tip modelling and deconvolution

backend/nodes/tip_deconvolution.py

@@ -0,0 +1,44 @@
+from __future__ import annotations
+
+import numpy as np
+from scipy.ndimage import grey_erosion
+
+from backend.node_registry import register_node
+from backend.data_types import DataField
+
+
+@register_node(display_name="Tip Deconvolution")
+class TipDeconvolution:
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "field": ("DATA_FIELD",),
+                "tip": ("DATA_FIELD",),
+            }
+        }
+
+    OUTPUTS = (
+        ('DATA_FIELD', 'surface'),
+    )
+    FUNCTION = "process"
+
+    DESCRIPTION = (
+        "Reconstruct the true surface from a tip-broadened measured image. "
+        "Uses morphological grey erosion (Villarrubia algorithm): "
+        "  mytip = flip(tip) − max(flip(tip))   [max shifted to 0] "
+        "  surface[y,x] = min_{dy,dx}[image[y+dy, x+dx] − mytip[dy,dx]] "
+        "Connect the tip output from a TipModel node. "
+        "The tip pixel size must match the image pixel size. "
+        "Equivalent to gwy_tip_erosion (tip.c)."
+    )
+
+    def process(self, field: DataField, tip: DataField) -> tuple:
+        # Gwyddion gwy_tip_erosion:
+        #   mytip = flip(tip) − max(flip(tip))   (values ≤ 0, apex = 0)
+        #   result[y,x] = min_{ty,tx}[surface[y+ty, x+tx] − mytip[ty,tx]]
+        tip_flipped = np.flipud(np.fliplr(tip.data))
+        mytip = tip_flipped - tip_flipped.max()     # shift so max = 0
+
+        result = grey_erosion(field.data, structure=mytip)
+        return (field.replace(data=result),)