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.
