deepstream-ssd-parser
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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 7.1 - NVIDIA Triton Inference Server - Python 3.10 - Gst-python - NumPy To set up Triton Inference Server: For x86_64 and Jetson Docker: 1. Use the provided docker container and follow directions for Triton Inference Server in the SDK README -- be sure to prepare the detector models. 2. Run the docker with this Python Bindings directory mapped 3. Install required Python packages inside the container: $ apt update $ apt install python3-gi python3-dev python3-gst-1.0 python3-numpy -y For Jetson without Docker: 1. Install NumPy: $ apt update $ apt install python3-numpy 2. Follow instructions in the DeepStream SDK README to set up Triton Inference Server: 2.1 Compile and install the nvdsinfer_customparser 2.2 Prepare at least the Triton detector models 3. Add to LD_PRELOAD: /usr/lib/aarch64-linux-gnu/libgomp.so.1 This is to work around the following problem with TLS usage limitation: https://gcc.gnu.org/bugzilla/show_bug.cgi?id=91938 4. Clear the GStreamer cache if pipeline creation fails: rm ~/.cache/gstreamer-1.0/* To run the test app: $ python3 deepstream_ssd_parser.py <h264_elementary_stream> This document shall describe the sample deepstream-ssd-parser application. It is meant for simple demonstration of how to make a custom neural network output parser and use it in the pipeline to extract meaningful insights from a video stream. This example: - Uses SSD neural network running on Triton Inference Server - Selects custom post-processing in the Triton Inference Server config file - Parses the inference output into bounding boxes - Performs post-processing on the generated boxes with NMS (Non-maximum Suppression) - Adds detected objects into the pipeline metadata for downstream processing - Encodes OSD output and saves to MP4 file. Note that there is no visual output on screen. Known Issue: 1. On Jetson, if libgomp is not preloaded, this error may occur: (python3:21041): GStreamer-WARNING **: 14:35:44.113: Failed to load plugin '/usr/lib/aarch64-linux-gnu/gstreamer-1.0/libgstlibav.so': /usr/lib/aarch64-linux-gnu/libgomp.so.1: cannot allocate memory in static TLS block Unable to create Encoder 2. On Jetson Nano, ssd_inception_v2 is not expected to run with GPU instance. Switch to CPU instance when running on Nano: update config.pbtxt files in samples/trtis_modeo_repo: # Switch to CPU instance for Nano since memory might not be enough for # certain Models. # Specify CPU instance. instance_group { count: 1 kind: KIND_CPU } # Specify GPU instance. #instance_group { # kind: KIND_GPU # count: 1 # gpus: 0 #}