hx_add_noise_contour — 2D `halcon_ext` op

• Data kinds: contour → contour

• Call: fullseye.apply(img, "hx_add_noise_contour", a=0.5, b=0.5) (the 2-D model is one image plus two scalar knobs a,b∈[0,1])

• HALCON equivalent: add_noise_white_contour_xld (the HALCON reference is a useful guide to its meaning and parameters)

hx_add_noise_contour: input → output

*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):

▸ hx_add_noise_contour: knob a sweep (docs site)

*Knob b does not change the output (measured: identical at 0.1 / 0.5 / 0.9).*

Stages (the ops that come before → this op, left to right):

▸ hx_add_noise_contour: stages (docs site)

On other images (synthetic scene / photo / coins. Top row: inputs, bottom row: their outputs. Knobs at default):

▸ hx_add_noise_contour: other inputs (docs site)

Usage

Add white Gaussian noise to the contour points (std from a; deterministic with a fixed seed).

Adds independent Gaussian noise with mean 0 and standard deviation std to all point coordinates (row, col) of each contour and returns the result. The random generator np.random.default_rng(12345) is recreated on every call, so the same input always gets the same noise (deterministic).

• a → standard deviation std = a*3 (0 to 3 pixels; no change at a=0).

• b is unused.

The noise sequence depends on the order and number of points of the contours, so if an upstream step changes the point order or the number of contours, the same a hits differently. Used for robustness checks (how much the residual of hx_fit_circle_contour grows, etc.) or to confirm the effect of smooth_contours_xld.

Detailed usage guide

• gallery2d_halcon_ext family guide

References (sample data, literature)

• 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.

Try it in Studio

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.

threshold 0.50 0.50
sk_find_contours 0.50 0.50
hx_add_noise_contour 0.50 0.50

▸ Load this pipeline · Load & run

Runnable examples (verified samples that actually call this op)

• gallery2d_halcon_ext — py -3.11 examples/gallery2d_halcon_ext.py

Ops the type connects to (they accept `contour` as input)

identity · select_contours · smooth_contours · fit_line_contours · contours_to_region · count_contours · total_length · select_contours_xld

Same category (`halcon_ext`)

hx_gen_circle · hx_gen_ellipse · hx_gen_rectangle2 · hx_gen_checker_region · hx_gen_grid_region · hx_gabor · hx_fit_surface1 · hx_fit_surface2

*Provenance: ops.py — 2D operator registry. This per-op note is generated by tools/opdocs.py md (do not hand-edit).*

hx_add_noise_contour — 2D halcon_ext op