/dlstreamer_gst

This repository is a home to Deep Learning (DL) Streamer. DL Streamer is a streaming media analytics framework, based on GStreamer* multimedia framework, for creating complex media analytics pipelines.

Primary LanguageC++MIT LicenseMIT

OpenVINO™ Toolkit - DL Streamer repository

Overview

This repository is a home to Deep Learning (DL) Streamer. DL Streamer is a streaming media analytics framework, based on GStreamer* multimedia framework, for creating complex media analytics pipelines. It ensures pipeline interoperability and provides optimized media, and inference operations using Intel® Distribution of OpenVINO™ Toolkit Inference Engine backend, across Intel® architecture - CPU, iGPU and Intel® Movidius™ VPU. DL Streamer prebuilt binaries can be installed with the Intel® Distribution of OpenVINO™ Toolkit installer.

Here's the canonical video analytics pipeline consturctued using DL Streamer. It performs detection and classification operations on a video stream, using face detection and emotion classification deep learning models. The results of this pipeline are demoed in the above video clip:

gst-launch-1.0 filesrc location=cut.mp4 ! decodebin ! videoconvert ! gvadetect model=face-detection-adas-0001.xml ! gvaclassify model=emotions-recognition-retail-0003.xml model-proc=emotions-recognition-retail-0003.json ! gvawatermark ! xvimagesink sync=false

The complete solution leverages:

  • Open source GStreamer* framework for pipeline management
  • GStreamer* plugins for input and output such as media files and real-time streaming from camera or network
  • Video decode and encode plugins, either CPU optimized plugins or GPU-accelerated plugins based on VAAPI
  • Deep Learning models converted from training frameworks TensorFlow*, Caffe* etc. from Open Model Zoo And, the following elements in the DL Streamer repository:

In addition, the solution uses the following Deep Learning-specific elements, also available in this repository:

  • Inference plugins leveraging OpenVINO™ Toolkit for high-performance inference using deep learning models
  • Visualization of the inference results, with bounding boxes and labels of detected objects, on top of video stream

Please refer to Elements for the complete DL Streamer elements list.

License

The GStreamer Video Analytics Plugin, part of OpenVINO™ Toolkit - DL Streamer, is licensed under the MIT license.

GStreamer is an open source framework licensed under LGPL. See license terms. You are solely responsible for determining if your use of Gstreamer requires any additional licenses. Intel is not responsible for obtaining any such licenses, nor liable for any licensing fees due, in connection with your use of Gstreamer

Prerequisites

Hardware

Software

  • Intel® Distribution of OpenVINO™ Toolkit Release 2021.1 (Inference Engine 2.1.0) or above
  • Linux* system with kernel 4.15 or above
  • GStreamer* framework 1.16 or above

Getting Started

For additional documentation, please see wiki and don't miss the documentation indexed on the right side of the wiki home page.

Develop in the Cloud

Try DL Streamer with Intel® DevCloud. You can build your pipeline, test and optimize for free. With an Intel® DevCloud account, you get 120 days of access to the latest Intel® hardware — CPUs, GPUs, FPGAs. No software downloads. No configuration steps. No installations. Check out DL Stramer Tutorial on Intel® DevCloud.

Other Useful Links

Reporting Bugs and Feature Requests

Report bugs and requests on the issues page

How to contribute

Pull requests aren't monitored, so if you have bug fix or an idea to improve this project, post a description on the issues page.

* Other names and brands may be claimed as the property of others.