273 lines
9.9 KiB
Python
273 lines
9.9 KiB
Python
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# Ultralytics YOLO 🚀, AGPL-3.0 license
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import json
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import cv2
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import numpy as np
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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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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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check_requirements("tkinter")
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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.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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self.master.mainloop()
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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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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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# 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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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.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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x0, y0 = event.x - 3, event.y - 3
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x1, y1 = event.x + 3, event.y + 3
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self.canvas.create_oval(x0, y0, x1, y1, fill="red")
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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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"""
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Draw bounding box on canvas.
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Args:
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box (list): Bounding box data
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"""
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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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from tkinter import messagebox # scope for multi-environment compatibility
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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=self.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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"""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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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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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 f:
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json.dump(bounding_boxes_data, f, 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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"""Manages parking occupancy and availability using YOLOv8 for real-time monitoring and visualization."""
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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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"""
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Initializes the parking management system with a YOLOv8 model and visualization settings.
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Args:
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model_path (str): Path to the YOLOv8 model.
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txt_color (tuple): RGB color tuple for text.
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bg_color (tuple): RGB color tuple for background.
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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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margin (int): Margin for text display.
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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 supports 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 YOLO model for inference and analytics."""
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from ultralytics import YOLO
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return YOLO(self.model_path)
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@staticmethod
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def parking_regions_extraction(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 f:
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return json.load(f)
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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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empty_slots, filled_slots = len(json_data), 0
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for region in json_data:
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points_array = np.array(region["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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