89 lines
3.5 KiB
Markdown
89 lines
3.5 KiB
Markdown
# ComfyUI Phase Stretch Transform Node
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A ComfyUI custom node that applies the **Phase Stretch Transform (PST)** to images. PST is a physics-inspired edge detection algorithm that enhances features by simulating wave propagation through a dispersive medium.
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## Installation
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1. Clone or copy this folder into your ComfyUI `custom_nodes` directory:
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```
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ComfyUI/custom_nodes/Comfyui_PST/
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```
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2. Install dependencies:
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```bash
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pip install numpy scipy
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```
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3. Restart ComfyUI.
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The node will appear under **image → processing** in the node menu.
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## Node: Phase Stretch Transform
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### Inputs
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| Name | Type | Default | Description |
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|---|---|---|---|
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| `image` | IMAGE | — | Input image (RGB or grayscale) |
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| `lpf_sigma` | FLOAT | 0.21 | Gaussian low-pass filter sigma applied before PST. Higher values smooth the image more, reducing noise sensitivity. |
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| `phase_strength` | FLOAT | 0.48 | PST phase strength (S). Controls how strongly phase variations are amplified. |
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| `warp_strength` | FLOAT | 12.14 | PST warp strength (W). Controls the frequency-domain warping; higher values emphasise finer edges. |
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| `min_thresh` | FLOAT | -1.0 | Lower phase threshold for binary edge extraction. |
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| `max_thresh` | FLOAT | 0.003 | Upper phase threshold for binary edge extraction. |
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| `morph_flag` | BOOLEAN | True | When enabled, applies morphological closing and opening to clean up the binary edge map. When disabled, outputs a soft (normalised) phase image instead. |
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| `output_mode` | COMBO | grayscale | `grayscale` – white edges on black background. `rgb_overlay` – edges drawn in red over the original image. |
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### Outputs
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| Name | Type | Description |
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|---|---|---|
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| `edge_image` | IMAGE | Processed edge map (binary or overlay depending on `output_mode`). |
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| `phase_image` | IMAGE | Raw PST phase output normalised to 0–1, useful for further processing or visualisation. |
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## Algorithm
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PST processes each image through the following steps:
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1. Convert input to grayscale (luminance weights: R×0.2126, G×0.7152, B×0.0722).
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2. Compute the 2D FFT and shift to centre zero frequency.
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3. Apply a Gaussian low-pass filter in the frequency domain.
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4. Apply the PST kernel — a warped phase ramp:
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$$K(r) = \frac{W \cdot r}{1 + W \cdot r} \cdot e^{-j \, S \cdot r \, / \, (1 + r)}$$
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where $r$ is the radial frequency and $W$, $S$ are the warp and phase strength parameters.
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5. Compute the inverse FFT.
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6. Extract the phase angle of the complex result.
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7. Threshold the phase to produce a binary edge map, then apply morphological clean-up (if `morph_flag` is enabled).
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## Parameter Tuning Guide
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- **More edges / finer detail** — increase `phase_strength` or `warp_strength`.
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- **Fewer false edges / less noise** — increase `lpf_sigma` or tighten the threshold range (`min_thresh` closer to 0, `max_thresh` closer to 0).
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- **Soft/continuous edge map** — disable `morph_flag`; use the `phase_image` output.
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- **Overlay on original** — set `output_mode` to `rgb_overlay`.
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## Example Workflow
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```
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Load Image → Phase Stretch Transform → Preview Image
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↓
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(edge_image or phase_image)
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```
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## Screenshots
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## Requirements
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- Python ≥ 3.10
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- numpy
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- scipy
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- torch (provided by ComfyUI)
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## License
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MIT
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