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ultralytics 8.3.16 PyTorch 2.5.0 support (#16998)

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Signed-off-by: UltralyticsAssistant <web@ultralytics.com>
Co-authored-by: UltralyticsAssistant <web@ultralytics.com>
Co-authored-by: RizwanMunawar <chr043416@gmail.com>
Co-authored-by: Muhammad Rizwan Munawar <muhammadrizwanmunawar123@gmail.com>
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Glenn Jocher 2024-10-18 13:54:45 +02:00 • committed by GitHub
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commit 8d7d1fe390
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90
ultralytics/solutions/parking_management.py
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@ -10,10 +10,44 @@ from ultralytics.utils.plotting import Annotator
class ParkingPtsSelection:
"""Class for selecting and managing parking zone points on images using a Tkinter-based UI."""
"""
A class for selecting and managing parking zone points on images using a Tkinter-based UI.
This class provides functionality to upload an image, select points to define parking zones, and save the
selected points to a JSON file. It uses Tkinter for the graphical user interface.
Attributes:
tk (module): The Tkinter module for GUI operations.
filedialog (module): Tkinter's filedialog module for file selection operations.
messagebox (module): Tkinter's messagebox module for displaying message boxes.
master (tk.Tk): The main Tkinter window.
canvas (tk.Canvas): The canvas widget for displaying the image and drawing bounding boxes.
image (PIL.Image.Image): The uploaded image.
canvas_image (ImageTk.PhotoImage): The image displayed on the canvas.
rg_data (List[List[Tuple[int, int]]]): List of bounding boxes, each defined by 4 points.
current_box (List[Tuple[int, int]]): Temporary storage for the points of the current bounding box.
imgw (int): Original width of the uploaded image.
imgh (int): Original height of the uploaded image.
canvas_max_width (int): Maximum width of the canvas.
canvas_max_height (int): Maximum height of the canvas.
Methods:
setup_ui: Sets up the Tkinter UI components.
initialize_properties: Initializes the necessary properties.
upload_image: Uploads an image, resizes it to fit the canvas, and displays it.
on_canvas_click: Handles mouse clicks to add points for bounding boxes.
draw_box: Draws a bounding box on the canvas.
remove_last_bounding_box: Removes the last bounding box and redraws the canvas.
redraw_canvas: Redraws the canvas with the image and all bounding boxes.
save_to_json: Saves the bounding boxes to a JSON file.
Examples:
>>> parking_selector = ParkingPtsSelection()
>>> # Use the GUI to upload an image, select parking zones, and save the data
"""
def __init__(self):
"""Class initialization method."""
"""Initializes the ParkingPtsSelection class, setting up UI and properties for parking zone point selection."""
check_requirements("tkinter")
import tkinter as tk
from tkinter import filedialog, messagebox
@ -24,7 +58,7 @@ class ParkingPtsSelection:
self.master.mainloop()
def setup_ui(self):
"""Sets up the Tkinter UI components."""
"""Sets up the Tkinter UI components for the parking zone points selection interface."""
self.master = self.tk.Tk()
self.master.title("Ultralytics Parking Zones Points Selector")
self.master.resizable(False, False)
@ -45,14 +79,14 @@ class ParkingPtsSelection:
self.tk.Button(button_frame, text=text, command=cmd).pack(side=self.tk.LEFT)
def initialize_properties(self):
"""Initialize the necessary properties."""
"""Initialize properties for image, canvas, bounding boxes, and dimensions."""
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."""
"""Uploads and displays an image on the canvas, resizing it to fit within specified dimensions."""
from PIL import Image, ImageTk # scope because ImageTk requires tkinter package
self.image = Image.open(self.filedialog.askopenfilename(filetypes=[("Image Files", "*.png;*.jpg;*.jpeg")]))
@ -76,7 +110,7 @@ class ParkingPtsSelection:
self.rg_data.clear(), self.current_box.clear()
def on_canvas_click(self, event):
"""Handles mouse clicks to add points for bounding boxes."""
"""Handles mouse clicks to add points for bounding boxes on the canvas."""
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:
@ -85,12 +119,12 @@ class ParkingPtsSelection:
self.current_box.clear()
def draw_box(self, box):
"""Draws a bounding box on the canvas."""
"""Draws a bounding box on the canvas using the provided coordinates."""
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."""
"""Removes the last bounding box from the list and redraws the canvas."""
if not self.rg_data:
self.messagebox.showwarning("Warning", "No bounding boxes to remove.")
return
@ -105,7 +139,7 @@ class ParkingPtsSelection:
self.draw_box(box)
def save_to_json(self):
"""Saves the bounding boxes to a JSON file."""
"""Saves the selected parking zone points to a JSON file with scaled coordinates."""
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:
@ -114,7 +148,30 @@ class ParkingPtsSelection:
class ParkingManagement(BaseSolution):
"""Manages parking occupancy and availability using YOLO model for real-time monitoring and visualization."""
"""
Manages parking occupancy and availability using YOLO model for real-time monitoring and visualization.
This class extends BaseSolution to provide functionality for parking lot management, including detection of
occupied spaces, visualization of parking regions, and display of occupancy statistics.
Attributes:
json_file (str): Path to the JSON file containing parking region details.
json (List[Dict]): Loaded JSON data containing parking region information.
pr_info (Dict[str, int]): Dictionary storing parking information (Occupancy and Available spaces).
arc (Tuple[int, int, int]): RGB color tuple for available region visualization.
occ (Tuple[int, int, int]): RGB color tuple for occupied region visualization.
dc (Tuple[int, int, int]): RGB color tuple for centroid visualization of detected objects.
Methods:
process_data: Processes model data for parking lot management and visualization.
Examples:
>>> from ultralytics.solutions import ParkingManagement
>>> parking_manager = ParkingManagement(model="yolov8n.pt", json_file="parking_regions.json")
>>> results = parking_manager(source="parking_lot_video.mp4")
>>> print(f"Occupied spaces: {parking_manager.pr_info['Occupancy']}")
>>> print(f"Available spaces: {parking_manager.pr_info['Available']}")
"""
def __init__(self, **kwargs):
"""Initializes the parking management system with a YOLO model and visualization settings."""
@ -136,10 +193,19 @@ class ParkingManagement(BaseSolution):
def process_data(self, im0):
"""
Process the model data for parking lot management.
Processes the model data for parking lot management.
This function analyzes the input image, extracts tracks, and determines the occupancy status of parking
regions defined in the JSON file. It annotates the image with occupied and available parking spots,
and updates the parking information.
Args:
im0 (ndarray): inference image.
im0 (np.ndarray): The input inference image.
Examples:
>>> parking_manager = ParkingManagement(json_file="parking_regions.json")
>>> image = cv2.imread("parking_lot.jpg")
>>> parking_manager.process_data(image)
"""
self.extract_tracks(im0) # extract tracks from im0
es, fs = len(self.json), 0 # empty slots, filled slots