry.core import Position
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from test.test_utils import mock_detections
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PREDICTIONS = np.array(
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[
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[2254, 906, 2447, 1353, 0.90538, 0],
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[2049, 1133, 2226, 1371, 0.59002, 56],
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[727, 1224, 838, 1601, 0.51119, 39],
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[808, 1214, 910, 1564, 0.45287, 39],
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[6, 52, 1131, 2133, 0.45057, 72],
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[299, 1225, 512, 1663, 0.45029, 39],
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[529, 874, 645, 945, 0.31101, 39],
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[8, 47, 1935, 2135, 0.28192, 72],
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[2265, 813, 2328, 901, 0.2714, 62],
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],
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dtype=np.float32,
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)
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DETECTIONS = Detections(
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xyxy=PREDICTIONS[:, :4],
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confidence=PREDICTIONS[:, 4],
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class_id=PREDICTIONS[:, 5].astype(int),
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)
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# Merge test
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TEST_MASK = np.zeros((1000, 1000), dtype=bool)
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TEST_MASK[300:351, 200:251] = True
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TEST_DET_1 = Detections(
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xyxy=np.array([[10, 10, 20, 20], [30, 30, 40, 40], [50, 50, 60, 60]]),
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mask=np.array([TEST_MASK, TEST_MASK, TEST_MASK]),
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confidence=np.array([0.1, 0.2, 0.3]),
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class_id=np.array([1, 2, 3]),
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tracker_id=np.array([1, 2, 3]),
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data={
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"some_key": [1, 2, 3],
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"other_key": [["1", "2"], ["3", "4"], ["5", "6"]],
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},
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)
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TEST_DET_2 = Detections(
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xyxy=np.array([[70, 70, 80, 80], [90, 90, 100, 100]]),
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mask=np.array([TEST_MASK, TEST_MASK]),
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confidence=np.array([0.4, 0.5]),
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class_id=np.array([4, 5]),
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tracker_id=np.array([4, 5]),
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data={
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"some_key": [4, 5],
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"other_key": [["7", "8"], ["9", "10"]],
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},
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)
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TEST_DET_1_2 = Detections(
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xyxy=np.array(
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[
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[10, 10, 20, 20],
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[30, 30, 40, 40],
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[50, 50, 60, 60],
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[70, 70, 80, 80],
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[90, 90, 100, 100],
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]
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),
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mask=np.array([TEST_MASK, TEST_MASK, TEST_MASK, TEST_MASK, TEST_MASK]),
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confidence=np.array([0.1, 0.2, 0.3, 0.4, 0.5]),
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class_id=np.array([1, 2, 3, 4, 5]),
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tracker_id=np.array([1, 2, 3, 4, 5]),
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data={
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"some_key": [1, 2, 3, 4, 5],
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"other_key": [["1", "2"], ["3", "4"], ["5", "6"], ["7", "8"], ["9", "10"]],
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},
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)
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TEST_DET_ZERO_LENGTH = Detections(
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xyxy=np.empty((0, 4), dtype=np.float32),
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mask=np.empty((0, *TEST_MASK.shape), dtype=bool),
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confidence=np.empty((0,)),
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class_id=np.empty((0,)),
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tracker_id=np.empty((0,)),
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data={
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"some_key": [],
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"other_key": [],
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},
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)
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TEST_DET_NONE = Detections(
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xyxy=np.empty((0, 4), dtype=np.float32),
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)
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TEST_DET_DIFFERENT_FIELDS = Detections(
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xyxy=np.array([[88, 88, 99, 99]]),
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mask=np.array([np.logical_not(TEST_MASK)]),
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confidence=None,
