adding more nodes

backend/nodes/grain_mark.py

@@ -0,0 +1,75 @@
+"""Grain marking — mark grains by height, slope, or curvature criteria."""
+
+from __future__ import annotations
+
+import numpy as np
+from scipy.ndimage import label, sobel
+
+from backend.node_registry import register_node
+from backend.data_types import DataField
+from backend.nodes.helpers import bool_to_mask
+
+
+@register_node(display_name="Grain Mark")
+class GrainMark:
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "field": ("DATA_FIELD",),
+                "criterion": (["height", "slope", "curvature"], {"default": "height"}),
+                "threshold_low": ("FLOAT", {"default": 0.3, "min": 0.0, "max": 1.0, "step": 0.01}),
+                "threshold_high": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}),
+                "min_size": ("INT", {"default": 10, "min": 1, "max": 100000, "step": 1}),
+                "inverted": ("BOOLEAN", {"default": False}),
+            }
+        }
+
+    OUTPUTS = (
+        ('IMAGE', 'mask'),
+    )
+    FUNCTION = "process"
+
+    DESCRIPTION = (
+        "Mark grains by thresholding height, slope magnitude, or curvature. "
+        "Thresholds are relative (0–1) to the data range. Small regions below "
+        "min_size pixels are removed. Use inverted to mark valleys instead of peaks. "
+    )
+
+    def process(self, field: DataField, criterion: str, threshold_low: float,
+                threshold_high: float, min_size: int, inverted: bool) -> tuple:
+        data = np.asarray(field.data, dtype=np.float64)
+
+        if criterion == "height":
+            values = data
+        elif criterion == "slope":
+            gx = sobel(data, axis=1)
+            gy = sobel(data, axis=0)
+            values = np.sqrt(gx**2 + gy**2)
+        elif criterion == "curvature":
+            gxx = sobel(sobel(data, axis=1), axis=1)
+            gyy = sobel(sobel(data, axis=0), axis=0)
+            values = np.abs(gxx + gyy)
+        else:
+            raise ValueError(f"Unknown criterion: {criterion!r}")
+
+        # Normalize to [0, 1]
+        vmin, vmax = values.min(), values.max()
+        if vmax > vmin:
+            norm = (values - vmin) / (vmax - vmin)
+        else:
+            norm = np.zeros_like(values)
+
+        # Apply thresholds
+        binary = (norm >= threshold_low) & (norm <= threshold_high)
+
+        if inverted:
+            binary = ~binary
+
+        # Remove small regions
+        labeled, n_labels = label(binary.astype(np.int32))
+        for gid in range(1, n_labels + 1):
+            if (labeled == gid).sum() < min_size:
+                binary[labeled == gid] = False
+
+        return (bool_to_mask(binary),)