Select Video Source:
1. Upload a video file
2. Use webcam for real-time recording
Enter your choice (1 or 2): 1
Enter the path to the video file: C:\Users\heman\Desktop\Coding\videos\DU06NCFazYI.mkv
2024-10-10 14:00:37,017 [INFO] Selected video file: C:\Users\heman\Desktop\Coding\videos\DU06NCFazYI.mkv
2024-10-10 14:00:38,128 [INFO] MobileNetV2 Embedder for Deep Sort initialised
2024-10-10 14:00:38,128 [INFO] - gpu enabled: False
2024-10-10 14:00:38,128 [INFO] - half precision: False
2024-10-10 14:00:38,128 [INFO] - max batch size: 16
2024-10-10 14:00:38,128 [INFO] - expects BGR: True
2024-10-10 14:00:38,226 [INFO] DeepSort Tracker initialised
2024-10-10 14:00:38,226 [INFO] - max age: 30
2024-10-10 14:00:38,227 [INFO] - appearance threshold: 0.2
2024-10-10 14:00:38,227 [INFO] - nms threshold: OFF
2024-10-10 14:00:38,228 [INFO] - max num of appearance features: None
2024-10-10 14:00:38,228 [INFO] - overriding track class : No
2024-10-10 14:00:38,228 [INFO] - today given : No
2024-10-10 14:00:38,229 [INFO] - in-build embedder : Yes
2024-10-10 14:00:38,229 [INFO] - polygon detections : No
2024-10-10 14:00:38,230 [INFO] Output folder is set to: C:\Users\heman\Desktop\Coding\traffic_reports  
2024-10-10 14:00:38,256 [INFO] YOLOv8 model loaded successfully.
2024-10-10 14:00:38,343 [INFO] Video source 'C:\Users\heman\Desktop\Coding\videos\DU06NCFazYI.mkv' opened successfully.
2024-10-10 14:00:38,528 [ERROR] An error occurred during video processing: DeepSort.update_tracks() got multiple values for argument 'frame'
2024-10-10 14:00:38,535 [INFO] Video capture released.
2024-10-10 14:00:38,539 [INFO] All OpenCV windows destroyed.
2024-10-10 14:00:38,540 [WARNING] No data available to generate report.
2024-10-10 14:00:38,540 [INFO] Report generation started in a separate thread.
PS C:\Users\heman\Desktop\Coding> 

import sys
import cv2
import logging
from typing import List, Union, Optional, Dict, Tuple
from datetime import datetime
from pathlib import Path
from threading import Thread
import numpy as np
from ultralytics import YOLO
from collections import defaultdict

# Configure logging
logging.basicConfig(
    level=logging.INFO,
    format='%(asctime)s [%(levelname)s] %(message)s',
    handlers=[
        logging.StreamHandler(sys.stdout)
    ]
)

# Constants for traffic density thresholds
LOW_THRESHOLD: int = 10      # Less than 10 vehicles per frame
MEDIUM_THRESHOLD: int = 30   # 10 to 30 vehicles per frame

# Vehicle classes based on YOLOv8's COCO dataset
VEHICLE_CLASSES: set = {'car', 'truck', 'bus', 'motorbike', 'bicycle'}

class ReportGenerator:
    """
    Generates and saves HTML reports with traffic density and vehicle type visualizations.
    """

    def __init__(self, output_folder: Path) -> None:
        self.output_folder: Path = output_folder

    def generate_html_report(
        self,
        timestamps: List[datetime],
        vehicle_counts: List[int],
        vehicle_type_counts: List[Dict[str, int]]
    ) -> None:
        """
        Generates an HTML report summarizing traffic metrics and vehicle types.

