79 lines
3.2 KiB
Python
79 lines
3.2 KiB
Python
"""Grain summary statistics — aggregate statistics for all grains."""
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from __future__ import annotations
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import numpy as np
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from scipy.ndimage import label
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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 mask_to_bool, _square_unit
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@register_node(display_name="Grain Summary")
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class GrainSummary:
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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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OUTPUTS = (
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('RECORD_TABLE', 'summary'),
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)
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FUNCTION = "process"
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DESCRIPTION = (
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"Compute aggregate statistics for all grains in a mask: count, density, "
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"coverage fraction, mean/median area, total volume, and height statistics. "
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)
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def process(self, field: DataField, mask: np.ndarray, min_size: int) -> tuple:
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data = np.asarray(field.data, dtype=np.float64)
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grain_mask = mask_to_bool(mask)
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labeled, n_grains = label(grain_mask.astype(np.int32))
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pixel_area = field.dx * field.dy
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total_area = field.xreal * field.yreal
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xy_unit = field.si_unit_xy or "m"
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z_unit = field.si_unit_z or "m"
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# Collect per-grain properties
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areas = []
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heights = []
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volumes = []
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base_height = float(data[~grain_mask].mean()) if (~grain_mask).any() else 0.0
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for gid in range(1, n_grains + 1):
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gpx = labeled == gid
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n_px = int(gpx.sum())
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if n_px < min_size:
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continue
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area = n_px * pixel_area
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areas.append(area)
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heights.append(float(data[gpx].mean()))
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volumes.append(float(np.sum(data[gpx] - base_height) * pixel_area))
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records = RecordTable()
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n_valid = len(areas)
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records.append({"quantity": "Grain count", "value": str(n_valid), "unit": ""})
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records.append({"quantity": "Grain density", "value": f"{n_valid / total_area:.4g}" if total_area > 0 else "0", "unit": f"1/{_square_unit(xy_unit)}"})
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coverage = sum(areas) / total_area if total_area > 0 else 0.0
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records.append({"quantity": "Coverage fraction", "value": f"{coverage:.4f}", "unit": ""})
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if n_valid > 0:
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records.append({"quantity": "Mean area", "value": f"{np.mean(areas):.4g}", "unit": _square_unit(xy_unit)})
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records.append({"quantity": "Median area", "value": f"{np.median(areas):.4g}", "unit": _square_unit(xy_unit)})
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records.append({"quantity": "Total volume", "value": f"{sum(volumes):.4g}", "unit": f"{_square_unit(xy_unit)}·{z_unit}"})
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records.append({"quantity": "Mean height", "value": f"{np.mean(heights):.4g}", "unit": z_unit})
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records.append({"quantity": "Median height", "value": f"{np.median(heights):.4g}", "unit": z_unit})
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records.append({"quantity": "Max area", "value": f"{max(areas):.4g}", "unit": _square_unit(xy_unit)})
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records.append({"quantity": "Min area", "value": f"{min(areas):.4g}", "unit": _square_unit(xy_unit)})
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return (records,)
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