# ComfyUI Phase Stretch Transform Node

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.

## Installation

1. Clone or copy this folder into your ComfyUI `custom_nodes` directory:
   ```
   ComfyUI/custom_nodes/Comfyui_PST/
   ```
2. Install dependencies:
   ```bash
   pip install numpy scipy
   ```
3. Restart ComfyUI.

The node will appear under **image → processing** in the node menu.

## Node: Phase Stretch Transform

### Inputs

| Name | Type | Default | Description |
|---|---|---|---|
| `image` | IMAGE | — | Input image (RGB or grayscale) |
| `lpf_sigma` | FLOAT | 0.21 | Gaussian low-pass filter sigma applied before PST. Higher values smooth the image more, reducing noise sensitivity. |
| `phase_strength` | FLOAT | 0.48 | PST phase strength (S). Controls how strongly phase variations are amplified. |
| `warp_strength` | FLOAT | 12.14 | PST warp strength (W). Controls the frequency-domain warping; higher values emphasise finer edges. |
| `min_thresh` | FLOAT | -1.0 | Lower phase threshold for binary edge extraction. |
| `max_thresh` | FLOAT | 0.003 | Upper phase threshold for binary edge extraction. |
| `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. |
| `output_mode` | COMBO | grayscale | `grayscale` – white edges on black background. `rgb_overlay` – edges drawn in red over the original image. |

### Outputs

| Name | Type | Description |
|---|---|---|
| `edge_image` | IMAGE | Processed edge map (binary or overlay depending on `output_mode`). |
| `phase_image` | IMAGE | Raw PST phase output normalised to 0–1, useful for further processing or visualisation. |

## Algorithm

PST processes each image through the following steps:

1. Convert input to grayscale (luminance weights: R×0.2126, G×0.7152, B×0.0722).
2. Compute the 2D FFT and shift to centre zero frequency.
3. Apply a Gaussian low-pass filter in the frequency domain.
4. Apply the PST kernel — a warped phase ramp:

$$K(r) = \frac{W \cdot r}{1 + W \cdot r} \cdot e^{-j \, S \cdot r \, / \, (1 + r)}$$

   where $r$ is the radial frequency and $W$, $S$ are the warp and phase strength parameters.

5. Compute the inverse FFT.
6. Extract the phase angle of the complex result.
7. Threshold the phase to produce a binary edge map, then apply morphological clean-up (if `morph_flag` is enabled).

## Parameter Tuning Guide

- **More edges / finer detail** — increase `phase_strength` or `warp_strength`.
- **Fewer false edges / less noise** — increase `lpf_sigma` or tighten the threshold range (`min_thresh` closer to 0, `max_thresh` closer to 0).
- **Soft/continuous edge map** — disable `morph_flag`; use the `phase_image` output.
- **Overlay on original** — set `output_mode` to `rgb_overlay`.

## Example Workflow

```
Load Image → Phase Stretch Transform → Preview Image
                       ↓
              (edge_image or phase_image)
```

## Screenshots

![ComfyUI Phase Stretch Transform Workflow](https://raw.githubusercontent.com/bemoregt/ComfyUI_PhaseStretchTransform/main/ScrShot%206.png)

![Phase Stretch Transform Result](https://raw.githubusercontent.com/bemoregt/ComfyUI_PhaseStretchTransform/main/126134017.jpeg)

## Requirements

- Python ≥ 3.10
- numpy
- scipy
- torch (provided by ComfyUI)

## License

MIT