While using list.streams detection is not getting performed in all the frames about ultralytics HOT 17 OPEN

Hello @VM8198,

Thank you for providing a detailed description of the issue you're facing with list.streams and for sharing your code example. Let's work together to resolve this.

Firstly, I noticed a few things in your code that might need adjustments. The source parameter in the model.predict() method should be the path to your list.streams file, not directly within the loop. Additionally, the device parameter should be set correctly. Here's a refined version of your code:

import cv2
from ultralytics import YOLO

# Load the model
model = YOLO("yolov8n.pt")

# Path to your list.streams file
streams_path = "path/to/your/list.streams"

# Open the video streams
cap = cv2.VideoCapture(streams_path)

while cap.isOpened():
    success, frame = cap.read()

    if success:
        # Perform prediction on the current frame
        results = model.predict(source=frame, device='cuda:0', stream=True, classes=[0], stream_buffer=True)

        # Visualize the results
        annotated_frame = results[0].plot()

        # Display the annotated frame
        cv2.imshow("YOLOv8 Inference", annotated_frame)

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

cap.release()
cv2.destroyAllWindows()

Steps to Troubleshoot:

1. Ensure Latest Versions: Verify that you are using the latest versions of torch and ultralytics. You can upgrade them using:

   pip install --upgrade torch ultralytics

2. Check Stream Configuration: Ensure that your list.streams file is correctly formatted and accessible. Each line should contain a valid stream URL.

3. Adjust Confidence Threshold: Sometimes, detections might be missed due to a high confidence threshold. Try lowering the conf parameter in the model.predict() method:

   results = model.predict(source=frame, device='cuda:0', stream=True, classes=[0], stream_buffer=True, conf=0.25)

4. Debugging: Add print statements to log the number of detections per frame to understand if the issue is with specific frames or a general problem:

   if success:
       results = model.predict(source=frame, device='cuda:0', stream=True, classes=[0], stream_buffer=True)
       print(f"Detections in frame: {len(results[0].boxes)}")

If the issue persists after these adjustments, please provide additional details or any error messages you encounter. This will help us further diagnose the problem.

Feel free to reach out with any more questions or updates on your progress. We're here to help! 😊

from ultralytics.

Thanks for your response @glenn-jocher

Firstly, the code I provided was copied from my class, but it's identical to what you've provided. Regarding the Ultralytics version, it is updated to the latest one.

I've already tried adjusting the confidence threshold, and it works fine with all the CCTV cameras when I run the detection independently. Also, I'm not getting any error messages.

I believe the issue lies in how list.streams processes the frames. It seems to be skipping frames due to the overload of streaming channels. I'm not sure how list.streams works in the background, but I think this might be the problem.

from ultralytics.

Hello @VM8198,

Thank you for the detailed follow-up! 😊 It's great to hear that you've already tried adjusting the confidence threshold and ensured that you're using the latest version of Ultralytics.

Given your observations, it does sound like the issue might be related to how list.streams processes multiple streams concurrently. Here are a few suggestions and potential solutions to help address this:

1. Frame Skipping and Buffering

When dealing with multiple streams, frame skipping can occur due to the processing load. To mitigate this, you can try increasing the buffer size or adjusting the frame rate. Ensure that stream_buffer is set to True to help manage the frames more efficiently.

2. Threading for Concurrent Streams

Using threading can help manage multiple streams more effectively by processing each stream in a separate thread. Here’s an example of how you can implement threading to handle multiple streams:

import threading
import cv2
from ultralytics import YOLO

def process_stream(stream_url, model):
    cap = cv2.VideoCapture(stream_url)
    while cap.isOpened():
        success, frame = cap.read()
        if success:
            results = model.predict(source=frame, device='cuda:0', stream=True, classes=[0], stream_buffer=True)
            annotated_frame = results[0].plot()
            cv2.imshow(f"Stream: {stream_url}", annotated_frame)
            if cv2.waitKey(1) & 0xFF == ord("q"):
                break
        else:
            break
    cap.release()
    cv2.destroyAllWindows()

# Load the model
model = YOLO("yolov8n.pt")

# List of stream URLs
stream_urls = ["stream1_url", "stream2_url", "stream3_url", "stream4_url", "stream5_url"]

# Create and start a thread for each stream
threads = []
for url in stream_urls:
    thread = threading.Thread(target=process_stream, args=(url, model))
    thread.start()
    threads.append(thread)

