Histropy: A Computer Program for Quantifications of Histograms of 2D Gray-scale Images
CoRR(2024)
摘要
The computer program "Histropy" is an interactive Python program for the
quantification of selected features of two-dimensional (2D) images/patterns (in
either JPG/JPEG, PNG, GIF, BMP, or baseline TIF/TIFF formats) using
calculations based on the pixel intensities in this data, their histograms, and
user-selected sections of those histograms. The histograms of these images
display pixel-intensity values along the x-axis (of a 2D Cartesian plot), with
the frequency of each intensity value within the image represented along the
y-axis. The images need to be of 8-bit information depth and can be of
arbitrary size. Histropy generates an image's histogram surrounded by a
graphical user interface that allows one to select any range of image-pixel
intensity levels, i.e. sections along the histograms' x-axis, using either the
computer mouse or numerical text entries. The program subsequently calculates
the (so-called Monkey Model) Shannon entropy and root-mean-square contrast for
the selected section and displays them as part of what we call a
"histogram-workspace-plot." To support the visual identification of small peaks
in the histograms, the user can switch between a linear and log-base-10 display
scale for the y-axis of the histograms. Pixel intensity data from different
images can be overlaid onto the same histogram-workspace-plot for visual
comparisons. The visual outputs of the program can be saved as
histogram-workspace-plots in the PNG format for future usage. The source code
of the program and a brief user manual are published in the supporting
materials and on GitHub. Its functionality is currently being extended to
16-bit unsigned TIF/TIFF images. Instead of taking only 2D images as inputs,
the program's functionality could be extended by a few lines of code to other
potential uses employing data tables with one or two dimensions in the CSV
format.
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