Get Started

• Time to Complete: 45–60 minutes

• Programming Language: Python 3

Prerequisites

• System Requirements

• Build from Source

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 https://github.com/intel-retail/digital-signage
cd digital-signage

Step 2: Build Docker Images

make build

Step 3: Download AI Models

Download YOLO11s Model (for PID)

Please review the YOLO11s license before downloading.

cd configs/pid && \
wget https://raw.githubusercontent.com/intel-retail/automated-self-checkout/v3.6.3/download_models/downloadAndQuantizeModel.sh && \
sed -i 's|MODELS_PATH="${MODELS_DIR:-/workspace/models}"|MODELS_PATH="${MODELS_DIR:-$PWD/models}"|g' downloadAndQuantizeModel.sh && \
sed -i 's/MODEL_NAME="yolo11n"/MODEL_NAME="yolo11s"/g' downloadAndQuantizeModel.sh && \
rm -rf .modelenv && \
python3 -m venv .modelenv && \
source .modelenv/bin/activate && \
pip3 install -r model_download_requirements.txt && \
rm -rf models && \
chmod +x downloadAndQuantizeModel.sh && \
./downloadAndQuantizeModel.sh && \
rm ./downloadAndQuantizeModel.sh && \
deactivate && \
cd ../..

The quantized model is saved to ./configs/pid/models/object_detection/yolo11s.

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.

cd aig && \
rm -rf .modelenv && \
python3 -m venv .modelenv && \
source ./.modelenv/bin/activate && \
pip3 install -r export-requirements.txt && \
export HF_HUB_ENABLE_HF_TRANSFER=1 && \
optimum-cli export openvino --model stabilityai/sdxl-turbo --task stable-diffusion-xl --weight-format int8 ./models/sdxl_turbo_ov/int8 && \
huggingface-cli download sentence-transformers/all-MiniLM-L12-v2 --local-dir ./models/all-MiniLM-L12-v2 && \
deactivate && \
cd ../

Models are downloaded to ./aig/models/.

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

• 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, verifies that required models are available, removes any previously running containers, and 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

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