Build from Source

Build the Image-Based Video Search sample application from source to customize, debug, or extend its functionality. In this guide, you will:

• Set up your development environment.

• Compile the source code and resolve dependencies.

• Generate a runnable build for local testing or deployment.

This guide is ideal for developers who want to work directly with the source code.

Prerequisites

Before you begin, ensure the following:

• System Requirements: Verify your system meets the minimum requirements.

• Dependencies Installed:

  • Git: Install Git

  • Docker: Install Docker

• Permissions: Confirm you have permissions to install software and modify environment configurations.

Installing behind a proxy

1. Follow this guide to configure your Docker environment: Use a proxy server with the Docker CLI | Docker Documentation

2. Pull base images:

   docker pull docker.io/library/node:23
   docker pull docker.io/library/python:3.11
   docker pull docker.io/intel/dlstreamer:2025.0.1.3-ubuntu24
   docker pull openvino/ubuntu22_dev:2024.6.0
   

Steps to Build

1. Clone the Repository:

   • Run:

     git clone https://github.com/open-edge-platform/edge-ai-suites.git
     cd ./edge-ai-suites/metro-ai-suite/image-based-video-search
     

2. Build Containers:

   • Run:

     docker compose build
     

3. Download Models:

   • For Linux:

     MODELS_PATH="$(pwd)/src/dlstreamer-pipeline-server/models"
     
     docker run --rm \
         -v $MODELS_PATH:/output \
         openvino/ubuntu22_dev:2024.6.0 bash -c \
         "omz_downloader --name resnet-50-pytorch --output_dir models && \
          omz_converter --name resnet-50-pytorch --download_dir models --output_dir models && \
          cp -r ./models/public/resnet-50-pytorch /output"
     
     docker run --rm \
         -v $MODELS_PATH:/output \
         openvino/ubuntu22_dev:2024.6.0 bash -c \
         "omz_downloader --name person-vehicle-bike-detection-2004 --output_dir models && \
          omz_converter --name person-vehicle-bike-detection-2004 --download_dir models --output_dir models && \
          cp -r ./models/intel/person-vehicle-bike-detection-2004 /output"
     

   • For Windows:

     $MODELS_PATH="$PWD\src\dlstreamer-pipeline-server\models"
     
     docker run --rm `
         -v ${MODELS_PATH}:/output `
         openvino/ubuntu22_dev:2024.6.0 bash -c `
         "omz_downloader --name resnet-50-pytorch --output_dir models && `
          omz_converter --name resnet-50-pytorch --download_dir models --output_dir models && `
          cp -r ./models/public/resnet-50-pytorch /output"
     
     docker run --rm `
         -v ${MODELS_PATH}:/output `
         openvino/ubuntu22_dev:2024.6.0 bash -c `
         "omz_downloader --name person-vehicle-bike-detection-2004 --output_dir models && `
          omz_converter --name person-vehicle-bike-detection-2004 --download_dir models --output_dir models && `
          cp -r ./models/intel/person-vehicle-bike-detection-2004 /output"
     

Validation

1. Start Containers:

   • Run:

     docker compose up -d
     

2. Access the Application:

   • The App has the following endpoints:

     • Stream UI: http://localhost:8889/stream

     • App UI: http://localhost:3000

     • Search UI: http://localhost:9000/docs

     • MilvusDB UI: http://localhost:8000

3. Verify Build Success:

   • Check the logs. Look for confirmation messages indicating the microservice started successfully.

4. Example Output:

   • Here is an example output:

     

Troubleshooting

1. Environment Configuration Issues:

   • Verify environment variables:

     echo $VARIABLE_NAME