Fred's ImageMagick Scripts


    Copyright © Fred Weinhaus

    My scripts are available free of charge for non-commercial (non-profit) use, ONLY.

    For use of my scripts in commercial (for-profit) environments or non-free applications, please contact me (Fred Weinhaus) for licensing arrangements. My email address is fmw at alink dot net.

    If you: 1) redistribute, 2) incorporate any of these scripts into other free applications or 3) reprogram them in another scripting language, then you must contact me for permission, especially if the result might be used in a commercial or for-profit environment.

    Usage, whether stated or not in the script, is restricted to the above licensing arrangements. It is also subject, in a subordinate manner, to the ImageMagick license, which can be found at:

    Please read the Pointers For Use on my home page to properly install and customize my scripts.


Automatically thresholds an image to binary (b/w) format using Otsu's between class variance technique.

Download Script

last modified: January 25, 2020

USAGE: otsuthresh [-g graph] infile outfile
USAGE: otsuthresh [-help]

-g ..... graph .......... graph specifies whether to generate a
......................... histogram graph image displaying the
......................... location and value of the threshold;
......................... choices are: view or save;
......................... default is no graph

PURPOSE: To automatically thresholds an image to binary (b/w) format using Otsu's technique.

DESCRIPTION: OTSUTHRESH automatically thresholds an image to binary (b/w) format. It assume the histogram is bimodal, i.e. is the composite of two bell-shaped distributions representing the foreground and background classes. The Otsu appoach computes the Between Class Variance from the foreground (above threshold data) and background (at and below threshold value) for every possible threshold value. The optimal threshold is the one that maximizes the Between Class Variance. This is equivalent to finding the threshold that minimizes the overlap between the two bell-shaped class curves.


-g graph ... GRAPH specifies whether to generate a graph (image) of the histogram, displaying the location and value of the threshold. The choices are: view, save and none. If graph=view is selected, the graph will be created and displayed automatically, but not saved. If graph=save is selected, then the graph will be created and saved to a file using the infile name, with "_histog_otsu.gif" appended, but the graph will not be displayed automatically. If -g option is not specified, then no graph will be created.

NOTE: It is highly recommended that the output not be specified as a JPG image as that will cause compression and potentially a non-binary (i.e. a graylevel) result. GIF is the recommended output format.

REFERENCES: see the following:

Mathematics Background (256KB PDF)

CAVEAT: No guarantee that this script will work on all platforms, nor that trapping of inconsistent parameters is complete and foolproof. Use At Your Own Risk.


Otsu Thresholding Of Various Images

Pictures Were Obtained from:
blood.jpg from
fingerprint.jpg from
flower.jpg from
house.jpg from's_method
kanji.jpg from
parts.gif from
rice.jpg from
tank.jpg from
textsample.jpg from
lena2g_edge1.jpg was created using the IM function -edge 1

Original Image

Thresholded Image


See A Comparison Of Each Image Against Each Thresholding Technique

What the script does is as follows:

  • Converts the image to grayscale
  • Generates the histogram
  • Computes the normalized histogram counts by dividing each bin count
    by the total pixels in the image
  • Generates cumulative arrays for each bin from the normalized count
    and from the product of normalized counts times bin graylevel
    starting from each end of the histogram. These are called histogram
    moments zero and one
  • Computes the cumulative histogram mean arrays from these moment arrays
  • For each possible bin, finds the between class variance from the
    two cumulative mean arrays and chooses the threshold with the
    largest between class variance
  • Thresholds the image at the graylevel corresponding to the selected bin