adding more nodes

backend/nodes/level_grains.py

@@ -0,0 +1,68 @@
+"""Level grains — shift individual grain regions to a common baseline."""
+
+from __future__ import annotations
+
+import numpy as np
+from scipy.ndimage import label
+
+from backend.node_registry import register_node
+from backend.data_types import DataField
+from backend.nodes.helpers import mask_to_bool
+
+
+@register_node(display_name="Level Grains")
+class LevelGrains:
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "field": ("DATA_FIELD",),
+                "mask": ("IMAGE",),
+                "reference": (["mean", "median", "minimum"], {"default": "mean"}),
+            }
+        }
+
+    OUTPUTS = (
+        ('DATA_FIELD', 'leveled'),
+    )
+    FUNCTION = "process"
+
+    DESCRIPTION = (
+        "Shift individual grain regions (from a mask) so they all share a "
+        "common baseline. Uses the selected reference statistic (mean, median, "
+        "or minimum) per grain to compute the offset. "
+        "Useful for consistent grain height comparisons. "
+    )
+
+    def process(self, field: DataField, mask: np.ndarray, reference: str) -> tuple:
+        data = np.asarray(field.data, dtype=np.float64).copy()
+        grain_mask = mask_to_bool(mask)
+        labeled, n_grains = label(grain_mask.astype(np.int32))
+
+        if n_grains == 0:
+            return (field.replace(data=data),)
+
+        # Compute reference value for each grain
+        refs = []
+        for gid in range(1, n_grains + 1):
+            pixels = data[labeled == gid]
+            if len(pixels) == 0:
+                refs.append(0.0)
+                continue
+            if reference == "mean":
+                refs.append(float(pixels.mean()))
+            elif reference == "median":
+                refs.append(float(np.median(pixels)))
+            else:  # minimum
+                refs.append(float(pixels.min()))
+
+        # Target: global reference across all grains
+        target = float(np.mean(refs))
+
+        # Shift each grain
+        for gid in range(1, n_grains + 1):
+            grain_pixels = labeled == gid
+            offset = target - refs[gid - 1]
+            data[grain_pixels] += offset
+
+        return (field.replace(data=data),)