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class_id=None,
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tracker_id=np.array([9]),
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data={"some_key": [9], "other_key": [["11", "12"]]},
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)
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TEST_DET_DIFFERENT_DATA = Detections(
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xyxy=np.array([[88, 88, 99, 99]]),
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mask=np.array([np.logical_not(TEST_MASK)]),
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confidence=np.array([0.9]),
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class_id=np.array([9]),
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tracker_id=np.array([9]),
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data={
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"never_seen_key": [9],
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},
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)
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TEST_DET_WITH_METADATA = Detections(
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xyxy=np.array([[10, 10, 20, 20]]),
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class_id=np.array([1]),
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metadata={"source": "camera1"},
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)
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TEST_DET_WITH_METADATA_2 = Detections(
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xyxy=np.array([[30, 30, 40, 40]]),
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class_id=np.array([2]),
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metadata={"source": "camera1"},
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)
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TEST_DET_NO_METADATA = Detections(
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xyxy=np.array([[10, 10, 20, 20]]),
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class_id=np.array([1]),
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)
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TEST_DET_DIFFERENT_METADATA = Detections(
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xyxy=np.array([[50, 50, 60, 60]]),
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class_id=np.array([3]),
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metadata={"source": "camera2"},
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)
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@pytest.mark.parametrize(
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("detections", "index", "expected_result", "exception"),
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[
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(
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DETECTIONS,
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DETECTIONS.class_id == 0,
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mock_detections(
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xyxy=[[2254, 906, 2447, 1353]], confidence=[0.90538], class_id=[0]
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),
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DoesNotRaise(),
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), # take only detections with class_id = 0
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(
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DETECTIONS,
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DETECTIONS.confidence > 0.5,
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mock_detections(
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xyxy=[
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[2254, 906, 2447, 1353],
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[2049, 1133, 2226, 1371],
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[727, 1224, 838, 1601],
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],
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confidence=[0.90538, 0.59002, 0.51119],
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class_id=[0, 56, 39],
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),
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DoesNotRaise(),
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), # take only detections with confidence > 0.5
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(
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DETECTIONS,
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np.array(
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[True, True, True, True, True, True, True, True, True], dtype=bool
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),
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DETECTIONS,
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DoesNotRaise(),
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), # take all detections
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(
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DETECTIONS,
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np.array(
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[False, False, False, False, False, False, False, False, False],
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dtype=bool,
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),
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Detections(
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xyxy=np.empty((0, 4), dtype=np.float32),
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confidence=np.array([], dtype=np.float32),
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class_id=np.array([], dtype=int),
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),
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DoesNotRaise(),
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), # take no detections
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(
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DETECTIONS,
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[0, 2],
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mock_detections(
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xyxy=[[2254, 906, 2447, 1353], [727, 1224, 838, 1601]],
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confidence=[0.90538, 0.51119],
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class_id=[0, 39],
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),
