import cv2
import numpy as np

class SpeedDetector:
    def __init__(self):
        # Initialize speed detection model
        self.model = self._load_model()
        
    def _load_model(self):
        """
        Load speed detection model
        Here we use a simple template matching method
        More complex deep learning models should be used in practical applications
        """
        # TODO: Implement actual model loading
        return None
        
    def detect_speed(self, frame):
        """
        Detect speed from image
        :param frame: Input image
        :return: Detected speed (km/h)
        """
        # Convert image to grayscale
        gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
        
        # Use Canny edge detection
        edges = cv2.Canny(gray, 50, 150)
        
        # Use Hough transform to detect lines
        lines = cv2.HoughLinesP(edges, 1, np.pi/180, 100, minLineLength=100, maxLineGap=10)
        
        if lines is None:
            return 0
            
        # Calculate speed (using a simple heuristic method)
        # More complex algorithms should be used in practical applications
        speed = len(lines) * 5  # Simple linear relationship
        
        return min(speed, 120)  # Limit maximum speed to 120km/h