low pri features

docs/nodes/Pixel Classification.md

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+# Pixel Classification
+
+Classify pixels into discrete classes based on height, slope, and/or curvature using threshold or clustering methods. Equivalent to Gwyddion's classify.c module.
+
+## Inputs
+
+| Name | Type | Required | Description |
+|------|------|----------|-------------|
+| field | DATA_FIELD | Yes | Input surface |
+
+## Outputs
+
+| Name | Type | Description |
+|------|------|-------------|
+| classified | DATA_FIELD | Integer class labels (0 to n_classes-1) |
+| mask | IMAGE | Binary mask of the first class (class 0) |
+
+## Controls
+
+| Name | Type | Default | Description |
+|------|------|---------|-------------|
+| n_classes | INT | 3 | Number of output classes (2–10) |
+| feature | dropdown | height | Feature used for classification: height, slope, curvature, height_slope, or all |
+| method | dropdown | otsu | Thresholding method: otsu, equal_range, or quantile |
+
+## Notes
+
+- **Feature types**: "height" uses raw data values; "slope" uses gradient magnitude (via `np.gradient`); "curvature" uses the Laplacian (sum of second derivatives). "height_slope" and "all" stack multiple features.
+- **Threshold methods** (single-feature only):
+  - *otsu*: Multi-Otsu thresholding that finds thresholds minimising intra-class variance.
+  - *equal_range*: Divides the feature value range into equal-width intervals.
+  - *quantile*: Divides by quantiles so each class contains roughly the same number of pixels.
+- **Multi-feature modes** ("height_slope", "all") ignore the method setting and use k-means clustering instead. Each feature is normalised to [0, 1] before clustering.
+- The mask output contains class 0 only — use the classified field for access to all class labels.