ultralytics 8.2.30 automated tags and release notes (#13164)

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

docs/en/guides/analytics.md

@@ -28,6 +28,7 @@ This guide provides a comprehensive overview of three fundamental types of data
 
         ```python
         import cv2
+
         from ultralytics import YOLO, solutions
 
         model = YOLO("yolov8s.pt")
@@ -76,6 +77,7 @@ This guide provides a comprehensive overview of three fundamental types of data
 
         ```python
         import cv2
+
         from ultralytics import YOLO, solutions
 
         model = YOLO("yolov8s.pt")
@@ -136,6 +138,7 @@ This guide provides a comprehensive overview of three fundamental types of data
 
         ```python
         import cv2
+
         from ultralytics import YOLO, solutions
 
         model = YOLO("yolov8s.pt")
@@ -185,6 +188,7 @@ This guide provides a comprehensive overview of three fundamental types of data
 
         ```python
         import cv2
+
         from ultralytics import YOLO, solutions
 
         model = YOLO("yolov8s.pt")
@@ -234,52 +238,53 @@ This guide provides a comprehensive overview of three fundamental types of data
 
         ```python
         import cv2
+
         from ultralytics import YOLO, solutions
+
         model = YOLO("yolov8s.pt")
-        
+
         cap = cv2.VideoCapture("path/to/video/file.mp4")
         assert cap.isOpened(), "Error reading video file"
         w, h, fps = (int(cap.get(x)) for x in (cv2.CAP_PROP_FRAME_WIDTH, cv2.CAP_PROP_FRAME_HEIGHT, cv2.CAP_PROP_FPS))
-        
+
         out = cv2.VideoWriter("area_plot.avi", cv2.VideoWriter_fourcc(*"MJPG"), fps, (w, h))
-        
+
         analytics = solutions.Analytics(
             type="area",
             writer=out,
             im0_shape=(w, h),
             view_img=True,
         )
-        
+
         clswise_count = {}
         frame_count = 0
-        
+
         while cap.isOpened():
             success, frame = cap.read()
             if success:
-        
                 frame_count += 1
                 results = model.track(frame, persist=True, verbose=True)
-        
+
                 if results[0].boxes.id is not None:
                     boxes = results[0].boxes.xyxy.cpu()
                     clss = results[0].boxes.cls.cpu().tolist()
-        
+
                     for box, cls in zip(boxes, clss):
                         if model.names[int(cls)] in clswise_count:
                             clswise_count[model.names[int(cls)]] += 1
                         else:
                             clswise_count[model.names[int(cls)]] = 1
-        
+
                 analytics.update_area(frame_count, clswise_count)
                 clswise_count = {}
                 if cv2.waitKey(1) & 0xFF == ord("q"):
                     break
             else:
                 break
-        
+
         cap.release()
         out.release()
-        cv2.destroyAllWindows()    
+        cv2.destroyAllWindows()
         ```
 
 ### Argument `Analytics`

docs/en/guides/distance-calculation.md

@@ -43,6 +43,7 @@ Measuring the gap between two objects is known as distance calculation within a
 
         ```python
         import cv2
+
         from ultralytics import YOLO, solutions
 
         model = YOLO("yolov8n.pt")

docs/en/guides/heatmaps.md

@@ -45,6 +45,7 @@ A heatmap generated with [Ultralytics YOLOv8](https://github.com/ultralytics/ult
 
         ```python
         import cv2
+
         from ultralytics import YOLO, solutions
 
         model = YOLO("yolov8n.pt")
@@ -82,6 +83,7 @@ A heatmap generated with [Ultralytics YOLOv8](https://github.com/ultralytics/ult
 
         ```python
         import cv2
+
         from ultralytics import YOLO, solutions
 
         model = YOLO("yolov8n.pt")
@@ -121,6 +123,7 @@ A heatmap generated with [Ultralytics YOLOv8](https://github.com/ultralytics/ult
     === "Polygon Counting"
         ```python
         import cv2
+
         from ultralytics import YOLO, solutions
 
         model = YOLO("yolov8n.pt")
@@ -162,6 +165,7 @@ A heatmap generated with [Ultralytics YOLOv8](https://github.com/ultralytics/ult
 
         ```python
         import cv2
+
         from ultralytics import YOLO, solutions
 
         model = YOLO("yolov8n.pt")
@@ -203,6 +207,7 @@ A heatmap generated with [Ultralytics YOLOv8](https://github.com/ultralytics/ult
 
         ```python
         import cv2
+
         from ultralytics import YOLO, solutions
 
         model = YOLO("yolov8s.pt")  # YOLOv8 custom/pretrained model
@@ -227,6 +232,7 @@ A heatmap generated with [Ultralytics YOLOv8](https://github.com/ultralytics/ult
 
         ```python
         import cv2
+
         from ultralytics import YOLO, solutions
 
         model = YOLO("yolov8n.pt")

docs/en/guides/instance-segmentation-and-tracking.md

@@ -40,6 +40,7 @@ There are two types of instance segmentation tracking available in the Ultralyti
 
         ```python
         import cv2
+
         from ultralytics import YOLO
         from ultralytics.utils.plotting import Annotator, colors
 
@@ -82,6 +83,7 @@ There are two types of instance segmentation tracking available in the Ultralyti
         from collections import defaultdict
 
         import cv2
+
         from ultralytics import YOLO
         from ultralytics.utils.plotting import Annotator, colors
 

docs/en/guides/isolating-segmentation-objects.md

@@ -21,6 +21,7 @@ After performing the [Segment Task](../tasks/segment.md), it's sometimes desirab
 
     import cv2
     import numpy as np
+
     from ultralytics import YOLO
     ```
 
@@ -272,6 +273,7 @@ from pathlib import Path
 
 import cv2
 import numpy as np
+
 from ultralytics import YOLO
 
 m = YOLO("yolov8n-seg.pt")  # (4)!

