157 lines
9.3 KiB
Markdown
157 lines
9.3 KiB
Markdown
---
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comments: true
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description: Optimize parking spaces and enhance safety with Ultralytics YOLOv8. Explore real-time vehicle detection and smart parking solutions.
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keywords: parking management, YOLOv8, Ultralytics, vehicle detection, real-time tracking, parking lot optimization, smart parking
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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 Aerial 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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!!! Example "Parking slots Annotator Ultralytics YOLOv8"
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=== "Parking Annotator"
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```python
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from ultralytics import solutions
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solutions.ParkingPtsSelection()
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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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## FAQ
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### How does Ultralytics YOLOv8 enhance parking management systems?
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Ultralytics YOLOv8 greatly enhances parking management systems by providing **real-time vehicle detection** and monitoring. This results in optimized usage of parking spaces, reduced congestion, and improved safety through continuous surveillance. The [Parking Management System](https://github.com/ultralytics/ultralytics) enables efficient traffic flow, minimizing idle times and emissions in parking lots, thereby contributing to environmental sustainability. For further details, refer to the [parking management code workflow](#python-code-for-parking-management).
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### What are the benefits of using Ultralytics YOLOv8 for smart parking?
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Using Ultralytics YOLOv8 for smart parking yields numerous benefits:
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- **Efficiency**: Optimizes the use of parking spaces and decreases congestion.
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- **Safety and Security**: Enhances surveillance and ensures the safety of vehicles and pedestrians.
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- **Environmental Impact**: Helps in reducing emissions by minimizing vehicle idle times. More details on the advantages can be seen [here](#advantages-of-parking-management-system).
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### How can I define parking spaces using Ultralytics YOLOv8?
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Defining parking spaces is straightforward with Ultralytics YOLOv8:
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1. Capture a frame from a video or camera stream.
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2. Use the provided code to launch a GUI for selecting an image and drawing polygons to define parking spaces.
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3. Save the labeled data in JSON format for further processing. For comprehensive instructions, check the [selection of points](#selection-of-points) section.
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### Can I customize the YOLOv8 model for specific parking management needs?
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Yes, Ultralytics YOLOv8 allows customization for specific parking management needs. You can adjust parameters such as the **occupied and available region colors**, margins for text display, and much more. Utilizing the `ParkingManagement` class's [optional arguments](#optional-arguments-parkingmanagement), you can tailor the model to suit your particular requirements, ensuring maximum efficiency and effectiveness.
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### What are some real-world applications of Ultralytics YOLOv8 in parking lot management?
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Ultralytics YOLOv8 is utilized in various real-world applications for parking lot management, including:
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- **Parking Space Detection**: Accurately identifying available and occupied spaces.
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- **Surveillance**: Enhancing security through real-time monitoring.
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- **Traffic Flow Management**: Reducing idle times and congestion with efficient traffic handling. Images showcasing these applications can be found in [real-world applications](#real-world-applications).
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