yolov8_test/docs/en/guides/queue-management.md

comments	description	keywords
true	Queue Management Using Ultralytics YOLOv8	Ultralytics, YOLOv8, Queue Management, Object Counting, Object Tracking, Object Detection, Notebook, IPython Kernel, CLI, Python SDK

Queue Management using Ultralytics YOLOv8 🚀

What is Queue Management?

Queue management using Ultralytics YOLOv8 involves organizing and controlling lines of people or vehicles to reduce wait times and enhance efficiency. It's about optimizing queues to improve customer satisfaction and system performance in various settings like retail, banks, airports, and healthcare facilities.

Advantages of Queue Management?

• Reduced Waiting Times: Queue management systems efficiently organize queues, minimizing wait times for customers. This leads to improved satisfaction levels as customers spend less time waiting and more time engaging with products or services.
• Increased Efficiency: Implementing queue management allows businesses to allocate resources more effectively. By analyzing queue data and optimizing staff deployment, businesses can streamline operations, reduce costs, and improve overall productivity.

Real World Applications

Logistics	Retail
Queue management at airport ticket counter Using Ultralytics YOLOv8	Queue monitoring in crowd Ultralytics YOLOv8

!!! Example "Queue Management using YOLOv8 Example"

=== "Queue Manager"

    ```python
    import cv2
    from ultralytics import YOLO
    from ultralytics.solutions import queue_management
    
    model = YOLO("yolov8n.pt")
    cap = cv2.VideoCapture("path/to/video/file.mp4")
    
    assert cap.isOpened(), "Error reading video file"
    w, h, fps = (int(cap.get(x)) for x in (cv2.CAP_PROP_FRAME_WIDTH,
                                           cv2.CAP_PROP_FRAME_HEIGHT,
                                           cv2.CAP_PROP_FPS))
    
    video_writer = cv2.VideoWriter("queue_management.avi",
                                   cv2.VideoWriter_fourcc(*'mp4v'),
                                   fps,
                                   (w, h))
    
    queue_region = [(20, 400), (1080, 404), (1080, 360), (20, 360)]
    
    queue = queue_management.QueueManager()
    queue.set_args(classes_names=model.names,
                   reg_pts=queue_region,
                   line_thickness=3,
                   fontsize=1.0,
                   region_color=(255, 144, 31))
    
    while cap.isOpened():
        success, im0 = cap.read()
    
        if success:
            tracks = model.track(im0, show=False, persist=True,
                                 verbose=False)
            out = queue.process_queue(im0, tracks)
    
            video_writer.write(im0)
            if cv2.waitKey(1) & 0xFF == ord('q'):
                break
            continue
    
        print("Video frame is empty or video processing has been successfully completed.")
        break
    
    cap.release()
    cv2.destroyAllWindows()
    ```

=== "Queue Manager Specific Classes"

    ```python
    import cv2
    from ultralytics import YOLO
    from ultralytics.solutions import queue_management
    
    model = YOLO("yolov8n.pt")
    cap = cv2.VideoCapture("path/to/video/file.mp4")
    
    assert cap.isOpened(), "Error reading video file"
    w, h, fps = (int(cap.get(x)) for x in (cv2.CAP_PROP_FRAME_WIDTH,
                                           cv2.CAP_PROP_FRAME_HEIGHT,
                                           cv2.CAP_PROP_FPS))
    
    video_writer = cv2.VideoWriter("queue_management.avi",
                                   cv2.VideoWriter_fourcc(*'mp4v'),
                                   fps,
                                   (w, h))
    
    queue_region = [(20, 400), (1080, 404), (1080, 360), (20, 360)]
    
    queue = queue_management.QueueManager()
    queue.set_args(classes_names=model.names,
                   reg_pts=queue_region,
                   line_thickness=3,
                   fontsize=1.0,
                   region_color=(255, 144, 31))
    
    while cap.isOpened():
        success, im0 = cap.read()
    
        if success:
            tracks = model.track(im0, show=False, persist=True,
                                 verbose=False, classes=0)  # Only person class
            out = queue.process_queue(im0, tracks)
    
            video_writer.write(im0)
            if cv2.waitKey(1) & 0xFF == ord('q'):
                break
            continue
    
        print("Video frame is empty or video processing has been successfully completed.")
        break
    
    cap.release()
    cv2.destroyAllWindows()
    ```

Optional Arguments set_args

Name	Type	Default	Description
view_img	bool	False	Display frames with counts
view_queue_counts	bool	True	Display Queue counts only on video frame
line_thickness	int	2	Increase bounding boxes thickness
reg_pts	list	[(20, 400), (1260, 400)]	Points defining the Region Area
classes_names	dict	model.model.names	Dictionary of Class Names
region_color	RGB Color	(255, 0, 255)	Color of the Object counting Region or Line
track_thickness	int	2	Thickness of Tracking Lines
draw_tracks	bool	False	Enable drawing Track lines
track_color	RGB Color	(0, 255, 0)	Color for each track line
count_txt_color	RGB Color	(255, 255, 255)	Foreground color for Object counts text
region_thickness	int	5	Thickness for object counter region or line
fontsize	float	0.6	Font size of counting text

Arguments model.track

Name	Type	Default	Description
source	im0	None	source directory for images or videos
persist	bool	False	persisting tracks between frames
tracker	str	botsort.yaml	Tracking method 'bytetrack' or 'botsort'
conf	float	0.3	Confidence Threshold
iou	float	0.5	IOU Threshold
classes	list	None	filter results by class, i.e. classes=0, or classes=[0,2,3]
verbose	bool	True	Display the object tracking results