Update parking-management solution (#16990)
Co-authored-by: UltralyticsAssistant <web@ultralytics.com>
This commit is contained in:
Muhammad Rizwan Munawar
GitHub
parent
22ebd44f62
commit
1c650ab04c
3 changed files with 110 additions and 181 deletions
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@ -103,11 +103,9 @@ Parking management with [Ultralytics YOLO11](https://github.com/ultralytics/ultr
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### Optional Arguments `ParkingManagement`
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| Name | Type | Default | Description |
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| ------------------------ | ------- | ------------- | -------------------------------------------------------------- |
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| ----------- | ----- | ------- | -------------------------------------------------------------- |
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| `model` | `str` | `None` | Path to the YOLO11 model. |
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| `json_file` | `str` | `None` | Path to the JSON file, that have all parking coordinates data. |
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| `occupied_region_color` | `tuple` | `(0, 0, 255)` | RGB color for occupied regions. |
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| `available_region_color` | `tuple` | `(0, 255, 0)` | RGB color for available regions. |
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### Arguments `model.track`
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@ -15,3 +15,4 @@ down_angle: 90 # Workouts down_angle for counts, 90 is default value. You can ch
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kpts: [6, 8, 10] # Keypoints for workouts monitoring, i.e. If you want to consider keypoints for pushups that have mostly values of [6, 8, 10].
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colormap: # Colormap for heatmap, Only OPENCV supported colormaps can be used. By default COLORMAP_PARULA will be used for visualization.
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analytics_type: "line" # Analytics type i.e "line", "pie", "bar" or "area" charts. By default, "line" analytics will be used for processing.
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json_file: # parking system regions file path.
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@ -4,8 +4,9 @@ import json
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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.solutions.solutions import LOGGER, BaseSolution, check_requirements
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from ultralytics.utils.plotting import Annotator
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@ -13,229 +14,158 @@ class ParkingPtsSelection:
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"""Class for selecting and managing parking zone points on images using a Tkinter-based UI."""
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def __init__(self):
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"""Initializes the UI for selecting parking zone points in a tkinter window."""
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"""Class initialization method."""
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check_requirements("tkinter")
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import tkinter as tk
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from tkinter import filedialog, messagebox
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import tkinter as tk # scope for multi-environment compatibility
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self.tk = tk
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self.master = tk.Tk()
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self.master.title("Ultralytics Parking Zones Points Selector")
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# Disable window resizing
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self.master.resizable(False, False)
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# Setup canvas for image display
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self.canvas = self.tk.Canvas(self.master, bg="white")
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# Setup buttons
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button_frame = self.tk.Frame(self.master)
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button_frame.pack(side=self.tk.TOP)
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self.tk.Button(button_frame, text="Upload Image", command=self.upload_image).grid(row=0, column=0)
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self.tk.Button(button_frame, text="Remove Last BBox", command=self.remove_last_bounding_box).grid(
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row=0, column=1
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)
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self.tk.Button(button_frame, text="Save", command=self.save_to_json).grid(row=0, column=2)
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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.rg_data = [] # region coordinates
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self.current_box = []
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self.imgw = 0 # image width
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self.imgh = 0 # image height
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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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self.tk, self.filedialog, self.messagebox = tk, filedialog, messagebox
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self.setup_ui()
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self.initialize_properties()
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self.master.mainloop()
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def setup_ui(self):
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"""Sets up the Tkinter UI components."""
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self.master = self.tk.Tk()
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self.master.title("Ultralytics Parking Zones Points Selector")
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self.master.resizable(False, False)
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# Canvas for image display
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self.canvas = self.tk.Canvas(self.master, bg="white")
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self.canvas.pack(side=self.tk.BOTTOM)
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# Button frame with buttons
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button_frame = self.tk.Frame(self.master)
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button_frame.pack(side=self.tk.TOP)
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for text, cmd in [
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("Upload Image", self.upload_image),
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("Remove Last BBox", self.remove_last_bounding_box),
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("Save", self.save_to_json),
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]:
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self.tk.Button(button_frame, text=text, command=cmd).pack(side=self.tk.LEFT)
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def initialize_properties(self):
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"""Initialize the necessary properties."""
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self.image = self.canvas_image = None
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self.rg_data, self.current_box = [], []
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self.imgw = self.imgh = 0
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self.canvas_max_width, self.canvas_max_height = 1280, 720
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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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from tkinter import filedialog
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from PIL import Image, ImageTk # scope because ImageTk requires tkinter package
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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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"""Uploads an image, resizes it to fit the canvas, and displays it."""
