1213 lines
42 KiB
Python
1213 lines
42 KiB
Python
from __future__ import annotations
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import warnings
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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.config import ORIENTED_BOX_COORDINATES
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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 supervision.utils.internal import SupervisionWarnings
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from tests.helpers import _create_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("mask_dtype", [bool, np.bool_])
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def test_detections_bool_mask_types_do_not_warn(mask_dtype) -> None:
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with warnings.catch_warnings(record=True) as recorded_warnings:
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warnings.simplefilter("always")
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Detections(
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xyxy=np.array([[1, 2, 3, 4]]),
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mask=np.array([[[1, 0], [0, 1]]], dtype=mask_dtype),
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)
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assert not any(
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warning.category is SupervisionWarnings for warning in recorded_warnings
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)
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def test_detections_non_bool_mask_warns_with_migration_path() -> None:
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with pytest.warns(
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SupervisionWarnings,
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match="supervision-0.28.0.*ValueError.*astype\\(bool\\)",
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):
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Detections(
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xyxy=np.array([[1, 2, 3, 4]]),
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mask=np.array([[[1, 0], [0, 1]]], dtype=np.uint8),
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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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# Scenario: Filter detections by class ID using a boolean mask.
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# Expected: Only detections matching the class ID are retained.
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(
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DETECTIONS,
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DETECTIONS.class_id == 0,
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_create_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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),
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# Scenario: Filter detections by confidence score threshold.
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# Expected: Only high-confidence detections are kept, filtering out noise.
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(
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DETECTIONS,
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DETECTIONS.confidence > 0.5,
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_create_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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),
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# Scenario: Select all detections using a full boolean mask.
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# Expected: Result is identical to input.
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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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),
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# Scenario: Select no detections using an empty boolean mask.
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# Expected: An empty Detections object with correct shapes.
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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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),
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# Scenario: Select specific detections using a list of integer indices.
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# Expected: Only requested indices are returned in specified order.
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(
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DETECTIONS,
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[0, 2],
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_create_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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),
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# Scenario: Select specific detections using a NumPy array of indices.
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# Expected: Only requested indices are returned.
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(
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DETECTIONS,
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np.array([0, 2]),
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_create_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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),
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# Scenario: Select a single detection using an integer index.
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# Expected: A Detections object containing only that element.
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(
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DETECTIONS,
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0,
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_create_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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),
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# Scenario: Select a range of detections using a slice.
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# Expected: Detections within the slice range are returned.
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(
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DETECTIONS,
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slice(1, 3),
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_create_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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),
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# Scenario: Index out of range.
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# Expected: IndexError is raised.
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(DETECTIONS, 10, None, pytest.raises(IndexError, match="index 10 is out")),
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(
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DETECTIONS,
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[0, 2, 10],
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None,
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pytest.raises(IndexError, match="out of bounds for axis 0"),
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),
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(
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DETECTIONS,
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np.array([0, 2, 10]),
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None,
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pytest.raises(IndexError, match="axis 0 with size"),
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),
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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, match="boolean index did not match"),
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),
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# Scenario: Filter an empty Detections object.
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# Expected: Returns an empty Detections object without crashing.
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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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),
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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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"""
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Ensures that `Detections.__getitem__` (indexing/slicing) works correctly for various
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input types. This is a core feature that allows users to filter and manipulate
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detection results easily.
