ultralytics/docs/en/guides/parking-management.md

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

Parking Management using Ultralytics YOLOv8 🚀

What is Parking Management System?

Parking management with Ultralytics YOLOv8 ensures efficient and safe parking by organizing spaces and monitoring availability. YOLOv8 can improve parking lot management through real-time vehicle detection, and insights into parking occupancy.

Advantages of Parking Management System?

• Efficiency: Parking lot management optimizes the use of parking spaces and reduces congestion.
• Safety and Security: Parking management using YOLOv8 improves the safety of both people and vehicles through surveillance and security measures.
• Reduced Emissions: Parking management using YOLOv8 manages traffic flow to minimize idle time and emissions in parking lots.

Real World Applications

Parking Management System	Parking Management System
Parking management Aeriel View using Ultralytics YOLOv8	Parking management Top View using Ultralytics YOLOv8

Parking Management System Code Workflow

Selection of Points

!!! Tip "Point Selection is now Easy"

Choosing parking points is a critical and complex task in parking management systems. Ultralytics streamlines this process by providing a tool that lets you define parking lot areas, which can be utilized later for additional processing.

• Capture a frame from the video or camera stream where you want to manage the parking lot.
• Use the provided code to launch a graphical interface, where you can select an image and start outlining parking regions by mouse click to create polygons.

!!! Warning "Image Size"

Max Image Size of 1920 * 1080 supported

from ultralytics.solutions.parking_management import ParkingPtsSelection, tk

root = tk.Tk()
ParkingPtsSelection(root)
root.mainloop()

• After defining the parking areas with polygons, click save to store a JSON file with the data in your working directory.

Python Code for Parking Management

!!! Example "Parking management using YOLOv8 Example"

=== "Parking Management"

    ```python
    import cv2
    from ultralytics import solutions

    # Path to json file, that created with above point selection app
    polygon_json_path = "bounding_boxes.json"

    # Video capture
    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
    video_writer = cv2.VideoWriter("parking management.avi", cv2.VideoWriter_fourcc(*"mp4v"), fps, (w, h))

    # Initialize parking management object
    management = solutions.ParkingManagement(model_path="yolov8n.pt")

    while cap.isOpened():
        ret, im0 = cap.read()
        if not ret:
            break

        json_data = management.parking_regions_extraction(polygon_json_path)
        results = management.model.track(im0, persist=True, show=False)

        if results[0].boxes.id is not None:
            boxes = results[0].boxes.xyxy.cpu().tolist()
            clss = results[0].boxes.cls.cpu().tolist()
            management.process_data(json_data, im0, boxes, clss)

        management.display_frames(im0)
        video_writer.write(im0)

    cap.release()
    video_writer.release()
    cv2.destroyAllWindows()
    ```

Optional Arguments ParkingManagement

Name	Type	Default	Description
model_path	str	None	Path to the YOLOv8 model.
txt_color	tuple	(0, 0, 0)	RGB color tuple for text.
bg_color	tuple	(255, 255, 255)	RGB color tuple for background.
occupied_region_color	tuple	(0, 255, 0)	RGB color tuple for occupied regions.
available_region_color	tuple	(0, 0, 255)	RGB color tuple for available regions.
margin	int	10	Margin for text display.

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