ilia
6a78c84bcd
- capture/: frame sampling at configurable fps, ffmpeg audio extraction (WAV, whisper-ready) - vision/: changed-pixel motion scoring, stable/moving segmentation, contour + perspective document detection, Laplacian sharpness scoring, optional CLAHE enhancement - pipeline: two-pass detect (motion timeline, then best-frame crop per stable window) writing crops, debug frames, report.json, and motion_scores.csv - CLI: probe / detect / extract-audio subcommands - config.yaml with tunable thresholds; placeholder packages for events, transcribe, ocr, naming, pdf, review_cli - synthetic sample video generator + 16 unit tests
45 lines
1.6 KiB
Python
45 lines
1.6 KiB
Python
import numpy as np
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from paperpod.vision.motion import find_segments, motion_score, prepare_gray
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def series(pattern: list[tuple[float, int]], dt: float = 0.125):
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"""Expand [(score, count), ...] into (timestamps, scores)."""
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scores = [s for s, n in pattern for _ in range(n)]
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timestamps = [i * dt for i in range(len(scores))]
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return timestamps, scores
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def test_basic_stable_and_moving_segments():
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# stable 2s, moving 1s, stable 2s
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ts, sc = series([(0.5, 16), (10.0, 8), (0.5, 16)])
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segs = find_segments(ts, sc, threshold=4.0, stable_min_duration_s=1.0)
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kinds = [s.kind for s in segs]
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assert kinds == ["stable", "moving", "stable"]
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assert segs[0].duration >= 1.0
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assert segs[2].duration >= 1.0
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def test_short_stable_blip_is_demoted_to_moving():
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# moving, tiny stable pause (0.25s), moving -> should merge into one moving segment
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ts, sc = series([(10.0, 8), (0.5, 2), (10.0, 8)])
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segs = find_segments(ts, sc, threshold=4.0, stable_min_duration_s=1.0)
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assert [s.kind for s in segs] == ["moving"]
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def test_empty_input():
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assert find_segments([], [], threshold=4.0, stable_min_duration_s=1.0) == []
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def test_motion_score_zero_for_identical_frames():
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img = np.random.default_rng(0).integers(0, 255, (100, 100, 3), dtype=np.uint8)
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g = prepare_gray(img)
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assert motion_score(g, g) == 0.0
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def test_motion_score_positive_for_changed_frames():
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rng = np.random.default_rng(0)
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a = prepare_gray(rng.integers(0, 255, (100, 100, 3), dtype=np.uint8))
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b = prepare_gray(np.full((100, 100, 3), 200, dtype=np.uint8))
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assert motion_score(a, b) > 0
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