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"""
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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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Detections.empty(),
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DoesNotRaise(),
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), # Single weakly-defined detection: now correctly treated as empty
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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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Detections.empty(),
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DoesNotRaise(),
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), # Zero-length fields: all treated as empty, result is canonical empty
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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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TEST_DET_1,
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DoesNotRaise(),
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), # Empty detection stripped; non-empty detection returned intact
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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, match="confidence' fields must be None"),
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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, match="same keys to merge"),
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), # Non-empty detections with different data keys
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(
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[
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_create_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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_create_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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Detections(
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xyxy=np.array([[10, 10, 20, 20]]),
|
|
class_id=np.array([1]),
|
|
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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],
|
|
None,
|
|
pytest.raises(ValueError, match="metadata dictionaries must have the same"),
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), # Empty and non-empty metadata
|
|
(
|
|
[
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|
Detections(
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xyxy=np.array([[10, 10, 20, 20]]),
|
|
class_id=np.array([1]),
|
|
metadata={"source": "camera1"},
|
|
)
|
|
],
|
|
Detections(
|
|
xyxy=np.array([[10, 10, 20, 20]]),
|
|
class_id=np.array([1]),
|
|
metadata={"source": "camera1"},
|
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),
|
|
DoesNotRaise(),
|
|
), # Single detection with metadata
|
|
(
|
|
[
|
|
Detections(
|
|
xyxy=np.array([[10, 10, 20, 20]]),
|
|
class_id=np.array([1]),
|
|
metadata={"source": "camera1"},
|
|
),
|
|
Detections(
|
|
xyxy=np.array([[30, 30, 40, 40]]),
|
|
class_id=np.array([2]),
|
|
metadata={"source": "camera1"},
|
|
),
|
|
],
|
|
Detections(
|
|
xyxy=np.array([[10, 10, 20, 20], [30, 30, 40, 40]]),
|
|
class_id=np.array([1, 2]),
|
|
metadata={"source": "camera1"},
|
|
),
|
|
DoesNotRaise(),
|
|
), # Multiple metadata entries with identical values
|
|
(
|
|
[
|
|
Detections(
|
|
xyxy=np.array([[10, 10, 20, 20]]),
|
|
class_id=np.array([1]),
|
|
metadata={"source": "camera1"},
|
|
),
|
|
Detections(
|
|
xyxy=np.array([[50, 50, 60, 60]]),
|
|
class_id=np.array([3]),
|
|
