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119 lines
6.6 KiB
119 lines
6.6 KiB
--- |
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comments: true |
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description: Parking Management System Using Ultralytics YOLOv8 |
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keywords: Ultralytics, YOLOv8, Object Detection, Object Counting, Parking lots, Object Tracking, Notebook, IPython Kernel, CLI, Python SDK |
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--- |
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# Parking Management using Ultralytics YOLOv8 🚀 |
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## What is Parking Management System? |
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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. |
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## Advantages of Parking Management System? |
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- **Efficiency**: Parking lot management optimizes the use of parking spaces and reduces congestion. |
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- **Safety and Security**: Parking management using YOLOv8 improves the safety of both people and vehicles through surveillance and security measures. |
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- **Reduced Emissions**: Parking management using YOLOv8 manages traffic flow to minimize idle time and emissions in parking lots. |
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## Real World Applications |
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| Parking Management System | Parking Management System | |
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|:-------------------------------------------------------------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------------------------------------------------------------------------:| |
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|  |  | |
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| Parking management Aeriel View using Ultralytics YOLOv8 | Parking management Top View using Ultralytics YOLOv8 | |
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## Parking Management System Code Workflow |
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### Selection of Points |
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!!! Tip "Point Selection is now Easy" |
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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. |
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- Capture a frame from the video or camera stream where you want to manage the parking lot. |
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- 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. |
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!!! Warning "Image Size" |
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Max Image Size of 1920 * 1080 supported |
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```python |
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from ultralytics.solutions.parking_management import ParkingPtsSelection, tk |
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root = tk.Tk() |
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ParkingPtsSelection(root) |
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root.mainloop() |
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``` |
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- After defining the parking areas with polygons, click `save` to store a JSON file with the data in your working directory. |
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### Python Code for Parking Management |
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!!! Example "Parking management using YOLOv8 Example" |
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=== "Parking Management" |
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```python |
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import cv2 |
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from ultralytics import solutions |
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# Path to json file, that created with above point selection app |
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polygon_json_path = "bounding_boxes.json" |
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# Video capture |
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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, cv2.CAP_PROP_FRAME_HEIGHT, cv2.CAP_PROP_FPS)) |
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# Video writer |
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video_writer = cv2.VideoWriter("parking management.avi", cv2.VideoWriter_fourcc(*"mp4v"), fps, (w, h)) |
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# Initialize parking management object |
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management = solutions.ParkingManagement(model_path="yolov8n.pt") |
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while cap.isOpened(): |
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ret, im0 = cap.read() |
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if not ret: |
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break |
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json_data = management.parking_regions_extraction(polygon_json_path) |
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results = management.model.track(im0, persist=True, show=False) |
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if results[0].boxes.id is not None: |
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boxes = results[0].boxes.xyxy.cpu().tolist() |
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clss = results[0].boxes.cls.cpu().tolist() |
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management.process_data(json_data, im0, boxes, clss) |
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management.display_frames(im0) |
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video_writer.write(im0) |
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cap.release() |
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video_writer.release() |
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cv2.destroyAllWindows() |
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``` |
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### Optional Arguments `ParkingManagement` |
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| Name | Type | Default | Description | |
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|--------------------------|---------|-------------------|----------------------------------------| |
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| `model_path` | `str` | `None` | Path to the YOLOv8 model. | |
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| `txt_color` | `tuple` | `(0, 0, 0)` | RGB color tuple for text. | |
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| `bg_color` | `tuple` | `(255, 255, 255)` | RGB color tuple for background. | |
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| `occupied_region_color` | `tuple` | `(0, 255, 0)` | RGB color tuple for occupied regions. | |
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| `available_region_color` | `tuple` | `(0, 0, 255)` | RGB color tuple for available regions. | |
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| `margin` | `int` | `10` | Margin for text display. | |
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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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