73 lines
2.4 KiB
Python
73 lines
2.4 KiB
Python
from __future__ import annotations
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import numpy as np
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from backend.node_registry import register_node
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from backend.data_types import DataField, RecordTable
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from backend.nodes.helpers import _square_unit
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@register_node(display_name="Grain Analysis")
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class GrainAnalysis:
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@classmethod
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def INPUT_TYPES(cls):
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return {
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"required": {
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"field": ("DATA_FIELD",),
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"mask": ("IMAGE",),
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"min_size": ("INT", {"default": 10, "min": 1, "max": 100000, "step": 1}),
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}
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}
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RETURN_TYPES = ("RECORD_TABLE",)
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RETURN_NAMES = ("grain_stats",)
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FUNCTION = "process"
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DESCRIPTION = (
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"Label connected grain regions in a binary mask and compute per-grain "
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"statistics: area, equivalent diameter, mean/max height, bounding box. "
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"Equivalent to Gwyddion's grain statistics tools."
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)
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def process(self, field: DataField, mask: np.ndarray, min_size: int) -> tuple:
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from scipy.ndimage import label
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binary = (mask > 127).astype(np.int32)
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labeled, n_grains = label(binary)
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pixel_area = field.dx * field.dy
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xy_unit = str(field.si_unit_xy or "").strip()
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z_unit = str(field.si_unit_z or "").strip()
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rows = RecordTable()
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for gid in range(1, n_grains + 1):
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grain_pixels = labeled == gid
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area_px = int(grain_pixels.sum())
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if area_px < min_size:
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continue
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area_m2 = area_px * pixel_area
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equiv_diam = float(2.0 * np.sqrt(area_m2 / np.pi))
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heights = field.data[grain_pixels]
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mean_h = float(heights.mean())
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max_h = float(heights.max())
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ys, xs = np.where(grain_pixels)
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bbox = f"({int(xs.min())},{int(ys.min())})-({int(xs.max())},{int(ys.max())})"
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rows.append({
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"grain_id": gid,
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"area_px": area_px,
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"area_px_unit": _square_unit("px"),
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"area_m2": area_m2,
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"area_m2_unit": _square_unit(xy_unit),
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"equiv_diam_m": equiv_diam,
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"equiv_diam_m_unit": xy_unit,
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"mean_height": mean_h,
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"mean_height_unit": z_unit,
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"max_height": max_h,
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"max_height_unit": z_unit,
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"bbox": bbox,
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})
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return (rows,)
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