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DoesNotRaise(),
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), # take only first and third detection using List[int] index
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(
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DETECTIONS,
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np.array([0, 2]),
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mock_detections(
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xyxy=[[2254, 906, 2447, 1353], [727, 1224, 838, 1601]],
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confidence=[0.90538, 0.51119],
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class_id=[0, 39],
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),
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DoesNotRaise(),
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), # take only first and third detection using np.ndarray index
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(
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DETECTIONS,
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0,
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mock_detections(
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xyxy=[[2254, 906, 2447, 1353]], confidence=[0.90538], class_id=[0]
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),
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DoesNotRaise(),
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), # take only first detection by index
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(
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DETECTIONS,
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slice(1, 3),
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mock_detections(
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xyxy=[[2049, 1133, 2226, 1371], [727, 1224, 838, 1601]],
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confidence=[0.59002, 0.51119],
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class_id=[56, 39],
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),
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DoesNotRaise(),
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), # take only first detection by index slice (1, 3)
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(DETECTIONS, 10, None, pytest.raises(IndexError)), # index out of range
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(DETECTIONS, [0, 2, 10], None, pytest.raises(IndexError)), # index out of range
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(DETECTIONS, np.array([0, 2, 10]), None, pytest.raises(IndexError)),
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(
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DETECTIONS,
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np.array(
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[True, True, True, True, True, True, True, True, True, True, True]
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),
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None,
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pytest.raises(IndexError),
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),
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(
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Detections.empty(),
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np.isin(Detections.empty()["class_name"], ["cat", "dog"]),
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Detections.empty(),
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DoesNotRaise(),
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), # Filter an empty detections by specific class names
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],
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)
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def test_getitem(
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detections: Detections,
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index: int | slice | list[int] | np.ndarray,
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expected_result: Detections | None,
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exception: Exception,
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) -> None:
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with exception:
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result = detections[index]
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assert result == expected_result
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@pytest.mark.parametrize(
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("detections_list", "expected_result", "exception"),
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[
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([], Detections.empty(), DoesNotRaise()), # empty detections list
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(
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[Detections.empty()],
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Detections.empty(),
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DoesNotRaise(),
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), # single empty detections
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(
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[Detections.empty(), Detections.empty()],
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Detections.empty(),
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DoesNotRaise(),
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), # two empty detections
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(
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[TEST_DET_1],
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TEST_DET_1,
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DoesNotRaise(),
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), # single detection with fields
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(
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[TEST_DET_NONE],
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TEST_DET_NONE,
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DoesNotRaise(),
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), # Single weakly-defined detection
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(
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[TEST_DET_1, TEST_DET_2],
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TEST_DET_1_2,
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DoesNotRaise(),