metadata={"source": "camera2"},
|
|
),
|
|
],
|
|
None,
|
|
pytest.raises(
|
|
ValueError, match="Conflicting metadata for key: 'source'\\."
|
|
),
|
|
), # Different metadata values
|
|
(
|
|
[
|
|
Detections(
|
|
xyxy=np.array([[10, 10, 20, 20]]),
|
|
metadata={"source": "camera1", "resolution": "1080p"},
|
|
),
|
|
Detections(
|
|
xyxy=np.array([[30, 30, 40, 40]]),
|
|
metadata={"source": "camera1", "resolution": "1080p"},
|
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),
|
|
],
|
|
Detections(
|
|
xyxy=np.array([[10, 10, 20, 20], [30, 30, 40, 40]]),
|
|
metadata={"source": "camera1", "resolution": "1080p"},
|
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),
|
|
DoesNotRaise(),
|
|
), # Large metadata with multiple identical entries
|
|
(
|
|
[
|
|
Detections(
|
|
xyxy=np.array([[10, 10, 20, 20]]), metadata={"source": "camera1"}
|
|
),
|
|
Detections(
|
|
xyxy=np.array([[30, 30, 40, 40]]), metadata={"source": ["camera1"]}
|
|
),
|
|
],
|
|
None,
|
|
pytest.raises(ValueError, match="metadata for key: 'source'"),
|
|
), # Inconsistent types in metadata values
|
|
(
|
|
[
|
|
Detections(
|
|
xyxy=np.array([[10, 10, 20, 20]]), metadata={"source": "camera1"}
|
|
),
|
|
Detections(
|
|
xyxy=np.array([[30, 30, 40, 40]]), metadata={"location": "indoor"}
|
|
),
|
|
],
|
|
None,
|
|
pytest.raises(ValueError, match="same keys to merge"),
|
|
), # Metadata key mismatch
|
|
(
|
|
[
|
|
Detections(
|
|
xyxy=np.array([[10, 10, 20, 20]]),
|
|
metadata={
|
|
"source": "camera1",
|
|
"settings": {"resolution": "1080p", "fps": 30},
|
|
},
|
|
),
|
|
Detections(
|
|
xyxy=np.array([[30, 30, 40, 40]]),
|
|
metadata={
|
|
"source": "camera1",
|
|
"settings": {"resolution": "1080p", "fps": 30},
|
|
},
|
|
),
|
|
],
|
|
Detections(
|
|
xyxy=np.array([[10, 10, 20, 20], [30, 30, 40, 40]]),
|
|
metadata={
|
|
"source": "camera1",
|
|
"settings": {"resolution": "1080p", "fps": 30},
|
|
},
|
|
),
|
|
DoesNotRaise(),
|
|
), # multi-field metadata
|
|
(
|
|
[
|
|
Detections(
|
|
xyxy=np.array([[10, 10, 20, 20]]),
|
|
metadata={"calibration_matrix": np.array([[1, 0], [0, 1]])},
|
|
),
|
|
Detections(
|
|
xyxy=np.array([[30, 30, 40, 40]]),
|
|
metadata={"calibration_matrix": np.array([[1, 0], [0, 1]])},
|
|
),
|
|
],
|
|
Detections(
|
|
xyxy=np.array([[10, 10, 20, 20], [30, 30, 40, 40]]),
|
|
metadata={"calibration_matrix": np.array([[1, 0], [0, 1]])},
|
|
),
|
|
DoesNotRaise(),
|
|
), # Identical 2D numpy arrays in metadata
|
|
(
|
|
[
|
|
Detections(
|
|
xyxy=np.array([[10, 10, 20, 20]]),
|
|
metadata={"calibration_matrix": np.array([[1, 0], [0, 1]])},
|
|
),
|
|
Detections(
|
|
xyxy=np.array([[30, 30, 40, 40]]),
|
|
metadata={"calibration_matrix": np.array([[2, 0], [0, 2]])},
|
|
),
|
|
],
|
|
None,
|
|
pytest.raises(ValueError, match="calibration_matrix"),
|
|
), # Mismatching 2D numpy arrays in metadata
|
|
],
|
|
)
|
|
def test_merge(
|
|
detections_list: list[Detections],
|
|
expected_result: Detections | None,
|
|
exception: Exception,
|
|
) -> None:
|
|
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
|
|
(
|
|
_create_detections(xyxy=[[10, 10, 20, 20]]),
|
|
Position.CENTER,
|
|
np.array([[15, 15]], dtype=np.float32),
|
|
DoesNotRaise(),
|
|
), # single detection; center anchor
|
|
(
|
|
_create_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
|
|
(
|
|
_create_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
|
|
(
|
|
_create_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
|
|
(
|
|
_create_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
|
|
(
|
|
_create_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
