EdgesGauging.jl

Sub-pixel edge detection and robust geometric fitting (lines, circles) for machine-vision gauging tasks, written in pure Julia.

What it does

• 1-D edge detection in intensity profiles, with Gaussian smoothing and parabolic sub-pixel interpolation of gradient extrema.
• 2-D edge detection across rectangular ROIs, multi-strip scans, and radial / ring scans from a reference point.
• Robust geometric fitting via a generic RANSAC engine with constraint support (angle, radius, arc completeness, inlier counts).
• Parametric element types — LineModel{T}, CircleModel{T}, etc. so Float32 pipelines work end-to-end at the model layer.

Quick start

using EdgesGauging

result = gauge_edges_in_profile(profile, 2.0, 0.1,
                                POLARITY_POSITIVE, SELECT_BEST)

cc  = CircleConstraints{Float64}(min_radius=10.0, max_radius=200.0)
fit = gauge_circle(image, (row_c, col_c), 0.0, 2π, deg2rad(3.0), 80, 2.0, 0.1;
                   constraints = cc)

Conventions

• Image arrays are (row, col) — matching Julia's column-major indexing.
• center_rc arguments are therefore (row, col) tuples.
• Detected edges in ImageEdge are exposed as Cartesian (x = col, y = row) for consumers that prefer image-space coordinates.

See the API reference for the complete public interface.