Apply new Ruff actions to Python codeblocks (#13783)

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

README.md

@@ -9,13 +9,13 @@
 <div>
     <a href="https://github.com/ultralytics/ultralytics/actions/workflows/ci.yaml"><img src="https://github.com/ultralytics/ultralytics/actions/workflows/ci.yaml/badge.svg" alt="Ultralytics CI"></a>
     <a href="https://codecov.io/github/ultralytics/ultralytics"><img src="https://codecov.io/github/ultralytics/ultralytics/branch/main/graph/badge.svg?token=HHW7IIVFVY" alt="Ultralytics Code Coverage"></a>
-    <a href="https://zenodo.org/badge/latestdoi/264818686"><img src="https://zenodo.org/badge/264818686.svg" alt="YOLOv8 Citation"></a>
+    <a href="https://zenodo.org/badge/latestdoi/264818686"><img src="https://zenodo.org/badge/264818686.svg" alt="Ultralytics YOLOv8 Citation"></a>
-    <a href="https://hub.docker.com/r/ultralytics/ultralytics"><img src="https://img.shields.io/docker/pulls/ultralytics/ultralytics?logo=docker" alt="Docker Pulls"></a>
+    <a href="https://hub.docker.com/r/ultralytics/ultralytics"><img src="https://img.shields.io/docker/pulls/ultralytics/ultralytics?logo=docker" alt="Ultralytics Docker Pulls"></a>
-    <a href="https://ultralytics.com/discord"><img alt="Discord" src="https://img.shields.io/discord/1089800235347353640?logo=discord&logoColor=white&label=Discord&color=blue"></a>
+    <a href="https://ultralytics.com/discord"><img alt="Ultralytics Discord" src="https://img.shields.io/discord/1089800235347353640?logo=discord&logoColor=white&label=Discord&color=blue"></a>
     <br>
-    <a href="https://console.paperspace.com/github/ultralytics/ultralytics"><img src="https://assets.paperspace.io/img/gradient-badge.svg" alt="Run on Gradient"></a>
+    <a href="https://console.paperspace.com/github/ultralytics/ultralytics"><img src="https://assets.paperspace.io/img/gradient-badge.svg" alt="Run Ultralytics on Gradient"></a>
-    <a href="https://colab.research.google.com/github/ultralytics/ultralytics/blob/main/examples/tutorial.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a>
+    <a href="https://colab.research.google.com/github/ultralytics/ultralytics/blob/main/examples/tutorial.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open Ultralytics In Colab"></a>
-    <a href="https://www.kaggle.com/ultralytics/yolov8"><img src="https://kaggle.com/static/images/open-in-kaggle.svg" alt="Open In Kaggle"></a>
+    <a href="https://www.kaggle.com/ultralytics/yolov8"><img src="https://kaggle.com/static/images/open-in-kaggle.svg" alt="Open Ultralytics In Kaggle"></a>
 </div>
 <br>
 

