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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.
annotator Annotator

An instance of the Annotator class for drawing on frames.
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

Initializes the queue region.
process_queue

Processes a single frame for queue management.
extract_tracks

Extracts object tracks from the current frame.
store_tracking_history

Stores the tracking history for an object.
display_output

Displays the processed output.

Examples:

>>> cap = cv2.VideoCapture("Path/to/video/file.mp4")
>>> queue_manager = QueueManager(region=[100, 100, 200, 200, 300, 300])
>>> while cap.isOpened():
>>>     success, im0 = cap.read()
>>>     if not success:
>>>         break
>>>     out = queue.process_queue(im0)
Source code in ultralytics/solutions/queue_management.py 
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def __init__(self, **kwargs):
    """Initializes 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
    self.region_length = len(self.region)  # Store region length for further usage

process_queue 

process_queue(im0)

Processes the 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
ndarray

Processed image with annotations, bounding boxes, and queue counts.

This method performs the following steps: 1. Resets the queue count for the current frame. 2. Initializes an Annotator object for drawing on the image. 3. Extracts tracks from the image. 4. Draws the counting region on the image. 5. For each detected object: - Draws bounding boxes and labels. - Stores tracking history. - Draws centroids and tracks. - Checks if the object is inside the counting region and updates the count. 6. Displays the queue count on the image. 7. Displays the processed output.

Examples:

>>> queue_manager = QueueManager()
>>> frame = cv2.imread("frame.jpg")
>>> processed_frame = queue_manager.process_queue(frame)
Source code in ultralytics/solutions/queue_management.py 
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def process_queue(self, im0):
    """
    Processes the queue management for a single frame of video.

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

    Returns:
        (numpy.ndarray): Processed image with annotations, bounding boxes, and queue counts.

    This method performs the following steps:
    1. Resets the queue count for the current frame.
    2. Initializes an Annotator object for drawing on the image.
    3. Extracts tracks from the image.
    4. Draws the counting region on the image.
    5. For each detected object:
       - Draws bounding boxes and labels.
       - Stores tracking history.
       - Draws centroids and tracks.
       - Checks if the object is inside the counting region and updates the count.
    6. Displays the queue count on the image.
    7. Displays the processed output.

    Examples:
        >>> queue_manager = QueueManager()
        >>> frame = cv2.imread("frame.jpg")
        >>> processed_frame = queue_manager.process_queue(frame)
    """
    self.counts = 0  # Reset counts every frame
    self.annotator = Annotator(im0, line_width=self.line_width)  # Initialize annotator
    self.extract_tracks(im0)  # Extract tracks

    self.annotator.draw_region(
        reg_pts=self.region, color=self.rect_color, thickness=self.line_width * 2
    )  # Draw region

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

        # Draw tracks of objects
        self.annotator.draw_centroid_and_tracks(
            self.track_line, color=colors(int(track_id), True), track_thickness=self.line_width
        )

        # 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
    self.annotator.queue_counts_display(
        f"Queue Counts : {str(self.counts)}",
        points=self.region,
        region_color=self.rect_color,
        txt_color=(104, 31, 17),
    )
    self.display_output(im0)  # display output with base class function

    return im0  # return output image for more usage



📅 Created 8 months ago ✏️ Updated 2 months ago
jk4e glenn-jocher Burhan-Q