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