New 🌟 Queue-Management code & docs (#9494)
Co-authored-by: UltralyticsAssistant <web@ultralytics.com> Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>
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@ -56,6 +56,7 @@ Here's a compilation of in-depth guides to help you master different aspects of
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- [VisionEye View Objects Mapping](vision-eye.md) 🚀 NEW: This feature aim computers to discern and focus on specific objects, much like the way the human eye observes details from a particular viewpoint.
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- [Speed Estimation](speed-estimation.md) 🚀 NEW: Speed estimation in computer vision relies on analyzing object motion through techniques like [object tracking](https://docs.ultralytics.com/modes/track/), crucial for applications like autonomous vehicles and traffic monitoring.
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- [Distance Calculation](distance-calculation.md) 🚀 NEW: Distance calculation, which involves measuring the separation between two objects within a defined space, is a crucial aspect. In the context of Ultralytics YOLOv8, the method employed for this involves using the bounding box centroid to determine the distance associated with user-highlighted bounding boxes.
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- [Queue Management](queue-management.md) 🚀 NEW: Queue management is the practice of efficiently controlling and directing the flow of people or tasks, often through strategic planning and technology implementation, to minimize wait times and improve overall productivity.
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## Contribute to Our Guides
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docs/en/guides/queue-management.md
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docs/en/guides/queue-management.md
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---
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comments: true
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description: Queue Management Using Ultralytics YOLOv8
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keywords: Ultralytics, YOLOv8, Queue Management, Object Counting, Object Tracking, Object Detection, Notebook, IPython Kernel, CLI, Python SDK
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---
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# Queue Management using Ultralytics YOLOv8 🚀
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## What is Queue Management?
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Queue management using [Ultralytics YOLOv8](https://github.com/ultralytics/ultralytics/) 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.
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## Advantages of Queue Management?
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- **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.
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- **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.
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## Real World Applications
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| Logistics | Retail |
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|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|:----------------------------------------------------------------------------------------------------------------------------------------------------------:|
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|  |  |
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| Queue management at airport ticket counter Using Ultralytics YOLOv8 | Queue monitoring in crowd Ultralytics YOLOv8 |
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!!! Example "Queue Management using YOLOv8 Example"
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=== "Queue Manager"
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```python
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import cv2
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from ultralytics import YOLO
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from ultralytics.solutions import queue_management
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model = YOLO("yolov8n.pt")
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cap = cv2.VideoCapture("path/to/video/file.mp4")
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assert cap.isOpened(), "Error reading video file"
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w, h, fps = (int(cap.get(x)) for x in (cv2.CAP_PROP_FRAME_WIDTH,
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cv2.CAP_PROP_FRAME_HEIGHT,
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cv2.CAP_PROP_FPS))
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video_writer = cv2.VideoWriter("queue_management.avi",
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cv2.VideoWriter_fourcc(*'mp4v'),
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fps,
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(w, h))
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queue_region = [(20, 400), (1080, 404), (1080, 360), (20, 360)]
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queue = queue_management.QueueManager()
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queue.set_args(classes_names=model.names,
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reg_pts=queue_region,
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line_thickness=3,
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fontsize=1.0,
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region_color=(255, 144, 31))
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while cap.isOpened():
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success, im0 = cap.read()
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if success:
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tracks = model.track(im0, show=False, persist=True,
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verbose=False)
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out = queue.process_queue(im0, tracks)
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video_writer.write(im0)
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if cv2.waitKey(1) & 0xFF == ord('q'):
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break
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continue
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print("Video frame is empty or video processing has been successfully completed.")
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break
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cap.release()
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cv2.destroyAllWindows()
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```
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=== "Queue Manager Specific Classes"
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```python
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import cv2
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from ultralytics import YOLO
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from ultralytics.solutions import queue_management
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model = YOLO("yolov8n.pt")
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cap = cv2.VideoCapture("path/to/video/file.mp4")
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assert cap.isOpened(), "Error reading video file"
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w, h, fps = (int(cap.get(x)) for x in (cv2.CAP_PROP_FRAME_WIDTH,
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cv2.CAP_PROP_FRAME_HEIGHT,
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cv2.CAP_PROP_FPS))
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video_writer = cv2.VideoWriter("queue_management.avi",
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cv2.VideoWriter_fourcc(*'mp4v'),
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fps,
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(w, h))
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queue_region = [(20, 400), (1080, 404), (1080, 360), (20, 360)]
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queue = queue_management.QueueManager()
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queue.set_args(classes_names=model.names,
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reg_pts=queue_region,
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line_thickness=3,
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fontsize=1.0,
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region_color=(255, 144, 31))
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while cap.isOpened():
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success, im0 = cap.read()
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if success:
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tracks = model.track(im0, show=False, persist=True,
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verbose=False, classes=0) # Only person class
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out = queue.process_queue(im0, tracks)
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video_writer.write(im0)
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if cv2.waitKey(1) & 0xFF == ord('q'):
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break
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continue
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print("Video frame is empty or video processing has been successfully completed.")
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break
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cap.release()
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cv2.destroyAllWindows()
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```
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### Optional Arguments `set_args`
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| Name | Type | Default | Description |
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|-----------------------|-------------|----------------------------|---------------------------------------------|
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| `view_img` | `bool` | `False` | Display frames with counts |
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| `view_queue_counts` | `bool` | `True` | Display Queue counts only on video frame |
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| `line_thickness` | `int` | `2` | Increase bounding boxes thickness |
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| `reg_pts` | `list` | `[(20, 400), (1260, 400)]` | Points defining the Region Area |
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| `classes_names` | `dict` | `model.model.names` | Dictionary of Class Names |
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| `region_color` | `RGB Color` | `(255, 0, 255)` | Color of the Object counting Region or Line |
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| `track_thickness` | `int` | `2` | Thickness of Tracking Lines |
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| `draw_tracks` | `bool` | `False` | Enable drawing Track lines |
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| `track_color` | `RGB Color` | `(0, 255, 0)` | Color for each track line |
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| `count_txt_color` | `RGB Color` | `(255, 255, 255)` | Foreground color for Object counts text |
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| `region_thickness` | `int` | `5` | Thickness for object counter region or line |
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| `fontsize` | `float` | `0.6` | Font size of counting text |
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### Arguments `model.track`
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| Name | Type | Default | Description |
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|-----------|---------|----------------|-------------------------------------------------------------|
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| `source` | `im0` | `None` | source directory for images or videos |
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| `persist` | `bool` | `False` | persisting tracks between frames |
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| `tracker` | `str` | `botsort.yaml` | Tracking method 'bytetrack' or 'botsort' |
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| `conf` | `float` | `0.3` | Confidence Threshold |
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| `iou` | `float` | `0.5` | IOU Threshold |
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| `classes` | `list` | `None` | filter results by class, i.e. classes=0, or classes=[0,2,3] |
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| `verbose` | `bool` | `True` | Display the object tracking results |
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