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self.image = Image.open(self.filedialog.askopenfilename(filetypes=[("Image Files", "*.png;*.jpg;*.jpeg")]))
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if not self.image:
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return
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self.image = Image.open(self.image_path)
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self.imgw, self.imgh = self.image.size
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# Calculate the aspect ratio and resize image
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aspect_ratio = self.imgw / self.imgh
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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.imgw)
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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.imgh)
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canvas_width = int(canvas_height * aspect_ratio)
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canvas_width = (
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min(self.canvas_max_width, self.imgw) if aspect_ratio > 1 else int(self.canvas_max_height * aspect_ratio)
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)
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canvas_height = (
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min(self.canvas_max_height, self.imgh) if aspect_ratio <= 1 else int(canvas_width / aspect_ratio)
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)
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# Check if canvas is already initialized
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if self.canvas:
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self.canvas.destroy() # Destroy previous canvas
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self.canvas.config(width=canvas_width, height=canvas_height)
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self.display_image(canvas_width, canvas_height)
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self.rg_data.clear(), self.current_box.clear()
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self.canvas = self.tk.Canvas(self.master, bg="white", 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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def display_image(self, width, height):
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"""Displays the resized image on the canvas."""
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self.canvas_image = ImageTk.PhotoImage(self.image.resize((width, height), Image.LANCZOS))
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self.canvas.create_image(0, 0, anchor=self.tk.NW, image=self.canvas_image)
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self.canvas.pack(side=self.tk.BOTTOM)
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self.canvas.bind("<Button-1>", self.on_canvas_click)
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# Reset bounding boxes and current box
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self.rg_data = []
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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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"""Handles mouse clicks to add points for bounding boxes."""
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self.current_box.append((event.x, event.y))
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self.canvas.create_oval(event.x - 3, event.y - 3, event.x + 3, event.y + 3, fill="red")
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if len(self.current_box) == 4:
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self.rg_data.append(self.current_box)
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[
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self.canvas.create_line(self.current_box[i], self.current_box[(i + 1) % 4], fill="blue", width=2)
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for i in range(4)
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]
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self.current_box = []
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self.rg_data.append(self.current_box.copy())
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self.draw_box(self.current_box)
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self.current_box.clear()
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def draw_box(self, box):
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"""Draws a bounding box on the canvas."""
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for i in range(4):
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self.canvas.create_line(box[i], box[(i + 1) % 4], 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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from tkinter import messagebox # scope for multi-environment compatibility
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"""Removes the last bounding box and redraws the canvas."""
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if not self.rg_data:
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self.messagebox.showwarning("Warning", "No bounding boxes to remove.")
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return
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self.rg_data.pop()
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self.redraw_canvas()
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if self.rg_data:
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self.rg_data.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=self.tk.NW, image=self.canvas_image) # Redraw the image
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# Redraw all bounding boxes
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def redraw_canvas(self):
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"""Redraws the canvas with the image and all bounding boxes."""
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self.canvas.delete("all")
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self.canvas.create_image(0, 0, anchor=self.tk.NW, image=self.canvas_image)
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for box in self.rg_data:
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[self.canvas.create_line(box[i], box[(i + 1) % 4], fill="blue", width=2) for i in range(4)]
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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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self.draw_box(box)
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def save_to_json(self):
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"""Saves rescaled bounding boxes to 'bounding_boxes.json' based on image-to-canvas size ratio."""
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from tkinter import messagebox # scope for multi-environment compatibility
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rg_data = [] # regions data
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for box in self.rg_data:
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rs_box = [
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(
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int(x * self.imgw / self.canvas.winfo_width()), # width scaling
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int(y * self.imgh / self.canvas.winfo_height()), # height scaling
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)
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for x, y in box
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]
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rg_data.append({"points": rs_box})
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"""Saves the bounding boxes to a JSON file."""
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scale_w, scale_h = self.imgw / self.canvas.winfo_width(), self.imgh / self.canvas.winfo_height()
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data = [{"points": [(int(x * scale_w), int(y * scale_h)) for x, y in box]} for box in self.rg_data]
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with open("bounding_boxes.json", "w") as f:
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json.dump(rg_data, f, indent=4)
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messagebox.showinfo("Success", "Bounding boxes saved to bounding_boxes.json")
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json.dump(data, f, indent=4)
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self.messagebox.showinfo("Success", "Bounding boxes saved to bounding_boxes.json")
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class ParkingManagement:
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class ParkingManagement(BaseSolution):
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"""Manages parking occupancy and availability using YOLO model for real-time monitoring and visualization."""
