# OmniShotCut MLX

Shot Boundary Detection with OmniShotCut, ported to Apple MLX for native Mac inference.

Based on the paper: [OmniShotCut: Holistic Relational Shot Boundary Detection with Shot-Query Transformer](https://arxiv.org/abs/2604.24762).

## Features

- Pure MLX inference — runs natively on Apple Silicon, zero PyTorch dependency at runtime
- Detects hard cuts, dissolves, fades, wipes, slides, zooms, doorways, and sudden jumps
- Tunable sensitivity for different video types (action, interview, vlog, film)

## Requirements

- macOS with Apple Silicon (M1/M2/M3/M4)
- Python 3.10+
- `ffmpeg` (for video I/O)

```bash
pip install mlx mlx-metal numpy
```

## Quick Start

```bash
# 1. Clone and install
git clone https://github.com/eisneim/OmniShotCut_mlx.git
cd OmniShotCut_mlx

# 2. Download weights from HuggingFace
python omnishotcut_mlx/download_weights.py

# 3. Run on test videos
python run_inference.py
```

## Download Weights

```bash
# Auto-download from HuggingFace Hub (requires huggingface_hub)
pip install huggingface_hub
python omnishotcut_mlx/download_weights.py

# Or manually download from:
# https://huggingface.co/eisneim/OmniShotCut_mlx
# Place OmniShotCut.safetensors and config.json into ./weights/

# Alternative: download without huggingface_hub
curl -L -o weights/OmniShotCut.safetensors https://huggingface.co/eisneim/OmniShotCut_mlx/resolve/main/OmniShotCut.safetensors
curl -L -o weights/config.json https://huggingface.co/eisneim/OmniShotCut_mlx/resolve/main/config.json
```

## Usage

```bash
# Default: balanced detection
python run_inference.py

# Sensitive mode: more cuts, good for action/vlog videos
python run_inference.py --sensitive

# Conservative mode: fewer false positives, good for interviews/long takes
python run_inference.py --conservative

# Single video
python run_inference.py --video /path/to/video.mp4

# Custom output directory
python run_inference.py --output ./my_shots

# Fine-tuned control
python run_inference.py --context 12 --min-shot 0.8 --conf 0.1
```

### Tunable Parameters

| Parameter | Default | Range | Effect |
|-----------|---------|-------|--------|
| `--context` | 10 | 0–20 | Overlap frames between windows. Higher = fewer missed boundaries, but slower |
| `--min-shot` | 0.5 | 0.1–5.0 | Minimum shot duration in seconds. Higher = fewer false positives |
| `--conf` | 0.0 | 0.0–1.0 | Intra-class confidence threshold. E.g. 0.3 = keep only predictions model is >30% sure about |
| `--sensitive` | — | — | Shortcut: context=15, min-shot=0.3, conf=0 |
| `--conservative` | — | — | Shortcut: context=5, min-shot=1.5, conf=0.15 |

### Parameter Guide by Video Type

| Video Type | Recommended | Why |
|------------|------------|-----|
| Action / Sports | `--sensitive` | Fast cuts, many short shots |
| Vlog / YouTube | default or `--context 15` | Moderate pace, varied editing |
| Interview / Podcast | `--conservative` | Long takes, few cuts |
| Film / Cinema | default | Balanced |
| Animation | `--sensitive` | Frequent scene changes |
| Screen Recording | `--conservative` or `--min-shot 2.0` | Mostly static |

## Project Structure

```
OmniShotCut_mlx/
├── run_inference.py               # Main entry point
├── omnishotcut_mlx/
│   ├── model.py                   # OmniShotCut MLX model
│   ├── transformer.py             # Transformer encoder/decoder
│   ├── resnet.py                  # ResNet18 backbone
│   ├── position_encoding.py       # 3D sinusoidal position encoding
│   ├── load_weights.py            # Weight loader (from safetensors)
│   └── download_weights.py        # HuggingFace weight downloader
├── weights/
│   ├── OmniShotCut.safetensors    # MLX-native weights (~157MB)
│   └── config.json                # Model configuration
└── test_data/                     # Place test videos here
```

## Output

Shots are saved as `shot_0000.mp4`, `shot_0001.mp4`, ... under `test_data/output/<video_name>/`.

Each shot file is a self-contained H.264/AAC MP4 clip with the detected shot boundary transitions removed.

## Model

- **Architecture**: Shot-Query Transformer (DETR-style), 6 encoder + 6 decoder layers, ResNet18 backbone
- **Input**: 100-frame windows at 128×96, ImageNet normalization
- **Output**: Shot boundary frame indices + intra-shot relation (dissolve, wipe, fade, ...) + inter-shot relation (hard cut, sudden jump, ...)
- **Weights**: Converted from the official PyTorch checkpoint, 363 tensors, float32

## License & Credits

Paper: [OmniShotCut (arXiv 2604.24762)](https://arxiv.org/abs/2604.24762) by Boyang Wang et al.

MLX port by [@eisneim](https://github.com/eisneim). Weights hosted at [eisneim/OmniShotCut_mlx](https://huggingface.co/eisneim/OmniShotCut_mlx).