---
comments: true
description: Parking Management System Using Ultralytics YOLOv8
keywords: 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](https://github.com/ultralytics/ultralytics/) 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 lots Analytics Using Ultralytics YOLOv8](https://github.com/RizwanMunawar/RizwanMunawar/assets/62513924/e3d4bc3e-cf4a-4da9-b42e-0da55cc74ad6) | ![Parking management top view using Ultralytics YOLOv8](https://github.com/RizwanMunawar/RizwanMunawar/assets/62513924/fe186719-1aca-43c9-b388-1ded91280eb5) |
|                                                 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

```python
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.

![Ultralytics YOLOv8 Points Selection Demo](https://github.com/RizwanMunawar/RizwanMunawar/assets/62513924/72737b8a-0f0f-4efb-98ad-b917a0039535)


### Python Code for Parking Management

!!! Example "Parking management using YOLOv8 Example"

    === "Parking Management"

        ```python
        import cv2
        from ultralytics.solutions.parking_management import ParkingManagement

        # 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 = 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                                         |
|--------------------------|-------------|-------------------|-----------------------------------------------------|
| `occupied_region_color`  | `RGB Color` | `(0, 255, 0)`     | Parking space occupied region color                 |
| `available_region_color` | `RGB Color` | `(0, 0, 255)`     | Parking space available region color                |
| `margin`                 | `int`       | `10`              | Gap between text display for multiple classes count |
| `txt_color`              | `RGB Color` | `(255, 255, 255)` | Foreground color for object counts text             |
| `bg_color`               | `RGB Color` | `(255, 255, 255)` | Rectangle behind text background color              |

### 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                         |