`ultralytics 8.2.5` New 🌟 Parking Management Solution (#10385)
Co-authored-by: UltralyticsAssistant <web@ultralytics.com> Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>pull/10404/head v8.2.5
Muhammad Rizwan Munawar
7 months ago
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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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 |
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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.solutions.parking_management import ParkingManagement |
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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 = 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 = 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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| `occupied_region_color` | `RGB Color` | `(0, 255, 0)` | Parking space occupied region color | |
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| `available_region_color` | `RGB Color` | `(0, 0, 255)` | Parking space available region color | |
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| `margin` | `int` | `10` | Gap between text display for multiple classes count | |
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| `txt_color` | `RGB Color` | `(255, 255, 255)` | Foreground color for object counts text | |
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| `bg_color` | `RGB Color` | `(255, 255, 255)` | Rectangle behind text background color | |
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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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@ -0,0 +1,235 @@ |
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import json |
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from tkinter import filedialog, messagebox |
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import cv2 |
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import numpy as np |
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from PIL import Image, ImageTk |
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from ultralytics.utils.checks import check_imshow, check_requirements |
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from ultralytics.utils.plotting import Annotator |
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check_requirements("tkinter") |
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import tkinter as tk |
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class ParkingPtsSelection: |
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def __init__(self, master): |
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# Initialize window and widgets. |
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self.master = master |
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master.title("Ultralytics Parking Zones Points Selector") |
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self.initialize_ui() |
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# Initialize properties |
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self.image_path = None |
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self.image = None |
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self.canvas_image = None |
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self.canvas = None |
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self.bounding_boxes = [] |
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self.current_box = [] |
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self.img_width = 0 |
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self.img_height = 0 |
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# Constants |
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self.canvas_max_width = 1280 |
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self.canvas_max_height = 720 |
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def initialize_ui(self): |
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"""Setup UI components.""" |
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# Setup buttons |
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button_frame = tk.Frame(self.master) |
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button_frame.pack(side=tk.TOP) |
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tk.Button(button_frame, text="Upload Image", command=self.upload_image).grid(row=0, column=0) |
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tk.Button(button_frame, text="Remove Last BBox", command=self.remove_last_bounding_box).grid(row=0, column=1) |
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tk.Button(button_frame, text="Save", command=self.save_to_json).grid(row=0, column=2) |
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# Setup canvas for image display |
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self.canvas = tk.Canvas(self.master, bg="white") |
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self.canvas.pack(side=tk.BOTTOM) |
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self.canvas.bind("<Button-1>", self.on_canvas_click) |
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def upload_image(self): |
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"""Upload an image and resize it to fit canvas.""" |
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self.image_path = filedialog.askopenfilename(filetypes=[("Image Files", "*.png;*.jpg;*.jpeg")]) |
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if not self.image_path: |
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return |
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self.image = Image.open(self.image_path) |
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self.img_width, self.img_height = self.image.size |
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# Calculate the aspect ratio and resize image |
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aspect_ratio = self.img_width / self.img_height |
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if aspect_ratio > 1: |
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# Landscape orientation |
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canvas_width = min(self.canvas_max_width, self.img_width) |
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canvas_height = int(canvas_width / aspect_ratio) |
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else: |
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# Portrait orientation |
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canvas_height = min(self.canvas_max_height, self.img_height) |
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canvas_width = int(canvas_height * aspect_ratio) |
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self.canvas.config(width=canvas_width, height=canvas_height) |
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resized_image = self.image.resize((canvas_width, canvas_height), Image.LANCZOS) |
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self.canvas_image = ImageTk.PhotoImage(resized_image) |
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self.canvas.create_image(0, 0, anchor=tk.NW, image=self.canvas_image) |
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# Reset bounding boxes and current box |
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self.bounding_boxes = [] |
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self.current_box = [] |
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def on_canvas_click(self, event): |
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"""Handle mouse clicks on canvas to create points for bounding boxes.""" |
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self.current_box.append((event.x, event.y)) |
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if len(self.current_box) == 4: |
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self.bounding_boxes.append(self.current_box) |
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self.draw_bounding_box(self.current_box) |
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self.current_box = [] |
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def draw_bounding_box(self, box): |
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"""Draw bounding box on canvas.""" |
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for i in range(4): |
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x1, y1 = box[i] |
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x2, y2 = box[(i + 1) % 4] |
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self.canvas.create_line(x1, y1, x2, y2, fill="blue", width=2) |
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def remove_last_bounding_box(self): |
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"""Remove the last drawn bounding box from canvas.""" |
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if self.bounding_boxes: |
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self.bounding_boxes.pop() # Remove the last bounding box |
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self.canvas.delete("all") # Clear the canvas |
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self.canvas.create_image(0, 0, anchor=tk.NW, image=self.canvas_image) # Redraw the image |
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# Redraw all bounding boxes |
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for box in self.bounding_boxes: |
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self.draw_bounding_box(box) |
