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firestar5683
68 lines
1.8 KiB
Python
68 lines
1.8 KiB
Python
from __future__ import annotations
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import argparse
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import importlib.util
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from pathlib import Path
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import cv2
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import numpy as np
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def load_local_module(name: str):
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path = Path(__file__).resolve().with_name(f"{name}.py")
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spec = importlib.util.spec_from_file_location(f"test_local_{name}", path)
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assert spec is not None and spec.loader is not None
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module = importlib.util.module_from_spec(spec)
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spec.loader.exec_module(module)
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return module
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dataset = load_local_module("build_track_classifier_dataset")
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def options(**overrides) -> argparse.Namespace:
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values = {
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"min_exact_confidence": 0.80,
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"min_detector_confidence": 0.30,
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"min_tracking_confidence": 0.75,
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"min_growth": 0.30,
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"max_growth": 8.0,
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"max_track_rank": 6,
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}
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values.update(overrides)
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return argparse.Namespace(**values)
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def test_trusted_track_row_accepts_high_confidence_optical_flow() -> None:
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row = {
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"expected_speed_limit_mph": "35",
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"predicted_speed_limit_mph": "",
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"read_confidence": "0",
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"detector_confidence": "0",
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"tracking_confidence": "1.0",
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"area_ratio_to_anchor": "5.1",
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"rank": "6",
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}
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assert dataset.trusted_track_row(row, options())
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assert not dataset.trusted_track_row(row, options(min_tracking_confidence=1.01))
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assert not dataset.trusted_track_row(row, options(max_growth=5.0))
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def test_stage_runtime_expansions_writes_each_view_and_repeat(tmp_path: Path) -> None:
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frame = np.zeros((100, 200, 3), dtype=np.uint8)
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frame[20:60, 50:70] = 255
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frame_path = tmp_path / "frame.jpg"
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assert cv2.imwrite(str(frame_path), frame)
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row = {
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"frame_path": str(frame_path),
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"bbox": "50,20,70,60",
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"track_key": "track",
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"rank": "2",
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}
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output = tmp_path / "crops"
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assert dataset.stage_runtime_expansions(row, output, repeat_count=2) == 6
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assert len(list(output.glob("*.jpg"))) == 6
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