"""Placement: find low-variance regions and resolve a position to a pixel box.

Low-variance regions (sky, walls, uniform backgrounds) hide faint text best and
also give the VLM a clean patch to read. We rank a small grid of candidate
boxes by local variance and a position prior.
"""

from __future__ import annotations

import numpy as np
from PIL import Image

from veil_pgd.types import Position

REFERENCE = 1024  # canonical size specs are defined against


def _position_anchor(position: Position, w: int, h: int, bw: int, bh: int) -> tuple[int, int]:
    """Top-left pixel for a text box of size (bw, bh) at a named position."""
    pad = int(0.03 * max(w, h))
    xmid = (w - bw) // 2
    ymid = (h - bh) // 2
    table = {
        "center": (xmid, ymid),
        "top_middle": (xmid, pad),
        "bottom_middle": (xmid, h - bh - pad),
        "top_left": (pad, pad),
        "top_right": (w - bw - pad, pad),
        "bottom_left": (pad, h - bh - pad),
        "bottom_right": (w - bw - pad, h - bh - pad),
    }
    return table.get(position, table["bottom_middle"])


def resolve_box(
    image: Image.Image, position: Position, box_w: int, box_h: int
) -> tuple[int, int, int, int]:
    """Return (x0, y0, x1, y1) for the overlay text box, clamped to the image."""
    w, h = image.size
    x0, y0 = _position_anchor(position, w, h, box_w, box_h)
    x0 = max(0, min(x0, w - box_w))
    y0 = max(0, min(y0, h - box_h))
    return x0, y0, x0 + box_w, y0 + box_h


def region_variance(image: Image.Image, box: tuple[int, int, int, int]) -> float:
    """Mean per-channel variance inside a box (lower = flatter = stealthier)."""
    crop = np.asarray(image.convert("RGB").crop(box), dtype=np.float32)
    if crop.size == 0:
        return float("inf")
    return float(crop.reshape(-1, 3).var(axis=0).mean())


def rank_positions(
    image: Image.Image, positions: list[Position], box_w: int, box_h: int
) -> list[tuple[Position, float]]:
    """Rank candidate positions by ascending region variance."""
    scored = []
    for p in positions:
        box = resolve_box(image, p, box_w, box_h)
        scored.append((p, region_variance(image, box)))
    return sorted(scored, key=lambda kv: kv[1])


def scale_px(font_px: int, image: Image.Image) -> int:
    """Scale a reference (1024px) font size to the true image's short side."""
    short = min(image.size)
    return max(6, round(font_px * short / REFERENCE))