104 lines
4.1 KiB
Python
104 lines
4.1 KiB
Python
"""Hough transform — detect lines and circles in images."""
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from __future__ import annotations
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import numpy as np
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from skimage.transform import hough_line, hough_line_peaks, hough_circle, hough_circle_peaks
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from skimage.feature import canny
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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="Hough Transform")
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class HoughTransform:
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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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"detect": (["lines", "circles"], {"default": "lines"}),
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"n_features": ("INT", {"default": 5, "min": 1, "max": 50}),
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"canny_sigma": ("FLOAT", {"default": 1.0, "min": 0.1, "max": 10.0, "step": 0.1}),
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"min_radius_px": ("INT", {"default": 10, "min": 2, "max": 500}),
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"max_radius_px": ("INT", {"default": 100, "min": 5, "max": 1000}),
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}
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}
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OUTPUTS = (
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('DATA_FIELD', 'accumulator'),
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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 lines or circles using the Hough transform. "
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"First applies Canny edge detection, then accumulates votes in "
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"Hough parameter space. Reports detected features with their parameters. "
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"For lines: angle and distance from origin. "
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"For circles: centre coordinates and radius. "
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)
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KEYWORDS = ("line detection", "circle detection", "shape detection")
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def process(
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self,
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field: DataField,
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detect: str,
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n_features: int,
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canny_sigma: float,
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min_radius_px: int,
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max_radius_px: int,
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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] for edge detection
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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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edges = canny(norm, sigma=canny_sigma)
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records: RecordTable = RecordTable()
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if detect == "lines":
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tested_angles = np.linspace(-np.pi / 2, np.pi / 2, 180, endpoint=False)
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h, theta, d = hough_line(edges, theta=tested_angles)
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accum = field.replace(data=h.astype(np.float64), si_unit_z="", domain="frequency")
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peaks = hough_line_peaks(h, theta, d, num_peaks=n_features)
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for i, (hval, angle, dist) in enumerate(zip(*peaks)):
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angle_deg = np.degrees(angle)
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dist_phys = dist * field.dx
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records.append({"quantity": f"Line {i + 1} angle", "value": f"{angle_deg:.1f}", "unit": "deg"})
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records.append({"quantity": f"Line {i + 1} distance", "value": f"{dist_phys:.4g}", "unit": field.si_unit_xy})
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records.append({"quantity": f"Line {i + 1} votes", "value": f"{hval:.0f}", "unit": ""})
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elif detect == "circles":
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max_radius_px = max(min_radius_px + 1, max_radius_px)
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radii = np.arange(min_radius_px, max_radius_px + 1)
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h = hough_circle(edges, radii)
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# Sum over radii for the accumulator visualisation
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accum_data = h.sum(axis=0).astype(np.float64)
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accum = field.replace(data=accum_data, si_unit_z="")
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accum_list, cx_list, cy_list, rad_list = hough_circle_peaks(
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h, radii, total_num_peaks=n_features,
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)
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for i, (hval, cx, cy, r) in enumerate(zip(accum_list, cx_list, cy_list, rad_list)):
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cx_phys = cx * field.dx
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cy_phys = cy * field.dy
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r_phys = r * field.dx
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records.append({"quantity": f"Circle {i + 1} x", "value": f"{cx_phys:.4g}", "unit": field.si_unit_xy})
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records.append({"quantity": f"Circle {i + 1} y", "value": f"{cy_phys:.4g}", "unit": field.si_unit_xy})
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records.append({"quantity": f"Circle {i + 1} radius", "value": f"{r_phys:.4g}", "unit": field.si_unit_xy})
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else:
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raise ValueError(f"Unknown detect mode: {detect!r}")
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return (accum, records)
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