55 lines
1.6 KiB
Python
55 lines
1.6 KiB
Python
"""Outlier masking — mark statistical outlier pixels."""
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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
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from backend.nodes.helpers import bool_to_mask
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@register_node(display_name="Outlier Mask")
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class OutlierMask:
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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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"sigma_threshold": ("FLOAT", {"default": 3.0, "min": 1.0, "max": 10.0, "step": 0.1}),
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"mode": (["both", "high", "low"], {"default": "both"}),
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}
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}
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OUTPUTS = (
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('IMAGE', 'mask'),
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)
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FUNCTION = "process"
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DESCRIPTION = (
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"Create a mask marking pixels that deviate more than N standard "
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"deviations from the mean. Mode selects whether to flag high outliers, "
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"low outliers, or both. Quick way to identify noise spikes and defects. "
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)
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def process(self, field: DataField, sigma_threshold: float, mode: str) -> tuple:
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data = np.asarray(field.data, dtype=np.float64)
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mean = data.mean()
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std = data.std()
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if std < 1e-30:
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return (bool_to_mask(np.zeros(data.shape, dtype=bool)),)
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z = (data - mean) / std
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if mode == "both":
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outliers = np.abs(z) > sigma_threshold
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elif mode == "high":
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outliers = z > sigma_threshold
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elif mode == "low":
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outliers = z < -sigma_threshold
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else:
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raise ValueError(f"Unknown mode: {mode!r}")
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return (bool_to_mask(outliers),)
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