ultralytics-ascend/ultralytics/solutions/parking_management.py
Muhammad Rizwan Munawar 1c650ab04c
Update parking-management solution (#16990)
Co-authored-by: UltralyticsAssistant <web@ultralytics.com>
2024-10-17 18:51:52 +02:00

171 lines
7.2 KiB
Python

# Ultralytics YOLO 🚀, AGPL-3.0 license
import json
import cv2
import numpy as np
from PIL import Image, ImageTk
from ultralytics.solutions.solutions import LOGGER, BaseSolution, check_requirements
from ultralytics.utils.plotting import Annotator
class ParkingPtsSelection:
"""Class for selecting and managing parking zone points on images using a Tkinter-based UI."""
def __init__(self):
"""Class initialization method."""
check_requirements("tkinter")
import tkinter as tk
from tkinter import filedialog, messagebox
self.tk, self.filedialog, self.messagebox = tk, filedialog, messagebox
self.setup_ui()
self.initialize_properties()
self.master.mainloop()
def setup_ui(self):
"""Sets up the Tkinter UI components."""
self.master = self.tk.Tk()
self.master.title("Ultralytics Parking Zones Points Selector")
self.master.resizable(False, False)
# Canvas for image display
self.canvas = self.tk.Canvas(self.master, bg="white")
self.canvas.pack(side=self.tk.BOTTOM)
# Button frame with buttons
button_frame = self.tk.Frame(self.master)
button_frame.pack(side=self.tk.TOP)
for text, cmd in [
("Upload Image", self.upload_image),
("Remove Last BBox", self.remove_last_bounding_box),
("Save", self.save_to_json),
]:
self.tk.Button(button_frame, text=text, command=cmd).pack(side=self.tk.LEFT)
def initialize_properties(self):
"""Initialize the necessary properties."""
self.image = self.canvas_image = None
self.rg_data, self.current_box = [], []
self.imgw = self.imgh = 0
self.canvas_max_width, self.canvas_max_height = 1280, 720
def upload_image(self):
"""Uploads an image, resizes it to fit the canvas, and displays it."""
self.image = Image.open(self.filedialog.askopenfilename(filetypes=[("Image Files", "*.png;*.jpg;*.jpeg")]))
if not self.image:
return
self.imgw, self.imgh = self.image.size
aspect_ratio = self.imgw / self.imgh
canvas_width = (
min(self.canvas_max_width, self.imgw) if aspect_ratio > 1 else int(self.canvas_max_height * aspect_ratio)
)
canvas_height = (
min(self.canvas_max_height, self.imgh) if aspect_ratio <= 1 else int(canvas_width / aspect_ratio)
)
self.canvas.config(width=canvas_width, height=canvas_height)
self.display_image(canvas_width, canvas_height)
self.rg_data.clear(), self.current_box.clear()
def display_image(self, width, height):
"""Displays the resized image on the canvas."""
self.canvas_image = ImageTk.PhotoImage(self.image.resize((width, height), Image.LANCZOS))
self.canvas.create_image(0, 0, anchor=self.tk.NW, image=self.canvas_image)
self.canvas.bind("<Button-1>", self.on_canvas_click)
def on_canvas_click(self, event):
"""Handles mouse clicks to add points for bounding boxes."""
self.current_box.append((event.x, event.y))
self.canvas.create_oval(event.x - 3, event.y - 3, event.x + 3, event.y + 3, fill="red")
if len(self.current_box) == 4:
self.rg_data.append(self.current_box.copy())
self.draw_box(self.current_box)
self.current_box.clear()
def draw_box(self, box):
"""Draws a bounding box on the canvas."""
for i in range(4):
self.canvas.create_line(box[i], box[(i + 1) % 4], fill="blue", width=2)
def remove_last_bounding_box(self):
"""Removes the last bounding box and redraws the canvas."""
if not self.rg_data:
self.messagebox.showwarning("Warning", "No bounding boxes to remove.")
return
self.rg_data.pop()
self.redraw_canvas()
def redraw_canvas(self):
"""Redraws the canvas with the image and all bounding boxes."""
self.canvas.delete("all")
self.canvas.create_image(0, 0, anchor=self.tk.NW, image=self.canvas_image)
for box in self.rg_data:
self.draw_box(box)
def save_to_json(self):
"""Saves the bounding boxes to a JSON file."""
scale_w, scale_h = self.imgw / self.canvas.winfo_width(), self.imgh / self.canvas.winfo_height()
data = [{"points": [(int(x * scale_w), int(y * scale_h)) for x, y in box]} for box in self.rg_data]
with open("bounding_boxes.json", "w") as f:
json.dump(data, f, indent=4)
self.messagebox.showinfo("Success", "Bounding boxes saved to bounding_boxes.json")
class ParkingManagement(BaseSolution):
"""Manages parking occupancy and availability using YOLO model for real-time monitoring and visualization."""
def __init__(self, **kwargs):
"""Initializes the parking management system with a YOLO model and visualization settings."""
super().__init__(**kwargs)
self.json_file = self.CFG["json_file"] # Load JSON data
if self.json_file is None:
LOGGER.warning("❌ json_file argument missing. Parking region details required.")
raise ValueError("❌ Json file path can not be empty")
with open(self.json_file) as f:
self.json = json.load(f)
self.pr_info = {"Occupancy": 0, "Available": 0} # dictionary for parking information
self.arc = (0, 0, 255) # available region color
self.occ = (0, 255, 0) # occupied region color
self.dc = (255, 0, 189) # centroid color for each box
def process_data(self, im0):
"""
Process the model data for parking lot management.
Args:
im0 (ndarray): inference image.
"""
self.extract_tracks(im0) # extract tracks from im0
es, fs = len(self.json), 0 # empty slots, filled slots
annotator = Annotator(im0, self.line_width) # init annotator
for region in self.json:
# Convert points to a NumPy array with the correct dtype and reshape properly
pts_array = np.array(region["points"], dtype=np.int32).reshape((-1, 1, 2))
rg_occupied = False # occupied region initialization
for box, cls in zip(self.boxes, self.clss):
xc, yc = int((box[0] + box[2]) / 2), int((box[1] + box[3]) / 2)
dist = cv2.pointPolygonTest(pts_array, (xc, yc), False)
if dist >= 0:
# cv2.circle(im0, (xc, yc), radius=self.line_width * 4, color=self.dc, thickness=-1)
annotator.display_objects_labels(
im0, self.model.names[int(cls)], (104, 31, 17), (255, 255, 255), xc, yc, 10
)
rg_occupied = True
break
fs, es = (fs + 1, es - 1) if rg_occupied else (fs, es)
# Plotting regions
cv2.polylines(im0, [pts_array], isClosed=True, color=self.occ if rg_occupied else self.arc, thickness=2)
self.pr_info["Occupancy"], self.pr_info["Available"] = fs, es
annotator.display_analytics(im0, self.pr_info, (104, 31, 17), (255, 255, 255), 10)
self.display_output(im0) # display output with base class function
return im0 # return output image for more usage