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), # Fields with same keys
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(
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[TEST_DET_1, Detections.empty()],
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TEST_DET_1,
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DoesNotRaise(),
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), # single detection with fields
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(
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[
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TEST_DET_1,
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TEST_DET_ZERO_LENGTH,
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],
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TEST_DET_1,
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DoesNotRaise(),
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), # Single detection and empty-array fields
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(
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[TEST_DET_ZERO_LENGTH, TEST_DET_ZERO_LENGTH],
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TEST_DET_ZERO_LENGTH,
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DoesNotRaise(),
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), # Zero-length fields across all Detections
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(
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[
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TEST_DET_1,
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TEST_DET_NONE,
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],
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None,
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pytest.raises(ValueError),
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), # Empty detection, but not Detections.empty()
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# Errors: Non-zero-length differently defined keys & data
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(
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[TEST_DET_1, TEST_DET_DIFFERENT_FIELDS],
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None,
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pytest.raises(ValueError),
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), # Non-empty detections with different fields
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(
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[TEST_DET_1, TEST_DET_DIFFERENT_DATA],
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None,
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pytest.raises(ValueError),
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), # Non-empty detections with different data keys
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(
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[
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mock_detections(
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xyxy=[[10, 10, 20, 20]],
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class_id=[1],
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mask=[np.zeros((4, 4), dtype=bool)],
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),
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Detections.empty(),
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],
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mock_detections(
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xyxy=np.array([[10, 10, 20, 20]]),
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class_id=[1],
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mask=[np.zeros((4, 4), dtype=bool)],
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),
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DoesNotRaise(),
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), # Segmentation + Empty
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# Metadata
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(
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[
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Detections(
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xyxy=np.array([[10, 10, 20, 20]]),
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class_id=np.array([1]),
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metadata={"source": "camera1"},
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),
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Detections.empty(),
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],
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Detections(
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xyxy=np.array([[10, 10, 20, 20]]),
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class_id=np.array([1]),
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metadata={"source": "camera1"},
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),
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DoesNotRaise(),
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), # Metadata merge with empty detections
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(
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[
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Detections(
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xyxy=np.array([[10, 10, 20, 20]]),
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class_id=np.array([1]),
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metadata={"source": "camera1"},
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),
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Detections(xyxy=np.array([[30, 30, 40, 40]]), class_id=np.array([2])),
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],
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None,
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pytest.raises(ValueError),
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), # Empty and non-empty metadata
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(
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[
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Detections(
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xyxy=np.array([[10, 10, 20, 20]]),
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class_id=np.array([1]),
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metadata={"source": "camera1"},
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)
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],