# Wait for all threads to complete
for thread in threads:
    thread.join()

3. Optimizing Model Inference

Ensure that your model inference is optimized for performance. Using half-precision (FP16) can help speed up the inference process and reduce the load on your GPU:

results = model.predict(source=frame, device='cuda:0', stream=True, classes=[0], stream_buffer=True, half=True)

4. Monitoring System Resources

Monitor your system’s CPU and GPU usage to ensure that they are not being maxed out. High resource usage can lead to frame skipping and reduced detection performance. Tools like nvidia-smi for GPU monitoring and system resource monitors can be helpful.

5. Debugging and Logging

Add logging to track the performance and identify any bottlenecks. This can help you understand if certain streams are causing more issues than others:

import logging

logging.basicConfig(level=logging.INFO)

def process_stream(stream_url, model):
    cap = cv2.VideoCapture(stream_url)
    while cap.isOpened():
        success, frame = cap.read()
        if success:
            results = model.predict(source=frame, device='cuda:0', stream=True, classes=[0], stream_buffer=True)
            logging.info(f"Processed frame from {stream_url} with {len(results[0].boxes)} detections")
            annotated_frame = results[0].plot()
            cv2.imshow(f"Stream: {stream_url}", annotated_frame)
            if cv2.waitKey(1) & 0xFF == ord("q"):
                break
        else:
            break
    cap.release()
    cv2.destroyAllWindows()

I hope these suggestions help improve the performance and reliability of your multi-stream detection setup. If you continue to experience issues, please feel free to share more details or any additional observations. We're here to assist you further!

from ultralytics.

@glenn-jocher thanks for your detailed response.

I've already kept the stream_buffer=True and also kept vid_stride=10 can that be the issue? Because 3 frames per second are enough for these kind of detection. Please let me know if I'm wrong.

Previously, I used the threading approach you suggested, but it consumed too many resources, preventing me from running anything other than inference on the system. Since I often use the CPU rather than the GPU for inference, I switched to list.streams.

However, even with the threading approach, similar issues occurred. It is not entirely foolproof and tends to create problems at times.

from ultralytics.

Hello @VM8198,

Thank you for the additional details and for your patience as we work through this issue together. 😊

Given your setup and the constraints you're working with, let's explore a few more options to optimize your multi-stream detection process.

1. Frame Skipping with vid_stride

Using vid_stride=10 means that you're processing every 10th frame, which should indeed reduce the load on your system. However, if you're still experiencing missed detections, you might want to experiment with different stride values to find a balance between performance and detection accuracy. For example, try vid_stride=5 to see if it improves detection consistency without overloading your system.

2. Resource Management

Since threading consumed too many resources, consider using multiprocessing instead. Multiprocessing can help distribute the load across multiple CPU cores more efficiently. Here's an example of how you can implement multiprocessing for your streams:

import multiprocessing as mp
import cv2
from ultralytics import YOLO

def process_stream(stream_url, model):
    cap = cv2.VideoCapture(stream_url)
    while cap.isOpened():
        success, frame = cap.read()
        if success:
            results = model.predict(source=frame, device='cpu', stream=True, classes=[0], stream_buffer=True, vid_stride=10)
            annotated_frame = results[0].plot()
            cv2.imshow(f"Stream: {stream_url}", annotated_frame)
            if cv2.waitKey(1) & 0xFF == ord("q"):
                break
        else:
            break
    cap.release()
    cv2.destroyAllWindows()

if __name__ == "__main__":
    model = YOLO("yolov8n.pt")
    stream_urls = ["stream1_url", "stream2_url", "stream3_url", "stream4_url", "stream5_url"]

    processes = []
    for url in stream_urls:
        p = mp.Process(target=process_stream, args=(url, model))
        p.start()
        processes.append(p)

    for p in processes:
        p.join()

3. Optimizing Model Inference

Since you're using the CPU for inference, ensure that your model is optimized for CPU usage. You can try using the half precision mode, but note that this is typically more beneficial for GPU inference. For CPU, ensure that your model is not unnecessarily large (e.g., using YOLOv8n instead of YOLOv8x).

