adding more nodes

backend/nodes/poly_distort.py

@@ -0,0 +1,60 @@
+"""Polynomial distortion correction — correct nonlinear scanner distortions."""
+
+from __future__ import annotations
+
+import numpy as np
+from scipy.ndimage import map_coordinates
+
+from backend.node_registry import register_node
+from backend.data_types import DataField
+
+
+@register_node(display_name="Polynomial Distortion")
+class PolynomialDistortion:
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "field": ("DATA_FIELD",),
+                "k1_x": ("FLOAT", {"default": 0.0, "min": -1.0, "max": 1.0, "step": 0.001}),
+                "k1_y": ("FLOAT", {"default": 0.0, "min": -1.0, "max": 1.0, "step": 0.001}),
+                "k2_x": ("FLOAT", {"default": 0.0, "min": -1.0, "max": 1.0, "step": 0.001}),
+                "k2_y": ("FLOAT", {"default": 0.0, "min": -1.0, "max": 1.0, "step": 0.001}),
+                "k3_x": ("FLOAT", {"default": 0.0, "min": -0.5, "max": 0.5, "step": 0.001}),
+                "k3_y": ("FLOAT", {"default": 0.0, "min": -0.5, "max": 0.5, "step": 0.001}),
+            }
+        }
+
+    OUTPUTS = (
+        ('DATA_FIELD', 'corrected'),
+    )
+    FUNCTION = "process"
+
+    DESCRIPTION = (
+        "Correct nonlinear scanner distortions with polynomial coordinate "
+        "warping up to cubic order. Coefficients k1 (linear correction), "
+        "k2 (quadratic), k3 (cubic) are applied independently to x and y axes. "
+    )
+
+    def process(self, field: DataField,
+                k1_x: float, k1_y: float,
+                k2_x: float, k2_y: float,
+                k3_x: float, k3_y: float) -> tuple:
+        data = np.asarray(field.data, dtype=np.float64)
+        yres, xres = data.shape
+
+        # Normalised coordinates [-1, 1]
+        yy, xx = np.mgrid[:yres, :xres]
+        xn = 2.0 * xx / max(xres - 1, 1) - 1.0
+        yn = 2.0 * yy / max(yres - 1, 1) - 1.0
+
+        # Apply polynomial distortion (inverse mapping)
+        xn_src = xn + k1_x * xn + k2_x * xn**2 + k3_x * xn**3
+        yn_src = yn + k1_y * yn + k2_y * yn**2 + k3_y * yn**3
+
+        # Convert back to pixel coordinates
+        sx = (xn_src + 1.0) * max(xres - 1, 1) / 2.0
+        sy = (yn_src + 1.0) * max(yres - 1, 1) / 2.0
+
+        result = map_coordinates(data, [sy, sx], order=1, mode='nearest')
+        return (field.replace(data=result),)