DL Streamer Coding Agent — User Guide#

Overview#

The DL Streamer Coding Agent is a Claude Code skill that turns plain-English descriptions into working video-analytics applications. You describe what you want — it generates, builds, and validates a complete DL Streamer app.

Skill location: <dlstreamer-repo>/.github/skills/dlstreamer-coding-agent/ (where <dlstreamer-repo> is your local clone of the DL Streamer repository)

Quick Start#

  1. Open Claude Code inside your local dlstreamer repository

  2. Describe the video AI pipeline you want to build

  3. The agent asks clarifying questions if needed, then generates and runs the app

That’s it. No boilerplate, no manual GStreamer wiring.

What to Include in Your Prompt#

A good prompt answers these questions:

What

Why

Example

Video source

Where to read frames from

A Pexels URL, local file, or rtsp://...

AI model(s)

What intelligence to apply

“YOLOv11 for detection”

What to output

What the app should produce

“Annotated video + JSON with detections”

Target hardware

Which accelerator to use

“Intel Core Ultra 3, prefer GPU”

App language

Python, C++, or shell script

“Python application”

Where to save

Output directory name

“Save in my_app/”

If you skip any of these, the agent will ask before proceeding.

Example Prompts#

Simple — detection + tracking (shell script):

Create a bash script that detects and tracks people using YOLO26m and Mars-Small-128.
Input: https://videos.pexels.com/video-files/18552655/18552655-hd_1280_720_30fps.mp4
Output: annotated video file.
Optimized for Intel Core Ultra 3. Save in people_tracking/.

Medium — license plate OCR (Python):

Build a Python app for license plate recognition:
- YOLOv11 for plate detection, PaddleOCR for text
- Input: video file or RTSP camera
- Output: annotated video + JSON with plate text
Save in license_plate_recognition/. Include README.

Advanced — event-based recording:

Python app that records video clips only when people are detected:
- Input: RTSP camera (or file for testing)
- Detect people, start recording on detection, stop when gone
- Output: sequence of clips (save-1.mp4, save-2.mp4, ...)
Save in smart_nvr/.

Conversion — DeepStream to DL Streamer:

Convert this DeepStream app to DL Streamer: [paste code or path]
Keep the same detection + classification + JSON output.

What You Get#

The agent generates a ready-to-run project:

my_app/
├── my_app.py            # Main application (or .sh / .cpp)
├── export_models.py     # Downloads and converts AI models
├── requirements.txt     # Python dependencies
├── README.md            # How to set up and run
└── results/             # Output goes here at runtime

It also:

  • Pulls the intel/dlstreamer:latest Docker image

  • Downloads and converts models to OpenVINO format

  • Downloads your test video

  • Runs the app and checks that output is valid

Supported Use Cases#

Category

Examples

Detection

YOLO (v8/v11/v26), SSD, RTDETR

Tracking

DeepSORT, SORT with re-ID models

Text/OCR

PaddleOCR for license plates, signs

GenAI/VLM

InternVL, MiniCPM, Qwen2.5-VL, SmolVLM

Multi-camera

Shared models, cross-stream batching

Mosaic

Composite 2x2 / 3x3 grid views

Smart recording

Event-triggered start/stop clips

Streaming

WebRTC output, RTSP input

Conversion

DeepStream Python/C++ → DL Streamer

Tips#

  • Name exact models — “YOLOv11n” works better than “an object detector”

  • Provide a test video — the agent validates the pipeline end-to-end

  • Say “run and check output” — triggers automatic validation

  • Ask for README — ensures you get setup docs with the code

Troubleshooting#

Problem

Solution

Docker pull fails

Check network and docker login

Model export runs out of memory

Use a smaller model variant

Output video won’t play

Usually fixed by agent (EOS handling); re-run if needed

Very slow first run (5-10 min)

Normal — GPU compiles shaders on first inference

NPU inference fails

Not all models support NPU; agent falls back to GPU

More Examples#

The skill includes additional example prompts at: <dlstreamer-repo>/.github/skills/dlstreamer-coding-agent/examples/

  • People detection + tracking

  • License plate recognition

  • Event-based smart NVR

  • Multi-stream mosaic

  • Pose estimation

  • Safety compliance checks

  • DeepStream conversion (Python and C++)

Prerequisites#

  • Docker installed and running

  • Python 3.10+

  • Network access (Docker images, model downloads, test videos)

  • Intel hardware with GPU recommended