# dlstreamer_gst

**Repository Path**: openvinotoolkit-prc/dlstreamer_gst

## Basic Information

- **Project Name**: dlstreamer_gst
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: MIT
- **Default Branch**: main
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2020-09-25
- **Last Updated**: 2026-07-21

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

<div align="center">

# Deep Learning Streamer (DL Streamer)

**Hardware-accelerated video analytics pipelines — CPU, GPU and NPU, from a single line of code to production-grade edge AI**

[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](./LICENSE)
[![Ubuntu](https://img.shields.io/badge/Ubuntu-22.04%20%7C%2024.04-orange?logo=ubuntu)](https://docs.openedgeplatform.intel.com/dev/edge-ai-libraries/dlstreamer/get_started/system_requirements.html)
[![Windows](https://img.shields.io/badge/Windows-11-blue?logo=windows)](https://docs.openedgeplatform.intel.com/dev/edge-ai-libraries/dlstreamer/get_started/system_requirements.html)
[![OpenVINO](https://img.shields.io/badge/OpenVINO-powered-blue)](https://docs.openvino.ai)
[![GStreamer](https://img.shields.io/badge/GStreamer-based-brightgreen)](https://gstreamer.freedesktop.org)
[![Docker](https://img.shields.io/badge/Docker-available-2496ED?logo=docker)](https://hub.docker.com/r/intel/dlstreamer)
[![Part of Open Edge Platform](https://img.shields.io/badge/Open%20Edge%20Platform-member-0071C5)](https://github.com/open-edge-platform)

<img src="./hero.jpg" width="800" alt="DL Streamer sample outputs">

[Get Started](#quick-start---installation) • [Run Your Pipeline](#quick-start---run-your-pipeline) • [Samples](./samples/gstreamer/README.md) • [Elements](https://docs.openedgeplatform.intel.com/dev/edge-ai-libraries/dlstreamer/elements/elements.html) • [Documentation](https://docs.openedgeplatform.intel.com/dev/edge-ai-libraries/dlstreamer/index.html) • [Contributing](./CONTRIBUTING.md)

</div>

---

## What is DL Streamer?

**DL Streamer** is an open-source media analytics framework built on [GStreamer](https://gstreamer.freedesktop.org). It lets you build video and audio intelligence pipelines — from a simple object detection command line to a multi-stream, multi-sensor production deployment, all with minimal code.

- Powered by **[OpenVINO™](https://docs.openvino.ai)** for optimized inference on Intel CPU, GPU, and NPU.
- Pipelines are described as **simple strings** (or Python/C++ code) and executed with full hardware acceleration.
- Ships with **30+ ready-to-run samples** covering detection, classification, tracking, VLMs, LiDAR and more.
- Part of the **[Intel Open Edge Platform](https://github.com/open-edge-platform)**.

---

## Why DL Streamer?

| Benefit | Details |
|---|---|
| **One-line pipelines** | Build a working detection pipeline in a single `gst-launch-1.0` command |
| **Hardware acceleration** | Targets CPU, GPU, and NPU on Intel platforms from a single codebase |
| **VLM & GenAI ready** | Run Vision-Language Models (MiniCPM-V, CLIP, Whisper) in a GStreamer pipeline |
| **GstAnalytics compliance** | Supports the GStreamer industry metadata standard for interoperability with other GStreamer-based tools |
| **Messaging integration** | Publish inference results directly to MQTT or Kafka with built-in elements — no extra code required |
| **Python-first extensibility** | Add custom logic as Python callbacks or full Python GStreamer elements — no C++ required |
| **Multi-stream, multi-sensor** | Mux/demux dozens of RTSP streams, LiDAR frames, and radar point clouds in one process |
| **Geti™, Ultralytics & HuggingFace support** | Deploy models from Geti™ Studio, Ultralytics, Hugging Face, or any ONNX/OpenVINO IR model directly |

