How to do instance segmention on video or streaming data

[klein1213811]

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Question

How to do instance segmention on video or streaming data ,like yolov8

from ultralytics import YOLO

# Load a model
model = YOLO('yolov8n-seg.pt')  # pretrained YOLOv8n model

# Run batched inference on a list of images
results = model(['im1.jpg', 'im2.jpg'], stream=True)  # return a generator of Results objects

# Process results generator
for result in results:
    boxes = result.boxes  # Boxes object for bounding box outputs
    masks = result.masks  # Masks object for segmentation masks outputs
    keypoints = result.keypoints  # Keypoints object for pose outputs
    probs = result.probs  # Probs object for classification outputs
    result.show()  # display to screen
    result.save(filename='result.jpg')  # save to disk

Additional

No response

[github-actions]

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Introducing YOLOv8 🚀

We're excited to announce the launch of our latest state-of-the-art (SOTA) object detection model for 2023 - YOLOv8 🚀!

Designed to be fast, accurate, and easy to use, YOLOv8 is an ideal choice for a wide range of object detection, image segmentation and image classification tasks. With YOLOv8, you'll be able to quickly and accurately detect objects in real-time, streamline your workflows, and achieve new levels of accuracy in your projects.

Check out our YOLOv8 Docs for details and get started with:

pip install ultralytics

[glenn-jocher]

Hello there! 🌟

To adapt instance segmentation for video or streaming data similar to your YOLOv8 example but focusing on YOLOv5, please note that YOLOv5 primarily focuses on object detection tasks, evolving from bounding box predictions rather than instance segmentation.

For video or streaming data, you would typically pass frames one by one into the model in a loop, similar to processing a list of images but iterating over video frames. However, for instance segmentation specifically, you might want to explore integrating YOLOv5 with additional tools or libraries specialized in segmentation tasks, as YOLOv5 does not directly support this out-of-the-box.

Here's a generic snippet for processing video frames with YOLOv5, although this is focused on detection tasks:

from ultralytics import YOLO
import cv2

# Load the model
model = YOLO('yolov5s.pt')  # Adjust the model as per your requirements

# Open video
cap = cv2.VideoCapture('path/to/your/video.mp4')

while cap.isOpened():
    ret, frame = cap.read()
    if not ret:
        break
    
    # Inference
    results = model(frame)

    # For displaying, replace or add your custom code to process instance segmentation
    results.show()
    
    # Press Q on keyboard to exit
    if cv2.waitKey(25) & 0xFF == ord('q'):
        break

# When everything done, release the video capture object
cap.release()

Note: This code is focused on detection. For instance segmentation, YOLOv5 would require additional method integrations.

Unfortunately, direct instance segmentation as per your question isn't a feature in YOLOv5. For cutting-edge tasks like instance segmentation, I recommend checking the latest updates or discussing within the YOLOv5 community for potential custom solutions or recommendations.

Keep innovating! 😊

[github-actions]

👋 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.

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