        :param timestamps: List of timestamps corresponding to each frame.
        :param vehicle_counts: List of total vehicle counts per frame.
        :param vehicle_type_counts: List of vehicle type counts per frame.
        """
        try:
            if not timestamps or not vehicle_counts:
                logging.warning("No data available to generate report.")
                return

            report_filename: str = f"traffic_report_{datetime.now().strftime('%Y%m%d_%H%M%S')}.html"
            report_path: Path = self.output_folder / report_filename

            with report_path.open('w') as report_file:
                report_file.write("<html><head><title>Traffic Report</title></head><body>")
                report_file.write("<h1>Traffic Density Over Time</h1>")
                report_file.write("<table border='1'>")
                report_file.write("<tr><th>Timestamp</th><th>Total Vehicle Count</th>")
                for vehicle_type in VEHICLE_CLASSES:
                    report_file.write(f"<th>{vehicle_type.capitalize()} Count</th>")
                report_file.write("</tr>")
                for ts, count, type_counts in zip(timestamps, vehicle_counts, vehicle_type_counts):
                    report_file.write(f"<tr><td>{ts.strftime('%H:%M:%S')}</td><td>{count}</td>")
                    for vehicle_type in VEHICLE_CLASSES:
                        report_file.write(f"<td>{type_counts.get(vehicle_type, 0)}</td>")
                    report_file.write("</tr>")
                report_file.write("</table></body></html>")

            logging.info(f"HTML report generated at: {report_path.resolve()}")

        except Exception as e:
            logging.error(f"Failed to generate HTML report: {e}")

class TrafficMonitor:
    """
    Monitors traffic by processing video streams, detecting vehicles, tracking them, and maintaining traffic metrics.
    """

    def __init__(
        self,
        source: Union[str, int],
        model_path: str = 'yolov8n.pt',
        output_folder: str = 'traffic_reports'
    ) -> None:
        self.source: Union[str, int] = source
        self.output_folder: Path = Path(output_folder)
        self.model_path: str = model_path
        self.model: Optional[YOLO] = None
        self.cap: Optional[cv2.VideoCapture] = None
        self.timestamps: List[datetime] = []
        self.vehicle_counts: List[int] = []
        self.vehicle_type_counts: List[Dict[str, int]] = []
        self.report_generator: Optional[ReportGenerator] = None
        self.frame_number: int = 0
        self.frame_width: int = 0
        self.frame_height: int = 0
        self.legend_width: int = 300  # Width of the legend panel
        self.colors: Dict[str, Tuple[int, int, int]] = {
            'Low Traffic': (0, 255, 0),
            'Medium Traffic': (0, 165, 255),
            'High Traffic': (0, 0, 255)
        }
        self.tracked_objects: Dict[int, Dict[str, Union[int, Tuple[int, int, int, int]]]] = {}
        self.next_object_id: int = 0  # ID to assign to the next detected object
        self._initialize()

    def _initialize(self) -> None:
        """
        Initialize the traffic monitor by setting up the output folder and loading the YOLO model.
        """
        self._create_output_folder()
        self._load_yolo_model()
        self.report_generator = ReportGenerator(self.output_folder)

    def _create_output_folder(self) -> None:
        """
        Create an output folder if it doesn't exist.
        """
        try:
            self.output_folder.mkdir(parents=True, exist_ok=True)
            logging.info(f"Output folder is set to: {self.output_folder.resolve()}")
        except Exception as e:
            logging.error(f"Failed to create output folder '{self.output_folder}': {e}")
            sys.exit(1)

    def _load_yolo_model(self) -> None:
        """
        Load the YOLOv8 model.
        """
        try:
            self.model = YOLO(self.model_path)
            logging.info("YOLOv8 model loaded successfully.")
        except Exception as e:
            logging.error(f"Failed to load YOLOv8 model: {e}")
            sys.exit(1)

    @staticmethod
    def classify_traffic_density(vehicle_count: int) -> str:
        """
        Classify traffic density based on the number of vehicles detected.

        :param vehicle_count: Number of vehicles detected in the frame.
        :return: Traffic density category as a string.
        """
        if vehicle_count < LOW_THRESHOLD:
            return 'Low Traffic'
        elif LOW_THRESHOLD <= vehicle_count < MEDIUM_THRESHOLD:
            return 'Medium Traffic'
        else:
            return 'High Traffic'

    def _process_detections(self, detections) -> Tuple[int, Dict[str, int]]:
        """
        Process YOLO detections, track vehicles, and count vehicle types.

