Get Started#

  • Time to Complete: 45–60 minutes

  • Programming Language: Python 3

Prerequisites#

Configure Docker#

To configure Docker:

  1. Run Docker as Non-Root: Follow the steps in Manage Docker as a non-root user.

    Note

    Run all commands as a regular (non-root) user, without using sudo.

  2. Configure Proxy (if required):

    • Set up proxy settings for Docker client and containers as described in Docker Proxy Configuration.

    • Example ~/.docker/config.json:

      {
  "proxies": {
    "default": {
      "httpProxy": "http://<proxy_server>:<proxy_port>",
      "httpsProxy": "http://<proxy_server>:<proxy_port>",
      "noProxy": "127.0.0.1,localhost"
    }
  }
}

  3. Enable Log Rotation:

    • Add the following configuration to /etc/docker/daemon.json:

      {
  "log-driver": "json-file",
  "log-opts": {
    "max-size": "10m",
    "max-file": "5"
  }
}

    • Reload and restart Docker:

      sudo systemctl daemon-reload
sudo systemctl restart docker

Step 1: Clone the Repository#

git clone -b main https://github.com/intel-retail/digital-signage
cd digital-signage

Step 2: Build Docker Images#

make build

Step 3: Download AI Models#

Digital Signage uses the reusable Model Download microservice to download models and optimize it to run well with OpenVINO. Download models before deployment with make download_models; then start the app with make up.

Note

Please review the YOLO11s license and the SDXL-Turbo license before downloading.

make download_models

  • Uses the pinned intel/model-download image and downloads the model helper files from pinned jsDelivr mirrors.

  • Downloads and quantizes YOLO11s for PID

  • Downloads SDXL-Turbo (OpenVINO™ INT8) and all-MiniLM-L12-v2 for AIG

  • Maps the downloaded artifacts to the paths already used by Digital Signage:

    • ./configs/pid/models/object_detection/yolo11s

    • ./aig/models/sdxl_turbo_ov/int8

    • ./aig/models/all-MiniLM-L12-v2

Note

If the objects are not getting detected in the pretrained YOLO11s model, use a custom object-detection model instead. See Use Intel® Geti™ Exported Model for model training and export guidance.

Download SDXL-Turbo and MiniLM Models (for AIG)#

Please review the SDXL-Turbo license before downloading.

make download_models (via scripts/download_models.sh) already exports both of these for you, via the Model Download microservice. See scripts/download_models.sh if you need to reproduce the equivalent optimum-cli/hf download commands standalone (e.g. outside Docker).

Models are downloaded to ./aig/models/.

Download LTX-Video Model (optional, for trigger_video_ad)#

Please review the LTX-Video license before downloading. This model is only needed if you want trigger_video_ad to generate dynamic videos when no predefined video is provisioned for an item; the rest of the application works without it.

make download_models also exports this automatically (see the LTX-Video section of scripts/download_models.sh). It’s skipped by default in CI (SKIP_LTX_VIDEO_EXPORT) since the export is long-running; for local/manual use it runs by default. To run it standalone instead, see the script for the exact optimum-cli command.

Set AIG_VIDEO_MODEL_PATH in .env to match (defaults to /opt/models/ltx_video_ov/int8, matching this path). AIG_VIDEO_INFERENCE_DEVICE selects the device for this model independently of AIG_INFERENCE_DEVICE (see Change Inference Device).

Step 4: Configure Environment#

Edit the .env file in the repository root and set the following required variables:

Variable

Description

HOST_IP

Defaults to localhost; use the host system IP address if you want to access the web UI remotely

MTX_WEBRTCICESERVERS2_0_USERNAME

WebRTC ICE server username (minimum 5 alphabetic characters)

MTX_WEBRTCICESERVERS2_0_PASSWORD

WebRTC ICE server password (minimum 8 alphanumeric characters, at least one digit)

Optional variables:

  • RTSP_CAMERA_IP and related RTSP settings for live camera input.

  • AIG_INFERENCE_DEVICE to set the inference device for AIG (CPU or GPU).

  • AIG_VIDEO_MODEL_PATH and AIG_VIDEO_INFERENCE_DEVICE to enable and configure dynamic video ad generation (trigger_video_ad).

  • AIG_VIDEO_PRECISION and AIG_PRELOAD_VIDEO_MODEL to tune video generation speed and first-request latency (see Change Inference Device).

  • AIG_* and ASE_* variables for advanced AIG and ASe tuning.

  • OBJECT_CONFIDENCE_THRESHOLD and OBJECT_RECENCY_FRAME_COUNT for detection filtering.

  • TIME_TO_DISPLAY_AD_SECONDS for controlling ad rotation frequency.

To enable predefined advertisements (optional), update:

  • web-ui/ProductAssociations.csv — product, pricing, promo text, slogan, and optional image filename.

  • web-ui/pre-defined-ads/ — JPEG/JPG image assets referenced by the CSV.

Step 5: Deploy with Docker Compose#

make up

This command validates your environment configuration, stops any previous stack, downloads and prepares the required models, then starts all services.

Step 6: Access the Web UI#

Open Google Chrome and navigate to:

https://localhost:5000

Note

If you are accessing the UI remotely, replace localhost with the host system IP address.

You should see the live video stream and dynamic advertisements.

Note

For local systems with limited compute resources, launch Chrome with GPU acceleration disabled:

google-chrome --process-per-site --disable-plugins --disable-gpu https://localhost:5000

Alternatively, in Chrome go to Settings → System and disable Use graphics acceleration when available, then relaunch Chrome.

Click to see Chrome System settings screenshot

Chrome System settings

Step 7: Verify Output#

Check that all containers are running:

docker ps

If any container restarts, inspect its logs:

docker logs -f <container_name>

Undeploy#

To stop and remove all containers and volumes:

make down