docs/en/datasets/explorer/api.md

@@ -300,8 +300,6 @@ You can also visualize the embedding space using the plotting tool of your choic
 
 ```python
 import matplotlib.pyplot as plt
-import numpy as np
-from mpl_toolkits.mplot3d import Axes3D
 from sklearn.decomposition import PCA
 
 # Reduce dimensions using PCA to 3 components for visualization in 3D

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

@@ -14,20 +14,7 @@ After performing the [Segment Task](../tasks/segment.md), it's sometimes desirab
 
 ## Recipe Walk Through
 
-1.  Begin with the necessary imports
+1.  See the [Ultralytics Quickstart Installation section](../quickstart.md/#install-ultralytics) for a quick walkthrough on installing the required libraries.
-
-    ```python
-    from pathlib import Path
-
-    import cv2
-    import numpy as np
-
-    from ultralytics import YOLO
-    ```
-
-    ???+ tip "Ultralytics Install"
-
-        See the Ultralytics [Quickstart](../quickstart.md/#install-ultralytics) Installation section for a quick walkthrough on installing the required libraries.
 
     ***
 
@@ -61,6 +48,10 @@ After performing the [Segment Task](../tasks/segment.md), it's sometimes desirab
 3.  Now iterate over the results and the contours. For workflows that want to save an image to file, the source image `base-name` and the detection `class-label` are retrieved for later use (optional).
 
     ```{ .py .annotate }
+    from pathlib import Path
+
+    import numpy as np
+
     # (2) Iterate detection results (helpful for multiple images)
     for r in res:
         img = np.copy(r.orig_img)
@@ -86,6 +77,8 @@ After performing the [Segment Task](../tasks/segment.md), it's sometimes desirab
     ![Binary Mask Image](https://github.com/ultralytics/ultralytics/assets/62214284/59bce684-fdda-4b17-8104-0b4b51149aca){ width="240", align="right" }
 
     ```{ .py .annotate }
+    import cv2
+
     # Create binary mask
     b_mask = np.zeros(img.shape[:2], np.uint8)
 
@@ -178,12 +171,11 @@ After performing the [Segment Task](../tasks/segment.md), it's sometimes desirab
                 Additional steps required to crop image to only include object region.
 
                 ![Example Crop Isolated Object Image Black Background](https://github.com/ultralytics/ultralytics/assets/62214284/103dbf90-c169-4f77-b791-76cdf09c6f22){ align="right" }
-                ``` { .py .annotate }
+                ```{ .py .annotate }
                 # (1) Bounding box coordinates
                 x1, y1, x2, y2 = c.boxes.xyxy.cpu().numpy().squeeze().astype(np.int32)
                 # Crop image to object region
                 iso_crop = isolated[y1:y2, x1:x2]
-
                 ```
 
                 1.  For more information on bounding box results, see [Boxes Section from Predict Mode](../modes/predict.md/#boxes)
@@ -225,12 +217,11 @@ After performing the [Segment Task](../tasks/segment.md), it's sometimes desirab
                 Additional steps required to crop image to only include object region.
 
                 ![Example Crop Isolated Object Image No Background](https://github.com/ultralytics/ultralytics/assets/62214284/5910244f-d1e1-44af-af7f-6dea4c688da8){ align="right" }
-                ``` { .py .annotate }
+                ```{ .py .annotate }
                 # (1) Bounding box coordinates
                 x1, y1, x2, y2 = c.boxes.xyxy.cpu().numpy().squeeze().astype(np.int32)
                 # Crop image to object region
                 iso_crop = isolated[y1:y2, x1:x2]
-
                 ```
 
                 1.  For more information on bounding box results, see [Boxes Section from Predict Mode](../modes/predict.md/#boxes)

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

@@ -57,25 +57,13 @@ Without further ado, let's dive in!
 
 ## Generating Feature Vectors for Object Detection Dataset
 
-1. Start by creating a new Python file and import the required libraries.
+1. Start by creating a new `example.py` Python file for the steps below.
-
-    ```python
-    import datetime
-    import shutil
-    from collections import Counter
-    from pathlib import Path
-
-    import numpy as np
-    import pandas as pd
-    import yaml
-    from sklearn.model_selection import KFold
-
-    from ultralytics import YOLO
-    ```
 
 2. Proceed to retrieve all label files for your dataset.
 
     ```python
+    from pathlib import Path
+
     dataset_path = Path("./Fruit-detection")  # replace with 'path/to/dataset' for your custom data
     labels = sorted(dataset_path.rglob("*labels/*.txt"))  # all data in 'labels'
     ```
@@ -92,6 +80,8 @@ Without further ado, let's dive in!
 4. Initialize an empty `pandas` DataFrame.
 
     ```python
+    import pandas as pd
+
     indx = [l.stem for l in labels]  # uses base filename as ID (no extension)
     labels_df = pd.DataFrame([], columns=cls_idx, index=indx)
     ```
@@ -99,6 +89,8 @@ Without further ado, let's dive in!
 5. Count the instances of each class-label present in the annotation files.
 
     ```python
+    from collections import Counter
+
     for label in labels:
         lbl_counter = Counter()
 
@@ -142,6 +134,8 @@ The rows index the label files, each corresponding to an image in your dataset,
         - By setting `random_state=M` where `M` is a chosen integer, you can obtain repeatable results.
 
     ```python
+    from sklearn.model_selection import KFold
+
     ksplit = 5
     kf = KFold(n_splits=ksplit, shuffle=True, random_state=20)  # setting random_state for repeatable results
 
@@ -178,6 +172,8 @@ The rows index the label files, each corresponding to an image in your dataset,
 4. Next, we create the directories and dataset YAML files for each split.
 
     ```python
+    import datetime
+
     supported_extensions = [".jpg", ".jpeg", ".png"]
 
     # Initialize an empty list to store image file paths
@@ -222,6 +218,8 @@ The rows index the label files, each corresponding to an image in your dataset,
     - **NOTE:** The time required for this portion of the code will vary based on the size of your dataset and your system hardware.
 
     ```python
+    import shutil
+
     for image, label in zip(images, labels):
         for split, k_split in folds_df.loc[image.stem].items():
             # Destination directory
@@ -247,6 +245,8 @@ fold_lbl_distrb.to_csv(save_path / "kfold_label_distribution.csv")
 1. First, load the YOLO model.
 
     ```python
+    from ultralytics import YOLO
+
     weights_path = "path/to/weights.pt"
     model = YOLO(weights_path, task="detect")
     ```

docs/en/guides/sahi-tiled-inference.md

@@ -60,12 +60,6 @@ pip install -U ultralytics sahi
 Here's how to import the necessary modules and download a YOLOv8 model and some test images:
 