|
|
(
|
|
_create_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
|
|
(
|
|
_create_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
|
|
(
|
|
_create_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
|
|
(
|
|
_create_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
|
|
(
|
|
_create_detections(xyxy=[[10, 10, 20, 20]]),
|
|
_create_detections(xyxy=[[10, 10, 20, 20]]),
|
|
True,
|
|
), # detections with xyxy field
|
|
(
|
|
_create_detections(xyxy=[[10, 10, 20, 20]], confidence=[0.5]),
|
|
_create_detections(xyxy=[[10, 10, 20, 20]], confidence=[0.5]),
|
|
True,
|
|
), # detections with xyxy, confidence fields
|
|
(
|
|
_create_detections(xyxy=[[10, 10, 20, 20]], confidence=[0.5]),
|
|
_create_detections(xyxy=[[10, 10, 20, 20]]),
|
|
False,
|
|
), # detection with xyxy field + detection with xyxy, confidence fields
|
|
(
|
|
_create_detections(xyxy=[[10, 10, 20, 20]], data={"test": [1]}),
|
|
_create_detections(xyxy=[[10, 10, 20, 20]], data={"test": [1]}),
|
|
True,
|
|
), # detections with xyxy, data fields
|
|
(
|
|
_create_detections(xyxy=[[10, 10, 20, 20]], data={"test": [1]}),
|
|
_create_detections(xyxy=[[10, 10, 20, 20]]),
|
|
False,
|
|
), # detection with xyxy field + detection with xyxy, data fields
|
|
(
|
|
_create_detections(xyxy=[[10, 10, 20, 20]], data={"test_1": [1]}),
|
|
_create_detections(xyxy=[[10, 10, 20, 20]], data={"test_2": [1]}),
|
|
False,
|
|
), # detections with xyxy, and different data field names
|
|
(
|
|
_create_detections(xyxy=[[10, 10, 20, 20]], data={"test_1": [1]}),
|
|
_create_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"),
|
|
[
|
|
(
|
|
_create_detections(
|
|
xyxy=[[10, 10, 30, 30]],
|
|
),
|
|
_create_detections(
|
|
xyxy=[[10, 10, 30, 30]],
|
|
),
|
|
_create_detections(
|
|
xyxy=[[10, 10, 30, 30]],
|
|
),
|
|
DoesNotRaise(),
|
|
), # Merge with self
|
|
(
|
|
_create_detections(
|
|
xyxy=[[10, 10, 30, 30]],
|
|
),
|
|
Detections.empty(),
|
|
None,
|
|
pytest.raises(ValueError, match="exactly 1 detected object"),
|
|
), # merge with empty: error
|
|
(
|
|
_create_detections(
|
|
xyxy=[[10, 10, 30, 30]],
|
|
),
|
|
_create_detections(
|
|
xyxy=[[10, 10, 30, 30], [40, 40, 60, 60]],
|
|
),
|
|
None,
|
|
pytest.raises(ValueError, match="Both Detections should have"),
|
|
), # merge with 2+ objects: error
|
|
(
|
|
_create_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]},
|
|
),
|
|
_create_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]},
|
|
),
|
|
_create_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
|
|
(
|
|
_create_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]},
|
|
),
|
|
_create_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]},
|
|
),
|
|
_create_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
|
|
(
|
|
_create_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]},
|
|
),
|
|
_create_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]},
|
|
),
|
|
_create_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
|
|
(
|
|
_create_detections(
|
|
xyxy=[[0, 0, 20, 20]],
|
|
confidence=None,
|
|
),
|
|
_create_detections(
|
|
xyxy=[[10, 10, 30, 30]],
|
|
confidence=[0.2],
|
|
),
|
|
None,
|
|
pytest.raises(ValueError, match="Field 'confidence'"),
|
|
), # confidence: None + [x]
|
|
(
|
|
_create_detections(
|
|
xyxy=[[0, 0, 20, 20]],
|
|
mask=[np.array([[1, 1, 0], [1, 1, 0], [0, 0, 0]], dtype=bool)],
|
|
),
|
|
_create_detections(
|
|
xyxy=[[10, 10, 30, 30]],
|
|
mask=None,
|
|
),
|
|
None,
|
|
pytest.raises(ValueError, match="Field 'mask'"),
|
|
), # mask: None + [x]
|
|
(
|
|
