This is a perceptual image hash calculation tool based on algorithm descibed in Block Mean Value Based Image Perceptual Hashing by Bian Yang, Fan Gu and Xiamu Niu.
This module is installed via npm:
$ npm install blockhash
To use this library in the browser, you can build it with Browserify
with something like browserify index.js --standalone blockhashjs >
blockhash.js
Include it and zlib.js on your page:
<!DOCTYPE html>
<html>
<head>
<title>Blockhash</title>
</head>
<body>
<script src="https://github.com/commonsmachinery/blockhash-js/raw/v0.3/node_modules/png-js/zlib.js"></script>
<script src="https://github.com/commonsmachinery/blockhash-js/raw/v0.3/blockhash.js"></script>
<script>
var blockhash = blockhashjs.blockhash;
</script>
</body>
</html>
Call blockhash(src, bits, method, callback), where
src is an image URL, bits is the number of bits in a row, method
is a number 1-2 (see below), and callback is a function with
(error, result) signature. On success, result will be array of
binary values.
The available methods are:
Method 2 is recommended as a good tradeoff between speed and good matches on any image size. The quick ones are only advisable when the image width and height are an even multiple of the number of blocks used.
Copyright 2014 Commons Machinery http://commonsmachinery.se/
Distributed under an MIT license, please see LICENSE in the top dir.
Contact: dev@commonsmachinery.se
$ claude mcp add blockhash-js \
-- python -m otcore.mcp_server <graph>