 ```python
-from pathlib import Path
-
-from IPython.display import Image
-from sahi import AutoDetectionModel
-from sahi.predict import get_prediction, get_sliced_prediction, predict
-from sahi.utils.cv import read_image
 from sahi.utils.file import download_from_url
 from sahi.utils.yolov8 import download_yolov8s_model
 
@@ -91,6 +85,8 @@ download_from_url(
 You can instantiate a YOLOv8 model for object detection like this:
 
 ```python
+from sahi import AutoDetectionModel
+
 detection_model = AutoDetectionModel.from_pretrained(
     model_type="yolov8",
     model_path=yolov8_model_path,
@@ -104,6 +100,8 @@ detection_model = AutoDetectionModel.from_pretrained(
 Perform standard inference using an image path or a numpy image.
 
 ```python
+from sahi.predict import get_prediction
+
 # With an image path
 result = get_prediction("demo_data/small-vehicles1.jpeg", detection_model)
 
@@ -125,6 +123,8 @@ Image("demo_data/prediction_visual.png")
 Perform sliced inference by specifying the slice dimensions and overlap ratios:
 
 ```python
+from sahi.predict import get_sliced_prediction
+
 result = get_sliced_prediction(
     "demo_data/small-vehicles1.jpeg",
     detection_model,
@@ -155,6 +155,8 @@ result.to_fiftyone_detections()[:3]
 For batch prediction on a directory of images:
 
 ```python
+from sahi.predict import predict
+
 predict(
     model_type="yolov8",
     model_path="path/to/yolov8n.pt",

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

@@ -27,22 +27,6 @@ The Security Alarm System Project utilizing Ultralytics YOLOv8 integrates advanc
 
 ### Code
 
-#### Import Libraries
-
-```python
-import smtplib
-from email.mime.multipart import MIMEMultipart
-from email.mime.text import MIMEText
-from time import time
-
-import cv2
-import numpy as np
-import torch
-
-from ultralytics import YOLO
-from ultralytics.utils.plotting import Annotator, colors
-```
-
 #### Set up the parameters of the message
 
