adding more nodes

backend/nodes/immerse_detail.py

@@ -0,0 +1,89 @@
+"""Immerse detail — overlay high-resolution detail onto lower-resolution overview."""
+
+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="Immerse Detail")
+class ImmerseDetail:
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "overview": ("DATA_FIELD",),
+                "detail": ("DATA_FIELD",),
+                "blend": (["replace", "average"], {"default": "replace"}),
+            }
+        }
+
+    OUTPUTS = (
+        ('DATA_FIELD', 'combined'),
+    )
+    FUNCTION = "process"
+
+    DESCRIPTION = (
+        "Overlay a high-resolution detail scan onto a lower-resolution overview "
+        "image using cross-correlation to find the best position. "
+    )
+
+    def process(self, overview: DataField, detail: DataField, blend: str) -> tuple:
+        ov = np.asarray(overview.data, dtype=np.float64)
+        dt = np.asarray(detail.data, dtype=np.float64)
+
+        # Resample detail to overview pixel size if needed
+        scale_x = detail.dx / overview.dx
+        scale_y = detail.dy / overview.dy
+
+        if abs(scale_x - 1.0) > 0.01 or abs(scale_y - 1.0) > 0.01:
+            from scipy.ndimage import zoom
+            dt = zoom(dt, (scale_y, scale_x), order=1)
+
+        dy_res, dx_res = dt.shape
+        oy_res, ox_res = ov.shape
+
+        if dy_res >= oy_res or dx_res >= ox_res:
+            # Detail is larger than overview, just return overview
+            return (overview,)
+
+        # Cross-correlate to find best position
+        # Use a sliding window approach for small detail
+        best_score = -np.inf
+        best_y, best_x = 0, 0
+
+        dt_norm = dt - dt.mean()
+        dt_std = dt.std()
+        if dt_std < 1e-30:
+            dt_std = 1.0
+
+        # Coarse search with stride
+        stride = max(1, min(dy_res, dx_res) // 4)
+        for iy in range(0, oy_res - dy_res + 1, stride):
+            for ix in range(0, ox_res - dx_res + 1, stride):
+                patch = ov[iy:iy + dy_res, ix:ix + dx_res]
+                score = np.sum((patch - patch.mean()) * dt_norm) / (patch.std() * dt_std + 1e-30)
+                if score > best_score:
+                    best_score = score
+                    best_y, best_x = iy, ix
+
+        # Fine search around best position
+        for iy in range(max(0, best_y - stride), min(oy_res - dy_res + 1, best_y + stride + 1)):
+            for ix in range(max(0, best_x - stride), min(ox_res - dx_res + 1, best_x + stride + 1)):
+                patch = ov[iy:iy + dy_res, ix:ix + dx_res]
+                score = np.sum((patch - patch.mean()) * dt_norm) / (patch.std() * dt_std + 1e-30)
+                if score > best_score:
+                    best_score = score
+                    best_y, best_x = iy, ix
+
+        # Place detail into overview
+        result = ov.copy()
+        if blend == "replace":
+            result[best_y:best_y + dy_res, best_x:best_x + dx_res] = dt
+        else:  # average
+            result[best_y:best_y + dy_res, best_x:best_x + dx_res] = \
+                0.5 * (ov[best_y:best_y + dy_res, best_x:best_x + dx_res] + dt)
+
+        return (overview.replace(data=result),)