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Detections(
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xyxy=np.array([[10, 10, 20, 20]]),
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class_id=np.array([1]),
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metadata={"source": "camera1"},
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),
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DoesNotRaise(),
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), # Single detection with metadata
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(
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[
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Detections(
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xyxy=np.array([[10, 10, 20, 20]]),
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class_id=np.array([1]),
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metadata={"source": "camera1"},
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),
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Detections(
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xyxy=np.array([[30, 30, 40, 40]]),
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class_id=np.array([2]),
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metadata={"source": "camera1"},
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),
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],
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Detections(
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xyxy=np.array([[10, 10, 20, 20], [30, 30, 40, 40]]),
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class_id=np.array([1, 2]),
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metadata={"source": "camera1"},
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),
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DoesNotRaise(),
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), # Multiple metadata entries with identical values
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(
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[
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Detections(
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xyxy=np.array([[10, 10, 20, 20]]),
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class_id=np.array([1]),
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metadata={"source": "camera1"},
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),
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Detections(
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xyxy=np.array([[50, 50, 60, 60]]),
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class_id=np.array([3]),
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metadata={"source": "camera2"},
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),
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],
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None,
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pytest.raises(ValueError),
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), # Different metadata values
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(
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[
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Detections(
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xyxy=np.array([[10, 10, 20, 20]]),
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metadata={"source": "camera1", "resolution": "1080p"},
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),
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Detections(
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xyxy=np.array([[30, 30, 40, 40]]),
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metadata={"source": "camera1", "resolution": "1080p"},
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),
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],
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Detections(
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xyxy=np.array([[10, 10, 20, 20], [30, 30, 40, 40]]),
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metadata={"source": "camera1", "resolution": "1080p"},
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),
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DoesNotRaise(),
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), # Large metadata with multiple identical entries
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(
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[
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Detections(
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xyxy=np.array([[10, 10, 20, 20]]), metadata={"source": "camera1"}
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),
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Detections(
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xyxy=np.array([[30, 30, 40, 40]]), metadata={"source": ["camera1"]}
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),
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],
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None,
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pytest.raises(ValueError),
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), # Inconsistent types in metadata values
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(
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[
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Detections(
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xyxy=np.array([[10, 10, 20, 20]]), metadata={"source": "camera1"}
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),
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Detections(
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xyxy=np.array([[30, 30, 40, 40]]), metadata={"location": "indoor"}
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),
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],
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None,
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pytest.raises(ValueError),
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), # Metadata key mismatch
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(
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[
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Detections(
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xyxy=np.array([[10, 10, 20, 20]]),
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metadata={