_create_detections(xyxy=[[0, 0, 20, 20]], tracker_id=[1]),
|
|
_create_detections(
|
|
xyxy=[[10, 10, 30, 30]],
|
|
tracker_id=None,
|
|
),
|
|
None,
|
|
pytest.raises(ValueError, match="Field 'tracker_id'"),
|
|
), # tracker_id: None + []
|
|
(
|
|
_create_detections(xyxy=[[0, 0, 20, 20]], class_id=[1]),
|
|
_create_detections(
|
|
xyxy=[[10, 10, 30, 30]],
|
|
class_id=None,
|
|
),
|
|
None,
|
|
pytest.raises(ValueError, match="Field 'class_id'"),
|
|
), # 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
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
("detections", "expected"),
|
|
[
|
|
(
|
|
Detections.empty(),
|
|
True,
|
|
), # canonical empty
|
|
(
|
|
Detections(
|
|
xyxy=np.array([[0, 0, 10, 10]]),
|
|
class_id=np.array([1]),
|
|
confidence=np.array([0.9]),
|
|
),
|
|
False,
|
|
), # non-empty, no tracker_id
|
|
(
|
|
Detections(
|
|
xyxy=np.array([[0, 0, 10, 10], [0, 0, 20, 30]]),
|
|
class_id=np.array([1, 2]),
|
|
confidence=np.array([0.6, 0.7]),
|
|
tracker_id=np.array([1, 2]),
|
|
)[np.array([False, False])],
|
|
True,
|
|
), # filtered to empty with tracker_id — the regression case from #2195
|
|
(
|
|
Detections(
|
|
xyxy=np.array([[0, 0, 10, 10], [0, 0, 20, 30]]),
|
|
class_id=np.array([1, 2]),
|
|
confidence=np.array([0.6, 0.7]),
|
|
tracker_id=np.array([1, 2]),
|
|
)[np.array([True, False])],
|
|
False,
|
|
), # one detection remaining after filter
|
|
(
|
|
Detections(
|
|
xyxy=np.array([[0, 0, 10, 10], [0, 0, 20, 30]]),
|
|
mask=np.zeros((2, 4, 4), dtype=bool),
|
|
class_id=np.array([1, 2]),
|
|
)[np.array([False, False])],
|
|
True,
|
|
), # filtered to empty with mask — same bug could affect mask field
|
|
],
|
|
ids=[
|
|
"canonical_empty",
|
|
"non_empty_no_tracker",
|
|
"filtered_empty_with_tracker",
|
|
"one_remaining_after_filter",
|
|
"filtered_empty_with_mask",
|
|
],
|
|
)
|
|
def test_is_empty(detections: Detections, expected: bool) -> None:
|
|
"""Verify is_empty() returns True iff the Detections object has zero detections."""
|
|
assert detections.is_empty() == expected
|
|
|
|
|
|
def test_from_inference_empty_class_name_dtype_matches_non_empty() -> None:
|
|
"""Empty and non-empty results should produce string-kind class_name arrays."""
|
|
empty_result = {"predictions": [], "image": {"width": 100, "height": 100}}
|
|
non_empty_result = {
|
|
"predictions": [
|
|
{
|
|
"x": 50,
|
|
"y": 50,
|
|
"width": 20,
|
|
"height": 20,
|
|
"confidence": 0.9,
|
|
"class": "cat",
|
|
"class_id": 0,
|
|
}
|
|
],
|
|
"image": {"width": 100, "height": 100},
|
|
}
|
|
empty = Detections.from_inference(empty_result)
|
|
non_empty = Detections.from_inference(non_empty_result)
|
|
|
|
# null-safety: class_name must be an array, not None
|
|
assert empty["class_name"] is not None
|
|
assert non_empty["class_name"] is not None
|
|
|
|
# dtype kind must match between empty and non-empty paths
|
|
assert empty["class_name"].dtype.kind == non_empty["class_name"].dtype.kind == "U"
|
|
|
|
# all data keys and dtypes must match between empty and non-empty paths
|
|
assert set(empty.data.keys()) == set(non_empty.data.keys())
|
|
for key in non_empty.data:
|
|
assert empty.data[key].dtype.kind == non_empty.data[key].dtype.kind, key
|
|
|
|
# concatenation across empty+non-empty must produce a string-kind array
|
|
concat = np.concatenate([empty["class_name"], non_empty["class_name"]])
|
|
assert concat.dtype.kind == "U"
|
|
|
|
|
|
def test_from_inference_sdk_dict_path_empty_preserves_class_name_dtype() -> None:
|
|
"""SDK objects with .dict() and empty predictions produce string-kind class_name."""
