#!/usr/bin/env python3
"""SkinDetection.py

Runs several skin-detection algorithms on a single image and saves results.
"""

from __future__ import annotations

import argparse
import colorsys
import sys
from pathlib import Path
from typing import Callable

from PIL import Image


def load_image(path: Path) -> Image.Image:
    image = Image.open(path)
    return image.convert("RGB")


def save_result(image: Image.Image, path: Path) -> None:
    path.parent.mkdir(parents=True, exist_ok=True)
    image.save(path)


def mask_image(
    image: Image.Image,
    predicate: Callable[[int, int, int], bool],
    invert: bool = False,
) -> Image.Image:
    result = image.copy()
    pixels = result.load()

    for x in range(result.width):
        for y in range(result.height):
            r, g, b = image.getpixel((x, y))
            if predicate(r, g, b) ^ invert:
                continue
            pixels[x, y] = (0, 0, 0)

    return result


def explicitly_defined_skin_region(r: int, g: int, b: int) -> bool:
    return (
        r > 95
        and g > 40
        and b > 20
        and (max(r, g, b) - min(r, g, b)) > 15
        and abs(r - g) > 15
        and r > g
        and r > b
    )


def normalized_rg_skin_region(r: int, g: int, b: int) -> bool:
    total = r + g + b
    if total == 0:
        return False

    R = r / float(total)
    G = g / float(total)
    return 0.36 <= R <= 0.465 and 0.28 <= G <= 0.363


def hsv_skin_region(r: int, g: int, b: int) -> bool:
    r_norm = r / 255.0
    g_norm = g / 255.0
    b_norm = b / 255.0
    h, s, v = colorsys.rgb_to_hsv(r_norm, g_norm, b_norm)
    h *= 255
    return 0 <= h <= 50 and 0.2 <= s <= 0.68 and 0.35 <= v <= 1


def ycbcr_skin_region(r: int, g: int, b: int) -> bool:
    y = 0.299 * r + 0.587 * g + 0.114 * b
    cb = 128 - 0.168736 * r - 0.331264 * g + 0.5 * b
    cr = 128 + 0.5 * r - 0.418688 * g - 0.081312 * b
    return 97.5 <= cb <= 142.5 and 17 <= cr <= 134


def parse_args() -> argparse.Namespace:
    parser = argparse.ArgumentParser(
        description="Run multiple skin detection algorithms on an input image."
    )
    parser.add_argument(
        "image_path",
        nargs="?",
        default="Images/skin.jpg",
        help="Path to the input image.",
    )
    parser.add_argument(
        "--output-dir",
        default="Results",
        help="Directory to write output images.",
    )
    parser.add_argument(
        "--show",
        action="store_true",
        help="Open each generated result image after processing.",
    )
    return parser.parse_args()


def main() -> int:
    args = parse_args()
    image_path = Path(args.image_path)
    output_dir = Path(args.output_dir)
    show_results = args.show

    if not image_path.exists():
        print(f"Error: image path not found: {image_path}", file=sys.stderr)
        return 1

    try:
        image = load_image(image_path)
    except OSError as error:
        print(f"Error opening image: {error}", file=sys.stderr)
        return 1

    algorithms = [
        ("Explicitly-Defined-Skin-Region.png", explicitly_defined_skin_region, False),
        ("Normalizationrg.png", normalized_rg_skin_region, False),
        ("HSV.png", hsv_skin_region, False),
        ("YCBCR.png", ycbcr_skin_region, True),
    ]

    for filename, predicate, invert in algorithms:
        output_path = output_dir / filename
        result = mask_image(image, predicate, invert=invert)
        save_result(result, output_path)
        if show_results:
            result.show(title=filename)

    print(f"Saved {len(algorithms)} results to {output_dir}")
    return 0


if __name__ == "__main__":
    raise SystemExit(main())