low pri features

2026-04-03 22:09:19 -07:00
parent c24eed104e
commit 5d4c6dfcea
25 changed files with 1707 additions and 117 deletions
--- a/backend/nodes/feature_detection.py
+++ b/backend/nodes/feature_detection.py
@@ -0,0 +1,99 @@
+"""Feature detection — Canny edge and Harris corner detection."""
+
+from __future__ import annotations
+
+import numpy as np
+from skimage.feature import canny, corner_harris, corner_peaks
+
+from backend.node_registry import register_node
+from backend.data_types import DataField, RecordTable
+
+
+@register_node(display_name="Feature Detection")
+class FeatureDetection:
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "field": ("DATA_FIELD",),
+                "method": (["canny", "harris"], {"default": "canny"}),
+                "sigma": ("FLOAT", {"default": 1.0, "min": 0.1, "max": 20.0, "step": 0.1}),
+            },
+            "optional": {
+                "low_threshold": ("FLOAT", {"default": 0.1, "min": 0.0, "max": 1.0, "step": 0.01}),
+                "high_threshold": ("FLOAT", {"default": 0.2, "min": 0.0, "max": 1.0, "step": 0.01}),
+                "harris_k": ("FLOAT", {"default": 0.05, "min": 0.01, "max": 0.5, "step": 0.01}),
+                "min_distance": ("INT", {"default": 5, "min": 1, "max": 100}),
+            }
+        }
+
+    OUTPUTS = (
+        ('DATA_FIELD', 'result'),
+        ('RECORD_TABLE', 'features'),
+    )
+    FUNCTION = "process"
+
+    DESCRIPTION = (
+        "Detect edges or corners in a surface. "
+        "Canny: multi-stage edge detector with hysteresis thresholding. "
+        "Harris: corner/interest point detector based on structure tensor. "
+        "Outputs a feature map and a table of detected feature locations. "
+        "Equivalent to Gwyddion's edge/corner detection in filters.c."
+    )
+
+    def process(
+        self,
+        field: DataField,
+        method: str,
+        sigma: float,
+        low_threshold: float = 0.1,
+        high_threshold: float = 0.2,
+        harris_k: float = 0.05,
+        min_distance: int = 5,
+    ) -> tuple:
+        data = np.asarray(field.data, dtype=np.float64)
+
+        # Normalise to [0, 1]
+        dmin, dmax = data.min(), data.max()
+        if dmax > dmin:
+            norm = (data - dmin) / (dmax - dmin)
+        else:
+            norm = np.zeros_like(data)
+
+        records: RecordTable = RecordTable()
+
+        if method == "canny":
+            edges = canny(
+                norm,
+                sigma=sigma,
+                low_threshold=low_threshold,
+                high_threshold=high_threshold,
+            )
+            result = edges.astype(np.float64)
+            n_edge_pixels = int(edges.sum())
+            edge_fraction = n_edge_pixels / data.size
+            records.append({"quantity": "Edge pixels", "value": str(n_edge_pixels), "unit": ""})
+            records.append({"quantity": "Edge fraction", "value": f"{edge_fraction:.4f}", "unit": ""})
+
+        elif method == "harris":
+            response = corner_harris(norm, sigma=sigma, k=harris_k)
+            result = response.astype(np.float64)
+            corners = corner_peaks(
+                response,
+                min_distance=min_distance,
+                threshold_rel=0.1,
+            )
+            records.append({"quantity": "Corners detected", "value": str(len(corners)), "unit": ""})
+            for i, (row, col) in enumerate(corners[:20]):
+                x_phys = col * field.dx
+                y_phys = row * field.dy
+                records.append({
+                    "quantity": f"Corner {i + 1}",
+                    "value": f"({x_phys:.4g}, {y_phys:.4g})",
+                    "unit": field.si_unit_xy,
+                })
+
+        else:
+            raise ValueError(f"Unknown method: {method!r}")
+
+        return (field.replace(data=result, si_unit_z=""), records)