Deepstream python app - how to deepcopy and process part of frame?

• Hardware Platform (Jetson / GPU) Nvidia RTX A2000
• DeepStream Version 6.0.1
• TensorRT Version 8.0.1-1
• NVIDIA GPU Driver Version (valid for GPU only) 470.86


I have some question - I just want to process some frames in probe_fcn.

For instance I will get some bbox got from inference, want to crop the frame to retrieve some detected object, copy the object and process somehow - but not the whole frame - just an object.

And if in the probe_function I’m trying to deepcopy some frame - I got segmentation fault (core dumped).

I have prepared my own lib for gstlib but the problem also occurs in deepstream python example ( deepstream-imagedata-multistream

So is there any chance to get any information how it should be done?

It’s interesting cause as I have tested, it occurs only when I have uridecodebin/rtsp as source.
With usb/multifilesource it works ok.
The pipeline:


Is it possible the deepcopy cause the pipeline can’t process data in real time?

Yeah, I was thinking about this but why usb source works then?
And yeah, is there any possibility to process/deepcopy frames even when the source is rtsp?

maybe it’s important - the problem also occurs in deepstream 5

All right, I have found the solution for the problem.
Dont know why but using Gst.Bin as sink to the streammux does not work.

Finally I have append the uridecodebin inside Gst.Bin, converters and caps filter for every stream are outside of GstBin.
You can take a look at the pipeline and see the differencess between the first pipeline I have added.

The above pipeline is also solution for the problem what I mentioned in the topic:
multistream cropping problem

So the question is why the GstBin is not working as expected?
What did I do wrong?

Glad to know you have solution for you issue. Where is the different of the two pipeline?

In not working version - the source pipeline (before streammux) was packed in GstBin.
You can take a look at pipeline.png pictures (remember to enlarge picture to see details).

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