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Apply new Ruff actions to Python codeblocks (#13783)

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Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
Co-authored-by: UltralyticsAssistant <web@ultralytics.com>
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27
docs/en/guides/isolating-segmentation-objects.md
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@ -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
```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.
1. See the [Ultralytics Quickstart Installation section](../quickstart.md/#install-ultralytics) 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)

30
docs/en/guides/kfold-cross-validation.md
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@ -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.
```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
```
1. Start by creating a new `example.py` Python file for the steps below.
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")
```

14
docs/en/guides/sahi-tiled-inference.md
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@ -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",

33
docs/en/guides/security-alarm-system.md
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@ -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

2
docs/en/guides/triton-inference-server.md
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@ -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")

41
docs/en/guides/view-results-in-terminal.md
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@ -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">
<img width="800" src="https://github.com/ultralytics/ultralytics/assets/62214284/d158ab1c-893c-4397-a5de-2f9f74f81175" alt="VSCode enable terminal images setting">
</p>
<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">
</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
```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.
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 .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)!

2
docs/en/guides/vision-eye.md
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@ -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")