"""Mask manipulation: scaling organ regions and resolving overlaps."""

from __future__ import annotations

from pathlib import Path

import numpy as np
from PIL import Image
from scipy import ndimage
from scipy.ndimage import map_coordinates


def resolve_overlaps(
    mask: np.ndarray,
    priority: tuple[int, int, int] = (2, 0, 1),
    threshold: int = 10,
) -> np.ndarray:
    """Assign overlapping pixels to the highest-priority channel.

    Default priority: heart (2) > left lung (0) > right lung (1).
    """
    result = mask.copy()
    active = mask > threshold
    overlap_mask = active.sum(axis=2) > 1
    if not overlap_mask.any():
        return result

    for y, x in zip(*np.where(overlap_mask)):
        active_channels = [ch for ch in range(3) if mask[y, x, ch] > threshold]
        best = min(active_channels, key=lambda ch: priority.index(ch))
        for ch in active_channels:
            if ch != best:
                result[y, x, ch] = 0
    return result


def scale_mask_channel(
    mask: np.ndarray,
    channel: int,
    scale_factor: float,
    threshold: int = 10,
) -> np.ndarray:
    """Scale a single channel's region around its centroid.

    ``channel``: 0 = left lung (red), 1 = right lung (green), 2 = heart (blue).
    """
    result = mask.copy()
    channel_data = mask[:, :, channel]
    binary = channel_data > threshold
    if not binary.any():
        return result

    cy, cx = ndimage.center_of_mass(binary)
    h, w = mask.shape[:2]
    y_coords, x_coords = np.mgrid[0:h, 0:w]
    y_t = ((y_coords - cy) / scale_factor + cy).astype(np.float32)
    x_t = ((x_coords - cx) / scale_factor + cx).astype(np.float32)

    result[:, :, channel] = 0
    scaled = map_coordinates(
        channel_data.astype(np.float32),
        [y_t, x_t],
        order=1,
        mode="constant",
        cval=0,
    )
    result[:, :, channel] = np.clip(scaled, 0, 255).astype(np.uint8)
    return result


def modify_mask(
    input_path: Path,
    output_path: Path,
    heart_scale: float = 1.0,
    left_lung_scale: float = 1.0,
    right_lung_scale: float = 1.0,
) -> None:
    """Load a conditioning mask, apply scale factors, and save."""
    with Image.open(input_path) as img:
        mask = np.array(img.convert("RGB"))

    if left_lung_scale != 1.0:
        mask = scale_mask_channel(mask, channel=0, scale_factor=left_lung_scale)
    if right_lung_scale != 1.0:
        mask = scale_mask_channel(mask, channel=1, scale_factor=right_lung_scale)
    if heart_scale != 1.0:
        mask = scale_mask_channel(mask, channel=2, scale_factor=heart_scale)

    mask = resolve_overlaps(mask, priority=(2, 0, 1))

    output_path.parent.mkdir(parents=True, exist_ok=True)
    Image.fromarray(mask).save(output_path)
    print(f"[INFO] Saved modified mask to {output_path}")