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def __init__(
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self,
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model, # Ultralytics YOLO model file path
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json_file, # Parking management annotation file created from Parking Annotator
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occupied_region_color=(0, 0, 255), # occupied region color
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available_region_color=(0, 255, 0), # available region color
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):
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"""
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Initializes the parking management system with a YOLO model and visualization settings.
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def __init__(self, **kwargs):
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"""Initializes the parking management system with a YOLO model and visualization settings."""
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super().__init__(**kwargs)
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Args:
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model (str): Path to the YOLO model.
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json_file (str): file that have all parking slot points data
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occupied_region_color (tuple): RGB color tuple for occupied regions.
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available_region_color (tuple): RGB color tuple for available regions.
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"""
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# Model initialization
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from ultralytics import YOLO
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self.json_file = self.CFG["json_file"] # Load JSON data
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if self.json_file is None:
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LOGGER.warning("❌ json_file argument missing. Parking region details required.")
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raise ValueError("❌ Json file path can not be empty")
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self.model = YOLO(model)
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# Load JSON data
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with open(json_file) as f:
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self.json_data = json.load(f)
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with open(self.json_file) as f:
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self.json = json.load(f)
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self.pr_info = {"Occupancy": 0, "Available": 0} # dictionary for parking information
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self.occ = occupied_region_color
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self.arc = available_region_color
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self.env_check = check_imshow(warn=True) # check if environment supports imshow
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self.arc = (0, 0, 255) # available region color
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self.occ = (0, 255, 0) # occupied region color
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self.dc = (255, 0, 189) # centroid color for each box
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def process_data(self, im0):
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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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im0 (ndarray): inference image
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im0 (ndarray): inference image.
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"""
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results = self.model.track(im0, persist=True, show=False) # object tracking
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self.extract_tracks(im0) # extract tracks from im0
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es, fs = len(self.json), 0 # empty slots, filled slots
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annotator = Annotator(im0, self.line_width) # init annotator
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es, fs = len(self.json_data), 0 # empty slots, filled slots
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annotator = Annotator(im0) # init annotator
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# extract tracks data
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if results[0].boxes.id is None:
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self.display_frames(im0)
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return im0
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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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for region in self.json_data:
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for region in self.json:
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# Convert points to a NumPy array with the correct dtype and reshape properly
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pts_array = np.array(region["points"], dtype=np.int32).reshape((-1, 1, 2))
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rg_occupied = False # occupied region initialization
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for box, cls in zip(boxes, clss):
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xc = int((box[0] + box[2]) / 2)
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yc = int((box[1] + box[3]) / 2)
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for box, cls in zip(self.boxes, self.clss):
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xc, yc = int((box[0] + box[2]) / 2), int((box[1] + box[3]) / 2)
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dist = cv2.pointPolygonTest(pts_array, (xc, yc), False)
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if dist >= 0:
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# cv2.circle(im0, (xc, yc), radius=self.line_width * 4, color=self.dc, thickness=-1)
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annotator.display_objects_labels(
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im0, self.model.names[int(cls)], (104, 31, 17), (255, 255, 255), xc, yc, 10
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)
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dist = cv2.pointPolygonTest(pts_array, (xc, yc), False)
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if dist >= 0:
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rg_occupied = True
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break
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if rg_occupied:
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fs += 1
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es -= 1
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fs, es = (fs + 1, es - 1) if rg_occupied else (fs, es)
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# Plotting regions
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color = self.occ if rg_occupied else self.arc
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cv2.polylines(im0, [pts_array], isClosed=True, color=color, thickness=2)
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cv2.polylines(im0, [pts_array], isClosed=True, color=self.occ if rg_occupied else self.arc, thickness=2)
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self.pr_info["Occupancy"] = fs
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self.pr_info["Available"] = es
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self.pr_info["Occupancy"], self.pr_info["Available"] = fs, es
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annotator.display_analytics(im0, self.pr_info, (104, 31, 17), (255, 255, 255), 10)
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self.display_frames(im0)
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return im0
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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.imshow("Ultralytics Parking Manager", 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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self.display_output(im0) # display output with base class function
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return im0 # return output image for more usage
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