Slow Inference Pipeline with M3U8 Format

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Question

Hi,

Introduction
While working with the inference pipeline, I had a significant performance problem when processing M3U8 (HTTP Live Streaming) format files. Unlike MP4 files which process smoothly, M3U8 inputs seem to cause skipping of frames in the pipeline. Could this be investigated? It's needed for all live-streaming services and I would really like to use it for my workflow as well.

I am not testing live, so I have a video (which plays as a livestream at 25 fps in m3u8 format) and when I manually use openCV to look at the frames and process them everything works and looks fine. When I use the inference pipeline, it starts skipping a lot of the frames. I also tried different max_fps (from 1 to 100), but the problem stays.

Question
How can I make sure that none of the frames from my m3u8 stream are skipped using the inference pipeline?

My code is the following:

self.pipeline = InferencePipeline.init_with_workflow(
            api_key=self.api_key,
            workspace_name=self.workspace_name,
            workflow_id=self.workflow_id,
            video_reference=video_url,
            max_fps=100,
            on_prediction=self.process_frame
        )
       
self.pipeline.start()
self.pipeline.join()

Any help would be appreciated!

With kind regards,
Emma

Additional

No response

Start by reproducing the frame-skipping behavior through InferencePipeline.init_with_workflow using the reported M3U8 video_reference and different max_fps values, then compare it with the author's direct OpenCV processing. Trace how the pipeline handles M3U8 input and define done as processing the stream without unexplained frame loss.