75 lines
3.4 KiB
Python
75 lines
3.4 KiB
Python
"""Application configuration and tunable defaults.
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Plain dataclasses. The detection thresholds matter most here — this tool is now
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an image-folder inspector for tuning the detectors, so keep them easy to tweak.
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"""
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from __future__ import annotations
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from dataclasses import dataclass, field
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@dataclass(frozen=True)
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class DetectionConfig:
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"""Parameters for the detector. Thresholds tuned for the classic-CV detector."""
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proc_max_dim: int = 720 # downscale longer side to this before detection (speed)
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min_area_frac: float = 0.0008 # ignore regions smaller than this fraction of the image
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# --- solid bars (black or white, achromatic, rectangular) ---
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black_intensity: int = 40 # V below this = dark-bar candidate
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white_intensity: int = 225 # V above this = light-bar candidate
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bar_saturation_max: int = 45 # S below this = achromatic (excludes colored fills)
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bar_min_extent: float = 0.80 # contour area / bbox area — how rectangular a bar must be
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# --- mosaic / pixelation ---
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mosaic_block_sizes: tuple[int, ...] = (8, 12, 16, 24) # candidate tile sizes (px, proc space)
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mosaic_residual_max: float = 6.0 # max reconstruction error to count as "blocky"
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mosaic_contrast_min: float = 14.0 # min local contrast (excludes flat gradients)
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mosaic_grad_min: float = 8.0 # min edge energy in BOTH x and y (excludes straight edges)
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mosaic_min_side: int = 24 # reject thin regions (px) — kills edge false-positives
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# --- blur ---
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blur_window: int = 31 # sliding window for local sharpness (odd)
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blur_sharpness_ratio: float = 0.35 # below this fraction of median sharpness => blurry
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blur_contrast_min: float = 8.0 # min local contrast (excludes flat regions)
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# --- YOLO detector (used only when detector == "yolo"/"combined") ---
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yolo_conf: float = 0.2 # confidence threshold (LADA recommends ~0.2)
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yolo_imgsz: int = 640 # inference image size
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yolo_device: str | None = None # None => auto ("cuda" if available, else "cpu")
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@dataclass(frozen=True)
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class OverlayConfig:
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"""How detections are drawn over the image."""
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# RGB per CensorType value
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colors: dict[str, tuple[int, int, int]] = field(
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default_factory=lambda: {
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"mosaic": (231, 76, 60), # red
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"blur": (241, 196, 15), # yellow
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"black_bar": (26, 188, 156), # teal
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"unknown": (155, 89, 182), # purple
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}
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)
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line_width: int = 2
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fill_alpha: int = 48 # 0..255 translucency of the region fill
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show_labels: bool = True
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@dataclass
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class AppConfig:
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detection: DetectionConfig = field(default_factory=DetectionConfig)
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overlay: OverlayConfig = field(default_factory=OverlayConfig)
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detector: str = "classic" # "classic" | "yolo" | "combined"
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model_path: str | None = None # weights path, used by the YOLO detector
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default_threshold: float = 0.20 # initial overlay confidence threshold
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# --- restoration ("расцензурить") ---
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restorer: str = "inpaint" # "inpaint" | "deepmosaics"
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dm_dir: str | None = None # DeepMosaics repo dir (contains deepmosaic.py)
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dm_model: str | None = None # DeepMosaics clean weights (clean_*.pth)
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dm_python: str | None = None # python exe for DeepMosaics (None = current)
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dm_gpu: str = "0" # CUDA device id, "-1" for CPU
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