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"source": "camera1",
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"settings": {"resolution": "1080p", "fps": 30},
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},
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),
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Detections(
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xyxy=np.array([[30, 30, 40, 40]]),
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metadata={
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"source": "camera1",
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"settings": {"resolution": "1080p", "fps": 30},
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},
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),
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],
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Detections(
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xyxy=np.array([[10, 10, 20, 20], [30, 30, 40, 40]]),
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metadata={
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"source": "camera1",
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"settings": {"resolution": "1080p", "fps": 30},
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},
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),
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DoesNotRaise(),
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), # multi-field metadata
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(
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[
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Detections(
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xyxy=np.array([[10, 10, 20, 20]]),
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metadata={"calibration_matrix": np.array([[1, 0], [0, 1]])},
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),
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Detections(
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xyxy=np.array([[30, 30, 40, 40]]),
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metadata={"calibration_matrix": np.array([[1, 0], [0, 1]])},
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),
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],
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Detections(
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xyxy=np.array([[10, 10, 20, 20], [30, 30, 40, 40]]),
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metadata={"calibration_matrix": np.array([[1, 0], [0, 1]])},
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),
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DoesNotRaise(),
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), # Identical 2D numpy arrays in metadata
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(
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[
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Detections(
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xyxy=np.array([[10, 10, 20, 20]]),
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metadata={"calibration_matrix": np.array([[1, 0], [0, 1]])},
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),
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Detections(
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xyxy=np.array([[30, 30, 40, 40]]),
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metadata={"calibration_matrix": np.array([[2, 0], [0, 2]])},
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),
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],
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None,
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pytest.raises(ValueError),
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), # Mismatching 2D numpy arrays in metadata
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],
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)
|
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def test_merge(
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detections_list: list[Detections],
|
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expected_result: Detections | None,
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exception: Exception,
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) -> None:
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with exception:
|
|
result = Detections.merge(detections_list=detections_list)
|
|
assert result == expected_result, f"Expected: {expected_result}, Got: {result}"
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
("detections", "anchor", "expected_result", "exception"),
|
|
[
|
|
(
|
|
Detections.empty(),
|
|
Position.CENTER,
|
|
np.empty((0, 2), dtype=np.float32),
|
|
DoesNotRaise(),
|
|
), # empty detections
|
|
(
|
|
mock_detections(xyxy=[[10, 10, 20, 20]]),
|
|
Position.CENTER,
|
|
np.array([[15, 15]], dtype=np.float32),
|
|
DoesNotRaise(),
|
|
), # single detection; center anchor
|
|
(
|
|
mock_detections(xyxy=[[10, 10, 20, 20], [20, 20, 30, 30]]),
|
|
Position.CENTER,
|
|
np.array([[15, 15], [25, 25]], dtype=np.float32),
|
|
DoesNotRaise(),
|
|
), # two detections; center anchor
|
|
(
|
|
mock_detections(xyxy=[[10, 10, 20, 20], [20, 20, 30, 30]]),
|
|
Position.CENTER_LEFT,
|
|
np.array([[10, 15], [20, 25]], dtype=np.float32),
|
|
DoesNotRaise(),
|
|
), # two detections; center left anchor
|
|
(
|
|
mock_detections(xyxy=[[10, 10, 20, 20], [20, 20, 30, 30]]),
|
|
Position.CENTER_RIGHT,
|
|
np.array([[20, 15], [30, 25]], dtype=np.float32),
|
|
DoesNotRaise(),
|
|
), # two detections; center right anchor
|
|
(
|
|
mock_detections(xyxy=[[10, 10, 20, 20], [20, 20, 30, 30]]),
|
|
Position.TOP_CENTER,
|
|
np.array([[15, 10], [25, 20]], dtype=np.float32),
|
|
DoesNotRaise(),
|
|
), # two detections; top center anchor
|
|
(
|
|
mock_detections(xyxy=[[10, 10, 20, 20], [20, 20, 30, 30]]),
|
|
Position.TOP_LEFT,
|
|
np.array([[10, 10], [20, 20]], dtype=np.float32),
|
|
DoesNotRaise(),
|
|
), # two detections; top left anchor
|
|
(
|
|
mock_detections(xyxy=[[10, 10, 20, 20], [20, 20, 30, 30]]),
|
|
Position.TOP_RIGHT,
|
|
np.array([[20, 10], [30, 20]], dtype=np.float32),
|
|
DoesNotRaise(),
|
|
), # two detections; top right anchor
|
|
(
|
|
mock_detections(xyxy=[[10, 10, 20, 20], [20, 20, 30, 30]]),
|
|
Position.BOTTOM_CENTER,
|
|
np.array([[15, 20], [25, 30]], dtype=np.float32),
|
|
DoesNotRaise(),
|
|
), # two detections; bottom center anchor
|
|
(
|
|
