deepstream-test1-rtsp-out
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################################################################################ # SPDX-FileCopyrightText: Copyright (c) 2020-2021 NVIDIA CORPORATION & AFFILIATES. All rights reserved. # SPDX-License-Identifier: Apache-2.0 # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. ################################################################################ Prequisites: - DeepStreamSDK 6.1.1 - Python 3.8 - Gst-python - GstRtspServer Installing GstRtspServer and instrospection typelib =================================================== $ sudo apt update $ 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_out.py -h To run the test app with default settings: $ python3 deepstream_test1_rtsp_out.py -i <h264_elementary_stream> Default RTSP streaming location: rtsp://<server IP>:8554/ds-test This document shall describe the sample deepstream-test1-rtsp-out application. It is meant for simple demonstration of how to use the various DeepStream SDK elements in the pipeline and extract meaningful insights from a video stream. The output video data with inference output overlayed is encoded and streamed using GstRtspServer. This sample creates instance of "nvinfer" element. Instance of the "nvinfer" uses TensorRT API to execute inferencing on a model. Using a correct configuration for a nvinfer element instance is therefore very important as considerable behaviors of the instance are parameterized through these configs. 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 In this sample, we first create one instance of "nvinfer", referred as the pgie. This is our 4 class detector and it detects for "Vehicle , RoadSign, TwoWheeler, Person". nvinfer element attach some MetaData to the buffer. By attaching the probe function at the end of the pipeline, one can extract meaningful information from this inference. Please refer the "osd_sink_pad_buffer_probe" function in the sample code. For details on the Metadata format, refer to the file "gstnvdsmeta.h".