"""Application configuration and tunable defaults.

Plain dataclasses. The detection thresholds matter most here — this tool is now
an image-folder inspector for tuning the detectors, so keep them easy to tweak.
"""

from __future__ import annotations

from dataclasses import dataclass, field


@dataclass(frozen=True)
class DetectionConfig:
    """Parameters for the detector. Thresholds tuned for the classic-CV detector."""

    proc_max_dim: int = 720      # downscale longer side to this before detection (speed)
    min_area_frac: float = 0.0008  # ignore regions smaller than this fraction of the image

    # --- solid bars (black or white, achromatic, rectangular) ---
    black_intensity: int = 40    # V below this = dark-bar candidate
    white_intensity: int = 225   # V above this = light-bar candidate
    bar_saturation_max: int = 45  # S below this = achromatic (excludes colored fills)
    bar_min_extent: float = 0.80  # contour area / bbox area — how rectangular a bar must be

    # --- mosaic / pixelation ---
    mosaic_block_sizes: tuple[int, ...] = (8, 12, 16, 24)  # candidate tile sizes (px, proc space)
    mosaic_residual_max: float = 6.0   # max reconstruction error to count as "blocky"
    mosaic_contrast_min: float = 14.0  # min local contrast (excludes flat gradients)
    mosaic_grad_min: float = 8.0       # min edge energy in BOTH x and y (excludes straight edges)
    mosaic_min_side: int = 24          # reject thin regions (px) — kills edge false-positives

    # --- blur ---
    blur_window: int = 31              # sliding window for local sharpness (odd)
    blur_sharpness_ratio: float = 0.35  # below this fraction of median sharpness => blurry
    blur_contrast_min: float = 8.0     # min local contrast (excludes flat regions)

    # --- YOLO detector (used only when detector == "yolo"/"combined") ---
    yolo_conf: float = 0.2             # confidence threshold (LADA recommends ~0.2)
    yolo_imgsz: int = 640              # inference image size
    yolo_device: str | None = None     # None => auto ("cuda" if available, else "cpu")


@dataclass(frozen=True)
class OverlayConfig:
    """How detections are drawn over the image."""

    # RGB per CensorType value
    colors: dict[str, tuple[int, int, int]] = field(
        default_factory=lambda: {
            "mosaic": (231, 76, 60),     # red
            "blur": (241, 196, 15),      # yellow
            "black_bar": (26, 188, 156),  # teal
            "unknown": (155, 89, 182),   # purple
        }
    )
    line_width: int = 2
    fill_alpha: int = 48  # 0..255 translucency of the region fill
    show_labels: bool = True


@dataclass
class AppConfig:
    detection: DetectionConfig = field(default_factory=DetectionConfig)
    overlay: OverlayConfig = field(default_factory=OverlayConfig)
    detector: str = "classic"          # "classic" | "yolo" | "combined"
    model_path: str | None = None      # weights path, used by the YOLO detector
    default_threshold: float = 0.20    # initial overlay confidence threshold

    # --- restoration ("расцензурить") ---
    restorer: str = "inpaint"          # "inpaint" | "deepmosaics"
    dm_dir: str | None = None          # DeepMosaics repo dir (contains deepmosaic.py)
    dm_model: str | None = None        # DeepMosaics clean weights (clean_*.pth)
    dm_python: str | None = None       # python exe for DeepMosaics (None = current)
    dm_gpu: str = "0"                  # CUDA device id, "-1" for CPU