remaining med value features

backend/nodes/local_contrast.py

@@ -0,0 +1,64 @@
+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="Local Contrast")
+class LocalContrast:
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "field": ("DATA_FIELD",),
+                "kernel_size": ("INT", {"default": 10, "min": 2, "max": 100}),
+                "weight": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.05}),
+            }
+        }
+
+    OUTPUTS = (
+        ('DATA_FIELD', 'result'),
+    )
+    FUNCTION = "process"
+
+    DESCRIPTION = (
+        "Expand the local dynamic range at each pixel. "
+        "Reveals fine surface features that are hidden by global contrast range. "
+        "Equivalent to Gwyddion local_contrast.c."
+    )
+
+    def process(self, field: DataField, kernel_size: int, weight: float) -> tuple:
+        from scipy.ndimage import minimum_filter, maximum_filter
+
+        data = np.asarray(field.data, dtype=np.float64)
+        kernel_size = max(2, int(kernel_size))
+        weight = float(np.clip(weight, 0.0, 1.0))
+
+        local_min = minimum_filter(data, size=kernel_size, mode="reflect")
+        local_max = maximum_filter(data, size=kernel_size, mode="reflect")
+
+        global_min = float(data.min())
+        global_max = float(data.max())
+
+        local_range = local_max - local_min
+        eps = np.finfo(np.float64).eps * max(abs(global_max), abs(global_min), 1.0)
+
+        # Remap each pixel from its local range to the global range.
+        # Where local_range is near zero, the pixel is already flat: leave it
+        # unchanged (enhancement factor = 1).
+        safe_range = np.where(local_range > eps, local_range, 1.0)
+        global_span = global_max - global_min
+        if global_span <= eps:
+            # Uniform field – nothing to enhance.
+            return (field.replace(data=data.copy()),)
+
+        enhancement_factor = global_span / safe_range
+        # Locally enhanced value: remap v from [local_min, local_max] → [global_min, global_max]
+        v_enhanced = global_min + enhancement_factor * (data - local_min)
+
+        # Blend between original and enhanced.
+        result = (1.0 - weight) * data + weight * v_enhanced
+
+        return (field.replace(data=result),)