low pri features

backend/nodes/mark_disconnected.py

@@ -0,0 +1,71 @@
+"""Mark disconnected regions — mask topologically isolated surface regions."""
+
+from __future__ import annotations
+import numpy as np
+from scipy.ndimage import grey_opening, grey_closing
+from backend.node_registry import register_node
+from backend.data_types import DataField
+from backend.nodes.helpers import bool_to_mask, _mask_structure, emit_mask_preview
+
+
+@register_node(display_name="Mark Disconnected")
+class MarkDisconnected:
+    """
+    Detect topologically disconnected (isolated) surface regions using
+    morphological opening/closing to build a defect-free reference, then
+    thresholding the residual difference.
+    """
+    _CUSTOM_PREVIEW = True
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "field": ("DATA_FIELD",),
+                "defect_type": (["positive", "negative", "both"],),
+                "radius": ("INT", {"default": 5, "min": 1, "max": 100, "step": 1}),
+                "threshold": ("FLOAT", {"default": 0.1, "min": 0.001, "max": 1.0, "step": 0.001}),
+            }
+        }
+
+    OUTPUTS = (
+        ('IMAGE', 'mask'),
+    )
+    FUNCTION = "process"
+
+    DESCRIPTION = (
+        "Mark topologically disconnected (isolated) surface regions. "
+        "A morphological opening followed by closing builds a smooth "
+        "defect-free reference surface; pixels whose deviation from that "
+        "reference exceeds the sensitivity threshold are flagged. "
+        "Equivalent to Gwyddion's mark_disconn module."
+    )
+
+    def process(self, field: DataField, defect_type: str, radius: int, threshold: float) -> tuple:
+        data = field.data.astype(np.float64)
+
+        # Build a disk structuring element for grey-scale morphology.
+        struct = _mask_structure(radius, "disk")
+
+        # Morphological opening then closing produces a defect-free reference.
+        reference = grey_opening(data, footprint=struct)
+        reference = grey_closing(reference, footprint=struct)
+
+        difference = data - reference
+        diff_range = difference.max() - difference.min()
+
+        # Avoid division-by-zero on perfectly flat surfaces.
+        if diff_range == 0:
+            mask = np.zeros(data.shape, dtype=bool)
+        else:
+            abs_threshold = threshold * diff_range
+            if defect_type == "positive":
+                mask = difference > abs_threshold
+            elif defect_type == "negative":
+                mask = difference < -abs_threshold
+            else:  # "both"
+                mask = np.abs(difference) > abs_threshold
+
+        out = bool_to_mask(mask)
+        emit_mask_preview(field, out)
+        return (out,)