Comments (6)
π Hello @jules-cp, thank you for your interest in Ultralytics YOLOv8 π! We recommend a visit to the Docs for new users where you can find many Python and CLI usage examples and where many of the most common questions may already be answered.
If this is a π Bug Report, please provide a minimum reproducible example to help us debug it.
If this is a custom training β Question, please provide as much information as possible, including dataset image examples and training logs, and verify you are following our Tips for Best Training Results.
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Install
Pip install the ultralytics
package including all requirements in a Python>=3.8 environment with PyTorch>=1.8.
pip install ultralytics
Environments
YOLOv8 may be run in any of the following up-to-date verified environments (with all dependencies including CUDA/CUDNN, Python and PyTorch preinstalled):
- Notebooks with free GPU:
- Google Cloud Deep Learning VM. See GCP Quickstart Guide
- Amazon Deep Learning AMI. See AWS Quickstart Guide
- Docker Image. See Docker Quickstart Guide
Status
If this badge is green, all Ultralytics CI tests are currently passing. CI tests verify correct operation of all YOLOv8 Modes and Tasks on macOS, Windows, and Ubuntu every 24 hours and on every commit.
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Hello,
Thank you for reaching out with your question. It looks like there's a small issue in the way you're trying to access the detection results. In YOLOv8, the results from the model prediction are stored in a Results
object, not directly as a list. Here's how you can modify your code to correctly access and print the bounding box coordinates:
from ultralytics import YOLO
# Load a pretrained YOLOv8n model
model = YOLO('best.pt')
# Run prediction
results = model('WEED100151.jpg')
# Loop through detections
for result in results:
for det in result.boxes:
x1, y1, x2, y2 = det.xyxy.int().tolist() # Bounding box coordinates
print(f"Coordinates: {x1}, {y1}, {x2}, {y2}")
result.show() # Display the image with bounding boxes
Make sure to iterate over results
and then access boxes
for each result
. This should resolve the AttributeError
you're encountering.
If you have any more questions or need further assistance, feel free to ask. Happy coding! π
from ultralytics.
Thank you very much for the help, I tested the code you provided me, but I got the following error:
x1, y1, x2, y2 = det.xyxy.int().tolist() # Bounding box coordinates
ValueError: not enough values to unpack (expected 4, got 1)
I don't know if it's a problem with the image I'm using for the test or if it's a code problem?
from ultralytics.
I add an example of the object that I am detecting and that I want to obtain its coordinates
from ultralytics.
@jules-cp hello,
Thank you for providing the image example! It helps in understanding the context better. The issue you're encountering is likely due to the way the bounding box coordinates are being accessed. Let's refine the code to ensure it works correctly.
Here's an updated version of the code that should correctly extract and print the bounding box coordinates:
from ultralytics import YOLO
# Load a pretrained YOLOv8n model
model = YOLO('best.pt')
# Run prediction
results = model('WEED100151.jpg')
# Loop through detections
for result in results:
for box in result.boxes:
x1, y1, x2, y2 = box.xyxy[0].int().tolist() # Bounding box coordinates
print(f"Coordinates: {x1}, {y1}, {x2}, {y2}")
result.show() # Display the image with bounding boxes
In this code, box.xyxy[0].int().tolist()
ensures that we are correctly accessing the bounding box coordinates.
If the issue persists, please ensure you are using the latest versions of torch
and ultralytics
. You can upgrade them using the following commands:
pip install --upgrade torch ultralytics
If you continue to experience issues, could you please provide a minimum reproducible example? This will help us investigate the problem more effectively. You can find guidelines for creating a minimum reproducible example here.
Feel free to reach out if you have any more questions or need further assistance. We're here to help! π
from ultralytics.
π Hello there! We wanted to give you a friendly reminder that this issue has not had any recent activity and may be closed soon, but don't worry - you can always reopen it if needed. If you still have any questions or concerns, please feel free to let us know how we can help.
For additional resources and information, please see the links below:
- Docs: https://docs.ultralytics.com
- HUB: https://hub.ultralytics.com
- Community: https://community.ultralytics.com
Feel free to inform us of any other issues you discover or feature requests that come to mind in the future. Pull Requests (PRs) are also always welcomed!
Thank you for your contributions to YOLO π and Vision AI β
from ultralytics.
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