|
|
|
|
class _FakeSdkResult:
|
|
def dict(self, **kwargs: object) -> dict:
|
|
return {"predictions": [], "image": {"width": 100, "height": 100}}
|
|
|
|
detections = Detections.from_inference(_FakeSdkResult())
|
|
assert detections["class_name"] is not None
|
|
assert detections["class_name"].dtype.kind == "U"
|
|
|
|
|
|
def _rotated_rect(
|
|
cx: float, cy: float, w: float, h: float, angle_deg: float
|
|
) -> np.ndarray:
|
|
angle = np.deg2rad(angle_deg)
|
|
cos, sin = np.cos(angle), np.sin(angle)
|
|
rot = np.array([[cos, -sin], [sin, cos]])
|
|
corners = np.array(
|
|
[[-w / 2, -h / 2], [w / 2, -h / 2], [w / 2, h / 2], [-w / 2, h / 2]]
|
|
)
|
|
return (corners @ rot.T + [cx, cy]).astype(np.float32)
|
|
|
|
|
|
def _make_obb_detections(
|
|
quads: list[np.ndarray], scores: list[float], class_ids: list[int]
|
|
) -> Detections:
|
|
"""Build OBB Detections from a list of (4, 2) corner arrays."""
|
|
oriented_boxes = np.stack(quads)
|
|
xyxy = np.array(
|
|
[[q[:, 0].min(), q[:, 1].min(), q[:, 0].max(), q[:, 1].max()] for q in quads],
|
|
dtype=np.float32,
|
|
)
|
|
return Detections(
|
|
xyxy=xyxy,
|
|
confidence=np.array(scores, dtype=np.float32),
|
|
class_id=np.array(class_ids, dtype=int),
|
|
data={ORIENTED_BOX_COORDINATES: oriented_boxes},
|
|
)
|
|
|
|
|
|
class TestDetectionsObbDispatch:
|
|
"""Shared OBB-aware dispatch behaviour for `with_nms` and `with_nmm`."""
|
|
|
|
@pytest.mark.parametrize(
|
|
"method",
|
|
[
|
|
pytest.param("with_nms", id="with_nms"),
|
|
pytest.param("with_nmm", id="with_nmm"),
|
|
],
|
|
)
|
|
def test_uses_obb_iou_when_oriented_box_coordinates_present(
|
|
self, method: str
|
|
) -> None:
|
|
"""X-pattern OBBs: both survive under either method because OBB IoU < 0.5."""
|
|
quad_a = _rotated_rect(50, 50, 100, 10, +45)
|
|
quad_b = _rotated_rect(50, 50, 100, 10, -45)
|
|
detections = _make_obb_detections([quad_a, quad_b], [0.9, 0.85], [0, 0])
|
|
|
|
result = getattr(detections, method)(threshold=0.5)
|
|
|
|
assert len(result) == 2
|
|
|
|
@pytest.mark.parametrize(
|
|
"method",
|
|
[
|
|
pytest.param("with_nms", id="with_nms"),
|
|
pytest.param("with_nmm", id="with_nmm"),
|
|
],
|
|
)
|
|
def test_falls_back_without_obb_data(self, method: str) -> None:
|
|
"""Non-OBB heavily-overlapping AABBs collapse to one under either method."""
|
|
detections = Detections(
|
|
xyxy=np.array([[0, 0, 100, 100], [10, 10, 110, 110]], dtype=np.float32),
|
|
confidence=np.array([0.9, 0.85], dtype=np.float32),
|
|
class_id=np.array([0, 0], dtype=int),
|
|
)
|
|
|
|
result = getattr(detections, method)(threshold=0.5)
|
|
|
|
assert len(result) == 1
|
|
|
|
|
|
class TestDetectionsWithNmm:
|
|
"""NMM-specific behaviour tests for `Detections.with_nmm`."""