        :param detections: YOLO detections for the current frame.
        :return: Tuple containing the number of unique vehicles detected and a dictionary of vehicle types counts.
        """
        vehicle_type_count: Dict[str, int] = defaultdict(int)
        for det in detections:
            try:
                cls_id: int = int(det.cls[0])
                cls_name: str = self.model.names.get(cls_id, '')
                if cls_name in VEHICLE_CLASSES:
                    vehicle_type_count[cls_name] += 1
                    # Draw bounding box and label
                    x1, y1, x2, y2 = map(int, det.xyxy[0])
                    cv2.rectangle(
                        self.frame, (x1, y1), (x2, y2), (0, 255, 0), 2
                    )
                    cv2.putText(
                        self.frame, cls_name, (x1, y1 - 10),
                        cv2.FONT_HERSHEY_SIMPLEX, 0.6, (36, 255, 12), 2
                    )
                    # Assign an ID to the detected object
                    self._assign_object_id(cls_name, (x1, y1, x2, y2))
            except Exception as e:
                logging.error(f"Error processing detection: {e}")
        unique_vehicles = len(self.tracked_objects)
        return unique_vehicles, dict(vehicle_type_count)

    def _assign_object_id(self, cls_name: str, bbox: Tuple[int, int, int, int]) -> None:
        """
        Assign a unique ID to the detected vehicle and update its position.

        :param cls_name: Class name of the detected vehicle.
        :param bbox: Bounding box coordinates of the detected vehicle.
        """
        # Simple tracking logic based on overlapping bounding boxes
        x1, y1, x2, y2 = bbox
        bbox_center = ((x1 + x2) // 2, (y1 + y2) // 2)
        assigned = False
        for obj_id, obj_info in self.tracked_objects.items():
            ox1, oy1, ox2, oy2 = obj_info['bbox']
            o_center = obj_info['center']
            distance = np.linalg.norm(np.array(bbox_center) - np.array(o_center))
            if distance < 50:  # Threshold for considering the same object
                self.tracked_objects[obj_id]['bbox'] = bbox
                self.tracked_objects[obj_id]['center'] = bbox_center
                assigned = True
                break
        if not assigned:
            self.tracked_objects[self.next_object_id] = {
                'class': cls_name,
                'bbox': bbox,
                'center': bbox_center
            }
            self.next_object_id += 1

    def _add_legend_panel(
        self, 
        traffic_density: str, 
        total_vehicles: int, 
        vehicle_type_counts: Dict[str, int]
    ) -> None:
        """
        Adds a legend panel on the right side of the frame displaying vehicle counts and traffic density.

        :param traffic_density: The classified traffic density.
        :param total_vehicles: Total number of vehicles detected.
        :param vehicle_type_counts: Dictionary of vehicle types and their counts.
        """
        try:
            # Create a blank legend panel
            legend = 255 * np.ones((self.frame_height, self.legend_width, 3), dtype=np.uint8)

            # Display current total vehicle count
            cv2.putText(
                legend, f"Total Vehicles: {total_vehicles}", (10, 50),
                cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 0), 2
            )

            # Display traffic density
            cv2.putText(
                legend, f"Traffic Density:", (10, 100),
                cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 0), 2
            )
            cv2.rectangle(
                legend, (170, 85), (200, 115),
                self.colors.get(traffic_density, (0, 0, 0)), -1
            )
            cv2.putText(
                legend, traffic_density, (210, 105),
                cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 0), 2
            )

            # Display vehicle type counts
            y_offset = 150
            for vehicle_type, count in vehicle_type_counts.items():
                text = f"{vehicle_type.capitalize()}: {count}"
                cv2.putText(
                    legend, text, (10, y_offset),
                    cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 0, 0), 2
                )
                y_offset += 30

            # Combine the original frame with the legend panel
            self.frame = cv2.hconcat([self.frame, legend])

        except Exception as e:
            logging.error(f"Failed to add legend panel: {e}")

    def process_stream(self) -> None:
        """
        Process the video stream, perform object detection, track vehicles, and collect traffic metrics.
        """
        try:
            self.cap = cv2.VideoCapture(self.source)
            if not self.cap.isOpened():
                logging.error(f"Cannot open video source: {self.source}")
                sys.exit(1)
            else:
                logging.info(f"Video source '{self.source}' opened successfully.")

