adding more nodes

backend/nodes/freq_split.py

@@ -0,0 +1,50 @@
+"""Frequency splitting — separate image into low-pass and high-pass components."""
+
+from __future__ import annotations
+
+import numpy as np
+
+from backend.node_registry import register_node
+from backend.data_types import DataField
+
+
+@register_node(display_name="Frequency Split")
+class FrequencySplit:
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "field": ("DATA_FIELD",),
+                "cutoff": ("FLOAT", {"default": 0.1, "min": 0.001, "max": 0.5, "step": 0.001}),
+            }
+        }
+
+    OUTPUTS = (
+        ('DATA_FIELD', 'low_pass'),
+        ('DATA_FIELD', 'high_pass'),
+    )
+    FUNCTION = "process"
+
+    DESCRIPTION = (
+        "Separate a field into low-frequency (background) and high-frequency "
+        "(detail) components using FFT. The cutoff is relative to the Nyquist "
+        "frequency (0.5 = no filtering, 0.001 = very aggressive). "
+    )
+
+    def process(self, field: DataField, cutoff: float) -> tuple:
+        data = np.asarray(field.data, dtype=np.float64)
+        yres, xres = data.shape
+
+        kx = np.fft.fftfreq(xres)
+        ky = np.fft.fftfreq(yres)
+        KX, KY = np.meshgrid(kx, ky)
+        K = np.sqrt(KX**2 + KY**2)
+
+        # Gaussian low-pass filter
+        lp_filter = np.exp(-0.5 * (K / cutoff)**2)
+
+        fft_data = np.fft.fft2(data)
+        low = np.real(np.fft.ifft2(fft_data * lp_filter))
+        high = data - low
+
+        return (field.replace(data=low), field.replace(data=high))