35 lines
1.6 KiB
Markdown
35 lines
1.6 KiB
Markdown
# 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.
|