low pri features

backend/nodes/affine_correction.py

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