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Reference for ultralytics/solutions/queue_management.py

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This file is available at https://github.com/ultralytics/ultralytics/blob/main/ultralytics/solutions/queue_management.py. If you spot a problem please help fix it by contributing a Pull Request 🛠️. Thank you 🙏!


ultralytics.solutions.queue_management.QueueManager 

QueueManager(**kwargs)

Bases: 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:

Name Type Description
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:

Name Description
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)
Source code in ultralytics/solutions/queue_management.py 
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def __init__(self, **kwargs):
    """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

process 

process(im0)

Process queue management for a single frame of video.

Parameters:

Name Type Description Default
im0 ndarray

Input image for processing, typically a frame from a video stream.

 required

Returns:

Type Description
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)
Source code in ultralytics/solutions/queue_management.py 
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def process(self, im0):
    """
    Process queue management for a single frame of video.

    Args:
        im0 (numpy.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 : {str(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))





📅 Created 1 year ago ✏️ Updated 8 months ago
glenn-jocher Burhan-Q jk4e