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messagebox.showinfo("Success", "Last bounding box removed.") |
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else: |
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messagebox.showwarning("Warning", "No bounding boxes to remove.") |
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def save_to_json(self): |
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canvas_width, canvas_height = self.canvas.winfo_width(), self.canvas.winfo_height() |
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width_scaling_factor = self.img_width / canvas_width |
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height_scaling_factor = self.img_height / canvas_height |
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bounding_boxes_data = [] |
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for box in self.bounding_boxes: |
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print("Bounding Box ", bounding_boxes_data) |
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rescaled_box = [] |
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for x, y in box: |
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rescaled_x = int(x * width_scaling_factor) |
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rescaled_y = int(y * height_scaling_factor) |
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rescaled_box.append((rescaled_x, rescaled_y)) |
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bounding_boxes_data.append({"points": rescaled_box}) |
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with open("bounding_boxes.json", "w") as json_file: |
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json.dump(bounding_boxes_data, json_file, indent=4) |
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messagebox.showinfo("Success", "Bounding boxes saved to bounding_boxes.json") |
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class ParkingManagement: |
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def __init__( |
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self, |
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model_path, |
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txt_color=(0, 0, 0), |
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bg_color=(255, 255, 255), |
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occupied_region_color=(0, 255, 0), |
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available_region_color=(0, 0, 255), |
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margin=10, |
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): |
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# Model path and initialization |
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self.model_path = model_path |
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self.model = self.load_model() |
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# Labels dictionary |
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self.labels_dict = {"Occupancy": 0, "Available": 0} |
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# Visualization details |
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self.margin = margin |
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self.bg_color = bg_color |
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self.txt_color = txt_color |
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self.occupied_region_color = occupied_region_color |
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self.available_region_color = available_region_color |
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self.window_name = "Ultralytics YOLOv8 Parking Management System" |
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# Check if environment support imshow |
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self.env_check = check_imshow(warn=True) |
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def load_model(self): |
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"""Load the Ultralytics YOLOv8 model for inference and analytics.""" |
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from ultralytics import YOLO |
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self.model = YOLO(self.model_path) |
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return self.model |
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def parking_regions_extraction(self, json_file): |
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""" |
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Extract parking regions from json file. |
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Args: |
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json_file (str): file that have all parking slot points |
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""" |
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with open(json_file, "r") as json_file: |
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json_data = json.load(json_file) |
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return json_data |
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def process_data(self, json_data, im0, boxes, clss): |
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""" |
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Process the model data for parking lot management. |
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Args: |
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json_data (str): json data for parking lot management |
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im0 (ndarray): inference image |
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boxes (list): bounding boxes data |
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clss (list): bounding boxes classes list |
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Returns: |
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filled_slots (int): total slots that are filled in parking lot |
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empty_slots (int): total slots that are available in parking lot |
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""" |
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annotator = Annotator(im0) |
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total_slots, filled_slots = len(json_data), 0 |
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empty_slots = total_slots |
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for region in json_data: |
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points = region["points"] |
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points_array = np.array(points, dtype=np.int32).reshape((-1, 1, 2)) |
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region_occupied = False |
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for box, cls in zip(boxes, clss): |
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x_center = int((box[0] + box[2]) / 2) |
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y_center = int((box[1] + box[3]) / 2) |
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text = f"{self.model.names[int(cls)]}" |
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annotator.display_objects_labels( |
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im0, text, self.txt_color, self.bg_color, x_center, y_center, self.margin |
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) |
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dist = cv2.pointPolygonTest(points_array, (x_center, y_center), False) |
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if dist >= 0: |
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region_occupied = True |
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break |
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color = self.occupied_region_color if region_occupied else self.available_region_color |
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cv2.polylines(im0, [points_array], isClosed=True, color=color, thickness=2) |
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if region_occupied: |
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filled_slots += 1 |
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empty_slots -= 1 |
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self.labels_dict["Occupancy"] = filled_slots |
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self.labels_dict["Available"] = empty_slots |
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annotator.display_analytics(im0, self.labels_dict, self.txt_color, self.bg_color, self.margin) |
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def display_frames(self, im0): |
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""" |
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Display frame. |
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Args: |
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im0 (ndarray): inference image |
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""" |
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if self.env_check: |
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cv2.namedWindow(self.window_name) |
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cv2.imshow(self.window_name, im0) |
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# Break Window |
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if cv2.waitKey(1) & 0xFF == ord("q"): |
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return |
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