ultralytics 8.2.22 Parking Management Solution fix (#13122)

Co-authored-by: UltralyticsAssistant <web@ultralytics.com>
Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>

docs/en/guides/parking-management.md

@@ -38,13 +38,14 @@ Parking management with [Ultralytics YOLOv8](https://github.com/ultralytics/ultr
 
     Max Image Size of 1920 * 1080 supported
 
-```python
-from ultralytics.solutions.parking_management import ParkingPtsSelection, tk
+!!! Example "Parking slots Annotator Ultralytics YOLOv8"
 
-root = tk.Tk()
-ParkingPtsSelection(root)
-root.mainloop()
-```
+    === "Parking Annotator"
+
+        ```python
+        from ultralytics import solutions
+        solutions.ParkingPtsSelection()
+        ```
 
 - After defining the parking areas with polygons, click `save` to store a JSON file with the data in your working directory.
 

docs/en/guides/speed-estimation.md

@@ -8,7 +8,7 @@ keywords: Ultralytics, YOLOv8, Object Detection, Speed Estimation, Object Tracki
 
 ## What is Speed Estimation?
 
-Speed estimation is the process of calculating the rate of movement of an object within a given context, often employed in computer vision applications. Using [Ultralytics YOLOv8](https://github.com/ultralytics/ultralytics/) you can now calculate the speed of object using [object tracking](../modes/track.md) alongside distance and time data, crucial for tasks like traffic and surveillance. The accuracy of speed estimation directly influences the efficiency and reliability of various applications, making it a key component in the advancement of intelligent systems and real-time decision-making processes.
+[Speed estimation](https://www.ultralytics.com/blog/ultralytics-yolov8-for-speed-estimation-in-computer-vision-projects) is the process of calculating the rate of movement of an object within a given context, often employed in computer vision applications. Using [Ultralytics YOLOv8](https://github.com/ultralytics/ultralytics/) you can now calculate the speed of object using [object tracking](../modes/track.md) alongside distance and time data, crucial for tasks like traffic and surveillance. The accuracy of speed estimation directly influences the efficiency and reliability of various applications, making it a key component in the advancement of intelligent systems and real-time decision-making processes.
 
 <p align="center">
   <br>
@@ -21,6 +21,10 @@ Speed estimation is the process of calculating the rate of movement of an object
   <strong>Watch:</strong> Speed Estimation using Ultralytics YOLOv8
 </p>
 
+!!! tip "Check Out Our Blog"
+
+    For deeper insights into speed estimation, check out our blog post: [Ultralytics YOLOv8 for Speed Estimation in Computer Vision Projects](https://www.ultralytics.com/blog/ultralytics-yolov8-for-speed-estimation-in-computer-vision-projects)
+
 ## Advantages of Speed Estimation?
 
 - **Efficient Traffic Control:** Accurate speed estimation aids in managing traffic flow, enhancing safety, and reducing congestion on roadways.

docs/en/models/yolov10.md

@@ -1,7 +1,7 @@
 ---
 comments: true
-description: Explore the YOLOv10, a real-time object detector. Understand its superior speed, impressive accuracy, and unique approach to end-to-end object detection optimization.
-keywords: YOLOv10, real-time object detector, state-of-the-art, Tsinghua University, COCO dataset, NMS-free training, holistic model design, efficient architecture
+description: Discover YOLOv10, a cutting-edge real-time object detector known for its exceptional speed and accuracy. Learn about NMS-free training, holistic model design, and performance across various scales.
+keywords: YOLOv10, real-time object detection, Tsinghua University, COCO dataset, NMS-free training, efficient architecture, object detection optimization, state-of-the-art AI
 ---
 
 # YOLOv10: Real-Time End-to-End Object Detection

ultralytics/__init__.py

@@ -1,6 +1,6 @@
 # Ultralytics YOLO 🚀, AGPL-3.0 license
 
-__version__ = "8.2.21"
+__version__ = "8.2.22"
 
 from ultralytics.data.explorer.explorer import Explorer
 from ultralytics.models import RTDETR, SAM, YOLO, YOLOWorld

ultralytics/solutions/__init__.py

@@ -5,7 +5,7 @@ from .analytics import Analytics
 from .distance_calculation import DistanceCalculation
 from .heatmap import Heatmap
 from .object_counter import ObjectCounter
-from .parking_management import ParkingManagement
+from .parking_management import ParkingManagement, ParkingPtsSelection
 from .queue_management import QueueManager
 from .speed_estimation import SpeedEstimator
 

ultralytics/solutions/analytics.py

@@ -1,3 +1,5 @@
+# Ultralytics YOLO 🚀, AGPL-3.0 license
+
 from itertools import cycle
 
 import cv2

ultralytics/solutions/parking_management.py

@@ -1,3 +1,5 @@
+# Ultralytics YOLO 🚀, AGPL-3.0 license
+
 import json
 from tkinter import filedialog, messagebox
 
