from typing import Any

import numpy as np
from PIL import Image


def segment_image(
    mask_generator: Any,
    image: Image.Image,
    min_area: int = 32 * 32,
    max_masks: int = 5,
    points_per_batch=64,
) -> list[dict[str, Any]]:
    """Segment image using SAM to extract object masks.

    Args:
        mask_generator: SAM2 mask generator.
        image: PIL Image to segment.
        min_area: Minimum mask area threshold in pixels.
        max_masks: Maximum number of masks to return.
        points_per_batch: Number of prompt points to process in each batch.
    Returns:
        List of mask dictionaries with keys:
            - segment: Binary mask (numpy array)
            - area: Mask area in pixels
            - bbox: Bounding box [x, y, width, height]
            - predicted_iou: Model's confidence in the mask
            - stability_score: Stability score for the mask
    """
    # Convert PIL Image to numpy array
    image_np = np.array(image.convert("RGB"))

    # Generate masks
    masks = mask_generator(image_np, points_per_batch=points_per_batch)["masks"]

    if not masks:
        return []

    # Filter by minimum area
    filtered_masks = [m for m in masks if m["area"] >= min_area]

    if not filtered_masks:
        return []

    # Sort by area (largest first) and limit to max_masks
    sorted_masks = sorted(filtered_masks, key=lambda x: x["area"], reverse=True)
    return sorted_masks[:max_masks]


def extract_masked_region(
    image: Image.Image,
    mask: np.ndarray,
) -> Image.Image:
    """Extract masked region from image.

    Args:
        image: Original PIL Image.
        mask: Binary mask as numpy array (True = keep).

    Returns:
        PIL Image with only the masked region visible.
    """
    image_np = np.array(image.convert("RGB"))

    # Apply mask
    masked_np = image_np * mask[:, :, np.newaxis]

    return Image.fromarray(masked_np.astype(np.uint8))