67 lines
2.3 KiB
Python
67 lines
2.3 KiB
Python
"""Affine correction — fix geometric distortions from scanner nonlinearity."""
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from __future__ import annotations
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import numpy as np
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from scipy.ndimage import affine_transform
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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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@register_node(display_name="Affine Correction")
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class AffineCorrection:
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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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"shear_x": ("FLOAT", {"default": 0.0, "min": -1.0, "max": 1.0, "step": 0.01}),
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"shear_y": ("FLOAT", {"default": 0.0, "min": -1.0, "max": 1.0, "step": 0.01}),
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"scale_x": ("FLOAT", {"default": 1.0, "min": 0.1, "max": 10.0, "step": 0.01}),
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"scale_y": ("FLOAT", {"default": 1.0, "min": 0.1, "max": 10.0, "step": 0.01}),
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"angle": ("FLOAT", {"default": 0.0, "min": -45.0, "max": 45.0, "step": 0.1}),
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}
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}
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OUTPUTS = (
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('DATA_FIELD', 'corrected'),
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)
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FUNCTION = "process"
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DESCRIPTION = (
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"Apply an affine correction to fix geometric distortions from scanner "
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"nonlinearity. Parameters specify shear, scale, and rotation corrections. "
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"The transform is applied about the centre of the field. "
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"Equivalent to Gwyddion's correct_affine.c module."
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)
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def process(
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self,
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field: DataField,
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shear_x: float,
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shear_y: float,
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scale_x: float,
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scale_y: float,
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angle: float,
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) -> tuple:
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data = np.asarray(field.data, dtype=np.float64)
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yres, xres = data.shape
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cy, cx = yres / 2.0, xres / 2.0
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theta = np.radians(angle)
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cos_t, sin_t = np.cos(theta), np.sin(theta)
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# Build the correction matrix: Scale * Shear * Rotation
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rotation = np.array([[cos_t, -sin_t], [sin_t, cos_t]])
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shear = np.array([[1.0, shear_x], [shear_y, 1.0]])
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scale = np.array([[1.0 / scale_x, 0.0], [0.0, 1.0 / scale_y]])
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matrix = scale @ shear @ rotation
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# Offset so the transform is centred
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offset = np.array([cy, cx]) - matrix @ np.array([cy, cx])
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corrected = affine_transform(data, matrix, offset=offset, order=3, mode="reflect")
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return (field.replace(data=corrected),)
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