---

## Quick Start - Installation

### Step 1 — Install GPU/NPU drivers (required for Docker and native install)

```bash
cd ~
wget https://raw.githubusercontent.com/open-edge-platform/dlstreamer/main/scripts/DLS_install_prerequisites.sh
chmod +x DLS_install_prerequisites.sh
./DLS_install_prerequisites.sh
```

> This script detects your Intel GPU/NPU, installs the correct drivers for Ubuntu 22.04 or 24.04, and adds your user to the required groups. Use `--reinstall-npu-driver=yes` to force-reinstall the NPU driver. Run `./DLS_install_prerequisites.sh --help` for all options.

### Step 2 — Install DL Streamer

**Option A — Docker (recommended, zero setup)**:

```bash
# Run once on the host to allow X11 forwarding from containers
xhost +local:docker

docker run -it --rm \
  --device /dev/dri \
  --group-add $(stat -c "%g" /dev/dri/render*) \
  -e DISPLAY=$DISPLAY \
  -e XDG_RUNTIME_DIR=/tmp \
  -v /tmp/.X11-unix:/tmp/.X11-unix \
  intel/dlstreamer:latest
```

> To use the NPU, also add `--device /dev/accel --group-add $(stat -c "%g" /dev/accel/accel*) -e ZE_ENABLE_ALT_DRIVERS=libze_intel_npu.so` to the `docker run` command.

**Option B — Native install (Ubuntu 24.04)**:

```bash
sudo -E wget -O- https://apt.repos.intel.com/intel-gpg-keys/GPG-PUB-KEY-INTEL-SW-PRODUCTS.PUB | gpg --dearmor | sudo tee /usr/share/keyrings/intel-gpg-archive-keyring.gpg > /dev/null
sudo -E wget -O- https://apt.repos.intel.com/edgeai/dlstreamer/GPG-PUB-KEY-INTEL-DLS.gpg | sudo tee /usr/share/keyrings/dls-archive-keyring.gpg > /dev/null
echo "deb [signed-by=/usr/share/keyrings/dls-archive-keyring.gpg] https://apt.repos.intel.com/edgeai/dlstreamer/ubuntu24 ubuntu24 main" | sudo tee /etc/apt/sources.list.d/intel-dlstreamer.list
sudo bash -c 'echo "deb [signed-by=/usr/share/keyrings/intel-gpg-archive-keyring.gpg] https://apt.repos.intel.com/openvino ubuntu24 main" | sudo tee /etc/apt/sources.list.d/intel-openvino.list'
sudo apt update && sudo apt-get install -y intel-dlstreamer
```

Full installation guide: [Install Guide for Ubuntu](https://docs.openedgeplatform.intel.com/dev/edge-ai-libraries/dlstreamer/get_started/install/install_guide_ubuntu.html) | [Windows](https://docs.openedgeplatform.intel.com/dev/edge-ai-libraries/dlstreamer/get_started/install/install_guide_windows.html)

---

## Quick Start - Run Your Pipeline

**Step 1 — Set up models and environment** (inside the container or on a native install):

```bash
cd ~
python3 -m venv .dls-venv && source .dls-venv/bin/activate
pip install openvino==2026.2.0 nncf==3.0.0 ultralytics==8.4.57
python3 /opt/intel/dlstreamer/scripts/download_models/download_ultralytics_models.py \
  --model yolo11n.pt \
  --outdir ~/models/yolo11n \
  --int8
source /opt/intel/dlstreamer/scripts/setup_dls_env.sh
```

**Step 2 — Run the pipeline.** Change `device=GPU` to `device=CPU` or `device=NPU` — no other code changes needed.

```bash
gst-launch-1.0 \
  urisourcebin buffer-size=4096 uri=https://videos.pexels.com/video-files/1192116/1192116-sd_640_360_30fps.mp4 ! \
  decodebin3 ! \
  gvadetect model=~/models/yolo11n/yolo11n_int8_openvino_model/yolo11n.xml device=GPU ! \
  queue ! \
  gvawatermark ! \
  gvafpscounter ! \
  videoconvert ! autovideosink sync=false
```

