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204 lines
7.3 KiB
Python
204 lines
7.3 KiB
Python
"""Tests for DetectionsSmoother bounding-box and confidence smoothing."""
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import numpy as np
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import pytest
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from numpy.testing import assert_allclose
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from supervision.detection.core import Detections
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from supervision.detection.tools.smoother import DetectionsSmoother
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from supervision.utils.internal import SupervisionWarnings
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class TestDetectionsSmoother:
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@pytest.mark.parametrize(
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("conf1", "conf2", "expected_confidence"),
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[
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pytest.param(
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np.array([0.5]),
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np.array([0.7]),
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np.array([0.6]),
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id="with_confidence",
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),
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pytest.param(
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None,
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None,
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None,
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id="no_confidence",
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),
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pytest.param(
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np.array([0.5]),
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None,
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np.array([0.5]),
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id="mixed_window_averages_present",
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),
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],
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)
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def test_smoother_confidence_scenarios(
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self,
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conf1: np.ndarray | None,
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conf2: np.ndarray | None,
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expected_confidence: np.ndarray | None,
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) -> None:
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"""Boxes average over window; confidence averages present values or None."""
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smoother = DetectionsSmoother(length=3)
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smoother.update_with_detections(
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Detections(
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xyxy=np.array([[0, 0, 10, 10]], dtype=np.float32),
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confidence=conf1,
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tracker_id=np.array([1]),
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)
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)
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smoothed = smoother.update_with_detections(
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Detections(
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xyxy=np.array([[2, 2, 12, 12]], dtype=np.float32),
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confidence=conf2,
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tracker_id=np.array([1]),
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)
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)
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assert_allclose(smoothed.xyxy, np.array([[1, 1, 11, 11]]), atol=1e-5)
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if expected_confidence is None:
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assert smoothed.confidence is None
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else:
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assert smoothed.confidence is not None
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assert_allclose(smoothed.confidence, expected_confidence, atol=1e-5)
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def test_smoother_reappearing_track_keeps_history(self) -> None:
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"""Missing tracks stay silent but still contribute when they return."""
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smoother = DetectionsSmoother(length=3)
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first = Detections(
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xyxy=np.array([[0, 0, 10, 10]], dtype=np.float32),
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confidence=np.array([0.5]),
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tracker_id=np.array([1]),
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)
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missing = Detections(
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xyxy=np.empty((0, 4), dtype=np.float32),
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tracker_id=np.array([], dtype=int),
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)
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returned = Detections(
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xyxy=np.array([[2, 2, 12, 12]], dtype=np.float32),
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confidence=np.array([0.7]),
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tracker_id=np.array([1]),
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)
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smoother.update_with_detections(first)
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smoothed_missing = smoother.update_with_detections(missing)
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smoothed_returned = smoother.update_with_detections(returned)
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assert len(smoothed_missing) == 0
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assert len(smoothed_returned) == 1
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assert smoothed_returned.confidence is not None
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assert_allclose(smoothed_returned.xyxy, np.array([[1, 1, 11, 11]]), atol=1e-5)
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assert_allclose(smoothed_returned.confidence, np.array([0.6]), atol=1e-5)
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def test_smoother_tracker_id_none_warns_and_returns_unchanged(self) -> None:
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"""update_with_detections warns and returns input when tracker_id is None."""
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smoother = DetectionsSmoother(length=3)
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detections = Detections(
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xyxy=np.array([[0, 0, 10, 10]], dtype=np.float32),
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tracker_id=None,
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)
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with pytest.warns(SupervisionWarnings):
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result = smoother.update_with_detections(detections)
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assert result is detections
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def test_smoother_window_full_averages_all_frames(self) -> None:
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"""Full window (length=3) averages all 3 frames, not just the last two."""
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smoother = DetectionsSmoother(length=3)
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smoother.update_with_detections(
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Detections(
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xyxy=np.array([[0, 0, 10, 10]], dtype=np.float32),
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confidence=np.array([0.3]),
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tracker_id=np.array([1]),
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)
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)
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smoother.update_with_detections(
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Detections(
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xyxy=np.array([[3, 3, 13, 13]], dtype=np.float32),
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confidence=np.array([0.6]),
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tracker_id=np.array([1]),
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)
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)
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smoothed = smoother.update_with_detections(
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Detections(
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xyxy=np.array([[6, 6, 16, 16]], dtype=np.float32),
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confidence=np.array([0.9]),
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tracker_id=np.array([1]),
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)
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)
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assert_allclose(smoothed.xyxy, np.array([[3, 3, 13, 13]]), atol=1e-5)
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assert smoothed.confidence is not None
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assert_allclose(smoothed.confidence, np.array([0.6]), atol=1e-5)
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def test_smoother_does_not_emit_missing_tracks(self) -> None:
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"""A missing track should keep history but stop emitting ghost boxes."""
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smoother = DetectionsSmoother(length=3)
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first = Detections(
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xyxy=np.array([[0, 0, 10, 10]], dtype=np.float32),
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confidence=np.array([0.3]),
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tracker_id=np.array([1]),
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)
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missing = Detections(
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xyxy=np.empty((0, 4), dtype=np.float32),
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tracker_id=np.array([], dtype=int),
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)
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second = Detections(
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xyxy=np.array([[2, 2, 12, 12]], dtype=np.float32),
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confidence=np.array([0.9]),
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tracker_id=np.array([1]),
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)
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smoother.update_with_detections(first)
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smoothed_missing = smoother.update_with_detections(missing)
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smoothed_returned = smoother.update_with_detections(second)
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assert len(smoothed_missing) == 0
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assert smoothed_returned.confidence is not None
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assert_allclose(smoothed_returned.xyxy, np.array([[1, 1, 11, 11]]), atol=1e-5)
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def test_reset_clears_track_history(self) -> None:
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"""reset() must drop cached frames so post-reset output ignores prior boxes."""
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smoother = DetectionsSmoother(length=3)
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smoother.update_with_detections(
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Detections(
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xyxy=np.array([[0, 0, 10, 10]], dtype=np.float32),
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confidence=np.array([0.5]),
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tracker_id=np.array([1]),
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)
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)
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smoother.reset()
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smoothed = smoother.update_with_detections(
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Detections(
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xyxy=np.array([[2, 2, 12, 12]], dtype=np.float32),
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confidence=np.array([0.7]),
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tracker_id=np.array([1]),
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)
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)
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assert len(smoother.tracks) == 1
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assert_allclose(smoothed.xyxy, np.array([[2, 2, 12, 12]]), atol=1e-5)
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def test_reset_preserves_window_length(self) -> None:
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"""reset() must keep the configured window so maxlen still bounds new tracks."""
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smoother = DetectionsSmoother(length=2)
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smoother.update_with_detections(
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Detections(
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xyxy=np.array([[0, 0, 10, 10]], dtype=np.float32),
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tracker_id=np.array([1]),
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)
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)
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smoother.reset()
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smoother.update_with_detections(
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Detections(
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xyxy=np.array([[0, 0, 10, 10]], dtype=np.float32),
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tracker_id=np.array([9]),
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)
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)
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assert smoother.tracks[9].maxlen == 2
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