4. Monitoring and Logging

To better understand where the bottlenecks might be, add logging to monitor the performance and resource usage. This can help identify if specific streams are causing more issues than others:

import logging

logging.basicConfig(level=logging.INFO)

def process_stream(stream_url, model):
    cap = cv2.VideoCapture(stream_url)
    while cap.isOpened():
        success, frame = cap.read()
        if success:
            results = model.predict(source=frame, device='cpu', stream=True, classes=[0], stream_buffer=True, vid_stride=10)
            logging.info(f"Processed frame from {stream_url} with {len(results[0].boxes)} detections")
            annotated_frame = results[0].plot()
            cv2.imshow(f"Stream: {stream_url}", annotated_frame)
            if cv2.waitKey(1) & 0xFF == ord("q"):
                break
        else:
            break
    cap.release()
    cv2.destroyAllWindows()

5. System Resource Monitoring

Keep an eye on your system's CPU and memory usage using tools like htop or top on Linux, or Task Manager on Windows. This can help you understand if your system is being overwhelmed and guide you in adjusting your setup accordingly.

I hope these suggestions help improve the performance and reliability of your multi-stream detection setup. If you continue to experience issues, please feel free to share more details or any additional observations. We're here to assist you further!

from ultralytics.

Hello @glenn-jocher

I have checked with different parameters in vid_stride but it still the issue. It's not accurate as it should

Also, I checked htop and top the maximum Ram utilization is not more than 40%.

from ultralytics.

Hello @VM8198,

Thank you for the update and for checking the resource utilization. Given that the RAM usage is within limits, it seems the issue might be related to how frames are being processed or skipped.

To help us investigate further, could you please provide a minimum reproducible example of your code? This will allow us to replicate the issue on our end. You can refer to our guide on creating a minimum reproducible example here: https://docs.ultralytics.com/help/minimum_reproducible_example.

Additionally, please ensure you are using the latest versions of torch and ultralytics. You can upgrade them using:

pip install --upgrade torch ultralytics

In the meantime, you might also want to experiment with slightly lower vid_stride values or try using multiprocessing as suggested earlier to see if it improves detection accuracy.

Looking forward to your response so we can assist you further! 😊

from ultralytics.

Hello @glenn-jocher Sorry for the late reply.

I have already provided the MRE in the issue alreay. Still below is the example.

import cv2
from ultralytics import YOLO

model = YOLO("yolov8n.pt")

cap = cv2.VideoCapture(video_path)

while cap.isOpened():
    success, frame = cap.read()

    if success:
        results = model.predict(source='list.streams', stream=True,  classes=[0],  stream_buffer=True, vid_stride=10)

        annotated_frame = results[0].plot()

        cv2.imshow("YOLOv8 Inference", annotated_frame)

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

cap.release()
cv2.destroyAllWindows()

The torch and ultralytics versions are already updated. I've tried with lower vid_stride value (I've also tried removing the vid_stride) but that does not resolves the issue 100%. Yes there is a bit improvement but still it is skipping the frames.

Thanks.

from ultralytics.

Hello @VM8198,

Thank you for providing the Minimum Reproducible Example (MRE) and confirming that you're using the latest versions of torch and ultralytics. 😊

Given that you've already experimented with different vid_stride values and still encounter frame skipping, let's explore a few additional strategies to improve detection consistency.

1. Adjusting stream_buffer and vid_stride

While you've already tried adjusting vid_stride, let's ensure that stream_buffer is effectively managing the frames. You might want to experiment with different buffer sizes or even disable it to see if it makes a difference.

2. Multiprocessing Approach

Since threading consumed too many resources, let's revisit the multiprocessing approach. This can help distribute the load across multiple CPU cores more efficiently. Here's an updated example using multiprocessing:

import multiprocessing as mp
import cv2
from ultralytics import YOLO

def process_stream(stream_url, model):
    cap = cv2.VideoCapture(stream_url)
    while cap.isOpened():
        success, frame = cap.read()
        if success:
            results = model.predict(source=frame, device='cpu', stream=True, classes=[0], stream_buffer=True, vid_stride=10)
            annotated_frame = results[0].plot()
            cv2.imshow(f"Stream: {stream_url}", annotated_frame)
            if cv2.waitKey(1) & 0xFF == ord("q"):
                break
        else:
            break
    cap.release()
    cv2.destroyAllWindows()

if __name__ == "__main__":
    model = YOLO("yolov8n.pt")
    stream_urls = ["stream1_url", "stream2_url", "stream3_url", "stream4_url", "stream5_url"]

    processes = []
    for url in stream_urls:
        p = mp.Process(target=process_stream, args=(url, model))
        p.start()
        processes.append(p)

    for p in processes:
        p.join()

