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Prequisites:
- DeepStreamSDK 7.1
- Python 3.10
- Gst-python
- GstRtspServer



Installing GstRtspServer and introspection typelib
===================================================
$ sudo apt update
$ sudo apt install python3-gi python3-dev python3-gst-1.0 -y
$ sudo apt-get install libgstrtspserver-1.0-0 gstreamer1.0-rtsp
For gst-rtsp-server (and other GStreamer stuff) to be accessible in
Python through gi.require_version(), it needs to be built with
gobject-introspection enabled (libgstrtspserver-1.0-0 is already).
Yet, we need to install the introspection typelib package:
$ sudo apt-get install libgirepository1.0-dev
$ sudo apt-get install gobject-introspection gir1.2-gst-rtsp-server-1.0

To get test app usage information:
-----------------------------------
  $ python3 deepstream_test1_rtsp_in_rtsp_out.py -h
  
To run the test app with default settings:
------------------------------------------
  1) NVInfer
      $ python3 deepstream_test1_rtsp_in_rtsp_out.py -i rtsp://sample_1.mp4 rtsp://sample_2.mp4 rtsp://sample_N.mp4  -g nvinfer
  2) NVInferserver
      bash /opt/nvidia/deepstream/deepstream-<Version>/samples/prepare_ds_trtis_model_repo.sh
      $ python3 deepstream_test1_rtsp_in_rtsp_out.py -i rtsp://sample_1.mp4 rtsp://sample_2.mp4 rtsp://sample_N.mp4  -g nvinferserver
  
Default RTSP streaming location:
  rtsp://<server IP>:8554/ds-test

This document shall describe the sample deepstream_test1_rtsp_in_rtsp_out application.

This sample app is derived from the deepstream-test3 and deepStream-test1-rtsp-out

This sample app specifically includes following : 
  - Accepts RTSP stream as input and gives out inference as RTSP stream
  - User can choose NVInfer and NVInferserver as GPU inference engine

  If NVInfer is selected then : 
    For reference, here are the config files used for this sample :
    1. The 4-class detector (referred to as pgie in this sample) uses
        dstest1_pgie_config.txt
    2. This 4 class detector detects "Vehicle , RoadSign, TwoWheeler, Person".


    In this sample, first create one instance of "nvinfer", referred as the pgie.
    This is our 4 class detector and it detects for "Vehicle , RoadSign, TwoWheeler,
    Person".

  If NVInferserver is selected then:
    1. Uses SSD neural network running on Triton Inference Server
    2. Selects custom post-processing in the Triton Inference Server config file
    3. Parses the inference output into bounding boxes
    4. Performs post-processing on the generated boxes with NMS (Non-maximum Suppression)
    5. Adds detected objects into the pipeline metadata for downstream processing
    6. Encodes OSD output and shows visual output over RTSP.

The "--rtsp-ts" option configures the RTSP source to attach the timestamp to the frame, as opposed to the streammux. Before using this option, please make sure the RTSP source sends RTCP Sender Reports. See this documentation for more details on how to check: https://docs.nvidia.com/metropolis/deepstream/dev-guide/text/DS_NTP_Timestamp.html.