            # Retrieve frame dimensions
            ret, frame = self.cap.read()
            if not ret:
                logging.error("Failed to read from video source.")
                sys.exit(1)
            self.frame_height, self.frame_width = frame.shape[:2]
            self.cap.set(cv2.CAP_PROP_POS_FRAMES, 0)  # Reset to first frame

            while True:
                ret, frame = self.cap.read()
                if not ret:
                    logging.warning("No frame received. Exiting...")
                    break

                self.frame_number += 1
                self.frame: np.ndarray = frame.copy()  # Make a copy to draw annotations
                timestamp: datetime = datetime.now()
                self.timestamps.append(timestamp)

                # Perform object detection with increased confidence threshold for better accuracy
                results = self.model.predict(self.frame, conf=0.5, verbose=False)

                # Extract detected classes and count vehicles
                detections = results[0].boxes
                total_vehicles, vehicle_type_count = self._process_detections(detections)
                self.vehicle_counts.append(total_vehicles)
                self.vehicle_type_counts.append(vehicle_type_count)
                traffic_density = self.classify_traffic_density(total_vehicles)

                # Add legend panel
                self._add_legend_panel(traffic_density, total_vehicles, vehicle_type_count)

                # Display the resulting frame with legend
                cv2.imshow('Real-Time Traffic Monitoring', self.frame)

                # Break the loop on 'q' key press
                if cv2.waitKey(1) & 0xFF == ord('q'):
                    logging.info("Exit signal received. Stopping video processing...")
                    break

                # Logging every 100 frames to avoid excessive log entries
                if self.frame_number % 100 == 0:
                    logging.info(f"Processed {self.frame_number} frames.")

            self._cleanup()

        except KeyboardInterrupt:
            logging.info("Keyboard interrupt received. Exiting gracefully...")
            self._cleanup()
        except Exception as e:
            logging.error(f"An error occurred during video processing: {e}")
            self._cleanup()
            sys.exit(1)

    def _cleanup(self) -> None:
        """
        Release video capture and destroy all OpenCV windows. Generate the report.
        """
        if self.cap and self.cap.isOpened():
            self.cap.release()
            logging.info("Video capture released.")
        cv2.destroyAllWindows()
        logging.info("All OpenCV windows destroyed.")

        if self.report_generator:
            # Generate the HTML report in a separate thread to avoid blocking
            report_thread: Thread = Thread(
                target=self.report_generator.generate_html_report,
                args=(self.timestamps, self.vehicle_counts, self.vehicle_type_counts),
                daemon=True
            )
            report_thread.start()
            logging.info("Report generation started in a separate thread.")

def get_video_source() -> Union[str, int]:
    """
    Prompt the user to choose between uploading a video or using the webcam.

    :return: Video source (file path or webcam index).
    """
    while True:
        print("\nSelect Video Source:")
        print("1. Upload a video file")
        print("2. Use webcam for real-time recording")
        choice: str = input("Enter your choice (1 or 2): ").strip()

        if choice == '1':
            file_path: str = input("Enter the path to the video file: ").strip()
            if Path(file_path).is_file():
                logging.info(f"Selected video file: {file_path}")
                return file_path
            else:
                logging.error(f"File not found: {file_path}")
        elif choice == '2':
            logging.info("Selected webcam for real-time recording.")
            return 0  # Typically, 0 is the default webcam index
        else:
            logging.error("Invalid choice. Please enter 1 or 2.")

def main() -> None:
    """
    Main function to set up and run the traffic monitoring system based on user choice.
    """
    try:
        # Get user choice for video source
        video_source: Union[str, int] = get_video_source()

        # Initialize and start the traffic monitor
        traffic_monitor: TrafficMonitor = TrafficMonitor(source=video_source)
        traffic_monitor.process_stream()

    except Exception as e:
        logging.error(f"An unexpected error occurred in the main function: {e}")
        sys.exit(1)

if __name__ == '__main__':
    main()