deepstream_python_apps/apps/deepstream-imagedata-multistream

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Prerequisites:
- DeepStreamSDK 5.1
- Python 3.6
- Gst-python
- NumPy package
- OpenCV package

To install required packages:
$ sudo apt update
$ sudo apt install python3-numpy python3-opencv -y

To run:
  $ python3 deepstream_imagedata-multistream.py <uri1> [uri2] ... [uriN] <FOLDER NAME TO SAVE FRAMES>
e.g.
  $ python3 deepstream_imagedata-multistream.py file:///home/ubuntu/video1.mp4 file:///home/ubuntu/video2.mp4 frames
  $ python3 deepstream_imagedata-multistream.py rtsp://127.0.0.1/video1 rtsp://127.0.0.1/video2 frames

This document describes the sample deepstream-imagedata-multistream application.

This sample builds on top of the deepstream-test3 sample to demonstrate how to:

* Access imagedata in a multistream source
* Modify the images in-place. Changes made to the buffer will reflect in the downstream but  
  color format, resolution and numpy transpose operations are not permitted.  
* Make a copy of the image, modify it and save to a file. These changes are made on the copy  
  of the image and will not be seen downstream.
* Extract the stream metadata, imagedata, which contains useful information about the
  frames in the batched buffer.
* Annotating detected objects within certain confidence interval
* Use OpenCV to draw bboxes on the image and save it to file.
* Use multiple sources in the pipeline.
* Use a uridecodebin so that any type of input (e.g. RTSP/File), any GStreamer
  supported container format, and any codec can be used as input.
* Configure the stream-muxer to generate a batch of frames and infer on the
  batch for better resource utilization.

NOTE:
- For x86, only CUDA unified memory is supported.
- Only RGBA color format is supported for access from Python. Color conversion
  is added in the pipeline for this reason.

This sample accepts one or more H.264/H.265 video streams as input. It creates
a source bin for each input and connects the bins to an instance of the
"nvstreammux" element, which forms the batch of frames. The batch of
frames is fed to "nvinfer" for batched inferencing. The batched buffer is
composited into a 2D tile array using "nvmultistreamtiler." The rest of the
pipeline is similar to the deepstream-test3 and deepstream-imagedata sample.

The "width" and "height" properties must be set on the stream-muxer to set the
output resolution. If the input frame resolution is different from
stream-muxer's "width" and "height", the input frame will be scaled to muxer's
output resolution.

The stream-muxer waits for a user-defined timeout before forming the batch. The
timeout is set using the "batched-push-timeout" property. If the complete batch
is formed before the timeout is reached, the batch is pushed to the downstream
element. If the timeout is reached before the complete batch can be formed
(which can happen in case of rtsp sources), the batch is formed from the
available input buffers and pushed. Ideally, the timeout of the stream-muxer
should be set based on the framerate of the fastest source. It can also be set
to -1 to make the stream-muxer wait infinitely.

The "nvmultistreamtiler" composite streams based on their stream-ids in
row-major order (starting from stream 0, left to right across the top row, then
across the next row, etc.).