"""Basic retouching tools: healing / inpainting and skin softening."""
from __future__ import annotations

from typing import Tuple

import numpy as np
from PIL import Image


def heal_spot(
    img: Image.Image,
    center: Tuple[int, int],
    radius: int,
) -> Image.Image:
    """Remove blemish at *center* using OpenCV Telea inpainting."""
    import cv2

    arr = np.asarray(img.convert("RGB"))
    bgr = cv2.cvtColor(arr, cv2.COLOR_RGB2BGR)

    mask = np.zeros(arr.shape[:2], dtype=np.uint8)
    cv2.circle(mask, center, radius, 255, -1)

    result = cv2.inpaint(bgr, mask, inpaintRadius=radius * 2, flags=cv2.INPAINT_TELEA)
    result = cv2.cvtColor(result, cv2.COLOR_BGR2RGB)
    return Image.fromarray(result)


def soften_skin(
    rgb_float01: np.ndarray,
    mask: np.ndarray,
    strength: float,
) -> np.ndarray:
    """Edge-preserving skin softening (bilateral filter) within *mask* area.

    *strength*: 0..100.  0 = no effect.
    *mask*: float01 (H, W) – typically the person/face mask.
    """
    if strength <= 0:
        return rgb_float01
    import cv2

    img8 = np.clip(rgb_float01 * 255, 0, 255).astype(np.uint8)

    d = max(3, int(strength / 10))
    sigma_color = 30 + strength * 0.7
    sigma_space = 30 + strength * 0.7
    smoothed = cv2.bilateralFilter(img8, d, sigma_color, sigma_space)

    smoothed_f = smoothed.astype(np.float32) / 255.0
    alpha = np.clip(mask * min(strength / 100.0, 1.0), 0.0, 1.0)[..., None]
    result = rgb_float01 * (1.0 - alpha) + smoothed_f * alpha
    return np.clip(result, 0.0, 1.0).astype(np.float32)