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- ################################################################################
- # Copyright (c) 2019-2021, NVIDIA CORPORATION. All rights reserved.
- #
- # Permission is hereby granted, free of charge, to any person obtaining a
- # copy of this software and associated documentation files (the "Software"),
- # to deal in the Software without restriction, including without limitation
- # the rights to use, copy, modify, merge, publish, distribute, sublicense,
- # and/or sell copies of the Software, and to permit persons to whom the
- # Software is furnished to do so, subject to the following conditions:
- #
- # The above copyright notice and this permission notice shall be included in
- # all copies or substantial portions of the Software.
- #
- # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
- # IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
- # FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL
- # THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
- # LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
- # FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER
- # DEALINGS IN THE SOFTWARE.
- ################################################################################
- Prequisites:
- - DeepStreamSDK 5.1
- - Python 3.6
- - Gst-python
- To run the test app:
- $ python3 deepstream_test_2.py <h264_elementary_stream> <0/1>
- To get the past-frame tracking meta use 1, otherwise 0, this argument is optional.
- This document shall describe about the sample deepstream-test2 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.
- This sample creates multiple instances of "nvinfer" element. Each instance of
- the "nvinfer" uses TensorRT API to infer on frames/objects. Every
- instance is configured through its respective config file. 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
- dstest2_pgie_config.txt
- 2. The vehicle color classifier (referred to as sgie1 in this sample) uses
- dstest2_sgie1_config.txt
- 3. The vehicle make classifier (referred to as sgie2 in this sample) uses
- dstest2_sgie2_config.txt
- 4. The vehicle type classifier (referred to as sgie3 in this sample) uses
- dstest2_sgie3_config.txt
- 5. The tracker (referred to as nvtracker in this sample) uses
- dstest2_tracker_config.txt
- To get the past-frame-tracking meta, the following changes have to be added to
- the dstest2_tracker_config.txt.
- 1. ll-lib-file=/opt/nvidia/deepstream/deepstream/lib/libnvds_nvdcf.so
- 2. ll-config-file=tracker_config.yml
- 3. enable-past-frame=1
- 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". After this we link a "nvtracker" instance which tracks the objects
- detected by the pgie. After this we create 3 more instances of "nvinfer"
- referred to as sgie1, sgie2, sgie3 respectively.
- Each of the nvinfer elements attach some MetaData to the buffer. By attaching
- the probe function at the end of the pipeline, one can extract meaningful
- information from these inferences. 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"
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