|
|
|
|
def test_obb_merged_xyxy_matches_winner_aabb(self) -> None:
|
|
"""OBB merge group: merged xyxy must equal winner's AABB, not union AABB.
|
|
|
|
Two near-identical OBBs merge into one group. The winner (score 0.9) occupies
|
|
[10, 10, 50, 30]; the lower-score box [20, 20, 60, 40] is offset. Union AABB
|
|
would be [10, 10, 60, 40]; fix must produce winner's AABB [10, 10, 50, 30].
|
|
"""
|
|
quad_winner = np.array(
|
|
[[10, 10], [50, 10], [50, 30], [10, 30]], dtype=np.float32
|
|
)
|
|
quad_other = np.array(
|
|
[[11, 11], [51, 11], [51, 31], [11, 31]], dtype=np.float32
|
|
)
|
|
detections = _make_obb_detections(
|
|
[quad_winner, quad_other], [0.9, 0.85], [0, 0]
|
|
)
|
|
|
|
result = detections.with_nmm(threshold=0.5)
|
|
|
|
assert len(result) == 1
|
|
expected_xyxy = np.array([[10.0, 10.0, 50.0, 30.0]], dtype=np.float32)
|
|
assert np.allclose(result.xyxy, expected_xyxy, atol=0.5)
|
|
|
|
|
|
class TestDetectionsArea:
|
|
"""Selection order for the `area` property: mask → OBB → AABB."""
|
|
|
|
@pytest.mark.parametrize(
|
|
("width", "height", "angle_deg", "expected_area"),
|
|
[
|
|
pytest.param(20, 10, 0, 200.0, id="axis-aligned"),
|
|
pytest.param(20, 10, 45, 200.0, id="45-deg rotation"),
|
|
pytest.param(20, 10, 30, 200.0, id="30-deg rotation"),
|
|
pytest.param(20, 10, -60, 200.0, id="negative rotation"),
|
|
],
|
|
)
|
|
def test_uses_oriented_box_corners_when_present(
|
|
self, width: float, height: float, angle_deg: float, expected_area: float
|
|
) -> None:
|
|
"""Area equals the rotated body's area regardless of rotation, not the AABB."""
|
|
quad = _rotated_rect(50, 50, width, height, angle_deg)
|
|
detections = _make_obb_detections([quad], [0.9], [0])
|
|
|
|
assert np.allclose(detections.area, [expected_area])
|
|
|
|
def test_falls_back_to_box_area_without_obb_data(self) -> None:
|
|
"""Without ORIENTED_BOX_COORDINATES, area mirrors box_area (AABB)."""
|
|
detections = Detections(
|
|
xyxy=np.array([[0, 0, 20, 10]], dtype=np.float32),
|
|
class_id=np.array([0], dtype=int),
|
|
)
|
|
|
|
assert np.allclose(detections.area, [200.0])
|
|
assert np.allclose(detections.area, detections.box_area)
|
|
|
|
def test_mask_takes_precedence_over_oriented_box(self) -> None:
|
|
"""When both `mask` and `ORIENTED_BOX_COORDINATES` are present, area is
|
|
computed from the mask."""
|
|
mask = np.zeros((40, 40), dtype=bool)
|
|
mask[10:30, 10:25] = True # 20 rows x 15 cols = 300 pixels
|
|
quad = _rotated_rect(20, 20, 20, 10, 0) # OBB area = 200
|
|
detections = Detections(
|
|
xyxy=np.array([[10, 10, 25, 30]], dtype=np.float32),
|
|
class_id=np.array([0], dtype=int),
|
|
mask=mask[None, ...],
|
|
data={ORIENTED_BOX_COORDINATES: quad[None, ...]},
|
|
)
|
|
|
|
assert np.allclose(detections.area, [300.0])
|
|
|
|
def test_empty_detections_with_obb_data_returns_empty_array(self) -> None:
|
|
"""Boundary case: empty Detections carrying an OBB data field must
|
|
return an empty area array (matches the mask / box_area branches)."""
