import cv2
import numpy as np
from PIL import Image

def is_blurry(image: np.ndarray, threshold: float = 100.0) -> bool:
    """
    Checks if an image is blurry using the Laplacian variance method.
    
    Args:
        image (np.ndarray): The image to check (BGR format).
        threshold (float): The variance threshold below which the image is considered blurry.
        
    Returns:
        bool: True if blurry, False otherwise.
    """
    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
    variance = cv2.Laplacian(gray, cv2.CV_64F).var()
    return variance < threshold

def crop_image(frame: np.ndarray, bbox: list[float], padding_percent: float = 0.1) -> np.ndarray:
    """
    Crops an image based on a bounding box with optional padding.
    
    Args:
        frame (np.ndarray): The full image frame.
        bbox (list): [x1, y1, x2, y2]
        padding_percent (float): Percentage of padding to add around the box.
        
    Returns:
        np.ndarray: The cropped image.
    """
    h, w, _ = frame.shape
    x1, y1, x2, y2 = bbox
    
    width = x2 - x1
    height = y2 - y1
    
    pad_w = width * padding_percent
    pad_h = height * padding_percent
    
    # Apply padding ensuring we stay within frame boundaries
    new_x1 = max(0, int(x1 - pad_w))
    new_y1 = max(0, int(y1 - pad_h))
    new_x2 = min(w, int(x2 + pad_w))
    new_y2 = min(h, int(y2 + pad_h))
    
    return frame[new_y1:new_y2, new_x1:new_x2]

def convert_cv2_to_pil(cv2_image: np.ndarray) -> Image.Image:
    """Conventional cv2 BGR to PIL RGB conversion."""
    color_converted = cv2.cvtColor(cv2_image, cv2.COLOR_BGR2RGB)
    return Image.fromarray(color_converted)