Output to JSON (works everywhere, including headless Docker):

```bash
gst-launch-1.0 \
  urisourcebin buffer-size=4096 uri=https://videos.pexels.com/video-files/1192116/1192116-sd_640_360_30fps.mp4 ! \
  decodebin3 ! \
  gvadetect model=~/models/yolo11n/yolo11n_int8_openvino_model/yolo11n.xml device=GPU ! \
  queue ! \
  gvafpscounter ! \
  gvametaconvert format=json ! \
  gvametapublish file-format=json-lines file-path=output.json ! fakesink async=false
```

### Python API

Create a file `detect.py`:

```python
import gi
gi.require_version("Gst", "1.0")
from gi.repository import Gst
import os

Gst.init([])

video_url = "https://videos.pexels.com/video-files/1192116/1192116-sd_640_360_30fps.mp4"
model = os.path.expanduser("~/models/yolo11n/yolo11n_int8_openvino_model/yolo11n.xml")
pipeline = Gst.parse_launch(f"""
    urisourcebin buffer-size=4096 uri={video_url} !
    decodebin3 !
    gvadetect model={model} device=GPU !
    queue !
    gvafpscounter !
    gvametaconvert format=json !
    gvametapublish file-format=json-lines file-path=output_from_python.json ! fakesink async=false
""")

pipeline.set_state(Gst.State.PLAYING)
bus = pipeline.get_bus()
bus.timed_pop_filtered(Gst.CLOCK_TIME_NONE, Gst.MessageType.EOS | Gst.MessageType.ERROR)
pipeline.set_state(Gst.State.NULL)
```

Then run it and wait for results:

```bash
python3 detect.py
# Detection results are written to output_from_python.json as the pipeline processes frames
# Each line is a JSON object with detected objects, labels, and bounding boxes
cat output_from_python.json
```

> If any Python dependencies are missing, refer to the [Python dependencies install guide](https://docs.openedgeplatform.intel.com/dev/edge-ai-libraries/dlstreamer/dev_guide/advanced_install/advanced_install_guide_compilation.html#step-10-install-python-dependencies-optional).

---

## DL Streamer Elements

| Category | Key Elements |
|---|---|
| **Inference** | `gvadetect` · `gvaclassify` · `gvainference` · `gvagenai` · `gvaaudiotranscribe` |
| **Analytics** | `gvatrack` · `gvaanalytics` · `gvastreammux` / `gvastreamdemux` · `gvamotiondetect` |
| **Output** | `gvawatermark` · `gvametaconvert` · `gvametapublish` · `gvafpscounter` |
| **3D / Sensors** | `g3dlidarparse` · `g3dinference` · `g3dradarprocess` |

[Full elements reference →](https://docs.openedgeplatform.intel.com/dev/edge-ai-libraries/dlstreamer/elements/elements.html)

---

## Samples

30+ samples across Python, C++, and `gst-launch` command lines:

