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author | Nick White <git@njw.name> | 2020-11-06 16:19:31 +0000 |
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committer | Nick White <git@njw.name> | 2020-11-06 16:19:31 +0000 |
commit | cfbecc747c3db15c307168e8b5baa24c80ad7f35 (patch) | |
tree | b28e2014f96168bd49385883f9c556574ceeaf17 /content/posts/adaptive-binarisation | |
parent | b5f11cf4910cef277defad486604a3aab7b7bf74 (diff) |
Add docs links to packages on adaptive binarisation page
Diffstat (limited to 'content/posts/adaptive-binarisation')
-rw-r--r-- | content/posts/adaptive-binarisation/index.md | 14 |
1 files changed, 7 insertions, 7 deletions
diff --git a/content/posts/adaptive-binarisation/index.md b/content/posts/adaptive-binarisation/index.md index 46316f2..507fa3c 100644 --- a/content/posts/adaptive-binarisation/index.md +++ b/content/posts/adaptive-binarisation/index.md @@ -90,12 +90,12 @@ So, we wrote a Go package which does Sauvola binarisation, which contains both standalone command line tools and can be used as a package in your own Go projects. The same package contains some other image preprocessing functionality, so the package is called -[rescribe.xyz/preproc](https://rescribe.xyz/preproc). The command -line binarisation tool in the package is *binarize*, and the -relevant Go functions are Sauvola() and IntegralSauvola(). +[rescribe.xyz/preproc](https://rescribe.xyz/preproc) ([docs](https://pkg.go.dev/rescribe.xyz/preproc)). +The command line binarisation tool in the package is *binarize*, and +the relevant Go functions are Sauvola() and IntegralSauvola(). The Integral Image support is provided by another package we wrote, -[rescribe.xyz/integralimg](https://rescribe.xyz/integralimg), which -is general purpose and has a test suite, so will also be useful for -other image processing tools which require calculating the mean or -standard deviation for different areas of an image. +[rescribe.xyz/integral](https://rescribe.xyz/integral) ([docs](https://pkg.go.dev/rescribe.xyz/integral)), +which will also be useful for other image processing tools which +require calculating the mean or standard deviation for different +areas of an image. |