measure1d op• Data kinds: image → contour
• Call: fullseye.apply(img, "m1_fuzzy_measure_pos", a=0.5, b=0.5) (the 2-D model is one image plus two scalar knobs a,b∈[0,1])
• HALCON equivalent: fuzzy_measure_pos (the HALCON reference is a useful guide to its meaning and parameters)
*The figure is the actual output on a synthetic 128×128 input. Left: input, right: output. Point clouds are drawn as a top-down scatter (brightness = z), 1-D series as a line plot, volumes as the maximum-intensity projection along z, videos as the middle frame, complex images as magnitude; return values that are not pictures are shown as the values themselves.*
Sweeping knob a (0.1 / 0.5 / 0.9, the other knob at its default):
▸ m1_fuzzy_measure_pos: knob a sweep (docs site)
*Knob b does not change the output (measured: identical at 0.1 / 0.5 / 0.9).*
On other images (synthetic scene / photo / coins. Top row: inputs, bottom row: their outputs. Knobs at default):
▸ m1_fuzzy_measure_pos: other inputs (docs site)
*The 4th column is a colour (H,W,3) input. This op handles colour without crossing channels (it does not convolve the colour channel as a third spatial axis).*
Sieves edges along a caliper line through the centre by a fuzzy membership score and returns them (equivalent to HALCON fuzzy_measure_pos: detects straight edges perpendicular to a rectangle/arc using fuzzy judgement).
Edge detection is the same as m1_measure_pos. The amplitude of each edge is mapped to a fuzzy score in [0,1] with (amp - lo) / (gmax - lo) (lo = 0.05*gmax), and only those of at least b (0 to 1) are kept (it differs from measure_pos in selecting by a continuous confidence according to amplitude rather than by a 0/1 hard threshold). a is the line orientation (theta = a*pi). Returns a CONTOUR; coordinates are in pixels.
• gallery2d_contour_measure family guide
• measurement_uncertainty — Measurement uncertainty and calibration — what it takes to claim you are measuring
• Sample-data catalog (download URLs / licences) — 2-D uses skimage.data (BSD/public domain) plus synthetic images; 3-D lists download URLs for real data sources (Stanford, PDS, …).
• Operator provenance and references — the sources of the research/methods this op family came from.
• The canonical algorithm (author, year) and its uses are named in the family usage guide above.
The program below has been verified to run (same input as the figure). In Studio's help this block becomes buttons that load and run it on the spot.
m1_fuzzy_measure_pos 0.50 0.50
▸ Load this pipeline · Load & run
• gallery2d_contour_measure — py -3.11 examples/gallery2d_contour_measure.py
contour as input)identity · select_contours · smooth_contours · fit_line_contours · contours_to_region · count_contours · total_length · select_contours_xld
measure1d)m1_measure_projection · m1_measure_pos · m1_measure_thresh · m1_measure_pairs
*Provenance: ops.py — 2D operator registry. This per-op note is generated by tools/opdocs.py md (do not hand-edit).*
© 2026 Kazufumi Furuse — Fullseye operator documentation. Licensed under Apache-2.0.