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

def process_missing_patches(frame: np.ndarray) -> Tuple[List[Dict[str, Any]], np.ndarray]:
    """
    Detect missing patches in the plantation area.
    Args:
        frame: Input frame as a numpy array.
    Returns:
        Tuple of (list of detections, annotated frame).
    """
    # Convert to HSV color space for soil detection
    hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)
    
    # Define range for soil color (brownish tones)
    lower_soil = np.array([10, 50, 50])
    upper_soil = np.array([30, 255, 255])
    mask = cv2.inRange(hsv, lower_soil, upper_soil)
    
    # Morphological operations to clean up the mask
    kernel = np.ones((5, 5), np.uint8)
    mask = cv2.morphologyEx(mask, cv2.MORPH_OPEN, kernel)
    
    # Find contours of missing patches
    contours, _ = cv2.findContours(mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
    
    detections = []
    for i, contour in enumerate(contours):
        area = cv2.contourArea(contour)
        if area < 200:  # Ignore small patches
            continue
        
        x, y, w, h = cv2.boundingRect(contour)
        x_min, y_min, x_max, y_max = x, y, x + w, y + h
        
        # 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": "Missing",
            "type": "missing_patch",
            "severity": severity
        })
    
    return detections, frame