 ???+ tip "Note"
@@ -60,6 +44,8 @@ to_email = ""  # receiver email
 #### Server creation and authentication
 
 ```python
+import smtplib
+
 server = smtplib.SMTP("smtp.gmail.com: 587")
 server.starttls()
 server.login(from_email, password)
@@ -68,6 +54,10 @@ server.login(from_email, password)
 #### Email Send Function
 
 ```python
+from email.mime.multipart import MIMEMultipart
+from email.mime.text import MIMEText
+
+
 def send_email(to_email, from_email, object_detected=1):
     """Sends an email notification indicating the number of objects detected; defaults to 1 object."""
     message = MIMEMultipart()
@@ -84,6 +74,15 @@ def send_email(to_email, from_email, object_detected=1):
 #### Object Detection and Alert Sender
 
 ```python
+from time import time
+
+import cv2
+import torch
+
+from ultralytics import YOLO
+from ultralytics.utils.plotting import Annotator, colors
+
+
 class ObjectDetection:
     def __init__(self, capture_index):
         """Initializes an ObjectDetection instance with a given camera index."""
@@ -109,7 +108,7 @@ class ObjectDetection:
     def display_fps(self, im0):
         """Displays the FPS on an image `im0` by calculating and overlaying as white text on a black rectangle."""
         self.end_time = time()
-        fps = 1 / np.round(self.end_time - self.start_time, 2)
+        fps = 1 / round(self.end_time - self.start_time, 2)
         text = f"FPS: {int(fps)}"
         text_size = cv2.getTextSize(text, cv2.FONT_HERSHEY_SIMPLEX, 1.0, 2)[0]
         gap = 10

docs/en/guides/triton-inference-server.md

@@ -126,7 +126,7 @@ Then run inference using the Triton Server model:
 from ultralytics import YOLO
 
 # Load the Triton Server model
-model = YOLO(f"http://localhost:8000/yolo", task="detect")
+model = YOLO("http://localhost:8000/yolo", task="detect")
 
 # Run inference on the server
 results = model("path/to/image.jpg")

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

@@ -18,7 +18,7 @@ When connecting to a remote machine, normally visualizing image results is not p
 
 !!! warning
 
-    Only compatible with Linux and MacOS. Check the VSCode [repository](https://github.com/microsoft/vscode), check [Issue status](https://github.com/microsoft/vscode/issues/198622), or [documentation](https://code.visualstudio.com/docs) for updates about Windows support to view images in terminal with `sixel`.
+    Only compatible with Linux and MacOS. Check the [VSCode repository](https://github.com/microsoft/vscode), check [Issue status](https://github.com/microsoft/vscode/issues/198622), or [documentation](https://code.visualstudio.com/docs) for updates about Windows support to view images in terminal with `sixel`.
 
 The VSCode compatible protocols for viewing images using the integrated terminal are [`sixel`](https://en.wikipedia.org/wiki/Sixel) and [`iTerm`](https://iterm2.com/documentation-images.html). This guide will demonstrate use of the `sixel` protocol.
 
@@ -31,28 +31,17 @@ The VSCode compatible protocols for viewing images using the integrated terminal
     "terminal.integrated.enableImages": false
     ```
 
-<p align="center">
+    <p align="center">
-  <img width="800" src="https://github.com/ultralytics/ultralytics/assets/62214284/d158ab1c-893c-4397-a5de-2f9f74f81175" alt="VSCode enable terminal images setting">
+      <img width="800" src="https://github.com/ultralytics/ultralytics/assets/62214284/d158ab1c-893c-4397-a5de-2f9f74f81175" alt="VSCode enable terminal images setting">
-</p>
+    </p>
 
-1. Install the `python-sixel` library in your virtual environment. This is a [fork](https://github.com/lubosz/python-sixel?tab=readme-ov-file) of the `PySixel` library, which is no longer maintained.
+2. Install the `python-sixel` library in your virtual environment. This is a [fork](https://github.com/lubosz/python-sixel?tab=readme-ov-file) of the `PySixel` library, which is no longer maintained.
 
     ```bash
     pip install sixel
     ```
 
-1. Import the relevant libraries
+3. Load a model and execute inference, then plot the results and store in a variable. See more about inference arguments and working with results on the [predict mode](../modes/predict.md) page.
-
-    ```py
-    import io
-
-    import cv2 as cv
-    from sixel import SixelWriter
-
-    from ultralytics import YOLO
-    ```
-
-1. Load a model and execute inference, then plot the results and store in a variable. See more about inference arguments and working with results on the [predict mode](../modes/predict.md) page.
 
     ```{ .py .annotate }
     from ultralytics import YOLO
@@ -69,11 +58,15 @@ The VSCode compatible protocols for viewing images using the integrated terminal
 
     1. See [plot method parameters](../modes/predict.md#plot-method-parameters) to see possible arguments to use.
 
-1. Now, use OpenCV to convert the `numpy.ndarray` to `bytes` data. Then use `io.BytesIO` to make a "file-like" object.
+4. Now, use OpenCV to convert the `numpy.ndarray` to `bytes` data. Then use `io.BytesIO` to make a "file-like" object.
 
     ```{ .py .annotate }
+    import io
+
+    import cv2
+
     # Results image as bytes
-    im_bytes = cv.imencode(
+    im_bytes = cv2.imencode(
         ".png",  # (1)!
         plot,
     )[1].tobytes()  # (2)!
@@ -85,9 +78,11 @@ The VSCode compatible protocols for viewing images using the integrated terminal
     1. It's possible to use other image extensions as well.
     2. Only the object at index `1` that is returned is needed.
 
-1. Create a `SixelWriter` instance, and then use the `.draw()` method to draw the image in the terminal.
+5. Create a `SixelWriter` instance, and then use the `.draw()` method to draw the image in the terminal.
+
+    ```python
+    from sixel import SixelWriter
 
-    ```py
     # Create sixel writer object
     w = SixelWriter()
 
@@ -110,7 +105,7 @@ The VSCode compatible protocols for viewing images using the integrated terminal
 ```{ .py .annotate }
 import io
 
