VisionFrameAnalyzer 📹🖼️

Extract frames from videos and analyze them using AI-powered image recognition.

🚀 Overview

VisionFrameAnalyzer is a Go-based tool that:
✅ Extracts frames from a video at set intervals using ffmpeg
✅ Uses an AI-powered vision model to analyze and describe each frame
✅ Provides a structured pipeline for video-to-image processing

✨ Features

• 🎞 Frame Extraction – Converts video frames into images
• 🖼 AI-Powered Analysis – Describes each frame using an LLM vision model
• ⚡ Multi-Frame Processing – Handles multiple frames efficiently
• 📝 Detailed Logging – Provides structured logs for debugging

🛠 Tech Stack

• Go (Golang)
• FFmpeg (Frame Extraction)
• Ollama (LLM-powered image analysis)
• Slog + Tint (Logging)
• Kubernetes Ready (Optional Multi-Cluster Support)

📦 Installation & Setup

Prerequisites

• Go (1.21+ recommended)
• FFmpeg
• Ollama

If you don't have Go installed or are experiencing GOROOT issues, install/fix it first:

# macOS with Homebrew
brew install go

# Verify installation
go version

# If you see GOROOT errors, set it explicitly in your shell profile
echo 'export GOROOT=$(brew --prefix go)/libexec' >> ~/.zshrc  # or ~/.bash_profile
echo 'export PATH=$PATH:$GOROOT/bin:$HOME/go/bin' >> ~/.zshrc
source ~/.zshrc  # or source ~/.bash_profile

Option 1: Local Installation

MacOS (Homebrew)

brew install ffmpeg
brew install ollama
go mod tidy

Option 2: Run Directly (No Installation)

# Navigate to project directory
cd /path/to/vision

# Build and run in one step
go run ./cmd/visionanalyzer --video path/to/video.mp4 --output output_directory

# Or build an executable and run it
go build -o ./bin/visionanalyzer ./cmd/visionanalyzer
./bin/visionanalyzer --video path/to/video.mp4 --output output_directory

Option 3: Docker Installation

# Build the container
docker build -t vision-analyzer .

# Run the container
docker run -v $(pwd):/data vision-analyzer --video /data/input.mp4 --output /data/frames

🔧 Configuration & Usage

Ollama Setup

1. Ensure Ollama is running locally on port 11434
2. The tool uses llama3.2-vision:11b model by default

Command Line Flags

• --video: Path to input video file (required)
• --output: Output directory for frames (default: "output_frames")

Basic Usage

# Build and run directly
go build -o visionanalyzer ./cmd/visionanalyzer
./visionanalyzer --video path/to/video.mp4

# Or run without building
go run ./cmd/visionanalyzer --video path/to/video.mp4

# Specify custom output directory
./visionanalyzer --video path/to/video.mp4 --output custom_output

Show help

./visionanalyzer --help

🚀 Quick Start

# Process a video
./visionanalyzer --video path/to/video.mp4

# Specify custom output directory
./visionanalyzer --video path/to/video.mp4 --output custom_output

# Search for frames containing specific content (requires PostgreSQL)
export DB_ENABLED=true
./visionanalyzer --search "person cooking" --limit 10 --video path/to/video.mp4

# Show help
./visionanalyzer --help

📂 Output Structure

output_frames/
└── video_name/
    ├── frame_0001.jpg
    ├── frame_0002.jpg
    ├── analysis_results.json
    └── ...

Analysis Results Format

The analysis_results.json file contains frame-by-frame analysis:

[
  {
    "frame": "frame_0001.jpg",
    "content": "Detailed analysis of frame contents..."
  }
]

🛢️ PostgreSQL with pgvector Setup

VisionFrameAnalyzer can store analysis results in PostgreSQL with pgvector for vector similarity search.

Prerequisites

1. Install PostgreSQL (14+ recommended)

# macOS with Homebrew
brew install postgresql@14
brew services start postgresql@14

# Verify installation
psql --version

Docker Compose Setup

For easy development, you can use Docker Compose to set up PostgreSQL with pgvector:

version: '3.8'

services:
  db:
    image: postgres:14
    environment:
      POSTGRES_USER: user
      POSTGRES_PASSWORD: password
      POSTGRES_DB: visiondb
    ports:
      - "5432:5432"
    volumes:
      - pgdata:/var/lib/postgresql/data

volumes:
  pgdata:

PostgreSQL Schema

Create the necessary tables and enable pgvector extension:

CREATE EXTENSION IF NOT EXISTS vector;

CREATE TABLE videos (
    id SERIAL PRIMARY KEY,
    name TEXT NOT NULL,
    created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP
);

CREATE TABLE frames (
    id SERIAL PRIMARY KEY,
    video_id INTEGER REFERENCES videos(id),
    frame_number INTEGER NOT NULL,
    image_path TEXT NOT NULL,
    created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP
);

CREATE TABLE analyses (
    id SERIAL PRIMARY KEY,
    frame_id INTEGER REFERENCES frames(id),
    content JSONB,
    vector VECTOR(768), -- Adjust dimension based on your model
    created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP
);

Vector Similarity Search

The pgvector implementation allows you to search for frames with similar content using vector similarity, which is much more powerful than basic text search.

Searching Frames

VisionFrameAnalyzer offers two ways to search for frames:

1. Vector Similarity Search - Find frames that are semantically similar to your query:

# Search for frames showing a person cooking (top 5 results)
export DB_ENABLED=true
./visionanalyzer --search "person cooking" --video path/to/video.mp4

# Increase the number of results
./visionanalyzer --search "person cooking" --limit 10 --video path/to/video.mp4

📁 Project Structure

vision/
├── cmd/
│   └── visionanalyzer/      # Main executable package
├── internal/
│   ├── analyzer/            # AI vision analysis functionality
│   ├── extractor/           # Video frame extraction functionality
│   ├── models/              # Shared data structures
│   └── storage/             # Result storage and persistence

📌 Use Cases

📽️ Automated Video Analysis – Extract insights from video feeds
🔍 Content Moderation – Detect and describe images in video content
🛠 Machine Learning Pipelines – Pre-process video datasets for AI models

📜 License

MIT License. See LICENSE for details.