diff --git a/README.md b/README.md index 9f123ebe..d781b999 100644 --- a/README.md +++ b/README.md @@ -9,13 +9,13 @@
Ultralytics CI Ultralytics Code Coverage - YOLOv8 Citation - Docker Pulls - Discord + Ultralytics YOLOv8 Citation + Ultralytics Docker Pulls + Ultralytics Discord
- Run on Gradient - Open In Colab - Open In Kaggle + Run Ultralytics on Gradient + Open Ultralytics In Colab + Open Ultralytics In Kaggle

diff --git a/docs/en/datasets/explorer/api.md b/docs/en/datasets/explorer/api.md index 37f5262d..33ba47e6 100644 --- a/docs/en/datasets/explorer/api.md +++ b/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 diff --git a/docs/en/guides/isolating-segmentation-objects.md b/docs/en/guides/isolating-segmentation-objects.md index 3885e624..e8c227b2 100644 --- a/docs/en/guides/isolating-segmentation-objects.md +++ b/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 - - ```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) diff --git a/docs/en/guides/kfold-cross-validation.md b/docs/en/guides/kfold-cross-validation.md index b18ab3fd..2c75da2e 100644 --- a/docs/en/guides/kfold-cross-validation.md +++ b/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. - - ```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") ``` diff --git a/docs/en/guides/sahi-tiled-inference.md b/docs/en/guides/sahi-tiled-inference.md index 4305f66c..e9287b3c 100644 --- a/docs/en/guides/sahi-tiled-inference.md +++ b/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", diff --git a/docs/en/guides/security-alarm-system.md b/docs/en/guides/security-alarm-system.md index 5ed15e1c..a8c83567 100644 --- a/docs/en/guides/security-alarm-system.md +++ b/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 diff --git a/docs/en/guides/triton-inference-server.md b/docs/en/guides/triton-inference-server.md index cc1c5b33..6b9b496b 100644 --- a/docs/en/guides/triton-inference-server.md +++ b/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") diff --git a/docs/en/guides/view-results-in-terminal.md b/docs/en/guides/view-results-in-terminal.md index ba99e3a2..2a2fedc4 100644 --- a/docs/en/guides/view-results-in-terminal.md +++ b/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 ``` -

- VSCode enable terminal images setting -

+

+ VSCode enable terminal images setting +

-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)! diff --git a/docs/en/guides/vision-eye.md b/docs/en/guides/vision-eye.md index e60fc31e..c5ade6fd 100644 --- a/docs/en/guides/vision-eye.md +++ b/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") diff --git a/docs/en/hub/inference-api.md b/docs/en/hub/inference-api.md index 68f9ef9e..8357e607 100644 --- a/docs/en/hub/inference-api.md +++ b/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"} diff --git a/docs/en/modes/predict.md b/docs/en/modes/predict.md index 1fb021dc..0f99456c 100644 --- a/docs/en/modes/predict.md +++ b/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 diff --git a/docs/en/usage/callbacks.md b/docs/en/usage/callbacks.md index e88ffac8..5a03ef56 100644 --- a/docs/en/usage/callbacks.md +++ b/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) diff --git a/docs/en/usage/simple-utilities.md b/docs/en/usage/simple-utilities.md index e68508da..3619eef3 100644 --- a/docs/en/usage/simple-utilities.md +++ b/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