mock_detections(xyxy=[[10, 10, 20, 20], [20, 20, 30, 30]]),
|
|
Position.BOTTOM_LEFT,
|
|
np.array([[10, 20], [20, 30]], dtype=np.float32),
|
|
DoesNotRaise(),
|
|
), # two detections; bottom left anchor
|
|
(
|
|
mock_detections(xyxy=[[10, 10, 20, 20], [20, 20, 30, 30]]),
|
|
Position.BOTTOM_RIGHT,
|
|
np.array([[20, 20], [30, 30]], dtype=np.float32),
|
|
DoesNotRaise(),
|
|
), # two detections; bottom right anchor
|
|
],
|
|
)
|
|
def test_get_anchor_coordinates(
|
|
detections: Detections,
|
|
anchor: Position,
|
|
expected_result: np.ndarray,
|
|
exception: Exception,
|
|
) -> None:
|
|
result = detections.get_anchors_coordinates(anchor)
|
|
with exception:
|
|
assert np.array_equal(result, expected_result)
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
("detections_a", "detections_b", "expected_result"),
|
|
[
|
|
(
|
|
Detections.empty(),
|
|
Detections.empty(),
|
|
True,
|
|
), # empty detections
|
|
(
|
|
mock_detections(xyxy=[[10, 10, 20, 20]]),
|
|
mock_detections(xyxy=[[10, 10, 20, 20]]),
|
|
True,
|
|
), # detections with xyxy field
|
|
(
|
|
mock_detections(xyxy=[[10, 10, 20, 20]], confidence=[0.5]),
|
|
mock_detections(xyxy=[[10, 10, 20, 20]], confidence=[0.5]),
|
|
True,
|
|
), # detections with xyxy, confidence fields
|
|
(
|
|
mock_detections(xyxy=[[10, 10, 20, 20]], confidence=[0.5]),
|
|
mock_detections(xyxy=[[10, 10, 20, 20]]),
|
|
False,
|
|
), # detection with xyxy field + detection with xyxy, confidence fields
|
|
(
|
|
mock_detections(xyxy=[[10, 10, 20, 20]], data={"test": [1]}),
|
|
mock_detections(xyxy=[[10, 10, 20, 20]], data={"test": [1]}),
|
|
True,
|
|
), # detections with xyxy, data fields
|
|
(
|
|
mock_detections(xyxy=[[10, 10, 20, 20]], data={"test": [1]}),
|
|
mock_detections(xyxy=[[10, 10, 20, 20]]),
|
|
False,
|
|
), # detection with xyxy field + detection with xyxy, data fields
|
|
(
|
|
mock_detections(xyxy=[[10, 10, 20, 20]], data={"test_1": [1]}),
|
|
mock_detections(xyxy=[[10, 10, 20, 20]], data={"test_2": [1]}),
|
|
False,
|
|
), # detections with xyxy, and different data field names
|
|
(
|
|
mock_detections(xyxy=[[10, 10, 20, 20]], data={"test_1": [1]}),
|
|
mock_detections(xyxy=[[10, 10, 20, 20]], data={"test_1": [3]}),
|
|
False,
|
|
), # detections with xyxy, and different data field values
|
|
],
|
|
)
|
|
def test_equal(
|
|
detections_a: Detections, detections_b: Detections, expected_result: bool
|
|
) -> None:
|
|
assert (detections_a == detections_b) == expected_result
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
("detection_1", "detection_2", "expected_result", "exception"),
|
|
[
|
|
(
|
|
mock_detections(
|
|
xyxy=[[10, 10, 30, 30]],
|
|
),
|
|
mock_detections(
|
|
xyxy=[[10, 10, 30, 30]],
|
|
),
|
|
mock_detections(
|
|
xyxy=[[10, 10, 30, 30]],
|
|
),
|
|
DoesNotRaise(),
|
|
), # Merge with self
|
|
(
|
|
mock_detections(
|
|
xyxy=[[10, 10, 30, 30]],
|
|
),
|
|
Detections.empty(),
|
|
None,
|
|
pytest.raises(ValueError),
|
|
), # merge with empty: error
|
|
(
|
|
mock_detections(
|
|
xyxy=[[10, 10, 30, 30]],
|
|
),
|
|
mock_detections(
|
|
xyxy=[[10, 10, 30, 30], [40, 40, 60, 60]],
|
|
),
|
|
None,
|
|
pytest.raises(ValueError),
|
|
), # merge with 2+ objects: error
|
|
(
|
|
mock_detections(
|
|
xyxy=[[10, 10, 30, 30]],
|
|
confidence=[0.1],
|
|
class_id=[1],
|
|
mask=[np.array([[1, 1, 0], [1, 1, 0], [0, 0, 0]], dtype=bool)],
|
|
tracker_id=[1],
|
|
data={"key_1": [1]},
|
|
),
|
|
mock_detections(
|
|
xyxy=[[20, 20, 40, 40]],
|
|
confidence=[0.1],
|
|
class_id=[2],
|
|
mask=[np.array([[0, 0, 0], [0, 1, 1], [0, 1, 1]], dtype=bool)],
|
|
tracker_id=[2],
|
|
data={"key_2": [2]},
|
|
),
|
|
mock_detections(
|
|
xyxy=[[10, 10, 40, 40]],
|
|
confidence=[0.1],
|
|
class_id=[1],
|
|
mask=[np.array([[1, 1, 0], [1, 1, 1], [0, 1, 1]], dtype=bool)],
|
|
tracker_id=[1],
|
|
data={"key_1": [1]},
|
|
),
|
|
DoesNotRaise(),
|
|
), # Same confidence - merge box & mask, tie-break to detection_1
|
|
(
|
|
mock_detections(
|
|
xyxy=[[0, 0, 20, 20]],
|
|
confidence=[0.1],
|
|
class_id=[1],
|
|
mask=[np.array([[1, 1, 0], [1, 1, 0], [0, 0, 0]], dtype=bool)],
|
|
tracker_id=[1],
|
|
data={"key_1": [1]},
|
|
),
|
|
mock_detections(
|
|
xyxy=[[10, 10, 50, 50]],
|
|
confidence=[0.2],
|
|
class_id=[2],
|
|
mask=[np.array([[0, 0, 0], [0, 1, 1], [0, 1, 1]], dtype=bool)],
|
|
tracker_id=[2],
|
|
data={"key_2": [2]},
|
|
),
|
|
mock_detections(
|
|
xyxy=[[0, 0, 50, 50]],
|
|
confidence=[(1 * 0.1 + 4 * 0.2) / 5],
|
|
class_id=[2],
|
|
mask=[np.array([[1, 1, 0], [1, 1, 1], [0, 1, 1]], dtype=bool)],
|
|
tracker_id=[2],
|
|
data={"key_2": [2]},
|
|
),
|
|
DoesNotRaise(),
|
|
), # Different confidence, different area
|
|
(
|
|
mock_detections(
|
|
xyxy=[[10, 10, 30, 30]],
|
|
confidence=None,
|
|
class_id=[1],
|
|
mask=[np.array([[1, 1, 0], [1, 1, 0], [0, 0, 0]], dtype=bool)],
|
|
tracker_id=[1],
|
|
data={"key_1": [1]},
|
|
),
|
|
mock_detections(
|
|
xyxy=[[20, 20, 40, 40]],
|
|
confidence=None,
|
|
class_id=[2],
|
|
mask=[np.array([[0, 0, 0], [0, 1, 1], [0, 1, 1]], dtype=bool)],
|
|
tracker_id=[2],
|
|
data={"key_2": [2]},
|
|
),
|
|
mock_detections(
|
|
xyxy=[[10, 10, 40, 40]],
|
|
confidence=None,
|
|
class_id=[1],
|
|
mask=[np.array([[1, 1, 0], [1, 1, 1], [0, 1, 1]], dtype=bool)],
|
|
tracker_id=[1],
|
|
data={"key_1": [1]},
|
|
),
|
|
DoesNotRaise(),
|
|
), # No confidence at all
|
|
(
|
|
mock_detections(
|
|
xyxy=[[0, 0, 20, 20]],
|
|
confidence=None,
|
|
),
|
|
mock_detections(
|
|
xyxy=[[10, 10, 30, 30]],
|
|
confidence=[0.2],
|
|
),
|
|
None,
|
|
pytest.raises(ValueError),
|
|
), # confidence: None + [x]
|
|
(
|
|
mock_detections(
|
|
xyxy=[[0, 0, 20, 20]],
|
|
mask=[np.array([[1, 1, 0], [1, 1, 0], [0, 0, 0]], dtype=bool)],
|
|
),
|
|
mock_detections(
|
|
xyxy=[[10, 10, 30, 30]],
|
|
mask=None,
|
|
),
|
|
None,
|
|
pytest.raises(ValueError),
|
|
), # mask: None + [x]
|
|
(
|
|
mock_detections(xyxy=[[0, 0, 20, 20]], tracker_id=[1]),
|
|
mock_detections(
|
|
xyxy=[[10, 10, 30, 30]],
|
|
tracker_id=None,
|
|
),
|
|
None,
|
|
pytest.raises(ValueError),
|
|
), # tracker_id: None + []
|
|
(
|
|
mock_detections(xyxy=[[0, 0, 20, 20]], class_id=[1]),
|
|
mock_detections(
|
|
xyxy=[[10, 10, 30, 30]],
|
|
class_id=None,
|
|
),
|
|
None,
|
|
pytest.raises(ValueError),
|
|
), # class_id: None + []
|
|
],
|
|
)
|
|
def test_merge_inner_detection_object_pair(
|
|
detection_1: Detections,
|
|
detection_2: Detections,
|
|
expected_result: Detections | None,
|
|
exception: Exception,
|
|
):
|
|
with exception:
|
|
result = merge_inner_detection_object_pair(detection_1, detection_2)
|
|
assert result == expected_result
|