-import cv2 as cv
+import cv2
 from sixel import SixelWriter
 
 from ultralytics import YOLO
@@ -125,7 +120,7 @@ results = model.predict(source="ultralytics/assets/bus.jpg")
 plot = results[0].plot()  # (3)!
 
 # Results image as bytes
-im_bytes = cv.imencode(
+im_bytes = cv2.imencode(
     ".png",  # (1)!
     plot,
 )[1].tobytes()  # (2)!

docs/en/guides/vision-eye.md

@@ -116,7 +116,7 @@ keywords: VisionEye, YOLOv8, Ultralytics, object mapping, object tracking, dista
         import cv2
 
         from ultralytics import YOLO
-        from ultralytics.utils.plotting import Annotator, colors
+        from ultralytics.utils.plotting import Annotator
 
         model = YOLO("yolov8s.pt")
         cap = cv2.VideoCapture("Path/to/video/file.mp4")

docs/en/hub/inference-api.md

@@ -28,7 +28,7 @@ To access the [Ultralytics HUB](https://bit.ly/ultralytics_hub) Inference API us
 import requests
 
 # API URL, use actual MODEL_ID
-url = f"https://api.ultralytics.com/v1/predict/MODEL_ID"
+url = "https://api.ultralytics.com/v1/predict/MODEL_ID"
 
 # Headers, use actual API_KEY
 headers = {"x-api-key": "API_KEY"}
@@ -117,7 +117,7 @@ The [Ultralytics HUB](https://bit.ly/ultralytics_hub) Inference API returns a JS
         import requests
 
         # API URL, use actual MODEL_ID
-        url = f"https://api.ultralytics.com/v1/predict/MODEL_ID"
+        url = "https://api.ultralytics.com/v1/predict/MODEL_ID"
 
