initial commit

backend/nodes/grains.py

@@ -0,0 +1,127 @@
+"""
+Grain/feature detection nodes.
+
+Gwyddion equivalents:
+  ThresholdMask → threshold.c / otsu_threshold.c
+  GrainAnalysis → gwy_data_field_grains_get_values (grains-values.c)
+"""
+
+from __future__ import annotations
+import numpy as np
+from backend.node_registry import register_node
+from backend.data_types import DataField
+
+
+# ---------------------------------------------------------------------------
+# ThresholdMask
+# ---------------------------------------------------------------------------
+
+@register_node(display_name="Threshold Mask")
+class ThresholdMask:
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "field": ("DATA_FIELD",),
+                "method": (["otsu", "absolute", "relative"],),
+                "threshold": ("FLOAT", {"default": 0.0, "min": -1e9, "max": 1e9, "step": 0.001}),
+                "direction": (["above", "below"],),
+            }
+        }
+
+    RETURN_TYPES = ("IMAGE",)
+    RETURN_NAMES = ("mask",)
+    FUNCTION = "process"
+    CATEGORY = "grains"
+    DESCRIPTION = (
+        "Create a binary mask by thresholding data. "
+        "Otsu automatically finds the optimal threshold. "
+        "Equivalent to Gwyddion's threshold and otsu_threshold modules."
+    )
+
+    def process(self, field: DataField, method: str, threshold: float, direction: str) -> tuple:
+        data = field.data
+
+        if method == "otsu":
+            from skimage.filters import threshold_otsu
+            t = threshold_otsu(data)
+        elif method == "absolute":
+            t = float(threshold)
+        elif method == "relative":
+            # threshold is a fraction [0, 1] of the data range
+            dmin, dmax = data.min(), data.max()
+            t = dmin + float(threshold) * (dmax - dmin)
+        else:
+            raise ValueError(f"Unknown threshold method: {method}")
+
+        if direction == "above":
+            mask = (data >= t).astype(np.uint8) * 255
+        else:
+            mask = (data < t).astype(np.uint8) * 255
+
+        return (mask,)
+
+
+# ---------------------------------------------------------------------------
+# GrainAnalysis
+# ---------------------------------------------------------------------------
+
+@register_node(display_name="Grain Analysis")
+class GrainAnalysis:
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "field": ("DATA_FIELD",),
+                "mask": ("IMAGE",),
+                "min_size": ("INT", {"default": 10, "min": 1, "max": 100000, "step": 1}),
+            }
+        }
+
+    RETURN_TYPES = ("TABLE",)
+    RETURN_NAMES = ("grain_stats",)
+    FUNCTION = "process"
+    CATEGORY = "grains"
+    DESCRIPTION = (
+        "Label connected grain regions in a binary mask and compute per-grain statistics: "
+        "area, equivalent diameter, mean/max height, bounding box. "
+        "Equivalent to gwy_data_field_grains_get_values."
+    )
+
+    def process(self, field: DataField, mask: np.ndarray, min_size: int) -> tuple:
+        from scipy.ndimage import label, find_objects
+
+        binary = (mask > 127).astype(np.int32)
+        labeled, n_grains = label(binary)
+
+        pixel_area = field.dx * field.dy  # m^2 per pixel
+
+        rows = []
+        for grain_id in range(1, n_grains + 1):
+            grain_pixels = labeled == grain_id
+            area_px = int(grain_pixels.sum())
+            if area_px < min_size:
+                continue
+
+            area_m2 = area_px * pixel_area
+            equiv_diam = float(2.0 * np.sqrt(area_m2 / np.pi))
+
+            heights = field.data[grain_pixels]
+            mean_h = float(heights.mean())
+            max_h = float(heights.max())
+
+            # Bounding box
+            ys, xs = np.where(grain_pixels)
+            bbox = f"({int(xs.min())},{int(ys.min())})-({int(xs.max())},{int(ys.max())})"
+
+            rows.append({
+                "grain_id":     grain_id,
+                "area_px":      area_px,
+                "area_m2":      area_m2,
+                "equiv_diam_m": equiv_diam,
+                "mean_height":  mean_h,
+                "max_height":   max_h,
+                "bbox":         bbox,
+            })
+
+        return (rows,)