3. Monitoring and Logging

Add logging to track the performance and identify any bottlenecks. This can help understand if specific streams are causing more issues than others:

import logging

logging.basicConfig(level=logging.INFO)

def process_stream(stream_url, model):
    cap = cv2.VideoCapture(stream_url)
    while cap.isOpened():
        success, frame = cap.read()
        if success:
            results = model.predict(source=frame, device='cpu', stream=True, classes=[0], stream_buffer=True, vid_stride=10)
            logging.info(f"Processed frame from {stream_url} with {len(results[0].boxes)} detections")
            annotated_frame = results[0].plot()
            cv2.imshow(f"Stream: {stream_url}", annotated_frame)
            if cv2.waitKey(1) & 0xFF == ord("q"):
                break
        else:
            break
    cap.release()
    cv2.destroyAllWindows()

4. System Resource Monitoring

Keep an eye on your system’s CPU and memory usage using tools like htop or top on Linux, or Task Manager on Windows. This can help you understand if your system is being overwhelmed and guide you in adjusting your setup accordingly.

If these strategies do not resolve the issue, please let us know, and we can explore further options. Thank you for your patience and collaboration!

from ultralytics.

Hello @glenn-jocher. Thanks for your reply.

I'm facing the same issues with multiprocessing also.
stream_buffer is also not making any difference. I've tried with and without stream_buffer. And for the system resource monitor it takes less resources in multiprocessing than threading

Overall the issue still persists.

Thanks

from ultralytics.

Hello @VM8198,

Thank you for your patience and for providing detailed feedback. 😊

Given that you've tried both threading and multiprocessing with similar results, and adjusting stream_buffer hasn't resolved the issue, let's explore a few more potential solutions:

1. Frame Rate Adjustment

Since vid_stride didn't fully resolve the issue, consider adjusting the frame rate directly in your video capture settings. This can help ensure that frames are processed at a consistent rate.

2. Model Optimization

Ensure that your model is optimized for CPU inference. Using a smaller model like YOLOv8n can help reduce the processing load.

3. Batch Processing

If your application allows, consider processing frames in batches. This can help improve efficiency and reduce the likelihood of frame skipping.

4. Alternative Streaming Libraries

Consider using alternative libraries for video streaming, such as ffmpeg or gstreamer, which might offer better performance and stability for handling multiple streams.

Example: Adjusting Frame Rate

import cv2
from ultralytics import YOLO

model = YOLO("yolov8n.pt")

cap = cv2.VideoCapture("path/to/your/list.streams")
cap.set(cv2.CAP_PROP_FPS, 3)  # Set frame rate to 3 FPS

while cap.isOpened():
    success, frame = cap.read()
    if success:
        results = model.predict(source=frame, device='cpu', stream=True, classes=[0], vid_stride=10)
        annotated_frame = results[0].plot()
        cv2.imshow("YOLOv8 Inference", annotated_frame)
        if cv2.waitKey(1) & 0xFF == ord("q"):
            break
    else:
        break

cap.release()
cv2.destroyAllWindows()

Monitoring and Logging

Continue to monitor and log the performance to identify any specific patterns or issues with certain streams.

If the issue persists, please let us know, and we can explore further options. Thank you for your collaboration and understanding!

from ultralytics.

Hello @glenn-jocher

I have tried everything you suggest. but not getting the desired results.

from ultralytics.

Hello @VM8198,

Thank you for your patience and for trying the suggested solutions. 😊

Given that you've already explored various approaches without achieving the desired results, let's take a closer look at your specific setup. To proceed effectively, could you please confirm the following:

1. Minimum Reproducible Example: Ensure that the code you provided is the exact setup you're using. If there are any differences, please share the updated code.
2. Package Versions: Verify that you are using the latest versions of torch and ultralytics. You can upgrade them using:

   pip install --upgrade torch ultralytics

If both points are confirmed and the issue persists, we might need to delve deeper into the specifics of your environment and the streams you're processing. Sometimes, external factors such as network latency or stream quality can impact performance.

