import cv2
import numpy as np
from typing import List, Tuple, Dict, Any

def detect_cracks_and_holes(frame: np.ndarray) -> Tuple[List[Dict[str, Any]], np.ndarray]:
    """
    Detect cracks and holes in the frame using edge detection and contour analysis.
    Args:
        frame: Input frame as a numpy array.
    Returns:
        Tuple of (list of detections, annotated frame).
    """
    # Convert to grayscale
    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
    
    # Apply Gaussian blur to reduce noise
    blurred = cv2.GaussianBlur(gray, (5, 5), 0)
    
    # Edge detection using Canny
    edges = cv2.Canny(blurred, 50, 150)
    
    # Dilate edges to connect nearby edges
    kernel = np.ones((3, 3), np.uint8)
    dilated = cv2.dilate(edges, kernel, iterations=1)
    
    # Find contours
    contours, _ = cv2.findContours(dilated, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
    
    detections = []
    for i, contour in enumerate(contours):
        # Calculate the area of the contour
        area = cv2.contourArea(contour)
        if area < 100:  # Ignore small contours
            continue
        
        # Get bounding box
        x, y, w, h = cv2.boundingRect(contour)
        x_min, y_min, x_max, y_max = x, y, x + w, y + h
        
        # Determine if it's a crack or hole based on shape and area
        perimeter = cv2.arcLength(contour, True)
        circularity = 4 * np.pi * area / (perimeter * perimeter) if perimeter > 0 else 0
        
        # Classify as hole if more circular, crack if elongated
        dtype = "hole" if circularity > 0.5 else "crack"
        label = f"{dtype.capitalize()} {i+1}"
        
        # Determine severity based on area
        severity = "Severe" if area > 1000 else "Moderate" if area > 500 else "Mild"
        
        detections.append({
            "box": [x_min, y_min, x_max, y_max],
            "label": label,
            "type": dtype,
            "severity": severity
        })
    
    return detections, frame