How to monitor images on the desktop in real time. Yolov8 about ultralytics HOT 5 CLOSED

@Mger96 hey there! 😄👋 Your English is fine, and I'm here to help.

For tracking objects on your monitor using YOLOv8, you can modify your script slightly to predict on the captured screen frames. Just replace the model.track() with results = model(imS) to make predictions, and then use results[0].plot() to visualize the detections on your screen capture.

Here’s how you can adjust your loop for object detection:

while time.time() - last_time < 100:
    img = numpy.asarray(sct.grab(mon))   
    imS = cv2.resize(img, (1000, 600))

    # Run YOLO inference on the screen capture
    results = model(imS)
    
    # Visualize the detections
    annotated_image = results[0].plot()
    cv2.imshow(title, annotated_image)

    if cv2.waitKey(1) & 0xFF == ord("q"):
        cv2.destroyAllWindows()
        break

This will allow YOLO to detect objects on your screen in real-time. Press 'q' to quit the display window. Hope this helps! Let me know if you have any more questions or need further assistance. 🚀

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@Mger96 hey there! 🎉

Fantastic news! I'm thrilled to hear you've successfully resolved the issue with the channels using cv2.cvtColor. Creative troubleshooting like yours is what keeps the community vibrant and resourceful. If you ever run into more puzzles or have insights to share, feel free to drop a message. Happy detecting with YOLOv8! 🚀

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👋 Hello @Mger96, thank you for your interest in Ultralytics YOLOv8 🚀! 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.

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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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@glenn-jocher , Hello!

Спасибо больше. Возникла проблема с каналами, но я решил проблему с помощью преобразования. cv2.cvtColor.

Now everything works!

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👋 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 🚀 and Vision AI ⭐

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