SceneSplit

Extract semantically distinct still images from video.

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SceneSplit analyzes video files and extracts representative frames that capture meaningful visual changes. Unlike simple keyframe extraction that relies on codec markers, SceneSplit uses vision embeddings to detect semantic transitions—scene changes, subject shifts, and visual content boundaries.

Features

• Semantic analysis — Uses ResNet50 embeddings to detect meaningful visual changes, not just codec keyframes
• Offline processing — All analysis runs locally after initial model download
• Two-knob configuration — Control output granularity (--detail) and processing fidelity (--quality)
• Auto-download model — ResNet50 ONNX model (~100MB) downloads automatically on first run
• Structured output — Numbered images plus metadata.json with timestamps and frame indices

Prerequisites

• Rust 1.70+ — Install Rust
• OpenCV 4.x — Required for video decoding
• ONNX Runtime — Required for ML inference

macOS (Homebrew)

brew install opencv onnxruntime

Ubuntu/Debian

sudo apt install libopencv-dev
# ONNX Runtime: download from https://github.com/microsoft/onnxruntime/releases

Installation

From crates.io

cargo install scenesplit

Pre-built Binaries (Linux)

Download from Releases:

curl -L https://github.com/wilmoore/scenesplit/releases/latest/download/scenesplit-linux-x64.tar.gz | tar xz
sudo mv scenesplit /usr/local/bin/

macOS users: Use cargo install scenesplit or build from source.

Build from Source

git clone https://github.com/wilmoore/scenesplit.git
cd scenesplit
cargo build --release

The binary is at target/release/scenesplit.

Quick Start

# Extract frames with default settings
scenesplit video.mp4

# Minimal output (5-10 frames)
scenesplit -d key video.mp4

# Comprehensive extraction with highest quality
scenesplit -d all -q best -o ./frames video.mp4

Usage

scenesplit [OPTIONS] <VIDEO>

Arguments

Argument	Description
<VIDEO>	Path to the input video file

Options

Option	Default	Description
-d, --detail <LEVEL>	summary	Granularity level
-q, --quality <PRESET>	balanced	Processing quality
-m, --model <PATH>	auto-download	Custom ONNX model file
-o, --output <DIR>	./scenesplit_output/	Output directory
-s, --quiet	off	Suppress progress output

Detail Levels

Level	Frames	Use Case
key	5-10	Major scene changes only
summary	10-20	Representative frames
all	20-30	Comprehensive semantic changes

Quality Presets

Preset	Speed	Fidelity
fast	Fastest	Lower
balanced	Moderate	Good
best	Slowest	Highest

Output

SceneSplit creates a directory containing:

scenesplit_output/
├── 0001.jpg
├── 0002.jpg
├── 0003.jpg
├── ...
└── metadata.json

metadata.json

{
  "source_video": "video.mp4",
  "video_duration_seconds": 120.5,
  "video_frame_count": 3615,
  "extracted_frames": 12,
  "detail_level": "summary",
  "quality_preset": "balanced",
  "frames": [
    {
      "filename": "0001.jpg",
      "segment_index": 0,
      "frame_index": 0,
      "timestamp_seconds": 0.0,
      "timestamp_formatted": "00:00:00.000"
    },
    {
      "filename": "0002.jpg",
      "segment_index": 1,
      "frame_index": 45,
      "timestamp_seconds": 1.5,
      "timestamp_formatted": "00:00:01.500"
    }
  ]
}

Supported Formats

• MP4
• AVI
• MOV
• MKV
• WebM

Format support depends on your OpenCV build and available codecs.

How It Works

1. Frame extraction — Samples frames from video at a rate determined by quality preset
2. Embedding computation — Passes frames through ResNet50 to generate semantic feature vectors
3. Similarity analysis — Computes cosine similarity between consecutive embeddings
4. Segmentation — Detects boundaries where similarity drops below threshold (controlled by detail level)
5. Selection — Chooses representative frames from each segment

License

MIT