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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, 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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self.tk = tk |
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self.master = tk.Tk() |
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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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# 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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# 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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# Setup buttons |
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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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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.master.mainloop() |
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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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# 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 = 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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self.canvas.create_image(0, 0, anchor=self.tk.NW, image=self.canvas_image) |
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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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self.canvas.pack(side=self.tk.BOTTOM) |
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self.canvas.bind("<Button-1>", self.on_canvas_click) |
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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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# 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 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.bind("<Button-1>", self.on_canvas_click) |
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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 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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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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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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def remove_last_bounding_box(self): |
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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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# Redraw all bounding boxes |
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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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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.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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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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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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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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Muhammad Rizwan Munawar
1 month ago
committed by
GitHub