dbbasic-video 0.1.0

AI-powered video analysis toolkit for extracting, analyzing, and summarizing video content

DBBasic Video

AI-powered video analysis toolkit for extracting, analyzing, and summarizing video content.

Features

• 📥 Download videos from YouTube
• 🎬 Extract keyframes using scene detection
• 📝 Get transcripts automatically
• 🖼️ Create thumbnail grids from keyframes
• 🤖 AI analysis of visual content using GPT-4 Vision
• 📊 Comprehensive summaries combining visuals and transcripts

Installation

pip install dbbasic-video

Or install from source:

git clone https://github.com/askrobots/dbbasic-video.git
cd dbbasic-video
pip install -e .

Requirements

• Python 3.8+
• OpenAI API key (set as OPENAI_API_KEY environment variable or in .env file)
• yt-dlp (for downloading videos)

Quick Start

Complete Pipeline

Run the entire analysis pipeline on a YouTube video:

dbbasic-video pipeline "https://www.youtube.com/watch?v=VIDEO_ID"

This will:

1. Download the video
2. Extract keyframes from scene changes
3. Create a thumbnail grid
4. Download the transcript
5. Analyze visuals with AI
6. Generate a comprehensive summary

Individual Commands

Download a video:

dbbasic-video download "https://www.youtube.com/watch?v=VIDEO_ID" -o video.mp4

Extract keyframes:

dbbasic-video extract video.mp4 -o keyframes/

Get transcript:

dbbasic-video transcript VIDEO_ID

Create thumbnail grid:

dbbasic-video grid keyframes/ -o thumbnail_grid.jpg

Analyze visuals:

dbbasic-video analyze thumbnail_grid.jpg -o analysis.txt

Find best thumbnail:

dbbasic-video find-thumbnail keyframes/ -t 120.5

Create comprehensive summary:

dbbasic-video summarize transcript.json analysis.txt -o summary.txt

Environment Variables

Create a .env file in your working directory:

OPENAI_API_KEY=your_api_key_here

Output Structure

When running the pipeline, output is organized as:

output/
├── video.mp4                    # Downloaded video
├── keyframes/                   # Extracted keyframes
│   ├── scene_0001_*.jpg
│   ├── scene_0002_*.jpg
│   └── scenes_metadata.txt
├── thumbnail_grid.jpg           # Grid of all keyframes
├── transcript.json              # Raw transcript
├── transcript.txt               # Readable transcript
├── visual_analysis.txt          # AI visual analysis
└── comprehensive_summary.txt    # Final summary

API Usage

from dbbasic_video.extract_keyframes import extract_keyframes
from dbbasic_video.make_grid import create_thumbnail_sheet
from dbbasic_video.analyze_visuals import analyze_grid

# Extract keyframes
extract_keyframes("video.mp4", "keyframes/")

# Create thumbnail grid
create_thumbnail_sheet("keyframes/", "grid.jpg")

# Analyze with AI
analyze_grid("grid.jpg", "analysis.txt")

Cost Considerations

• Scene detection and keyframe extraction: Free (local processing)
• Transcript download: Free (from YouTube)
• AI visual analysis: ~$0.01-0.02 per image with GPT-4 Vision
• Using thumbnail grid: 1 API call instead of analyzing each frame individually

License

MIT

Future: DBBasic Core Integration

This package is designed to be part of the larger DBBasic ecosystem. In the future, a unified db-basic CLI will provide:

# Unified configuration
db-basic config set output_dir ~/analysis

# Plugin-based architecture
db-basic video extract video.mp4     # uses dbbasic-video
db-basic audio transcribe audio.mp3  # uses dbbasic-audio
db-basic image analyze photo.jpg     # uses dbbasic-image

# Plugin discovery
db-basic list
  ✓ video  (dbbasic-video 0.1.0)
  ✓ audio  (dbbasic-audio 0.2.1)
  ✗ image  (not installed - pip install dbbasic-image)

Planned shared infrastructure:

• Global config: ~/.config/dbbasic/config.toml
• Shared cache and output structure
• Unified logging and error handling
• Plugin registry and version management
• Common utilities for AI, media processing, and data handling

For now, dbbasic-video works as a standalone tool. When db-basic core is released, it will automatically integrate while maintaining backwards compatibility.

Contributing

Contributions welcome! Please open an issue or PR.