|
|
detections = Detections(
|
|
xyxy=np.empty((0, 4), dtype=np.float32),
|
|
class_id=np.array([], dtype=int),
|
|
data={ORIENTED_BOX_COORDINATES: np.empty((0, 4, 2), dtype=np.float32)},
|
|
)
|
|
|
|
assert detections.area.shape == (0,)
|
|
|
|
def test_degenerate_oriented_box_has_zero_area(self) -> None:
|
|
"""An OBB whose four corners coincide has zero area — the shoelace
|
|
formula must not produce NaN or a negative value."""
|
|
quad = np.full((4, 2), 5.0, dtype=np.float32)
|
|
detections = _make_obb_detections([quad], [0.9], [0])
|
|
|
|
assert np.allclose(detections.area, [0.0])
|
|
|
|
def test_handles_batched_oriented_boxes(self) -> None:
|
|
"""Multiple OBBs in one `Detections` each get their own correct area.
|
|
Guards against the shoelace reduction collapsing across boxes instead
|
|
of along the per-box corner axis."""
|
|
quads = [
|
|
_rotated_rect(50, 50, 20, 10, 0), # 200
|
|
_rotated_rect(100, 100, 20, 10, 45), # 200 (rotation must not change it)
|
|
_rotated_rect(150, 150, 30, 5, 30), # 150
|
|
]
|
|
detections = _make_obb_detections(quads, [0.9, 0.9, 0.9], [0, 0, 0])
|
|
|
|
assert np.allclose(detections.area, [200.0, 200.0, 150.0])
|
|
|
|
@pytest.mark.parametrize(
|
|
"bad_shape",
|
|
[
|
|
pytest.param((1, 8), id="flat-N8"),
|
|
pytest.param((1, 3, 2), id="triangle"),
|
|
],
|
|
)
|
|
def test_raises_on_malformed_obb_coordinates_shape(self, bad_shape: tuple) -> None:
|
|
"""ValueError when OBB data shape is wrong for area computation."""
|
|
bad_corners = np.zeros(bad_shape, dtype=np.float32)
|
|
detections = Detections(
|
|
xyxy=np.array([[0, 0, 10, 10]], dtype=np.float32),
|
|
class_id=np.array([0]),
|
|
data={ORIENTED_BOX_COORDINATES: bad_corners},
|
|
)
|
|
|
|
with pytest.raises(ValueError, match="must have shape"):
|
|
_ = detections.area
|
|
|
|
@pytest.mark.parametrize(
|
|
("branch", "expected_dtype"),
|
|
[
|
|
pytest.param("obb", np.float64, id="obb-branch-float64"),
|
|
pytest.param("aabb", np.float32, id="aabb-branch-preserves-input-dtype"),
|
|
pytest.param("mask", np.int64, id="mask-branch-int64"),
|
|
],
|
|
)
|
|
def test_area_return_dtype_per_branch(
|
|
self, branch: str, expected_dtype: type
|
|
) -> None:
|
|
"""Area dtype matches the documented per-branch contract."""
|
|
if branch == "obb":
|
|
quad = _rotated_rect(50, 50, 20, 10, 0)
|
|
detections = _make_obb_detections([quad], [0.9], [0])
|
|
elif branch == "aabb":
|
|
detections = Detections(
|
|
xyxy=np.array([[0, 0, 20, 10]], dtype=np.float32),
|
|
class_id=np.array([0], dtype=int),
|
|
)
|
|
else:
|
|
mask = np.zeros((1, 40, 40), dtype=bool)
|
|
mask[0, 10:30, 10:30] = True
|
|
detections = Detections(
|
|
xyxy=np.array([[10, 10, 30, 30]], dtype=np.float32),
|
|
class_id=np.array([0], dtype=int),
|
|
mask=mask,
|
|
)
|
|
|
|
assert detections.area.dtype == expected_dtype
|