"""
Image processing functions
"""

import numpy as np
import cv2
from typing import List


def resize_mask_to_image(
    mask: np.ndarray,
    target_height: int,
    target_width: int
) -> np.ndarray:
    """
    Resize mask to match image dimensions.
    
    Args:
        mask: Mask array (H, W) in range [0, 1]
        target_height: Target height
        target_width: Target width
        
    Returns:
        Resized mask array (target_height, target_width) in range [0, 1]
    """
    if mask.shape[0] == target_height and mask.shape[1] == target_width:
        return mask
    
    # Use cv2.resize with INTER_LINEAR for smooth resizing
    mask_resized = cv2.resize(mask, (target_width, target_height), interpolation=cv2.INTER_LINEAR)
    # Ensure values stay in [0, 1] range
    mask_resized = np.clip(mask_resized, 0.0, 1.0)
    
    return mask_resized


def apply_mask_overlay(
    image: np.ndarray,
    mask: np.ndarray,
    background_color: List[float]
) -> np.ndarray:
    """
    Apply mask overlay to image with background color.
    Where mask is 1.0, use background_color; where mask is 0.0, use original image.
    
    Args:
        image: Image array (H, W, 3) in range [0, 1]
        mask: Mask array (H, W) in range [0, 1]
        background_color: RGB color [r, g, b] in range [0, 1]
        
    Returns:
        Image with mask overlay applied (H, W, 3) in range [0, 1]
    """
    # Ensure mask is 3D for broadcasting: (H, W) -> (H, W, 1)
    mask_3d = np.expand_dims(mask, axis=-1)
    
    # Expand background_color to match image shape: (3,) -> (1, 1, 3)
    bg_color_array = np.array(background_color, dtype=np.float32).reshape(1, 1, 3)
    
    # Apply overlay: image * (1 - mask) + background_color * mask
    masked_image = image * (1.0 - mask_3d) + bg_color_array * mask_3d
    
    # Ensure values stay in [0, 1] range
    masked_image = np.clip(masked_image, 0.0, 1.0)
    
    return masked_image