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
92 lines
2.3 KiB
Python
92 lines
2.3 KiB
Python
"""Configuration loading with built-in defaults, overridable via config.yaml."""
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from __future__ import annotations
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from dataclasses import dataclass, field, fields, is_dataclass
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from pathlib import Path
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from typing import Any
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import yaml
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@dataclass
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class CaptureConfig:
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sample_fps: float = 8.0
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processing_width: int = 960
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@dataclass
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class AudioConfig:
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sample_rate: int = 16000
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@dataclass
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class MotionConfig:
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blur_ksize: int = 21
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pixel_threshold: int = 12
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threshold: float = 0.02
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stable_min_duration_s: float = 1.0
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@dataclass
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class DocumentConfig:
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min_area_ratio: float = 0.04
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canny_low: int = 50
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canny_high: int = 150
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enhance: bool = False
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@dataclass
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class SpeechConfig:
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window_before_s: float = 3.0
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window_after_s: float = 5.0
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@dataclass
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class OutputConfig:
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dir: str = "./output"
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consume_dir: str | None = None
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@dataclass
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class Config:
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capture: CaptureConfig = field(default_factory=CaptureConfig)
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audio: AudioConfig = field(default_factory=AudioConfig)
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motion: MotionConfig = field(default_factory=MotionConfig)
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document: DocumentConfig = field(default_factory=DocumentConfig)
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speech: SpeechConfig = field(default_factory=SpeechConfig)
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output: OutputConfig = field(default_factory=OutputConfig)
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def _merge_into(instance: Any, data: dict[str, Any]) -> Any:
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"""Overlay a dict of overrides onto a dataclass instance, recursively."""
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for f in fields(instance):
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if f.name not in data:
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continue
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value = data[f.name]
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current = getattr(instance, f.name)
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if is_dataclass(current) and isinstance(value, dict):
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_merge_into(current, value)
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else:
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setattr(instance, f.name, value)
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return instance
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def load_config(path: str | Path | None = None) -> Config:
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"""Load config.yaml if present; unknown keys are ignored, missing keys use defaults."""
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cfg = Config()
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if path is None:
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return cfg
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path = Path(path)
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if not path.exists():
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raise FileNotFoundError(f"Config file not found: {path}")
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data = yaml.safe_load(path.read_text()) or {}
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return _merge_into(cfg, data)
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def config_to_dict(cfg: Config) -> dict[str, Any]:
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"""Serialize the effective config (for embedding in run reports)."""
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from dataclasses import asdict
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return asdict(cfg)
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