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")

docs/en/help/minimum_reproducible_example.md

@@ -50,6 +50,7 @@ When running inference on a 0-channel image, I get an error related to the dimen
 
 ```python
 import torch
+
 from ultralytics import YOLO
 
 # Load the model

docs/en/integrations/clearml.md

@@ -64,6 +64,7 @@ Before diving into the usage instructions, be sure to check out the range of [YO
 
         ```python
         from clearml import Task
+
         from ultralytics import YOLO
 
         # Step 1: Creating a ClearML Task

docs/en/integrations/gradio.md

@@ -57,6 +57,7 @@ This section provides the Python code used to create the Gradio interface with t
 ```python
 import gradio as gr
 import PIL.Image as Image
+
 from ultralytics import ASSETS, YOLO
 
 model = YOLO("yolov8n.pt")

docs/en/integrations/tensorrt.md

@@ -403,6 +403,7 @@ Expand sections below for information on how these models were exported and test
 
     ```py
     import cv2
+
     from ultralytics import YOLO
 
     model = YOLO("yolov8n.engine")

docs/en/integrations/weights-biases.md

@@ -64,9 +64,10 @@ Before diving into the usage instructions for YOLOv8 model training with Weights
     === "Python"
        ```python
        import wandb
-       from ultralytics import YOLO
        from wandb.integration.ultralytics import add_wandb_callback
 
+       from ultralytics import YOLO
+
        # Step 1: Initialize a Weights & Biases run
        wandb.init(project="ultralytics", job_type="training")
 

docs/en/modes/predict.md

@@ -178,6 +178,7 @@ Below are code examples for using each source type:
         Run inference on an image opened with Python Imaging Library (PIL).
         ```python
         from PIL import Image
+
         from ultralytics import YOLO
 
         # Load a pretrained YOLOv8n model
@@ -195,6 +196,7 @@ Below are code examples for using each source type:
         Run inference on an image read with OpenCV.
         ```python
         import cv2
+
         from ultralytics import YOLO
 
         # Load a pretrained YOLOv8n model
@@ -212,6 +214,7 @@ Below are code examples for using each source type:
         Run inference on an image represented as a numpy array.
         ```python
         import numpy as np
+
         from ultralytics import YOLO
 
         # Load a pretrained YOLOv8n model
@@ -229,6 +232,7 @@ Below are code examples for using each source type:
         Run inference on an image represented as a PyTorch tensor.
         ```python
         import torch
+
         from ultralytics import YOLO
 
         # Load a pretrained YOLOv8n model
@@ -246,6 +250,7 @@ Below are code examples for using each source type:
         Run inference on a collection of images, URLs, videos and directories listed in a CSV file.
         ```python
         import torch
+
         from ultralytics import YOLO
 
         # Load a pretrained YOLOv8n model
@@ -673,6 +678,7 @@ The `plot()` method in `Results` objects facilitates visualization of prediction
 
     ```python
     from PIL import Image
+
     from ultralytics import YOLO
 
     # Load a pretrained YOLOv8n model
@@ -754,6 +760,7 @@ Here's a Python script using OpenCV (`cv2`) and YOLOv8 to run inference on video
 
     ```python
     import cv2
+
     from ultralytics import YOLO
 
     # Load the YOLOv8 model

docs/en/modes/track.md

@@ -159,6 +159,7 @@ Here is a Python script using OpenCV (`cv2`) and YOLOv8 to run object tracking o
 
     ```python
     import cv2
+
     from ultralytics import YOLO
 
     # Load the YOLOv8 model
@@ -210,6 +211,7 @@ In the following example, we demonstrate how to utilize YOLOv8's tracking capabi
 
     import cv2
     import numpy as np
+
     from ultralytics import YOLO
 
     # Load the YOLOv8 model
@@ -285,6 +287,7 @@ Finally, after all threads have completed their task, the windows displaying the
     import threading
 
     import cv2
+
     from ultralytics import YOLO
 
 

docs/en/usage/python.md

@@ -127,6 +127,7 @@ Predict mode is used for making predictions using a trained YOLOv8 model on new
         ```python
         import cv2
         from PIL import Image
+
         from ultralytics import YOLO
 
         model = YOLO("model.pt")

docs/en/usage/simple-utilities.md

@@ -130,6 +130,7 @@ If you have a dataset that uses the [segmentation dataset format](../datasets/se
 
 ```python
 import numpy as np
+
 from ultralytics.utils.ops import segments2boxes
 
 segments = np.array(
@@ -194,6 +195,7 @@ Convert a single polygon (as list) to a binary mask of the specified image size.
 
 ```python
 import numpy as np
+
 from ultralytics.data.utils import polygon2mask
 
 imgsz = (1080, 810)
@@ -215,6 +217,7 @@ To manage bounding box data, the `Bboxes` class will help to convert between box
 
 ```python
 import numpy as np
+
 from ultralytics.utils.instance import Bboxes
 
 boxes = Bboxes(
@@ -259,6 +262,7 @@ When scaling and image up or down, corresponding bounding box coordinates can be
 ```{ .py .annotate }
 import cv2 as cv
 import numpy as np
+
 from ultralytics.utils.ops import scale_boxes
 
 image = cv.imread("ultralytics/assets/bus.jpg")
@@ -307,6 +311,7 @@ Convert bounding box coordinates from (x1, y1, x2, y2) format to (x, y, width, h
 
 ```python
 import numpy as np
+
 from ultralytics.utils.ops import xyxy2xywh
 
 xyxy_boxes = np.array(
@@ -359,6 +364,7 @@ Ultralytics includes an Annotator class that can be used to annotate any kind of
 ```{ .py .annotate }
 import cv2 as cv
 import numpy as np
+
 from ultralytics.utils.plotting import Annotator, colors
 
 names = {  # (1)!
@@ -402,6 +408,7 @@ image_with_bboxes = ann.result()
 ```python
 import cv2 as cv
 import numpy as np
+
 from ultralytics.utils.plotting import Annotator, colors
 
 obb_names = {10: "small vehicle"}