Reference for ultralytics/solutions/queue_management.py

class QueueManager(BaseSolution):
    """Manages queue counting in real-time video streams based on object tracks.

    This class extends BaseSolution to provide functionality for tracking and counting objects within a specified region
    in video frames.

    Attributes:
        counts (int): The current count of objects in the queue.
        rect_color (tuple[int, int, int]): RGB color tuple for drawing the queue region rectangle.
        region_length (int): The number of points defining the queue region.
        track_line (list[tuple[int, int]]): List of track line coordinates.
        track_history (dict[int, list[tuple[int, int]]]): Dictionary storing tracking history for each object.

    Methods:
        initialize_region: Initialize the queue region.
        process: Process a single frame for queue management.
        extract_tracks: Extract object tracks from the current frame.
        store_tracking_history: Store the tracking history for an object.
        display_output: Display the processed output.

    Examples:
        >>> cap = cv2.VideoCapture("path/to/video.mp4")
        >>> queue_manager = QueueManager(region=[100, 100, 200, 200, 300, 300])
        >>> while cap.isOpened():
        >>>     success, im0 = cap.read()
        >>>     if not success:
        >>>         break
        >>>     results = queue_manager.process(im0)
    """

    def __init__(self, **kwargs: Any) -> None:
        """Initialize the QueueManager with parameters for tracking and counting objects in a video stream."""
        super().__init__(**kwargs)
        self.initialize_region()
        self.counts = 0  # Queue counts information
        self.rect_color = (255, 255, 255)  # Rectangle color for visualization
        self.region_length = len(self.region)  # Store region length for further usage

def process(self, im0) -> SolutionResults:
    """Process queue management for a single frame of video.

    Args:
        im0 (np.ndarray): Input image for processing, typically a frame from a video stream.

    Returns:
        (SolutionResults): Contains processed image `im0`, 'queue_count' (int, number of objects in the queue) and
            'total_tracks' (int, total number of tracked objects).

    Examples:
        >>> queue_manager = QueueManager()
        >>> frame = cv2.imread("frame.jpg")
        >>> results = queue_manager.process(frame)
    """
    self.counts = 0  # Reset counts every frame
    self.extract_tracks(im0)  # Extract tracks from the current frame
    annotator = SolutionAnnotator(im0, line_width=self.line_width)  # Initialize annotator
    annotator.draw_region(reg_pts=self.region, color=self.rect_color, thickness=self.line_width * 2)  # Draw region

    for box, track_id, cls, conf in zip(self.boxes, self.track_ids, self.clss, self.confs):
        # Draw bounding box and counting region
        annotator.box_label(box, label=self.adjust_box_label(cls, conf, track_id), color=colors(track_id, True))
        self.store_tracking_history(track_id, box)  # Store track history

        # Cache frequently accessed attributes
        track_history = self.track_history.get(track_id, [])

        # Store previous position of track and check if the object is inside the counting region
        prev_position = None
        if len(track_history) > 1:
            prev_position = track_history[-2]
        if self.region_length >= 3 and prev_position and self.r_s.contains(self.Point(self.track_line[-1])):
            self.counts += 1

    # Display queue counts
    annotator.queue_counts_display(
        f"Queue Counts : {self.counts}",
        points=self.region,
        region_color=self.rect_color,
        txt_color=(104, 31, 17),
    )
    plot_im = annotator.result()
    self.display_output(plot_im)  # Display output with base class function

    # Return a SolutionResults object with processed data
    return SolutionResults(plot_im=plot_im, queue_count=self.counts, total_tracks=len(self.track_ids))