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1. `IG_MotionVideoSearch`: Takes an image input and returns the top 5 ranked URLs from the FAISS index.
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2. `IG_MotionVideoFrame`: Converts 24 consecutive frames into a single “dot frame,” which encodes motion features in a color-coded image.
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## ComfyUI
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We have some ComfyUI nodes ready to be used with a pre-existing video database consisting of roughly 100,000 videos. You can get started using the following workflow:
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[MotionSearch](workflows/MotionSearch.json)
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## Installation
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### 1. Set Up Environment
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- **IG_MotionVideoSearch**: Takes an image and returns URLs + ranks.
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- **IG_MotionVideoFrame**: Takes a stack of 24 frames and returns a single “dot frame.”
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## Repository Structure
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```
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├─ checkpoints/ # Checkpoints for the DOT motion model
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├─ configs/ # Model configuration files
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├─ data/ # Default location for data.sqlite & index.faiss
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│ ├─ datasets/
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│ └─ temp/
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├─ debug/ # Debug output (if enabled)
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├─ dot/ # DOT model code and configs
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├─ videos/ # Example video files (not in repo by default)
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├─ WatermarkRemoval/ # Optional submodule for removing watermarks
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├─ database.py # Database interactions (SQLite)
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├─ dot_functions.py # DOT model logic (motion extraction)
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├─ functions.py # Core logic for storing and handling frames
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├─ main.py # CLI entry point (Typer)
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├─ nodes.py # ComfyUI node definitions
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├─ pyproject.toml # Project metadata
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├─ README.md # This README
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└─ requirements.txt # Dependencies (for pip)
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```
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## Contributing
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Contributions and suggestions are welcome! Feel free to open issues or pull requests for enhancements or bug fixes.
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