docs/en/guides/kfold-cross-validation.md

@@ -69,6 +69,7 @@ Without further ado, let's dive in!
     import pandas as pd
     import yaml
     from sklearn.model_selection import KFold
+
     from ultralytics import YOLO
     ```
 

docs/en/guides/object-blurring.md

@@ -22,6 +22,7 @@ Object blurring with [Ultralytics YOLOv8](https://github.com/ultralytics/ultraly
 
         ```python
         import cv2
+
         from ultralytics import YOLO
         from ultralytics.utils.plotting import Annotator, colors
 

docs/en/guides/object-counting.md

@@ -52,6 +52,7 @@ Object counting with [Ultralytics YOLOv8](https://github.com/ultralytics/ultraly
 
         ```python
         import cv2
+
         from ultralytics import YOLO, solutions
 
         model = YOLO("yolov8n.pt")
@@ -93,6 +94,7 @@ Object counting with [Ultralytics YOLOv8](https://github.com/ultralytics/ultraly
 
         ```python
         import cv2
+
         from ultralytics import YOLO, solutions
 
         model = YOLO("yolov8n.pt")
@@ -134,6 +136,7 @@ Object counting with [Ultralytics YOLOv8](https://github.com/ultralytics/ultraly
 
         ```python
         import cv2
+
         from ultralytics import YOLO, solutions
 
         model = YOLO("yolov8n.pt")
@@ -175,6 +178,7 @@ Object counting with [Ultralytics YOLOv8](https://github.com/ultralytics/ultraly
 
         ```python
         import cv2
+
         from ultralytics import YOLO, solutions
 
         model = YOLO("yolov8n.pt")

docs/en/guides/object-cropping.md

@@ -31,6 +31,7 @@ Object cropping with [Ultralytics YOLOv8](https://github.com/ultralytics/ultraly
         import os
 
         import cv2
+
         from ultralytics import YOLO
         from ultralytics.utils.plotting import Annotator, colors
 

docs/en/guides/parking-management.md

@@ -60,6 +60,7 @@ Parking management with [Ultralytics YOLOv8](https://github.com/ultralytics/ultr
 
         ```python
         import cv2
+
         from ultralytics import solutions
 
         # Path to json file, that created with above point selection app

docs/en/guides/queue-management.md

@@ -28,6 +28,7 @@ Queue management using [Ultralytics YOLOv8](https://github.com/ultralytics/ultra
 
         ```python
         import cv2
+
         from ultralytics import YOLO, solutions
 
         model = YOLO("yolov8n.pt")
@@ -71,6 +72,7 @@ Queue management using [Ultralytics YOLOv8](https://github.com/ultralytics/ultra
 
         ```python
         import cv2
+
         from ultralytics import YOLO, solutions
 
         model = YOLO("yolov8n.pt")

docs/en/guides/raspberry-pi.md

@@ -286,6 +286,7 @@ There are 2 methods of using the Raspberry Pi Camera to inference YOLOv8 models.
                 ```python
                 import cv2
                 from picamera2 import Picamera2
+
                 from ultralytics import YOLO
 
                 # Initialize the Picamera2

docs/en/guides/security-alarm-system.md

@@ -38,6 +38,7 @@ from time import time
 import cv2
 import numpy as np
 import torch
+
 from ultralytics import YOLO
 from ultralytics.utils.plotting import Annotator, colors
 ```

docs/en/guides/speed-estimation.md

@@ -44,6 +44,7 @@ keywords: Ultralytics YOLOv8, speed estimation, object tracking, computer vision
 
         ```python
         import cv2
+
         from ultralytics import YOLO, solutions
 
         model = YOLO("yolov8n.pt")

docs/en/guides/view-results-in-terminal.md

@@ -48,6 +48,7 @@ The VSCode compatible protocols for viewing images using the integrated terminal
 
     import cv2 as cv
     from sixel import SixelWriter
+
     from ultralytics import YOLO
     ```
 
@@ -111,6 +112,7 @@ import io
 
 import cv2 as cv
 from sixel import SixelWriter
+
 from ultralytics import YOLO
 
 # Load a model

docs/en/guides/vision-eye.md

@@ -23,6 +23,7 @@ keywords: VisionEye, YOLOv8, Ultralytics, object mapping, object tracking, dista
 
         ```python
         import cv2
+
         from ultralytics import YOLO
         from ultralytics.utils.plotting import Annotator, colors
 
@@ -66,6 +67,7 @@ keywords: VisionEye, YOLOv8, Ultralytics, object mapping, object tracking, dista
 
         ```python
         import cv2
+
         from ultralytics import YOLO
         from ultralytics.utils.plotting import Annotator, colors
 
@@ -112,6 +114,7 @@ keywords: VisionEye, YOLOv8, Ultralytics, object mapping, object tracking, dista
         import math
 
         import cv2
+
         from ultralytics import YOLO
         from ultralytics.utils.plotting import Annotator, colors
 

docs/en/guides/workouts-monitoring.md

@@ -40,6 +40,7 @@ Monitoring workouts through pose estimation with [Ultralytics YOLOv8](https://gi
 
         ```python
         import cv2
+
         from ultralytics import YOLO, solutions
 
         model = YOLO("yolov8n-pose.pt")
@@ -72,6 +73,7 @@ Monitoring workouts through pose estimation with [Ultralytics YOLOv8](https://gi
 
         ```python
         import cv2
+
         from ultralytics import YOLO, solutions
 
         model = YOLO("yolov8n-pose.pt")