deepstream-test2
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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"