How can I use LoadStreams + object detection optimally about ultralytics HOT 10 OPEN

Hello! Working with multiple camera streams indeed challenges the computation resources. To optimize, you might consider batching your operations or using more efficient frames handling.

Here’s a suggestion:

• Use multi-threading to handle frame captures and detections separately. This way, frame capturing can occur in parallel to inference, thus better utilizing GPU resources.
• When using LoadStreams, ensure GPUs are efficiently used, especially if utilizing batch prediction. You might want to adjust the batch_size if supported by the model architecture.

Use this approach:

from ultralytics.data.loaders import LoadStreams
from ultralytics import YOLO
import torch
import threading
import cv2

device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')

model = YOLO('pywb3e.pt').to(device)
threads = []

def process_stream(stream):
    for path, imgs, im0s in stream:
        imgs = torch.stack([model.predict(img.unsqueeze(0), conf=0.6).xyxy[0] for img in imgs]).to(device)
        # Your visualization or further processing here

def camera_streams(sources):
    streams = LoadStreams(sources)
    for i, stream in enumerate(streams):
        thread = threading.Thread(target=process_stream, args=(stream,))
        threads.append(thread)
        thread.start()

    for thread in threads:
        thread.join()

if __name__ == '__main__':
    camera_streams('urls.txt')

With multi-threading, each stream is handled in its own thread, allowing for concurrent processing. Make sure that the hardware can handle the number of threads you plan to execute.

Let us know how it goes! 😊

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Hi there! The LoadStreams update function primarily serves to update the frame buffer for each stream in a LoadStreams object. Typically, you wouldn't need to call it directly in most use cases since LoadStreams internally handles fetching and updating the streams as you iterate through them.

However, if you want to manually manipulate or intervene in the stream updating process — for instance, to inject custom behavior or handle errors — you can use the update function. Here's a simplified example of how you might manually call update:

from ultralytics.data.loaders import LoadStreams

# Setup your stream
stream = LoadStreams('urls.txt')

# Manually update the stream
# The argument is the index of the stream from stream.sources
if some_condition:  # Define your condition or interaction
    stream.update(0, stream.caps[0], stream.streams[0])

# Continue processing your frames
for path, imgs, im0s in stream:
    # Your processing code here

This manually triggers an update for the first stream. You'd typically integrate such manual updates if you have specific reasons to refresh or adjust stream inputs dynamically.

Hope this clears things up! 😊 Let me know if you have any more questions!

from ultralytics.

Hi there! Currently, LoadStreams does handle all streams together, so if one rtsp stream disconnects, the whole system tries to reconnect, although targeting a specific stream for reconnection isn’t natively supported.

If stream-specific error handling is crucial, consider using individual VideoCapture objects for each stream and manage them separately. This way, you can isolate and handle connection issues per stream without affecting others.

Let me know if this helps or if you need further assistance! 😊

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👋 Hello @matiasnnr, 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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from ultralytics.

Hello! Working with multiple camera streams indeed challenges the computation resources. To optimize, you might consider batching your operations or using more efficient frames handling.

Here’s a suggestion:

• Use multi-threading to handle frame captures and detections separately. This way, frame capturing can occur in parallel to inference, thus better utilizing GPU resources.
• When using LoadStreams, ensure GPUs are efficiently used, especially if utilizing batch prediction. You might want to adjust the batch_size if supported by the model architecture.

Use this approach:

from ultralytics.data.loaders import LoadStreams
from ultralytics import YOLO
import torch
import threading
import cv2

device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')

model = YOLO('pywb3e.pt').to(device)
threads = []

def process_stream(stream):
    for path, imgs, im0s in stream:
        imgs = torch.stack([model.predict(img.unsqueeze(0), conf=0.6).xyxy[0] for img in imgs]).to(device)
        # Your visualization or further processing here

def camera_streams(sources):
    streams = LoadStreams(sources)
    for i, stream in enumerate(streams):
        thread = threading.Thread(target=process_stream, args=(stream,))
        threads.append(thread)
        thread.start()

    for thread in threads:
        thread.join()

if __name__ == '__main__':
    camera_streams('urls.txt')

With multi-threading, each stream is handled in its own thread, allowing for concurrent processing. Make sure that the hardware can handle the number of threads you plan to execute.

Let us know how it goes! 😊

Hey @glenn-jocher thanks, it's very helpful, I'm going to try threads, I thought the LoadStreams function itself had a function that helped do this, that's why I asked. Now the only question I have is how to use the LoadStreams update function, I see that it receives an index, caps and stream, I have been searching but I have not found examples, could you give me a basic example of its use please. I don't know if I should take values that LoadStreams already returns or generate everything from scratch to pass as parameters to the update function.

I'm not very clear about the uses it could have and how it is actually used. Sorry for the doubt, but I would like to understand how to use it.

Thank you 😊!

from ultralytics.

Thank you very much @glenn-jocher, that was the doubt I had, I didn't know that caps and streams could be obtained from the same LoadStreams instance, perhaps they could add that detail in the documentation (maybe it exists but I didn't see it hahaha).

Thank you very much for the example, it is clear to me. 😊

from ultralytics.

You're welcome! 😊 I'm glad the example was helpful for you. We'll take your feedback into consideration and look into making the documentation clearer about how caps and streams can be obtained from the LoadStreams instance. Thank you for suggesting this improvement—user feedback is invaluable in enhancing our resources!

If you have any more questions or need further assistance as you work with YOLOv8, feel free to reach out. Happy coding! 🚀

from ultralytics.

Hi again @glenn-jocher, question, ¿do you have any recommendation to handle the lost connections from an rtsp to try to reconnect automatically using LoadStreams without the need to close all and start again? 😮

from ultralytics.

Hello! For automatically handling RTSP connection losses in LoadStreams, you might consider wrapping your streaming code in a try-except block to catch connection errors and then implement a reconnection logic. Here’s a simple way to achieve this:

from ultralytics.data.loaders import LoadStreams
import time

def stream_videos(source):
    while True:
        try:
            streams = LoadStreams(source)
            for path, imgs, im0s in streams:
                # Your processing code here
                pass
        except Exception as e:
            print(f'Error: {e}. Reconnecting...')
            time.sleep(5)  # Wait for 5 seconds before reconnecting

if __name__ == '__main__':
    stream_videos('rtsp://your_stream_url')

This setup attempts a reconnection every time an exception occurs, which might handle occasional RTSP disconnects. 😊 Let me know if this helps or if you need more detailed implementation!

from ultralytics.

I understand, so there is no way to reconnect just the rtsp that was disconnected? Should I reconnect everything again when an error or loss of connection occurs... The example is clear to me, thank you very much again @glenn-jocher 🙌🏼😊.

from ultralytics.