"""Image quality assessment utilities."""

import numpy as np
from PIL import Image as PILImage
from scipy import ndimage


def calculate_blur_score(image_path: str) -> float:
    """
    Calculate blur score using Laplacian variance.
    Higher score = sharper image.

    Args:
        image_path: Path to image file

    Returns:
        Variance of Laplacian (higher = sharper)
    """
    try:
        img = PILImage.open(image_path).convert("L")
        img_array = np.array(img)
        laplacian = ndimage.laplace(img_array)
        return float(np.var(laplacian))
    except Exception:
        return 0.0


def is_too_blurry(image_path: str, threshold: float = 100.0) -> bool:
    """
    Check if image is too blurry for training.

    Args:
        image_path: Path to image file
        threshold: Minimum acceptable blur score (default 100)

    Returns:
        True if image is too blurry
    """
    score = calculate_blur_score(image_path)
    return score < threshold


def get_image_dimensions(image_path: str) -> tuple[int, int]:
    """
    Get image dimensions.

    Args:
        image_path: Path to image file

    Returns:
        Tuple of (width, height)
    """
    try:
        with PILImage.open(image_path) as img:
            return img.size
    except Exception:
        return (0, 0)


def is_too_small(image_path: str, min_size: int = 256) -> bool:
    """
    Check if image is too small for training.

    Args:
        image_path: Path to image file
        min_size: Minimum dimension size (default 256)

    Returns:
        True if image is too small
    """
    width, height = get_image_dimensions(image_path)
    return width < min_size or height < min_size


def resize_image(
    image_path: str,
    output_path: str = None,
    max_size: int = 512,
    quality: int = 95,
) -> bool:
    """
    Resize image to max dimension while preserving aspect ratio.

    Args:
        image_path: Path to input image
        output_path: Path for output (defaults to overwriting input)
        max_size: Maximum dimension size (default 512)
        quality: JPEG quality (default 95)

    Returns:
        True if successful
    """
    try:
        output_path = output_path or image_path

        with PILImage.open(image_path) as img:
            # Only resize if larger than max_size
            if max(img.size) > max_size:
                img.thumbnail((max_size, max_size), PILImage.Resampling.LANCZOS)

            # Convert to RGB if necessary (for JPEG)
            if img.mode in ("RGBA", "P"):
                img = img.convert("RGB")

            img.save(output_path, "JPEG", quality=quality)

        return True
    except Exception:
        return False