@@ -10,32 +12,31 @@ from ultralytics.utils.plotting import Annotator
 
 
 class ParkingPtsSelection:
-    def __init__(self, master):
-        """
-        Initializes the UI for selecting parking zone points in a tkinter window.
-
-        Args:
-            master (tk.Tk): The main tkinter window object.
-        """
+    def __init__(self):
+        """Initializes the UI for selecting parking zone points in a tkinter window."""
         check_requirements("tkinter")
+
         import tkinter as tk
 
-        self.master = master
-        master.title("Ultralytics Parking Zones Points Selector")
+        self.tk = tk
+        self.master = tk.Tk()
+        self.master.title("Ultralytics Parking Zones Points Selector")
 
         # Disable window resizing
-        master.resizable(False, False)
+        self.master.resizable(False, False)
 
         # Setup canvas for image display
-        self.canvas = tk.Canvas(master, bg="white")
+        self.canvas = self.tk.Canvas(self.master, bg="white")
 
         # Setup buttons
-        button_frame = tk.Frame(master)
-        button_frame.pack(side=tk.TOP)
+        button_frame = self.tk.Frame(self.master)
+        button_frame.pack(side=self.tk.TOP)
 
-        tk.Button(button_frame, text="Upload Image", command=self.upload_image).grid(row=0, column=0)
-        tk.Button(button_frame, text="Remove Last BBox", command=self.remove_last_bounding_box).grid(row=0, column=1)
-        tk.Button(button_frame, text="Save", command=self.save_to_json).grid(row=0, column=2)
+        self.tk.Button(button_frame, text="Upload Image", command=self.upload_image).grid(row=0, column=0)
+        self.tk.Button(button_frame, text="Remove Last BBox", command=self.remove_last_bounding_box).grid(
+            row=0, column=1
+        )
+        self.tk.Button(button_frame, text="Save", command=self.save_to_json).grid(row=0, column=2)
 
         # Initialize properties
         self.image_path = None
@@ -50,6 +51,8 @@ class ParkingPtsSelection:
         self.canvas_max_width = 1280
         self.canvas_max_height = 720
 
+        self.master.mainloop()
+
     def upload_image(self):
         """Upload an image and resize it to fit canvas."""
         self.image_path = filedialog.askopenfilename(filetypes=[("Image Files", "*.png;*.jpg;*.jpeg")])
@@ -74,12 +77,12 @@ class ParkingPtsSelection:
         if self.canvas:
             self.canvas.destroy()  # Destroy previous canvas
 
-        self.canvas = tk.Canvas(self.master, bg="white", width=canvas_width, height=canvas_height)
+        self.canvas = self.tk.Canvas(self.master, bg="white", width=canvas_width, height=canvas_height)
         resized_image = self.image.resize((canvas_width, canvas_height), Image.LANCZOS)
         self.canvas_image = ImageTk.PhotoImage(resized_image)
-        self.canvas.create_image(0, 0, anchor=tk.NW, image=self.canvas_image)
+        self.canvas.create_image(0, 0, anchor=self.tk.NW, image=self.canvas_image)
 
-        self.canvas.pack(side=tk.BOTTOM)
+        self.canvas.pack(side=self.tk.BOTTOM)
         self.canvas.bind("<Button-1>", self.on_canvas_click)
 
         # Reset bounding boxes and current box
@@ -115,7 +118,7 @@ class ParkingPtsSelection:
         if self.bounding_boxes:
             self.bounding_boxes.pop()  # Remove the last bounding box
             self.canvas.delete("all")  # Clear the canvas
-            self.canvas.create_image(0, 0, anchor=tk.NW, image=self.canvas_image)  # Redraw the image
+            self.canvas.create_image(0, 0, anchor=self.tk.NW, image=self.canvas_image)  # Redraw the image
 
             # Redraw all bounding boxes
             for box in self.bounding_boxes:
@@ -210,6 +213,7 @@ class ParkingManagement:
             im0 (ndarray): inference image
             boxes (list): bounding boxes data
             clss (list): bounding boxes classes list
+
         Returns:
             filled_slots (int): total slots that are filled in parking lot
             empty_slots (int): total slots that are available in parking lot