| Category | Samples |
|---|---|
| **Detection** | [YOLO detection](./samples/gstreamer/gst_launch/detection_with_yolo/README.md), [Face detection + classification](./samples/gstreamer/gst_launch/face_detection_and_classification/README.md), [Depth estimation](./samples/gstreamer/gst_launch/depth_estimation/README.md) |
| **Segmentation & Pose** | [Instance segmentation](./samples/gstreamer/gst_launch/instance_segmentation/README.md), [Human pose estimation](./samples/gstreamer/gst_launch/human_pose_estimation/README.md) |
| **Tracking** | [Vehicle & pedestrian tracking](./samples/gstreamer/gst_launch/vehicle_pedestrian_tracking/README.md), [Vehicle counter with tripwires](./samples/gstreamer/python/gvaanalytics_tripwire/README.md) |
| **VLM / GenAI** | [VLM video summarization](./samples/gstreamer/gst_launch/gvagenai/README.md), [VLM alerts](./samples/gstreamer/python/vlm_alerts/README.md), [VLM self-checkout](./samples/gstreamer/python/vlm_self_checkout/README.md) |
| **Multi-stream** | [Multi-camera deployment](./samples/gstreamer/gst_launch/multi_stream/README.md), [Stream mux/demux](./samples/gstreamer/gst_launch/stream_mux_and_demux/README.md) |
| **3D Sensors** | [LiDAR parsing](./samples/gstreamer/gst_launch/g3dlidarparse/README.md), [PointPillars 3D detection](./samples/gstreamer/gst_launch/g3dinference/README.md), [Radar processing](./samples/gstreamer/gst_launch/g3dradarprocess/README.md) |
| **Integration** | [ONVIF camera discovery](./samples/gstreamer/python/onvif_cameras_discovery/README.md), [Geti™ model deployment](./samples/gstreamer/gst_launch/geti_deployment/README.md), [Metadata to MQTT/Kafka](./samples/gstreamer/gst_launch/metapublish/README.md) |
| **Python extensibility** | [Custom Python GStreamer elements](./samples/gstreamer/gst_launch/python-elements/face_detection_and_classification/README.md), [Smart NVR with recording](./samples/gstreamer/python/smart_nvr/README.md) |

[Browse all samples →](./samples/gstreamer/README.md)

---

## Supported Platforms

| Hardware | CPU | GPU | NPU |
|---|:---:|:---:|:---:|
| Intel Core Ultra series 1–3 (Meteor / Lunar / Arrow / Panther Lake) | ✅ | ✅ | ✅ |
| Intel Arc discrete GPU (Alchemist, Battlemage) | — | ✅ | — |
| 11th–13th Gen Intel Core | ✅ | ✅ | — |

Operating systems: **Ubuntu 22.04 / 24.04**, **Windows 11**.

[Full system requirements →](https://docs.openedgeplatform.intel.com/dev/edge-ai-libraries/dlstreamer/get_started/system_requirements.html)

---

## Documentation

| Resource | Link |
|---|---|
| Get Started (tutorial + install) | [Get Started](https://docs.openedgeplatform.intel.com/dev/edge-ai-libraries/dlstreamer/get_started/get_started_index.html) |
| Developer Guide | [Developer Guide](https://docs.openedgeplatform.intel.com/dev/edge-ai-libraries/dlstreamer/dev_guide/dev_guide_index.html) |
| Elements Reference | [Elements Reference](https://docs.openedgeplatform.intel.com/dev/edge-ai-libraries/dlstreamer/elements/elements.html) |
| API Reference | [API Reference](https://docs.openedgeplatform.intel.com/dev/edge-ai-libraries/dlstreamer/api_ref/api_reference.html) |
| Metadata Guide | [Metadata Guide](https://docs.openedgeplatform.intel.com/dev/edge-ai-libraries/dlstreamer/dev_guide/metadata.html) |
| Supported Models | [Supported Models](https://docs.openedgeplatform.intel.com/dev/edge-ai-libraries/dlstreamer/supported_models.html) |

---

## Contributing

We welcome contributions! Please read [CONTRIBUTING.md](./CONTRIBUTING.md) and follow the [Code Style Guide](./CODESTYLE.md).

For security issues, see [SECURITY.md](./SECURITY.md).

---

## License

DL Streamer is licensed under the [MIT License](./LICENSE).

---

<div align="center">

*Intel, the Intel logo, OpenVINO, OpenVINO logo, Intel Geti, Intel Core, Intel Arc, and Intel Iris are trademarks of Intel Corporation or its subsidiaries.*
*GStreamer is a trademark of the GStreamer project.*

</div>