multithreshold

Builtin index · Images

result = multithreshold(x, levels)

Input: x, levels · Output: result

What it is

x is an Image, Signal, Vec, or scalar; levels is a Vec/matrix or tuple List of cut-point numbers (sorted ascending regardless of input order). Quantizes into len(levels)+1 bands at the given cut points. Image → posterized image of the same dimensions with evenly-spaced gray levels per band; Signal/plain numeric → raw 0-based band index.

Overloads

One call shape — this function is not overloaded.

Examples

From the documentation, and run by the build -- the output and the figure below are what the engine actually produced. Simplest first.

m = [10, 20, 30, 220; 15, 25, 200, 230; 120, 130, 210, 225; 125, 135, 205, 215]
img = image_from_matrix(m)

seg = otsu(img)                             # compute Otsu's level and apply it

imshow(seg)

cuts = multi_otsu(img, 3)                   # two cut points -> three bands

banded = multithreshold(img, cuts)
imshow(banded)

k = kapur_threshold(img)                    # entropy-based single threshold

fg = foreground_mask(img, k, radius=1)      # threshold + speck cleanup

print(cuts, k, seg.width, banded.width, fg.width)

[30.625, 135.46875] 130.3125 4 4 4

from images

How it works

multithreshold(x, levels) — quantizes into len(levels)+1 bands at the given cut points (levels a Vec/matrix literal or a List of numbers, sorted ascending regardless of input order); see apply_multithreshold's doc comment for the per-kind output convention (0-based band index for Vec/Signal, evenly-spaced posterized gray levels for Image).

From the note above this builtin in the interpreter, where the reasoning was written down as it was built.