integrate webcam support with real-time detection

GEMINI.md

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+# Media Analyzer
+
+## Project Overview
+
+This project is a real-time video streaming and AI analysis platform. It ingests RTMP video streams, processes them with computer vision AI models, and provides live analysis results through a responsive web dashboard.
+
+The architecture is based on microservices and includes:
+
+*   **Backend**: A Django application that handles video stream management, AI processing, and WebSocket communication for real-time updates.
+*   **Frontend**: An Angular single-page application that provides a user interface for stream viewing and analysis visualization.
+*   **AI Processing**: A Python-based analysis engine that uses various adapters for different types of analysis, such as object detection, logo detection, and motion analysis. The engine can be configured to run locally, on a remote LAN worker, or in the cloud.
+*   **Streaming**: An NGINX server with the RTMP module ingests video streams and converts them to HLS for web playback.
+*   **Infrastructure**: The entire platform is containerized using Docker and can be deployed with Docker Compose for development or Kubernetes for production.
+
+## Building and Running
+
+### Docker Compose (Development)
+
+To run the application in a development environment, use Docker Compose:
+
+```bash
+# Start all services
+docker compose up
+
+# Run migrations (in a separate terminal)
+docker compose --profile tools up migrate
+```
+
+The application will be accessible at the following URLs:
+
+*   **Frontend**: `http://localhost:4200`
+*   **Backend API**: `http://localhost:8000`
+*   **RTMP Stream**: `rtmp://localhost:1935/live`
+*   **HLS Stream**: `http://localhost:8081/hls`
+
+### Kubernetes (Production-like)
+
+To deploy the application to a Kubernetes cluster, you can use the provided scripts:
+
+```bash
+# Build and push images to a local registry
+./k8s/build-for-ctlptl.sh
+
+# Deploy to Kubernetes
+kubectl apply -k k8s/overlays/development
+
+# Check deployment status
+kubectl get pods -n media-analyzer
+
+# Access the application via port forwarding
+kubectl port-forward service/frontend -n media-analyzer 4200:80
+```
+
+## Development Conventions
+
+*   **Backend**: The backend is a Django application. Follow Django best practices for development.
+*   **Frontend**: The frontend is an Angular application. Follow Angular best practices for development.
+*   **AI Processing**: The AI processing engine is designed to be extensible. To add a new analysis capability, create a new adapter and integrate it with the `AnalysisEngine`.
+*   **Testing**: The project includes a `test_unified_ai.py` file, which suggests that there is a testing framework in place. Run existing tests and add new ones when making changes.