"""Image preprocessing utilities."""

import numpy as np
from PIL import Image
import hashlib
from typing import Tuple, Optional


def preprocess_image(image_input, max_size: int = 1024) -> Image.Image:
    """
    Preprocess any image input into a standardized PIL RGB image.
    
    Args:
        image_input: PIL Image, numpy array, or file path
        max_size: maximum dimension for resizing
        
    Returns:
        PIL Image in RGB mode, resized to max_size if needed
    """
    if isinstance(image_input, str):
        img = Image.open(image_input).convert("RGB")
    elif isinstance(image_input, np.ndarray):
        if image_input.ndim == 2:
            img = Image.fromarray(image_input).convert("RGB")
        elif image_input.ndim == 3:
            img = Image.fromarray(image_input).convert("RGB")
        else:
            raise ValueError(f"Unexpected array shape: {image_input.shape}")
    elif isinstance(image_input, Image.Image):
        img = image_input.convert("RGB")
    else:
        raise TypeError(f"Unsupported image type: {type(image_input)}")
    
    # Resize if too large
    w, h = img.size
    if max(w, h) > max_size:
        scale = max_size / max(w, h)
        new_w, new_h = int(w * scale), int(h * scale)
        img = img.resize((new_w, new_h), Image.LANCZOS)
    
    return img


def compute_image_hash(img: Image.Image) -> str:
    """Compute SHA256 hash of image pixels for caching."""
    return hashlib.sha256(np.array(img).tobytes()).hexdigest()


def get_image_size(img: Image.Image) -> Tuple[int, int]:
    """Return (width, height) of image."""
    return img.size


def validate_image(img: Image.Image, min_size: int = 50) -> Optional[str]:
    """
    Validate image meets minimum requirements.
    
    Returns error message if invalid, None if valid.
    """
    w, h = img.size
    if w < min_size or h < min_size:
        return f"Image too small: {w}x{h}. Minimum {min_size}x{min_size} required."
    return None