         # Headers, use actual API_KEY
         headers = {"x-api-key": "API_KEY"}
@@ -185,7 +185,7 @@ The [Ultralytics HUB](https://bit.ly/ultralytics_hub) Inference API returns a JS
         import requests
 
         # API URL, use actual MODEL_ID
-        url = f"https://api.ultralytics.com/v1/predict/MODEL_ID"
+        url = "https://api.ultralytics.com/v1/predict/MODEL_ID"
 
         # Headers, use actual API_KEY
         headers = {"x-api-key": "API_KEY"}
@@ -257,7 +257,7 @@ The [Ultralytics HUB](https://bit.ly/ultralytics_hub) Inference API returns a JS
         import requests
 
         # API URL, use actual MODEL_ID
-        url = f"https://api.ultralytics.com/v1/predict/MODEL_ID"
+        url = "https://api.ultralytics.com/v1/predict/MODEL_ID"
 
         # Headers, use actual API_KEY
         headers = {"x-api-key": "API_KEY"}
@@ -331,7 +331,7 @@ The [Ultralytics HUB](https://bit.ly/ultralytics_hub) Inference API returns a JS
         import requests
 
         # API URL, use actual MODEL_ID
-        url = f"https://api.ultralytics.com/v1/predict/MODEL_ID"
+        url = "https://api.ultralytics.com/v1/predict/MODEL_ID"
 
         # Headers, use actual API_KEY
         headers = {"x-api-key": "API_KEY"}
@@ -400,7 +400,7 @@ The [Ultralytics HUB](https://bit.ly/ultralytics_hub) Inference API returns a JS
         import requests
 
         # API URL, use actual MODEL_ID
-        url = f"https://api.ultralytics.com/v1/predict/MODEL_ID"
+        url = "https://api.ultralytics.com/v1/predict/MODEL_ID"
 
         # Headers, use actual API_KEY
         headers = {"x-api-key": "API_KEY"}

docs/en/modes/predict.md

@@ -249,8 +249,6 @@ 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

docs/en/usage/callbacks.md

@@ -41,7 +41,7 @@ def on_predict_batch_end(predictor):
 
 
 # Create a YOLO model instance
-model = YOLO(f"yolov8n.pt")
+model = YOLO("yolov8n.pt")
 
 # Add the custom callback to the model
 model.add_callback("on_predict_batch_end", on_predict_batch_end)

docs/en/usage/simple-utilities.md

@@ -349,6 +349,16 @@ from ultralytics.utils.ops import (
     xyxy2ltwh,  # xyxy → top-left corner, w, h
     xyxy2xywhn,  # pixel → normalized
 )
+
+for func in (
+    ltwh2xywh,
+    ltwh2xyxy,
+    xywh2ltwh,
+    xywh2xyxy,
+    xywhn2xyxy,
+    xyxy2ltwh,
+    xyxy2xywhn):
+  print(help(func))  # print function docstrings
 ```
 
 See docstring for each function or visit the `ultralytics.utils.ops` [reference page](../reference/utils/ops.md) to read more about each function.
@@ -467,7 +477,10 @@ Want or need to use the formats of [images or videos types supported](../modes/p
 from ultralytics.data.utils import IMG_FORMATS, VID_FORMATS
 
 print(IMG_FORMATS)
-# >>> ('bmp', 'dng', 'jpeg', 'jpg', 'mpo', 'png', 'tif', 'tiff', 'webp', 'pfm')
+# {'tiff', 'pfm', 'bmp', 'mpo', 'dng', 'jpeg', 'png', 'webp', 'tif', 'jpg'}
+
+print(VID_FORMATS)
+# {'avi', 'mpg', 'wmv', 'mpeg', 'm4v', 'mov', 'mp4', 'asf', 'mkv', 'ts', 'gif', 'webm'}
 ```
 
 ### Make Divisible