2 unstable releases
0.2.0 | Jan 14, 2023 |
---|---|
0.1.0 | May 1, 2021 |
#543 in Images
34KB
433 lines
skeletonize
A line thinning library for binary images, including edge detection and threshold functions for preprocessing images into binary images.
The goal of line thinning is to remove excess pixels from the image until the lines present are one pixel wide, resembling a "skeleton" of the original pattern. Thinning is useful for removing noise from images which have had image processing filters applied to them such as edge detection. Line thinning is similar to erosion, another morphological operator.
The thinning algorithms are based on the papers Zhang & Suen, 1984 and Chen & Hsu, 1988. See Reference.
This crate requires the input to be a type from the image
crate. To use this
crate, add the following to your Cargo.toml
.
[dependencies.skeletonize]
version = "0.2"
Features
- 2 line thinning algorithms
- support for black or white foreground color
- Sobel operator edge detection
- thresholding for binarization (turning an image into only black and white pixels)
The example skeletonize.rs
is a command line
program available for download as a binary executable from the repository
Releases
page.
Thinning Algorithms Comparison
Images should be viewed at 100% magnification to avoid scaling artifacts.
First image, left to right: Figures 7(b) and 7(c) from Chen & Hsu, 1988,
Standard
algorithm result, Modified
algorithm result.
Second image: Animated comparison of the Standard
and Modified
marking
methods.
The Chen & Hsu Modified
algorithm tends to produce better line connectivity,
less noise, and more consistent single pixel width lines than the Zhang & Suen
Standard
algorithm.
The original image can be found in the gfx
folder entitled chenhsu.png
. It
was thresholded at 0.3
to convert it into a binary image before line thinning.
The example program produced the image results with the following arguments. The
--method|-m
option allows for selecting the standard or modified pixel marking
algorithm, --threshold|-t
is the gray level threshold.
-i chenhsu.png -t 0.3 -m s -o chenhsu-standard.png
-i chenhsu.png -t 0.3 -m m -o chenhsu-modified.png
skeletonize.rs
The skeletonize
example program exposes library functions as a command line
application. It can be run with the following command or by invoking it directly
after downloading from Releases
/building it yourself.
cargo r --release --example skeletonize -- [args]
Examples
The next three examples use this image as the input. All examples include the equivalent library code.
Perform edge detection with sobel4
(4-way edge detection) and line thinning,
threshold the edge detection filter to 0.3
.
cargo r --release --example skeletonize -- -i rustacean.png -e sobel4 -t 0.3
let mut filtered = sobel4::<foreground::Black>(&img, Some(0.3))?;
thin_image_edges::<foreground::Black>(&mut filtered, method, None)?;
Perform edge detection with no line thinning, threshold the edge detection
filter to 0.3
, and set the --foreground|-f
color to white.
-i rustacean.png -e sobel4 -t 0.3 --no-thin -f white
let filtered = sobel4::<foreground::White>(&img, Some(0.3))?;
Return the grayscale edge detection image by omitting the --threshold|-t
and
using --no-thin
. Aliases are used for sobel4
and white
.
-i rustacean.png -e s4 --no-thin -f w
let filtered = sobel4::<foreground::White>(&img, None))?;
The title image was created with the following arguments.
-i skeletonize.png -t 0.3
skeletonize::threshold(&mut img, 0.3)?;
thin_image_edges::<foreground::Black>(&mut img, method, None)?;
Reference
Zhang, T. Y. & Suen, C. Y. (1984). A fast parallel algorithm for thinning digital patterns. Commun. ACM 27, 3 (March 1984), 236–239. DOI:10.1145/357994.358023
Chen, Yung-Sheng & Hsu, Wen-Hsing. (1988). A modified fast parallel algorithm for thinning digital patterns. Pattern Recognition Letters. 7. 99-106. DOI:10.1016/0167-8655(88)90124-9
License
This crate is licensed under either
- the MIT License, or
- the Apache License (Version 2.0)
at your option.
Unless you explicitly state otherwise, any contribution intentionally submitted for inclusion in the work by you, as defined in the Apache-2.0 license, shall be dual licensed as above, without any additional terms or conditions.
Dependencies
~9.5MB
~26K SLoC