Additionally, consider experimenting with alternative video processing libraries like ffmpeg or gstreamer to handle the streams more efficiently. These libraries are known for their robust handling of video streams and might offer better performance for your use case.

Feel free to share any additional observations or specific details about the streams you're working with. We're here to help you get to the bottom of this! 🚀

from ultralytics.

Okay @glenn-jocher thanks for your responses. I will check the camera stream network latency and quality and get back to you.

I've another question. Is there any way to change the list.stream file dynamically so that the inference does not restarts?

Let me explain. Suppose I've added 5 streams in list.stream and inference is started on those 5 streams. Now if I add or remove one stream from it than inference is not taking that change in effect. It will keep processing those 5 streams untill the inference is restarted.

Please let me know if there is any way to update the list.streams dynamically.

Thanks.

from ultralytics.

Hello @VM8198,

Thank you for your continued engagement and for considering the network latency and stream quality aspects. 😊

Regarding your new question about dynamically updating the list.streams file without restarting the inference, currently, the YOLOv8 framework does not support dynamic updates to the stream list during an active inference session. The streams are initialized at the start, and any changes to the list would require a restart to take effect.

However, you can implement a custom solution to handle dynamic stream updates. One approach is to use a separate thread or process to monitor changes to the list.streams file and restart the inference with the updated list when changes are detected. Here's a conceptual example using Python's watchdog library to monitor file changes:

Example: Dynamic Stream Update with Watchdog

1. Install Watchdog:

   pip install watchdog

2. Monitor and Restart Inference:

   import time
   from watchdog.observers import Observer
   from watchdog.events import FileSystemEventHandler
   import subprocess
   
   class StreamFileHandler(FileSystemEventHandler):
       def __init__(self, inference_process):
           self.inference_process = inference_process
   
       def on_modified(self, event):
           if event.src_path == "path/to/list.streams":
               print("Stream list updated. Restarting inference...")
               self.inference_process.terminate()
               self.inference_process = start_inference()
   
   def start_inference():
       return subprocess.Popen(["python", "your_inference_script.py"])
   
   if __name__ == "__main__":
       inference_process = start_inference()
       event_handler = StreamFileHandler(inference_process)
       observer = Observer()
       observer.schedule(event_handler, path="path/to", recursive=False)
       observer.start()
   
       try:
           while True:
               time.sleep(1)
       except KeyboardInterrupt:
           observer.stop()
       observer.join()

In this example, your_inference_script.py should contain your inference logic. The StreamFileHandler monitors the list.streams file and restarts the inference process whenever the file is modified.

This approach allows you to dynamically update the stream list without manually restarting the inference each time.

Feel free to adapt this solution to fit your specific needs. If you have any further questions or need additional assistance, please let us know!

from ultralytics.

Okay, thanks.

from ultralytics.

Hello @VM8198,

Thank you for your understanding and for considering the network latency and stream quality aspects. 😊

Regarding your question about dynamically updating the list.streams file without restarting the inference, currently, YOLOv8 does not support this feature natively. However, you can implement a custom solution to monitor changes to the list.streams file and restart the inference process when updates are detected.

Here's a conceptual example using Python's watchdog library to monitor file changes and restart the inference process:

1. Install Watchdog:

   pip install watchdog

2. Monitor and Restart Inference:

   import time
   from watchdog.observers import Observer
   from watchdog.events import FileSystemEventHandler
   import subprocess
   
   class StreamFileHandler(FileSystemEventHandler):
       def __init__(self, inference_process):
           self.inference_process = inference_process
   
       def on_modified(self, event):
           if event.src_path == "path/to/list.streams":
               print("Stream list updated. Restarting inference...")
               self.inference_process.terminate()
               self.inference_process = start_inference()
   
   def start_inference():
       return subprocess.Popen(["python", "your_inference_script.py"])
   
   if __name__ == "__main__":
       inference_process = start_inference()
       event_handler = StreamFileHandler(inference_process)
       observer = Observer()
       observer.schedule(event_handler, path="path/to", recursive=False)
       observer.start()
   
       try:
           while True:
               time.sleep(1)
       except KeyboardInterrupt:
           observer.stop()
       observer.join()

This approach allows you to dynamically update the stream list without manually restarting the inference each time. Feel free to adapt this solution to fit your specific needs.

If you have any further questions or need additional assistance, please let us know! 🚀

from ultralytics.