Professional video processing pipeline with AI analysis, 360° processing, and adaptive streaming capabilities. ✨ Core Features: • AI-powered content analysis with scene detection and quality assessment • Next-generation codec support (AV1, HEVC, HDR10) • Adaptive streaming (HLS/DASH) with smart bitrate ladders • Complete 360° video processing with multiple projection support • Spatial audio processing (Ambisonic, binaural, object-based) • Viewport-adaptive streaming with up to 75% bandwidth savings • Professional testing framework with video-themed HTML dashboards 🏗️ Architecture: • Modern Python 3.11+ with full type hints • Pydantic-based configuration with validation • Async processing with Procrastinate task queue • Comprehensive test coverage with 11 detailed examples • Professional documentation structure 🚀 Production Ready: • MIT License for open source use • PyPI-ready package metadata • Docker support for scalable deployment • Quality assurance with ruff, mypy, and pytest • Comprehensive example library From simple encoding to immersive experiences - complete multimedia processing platform for modern applications.
140 lines
3.8 KiB
YAML
140 lines
3.8 KiB
YAML
# Docker Compose setup for Video Processor with Procrastinate
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# Complete development and testing environment
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services:
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# PostgreSQL database for Procrastinate
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postgres:
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image: postgres:15-alpine
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environment:
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POSTGRES_DB: video_processor
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POSTGRES_USER: video_user
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POSTGRES_PASSWORD: video_password
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POSTGRES_HOST_AUTH_METHOD: trust
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volumes:
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- postgres_data:/var/lib/postgresql/data
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- ./docker/init-db.sql:/docker-entrypoint-initdb.d/init-db.sql
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ports:
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- "5432:5432"
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healthcheck:
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test: ["CMD-SHELL", "pg_isready -U video_user -d video_processor"]
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interval: 10s
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timeout: 5s
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retries: 5
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networks:
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- video_net
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# Video Processor API service
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app:
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build:
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context: .
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dockerfile: Dockerfile
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target: development
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environment:
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- DATABASE_URL=postgresql://video_user:video_password@postgres:5432/video_processor
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- PROCRASTINATE_DATABASE_URL=postgresql://video_user:video_password@postgres:5432/video_processor
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- PYTHONPATH=/app
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volumes:
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- .:/app
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- video_uploads:/app/uploads
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- video_outputs:/app/outputs
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ports:
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- "8000:8000"
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depends_on:
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postgres:
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condition: service_healthy
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networks:
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- video_net
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command: ["python", "examples/docker_demo.py"]
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# Procrastinate worker for background processing
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worker:
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build:
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context: .
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dockerfile: Dockerfile
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target: worker
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environment:
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- PROCRASTINATE_DATABASE_URL=postgresql://video_user:video_password@postgres:5432/video_processor
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- WORKER_CONCURRENCY=4
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- WORKER_TIMEOUT=300
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volumes:
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- video_uploads:/app/uploads
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- video_outputs:/app/outputs
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depends_on:
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postgres:
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condition: service_healthy
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networks:
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- video_net
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command: ["python", "-m", "video_processor.tasks.worker_compatibility", "worker"]
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# Migration service (runs once to setup DB)
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migrate:
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build:
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context: .
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dockerfile: Dockerfile
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target: migration
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environment:
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- PROCRASTINATE_DATABASE_URL=postgresql://video_user:video_password@postgres:5432/video_processor
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depends_on:
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postgres:
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condition: service_healthy
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networks:
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- video_net
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command: ["python", "-c", "
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import asyncio;
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from video_processor.tasks.migration import migrate_database;
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asyncio.run(migrate_database('postgresql://video_user:video_password@postgres:5432/video_processor'))
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"]
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# Test runner service
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test:
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build:
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context: .
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dockerfile: Dockerfile
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target: development
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environment:
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- DATABASE_URL=postgresql://video_user:video_password@postgres:5432/video_processor_test
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- PROCRASTINATE_DATABASE_URL=postgresql://video_user:video_password@postgres:5432/video_processor_test
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volumes:
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- .:/app
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depends_on:
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postgres:
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condition: service_healthy
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networks:
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- video_net
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command: ["uv", "run", "pytest", "tests/", "-v", "--cov=src/", "--cov-report=html", "--cov-report=term"]
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# Demo web interface (optional)
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demo:
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build:
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context: .
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dockerfile: Dockerfile
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target: development
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environment:
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- DATABASE_URL=postgresql://video_user:video_password@postgres:5432/video_processor
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- PROCRASTINATE_DATABASE_URL=postgresql://video_user:video_password@postgres:5432/video_processor
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ports:
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- "8080:8080"
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volumes:
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- .:/app
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- video_uploads:/app/uploads
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- video_outputs:/app/outputs
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depends_on:
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postgres:
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condition: service_healthy
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networks:
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- video_net
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command: ["python", "examples/web_demo.py"]
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volumes:
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postgres_data:
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driver: local
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video_uploads:
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driver: local
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video_outputs:
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driver: local
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networks:
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video_net:
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driver: bridge |