ComfyUI Custom Node: Mask Fourier Smoothing

A ComfyUI custom node that smooths mask contours using the Fourier Descriptor algorithm. It takes a mask as input, applies low-pass filtering in the frequency domain, and outputs a smoothed mask.

Example

Input Mask	Smoothed Mask (descriptors=32)
Jagged, noisy contour	Clean, smooth contour

Installation

1. Clone or copy this folder into your ComfyUI custom_nodes/ directory:

cd /path/to/ComfyUI/custom_nodes
git clone https://github.com/bemoregt/ComfyUI_CustomNode_MaskSmoothing.git

2. Install dependencies:

pip install -r custom_nodes/ComfyUI_CustomNode_MaskSmoothing/requirements.txt

3. Restart ComfyUI.

The node will appear under mask/processing → Mask Fourier Smoothing.

Usage

Connect a MASK output to the node's mask input. Adjust num_descriptors to control the smoothing strength.

[Load Image] ──► mask ──► [Mask Fourier Smoothing] ──► smoothed_mask ──► [Preview Mask]

Parameters

Parameter	Default	Range	Description
mask	—	—	Input mask (MASK type)
num_descriptors	32	4 – 512	Number of Fourier frequency components to keep. Lower = smoother contour. Higher = closer to original.

How It Works

The algorithm follows these steps:

1. Binarize the input mask (threshold at 0.5)
2. Extract contours using cv2.findContours
3. Encode contour points as complex numbers: z = x + j·y
4. Apply FFT to obtain the Fourier Descriptors
5. Low-pass filter: keep only the lowest num_descriptors frequency components (zero out the rest)
6. Apply inverse FFT to reconstruct the smoothed contour
7. Fill the reconstructed contour to produce the output mask

The key insight is that high-frequency Fourier components correspond to sharp corners and jagged edges, while low-frequency components capture the overall smooth shape. By discarding high-frequency components, the contour is effectively smoothed.

Dependencies

• numpy
• opencv-python
• torch

License

MIT License