https://github.com/shamazmazum/perceptual-hashes

Perceptual image hashing for Common Lisp
https://github.com/shamazmazum/perceptual-hashes

common-lisp image-analysis images lisp

Last synced: about 1 month ago
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Perceptual image hashing for Common Lisp

• Host: GitHub
• URL: https://github.com/shamazmazum/perceptual-hashes
• Owner: shamazmazum
• License: bsd-2-clause
• Created: 2020-03-22T05:31:26.000Z (about 5 years ago)
• Default Branch: master
• Last Pushed: 2022-04-15T12:30:09.000Z (about 3 years ago)
• Last Synced: 2025-02-09T10:11:17.941Z (3 months ago)
• Topics: common-lisp, image-analysis, images, lisp
• Language: Common Lisp
• Size: 503 KB
• Stars: 5
• Watchers: 3
• Forks: 0
• Open Issues: 1
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
perceptual-hashes

===============

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![CI](https://github.com/shamazmazum/perceptual-hashes/workflows/CI/badge.svg)

**perceptual-hashes** library computes perceptual hashes for images

(supported formats include formats supported by

[imago](https://github.com/tokenrove/imago) + jpeg). Perceptual hashes

are a measure of similarity between images.

Installation

------------

You can install **perceptual-hashes** from [Ultralisp](https://ultralisp.org/)

repository. Add Ultralisp repository to quicklisp (if you haven't already):

~~~~

(ql-dist:install-dist "http://dist.ultralisp.org/"

                      :prompt nil)

~~~~

and install **perceptual-hashes**:

~~~~

(ql:quickload :perceptual-hashes)

~~~~

Supported algorithms

--------------------

The following algorithms are supported:

* **aHash**: An algorithm based on whenever each pixel of an image is

  brighter or darker than the average luminance of all pixels.

* **dHash**: An algorithm based on whenever each pixel of an image is

  brighter or darker than its neighbours.

You can find more information

[here](http://www.hackerfactor.com/blog/?/archives/529-Kind-of-Like-That.html).

Examples

--------

~~~~~

(defconstant +threshold+ 45

    "The distance between hashes can vary from 0 to (length hash) =

1024. If it is small enough (<= ~45) the images are similar")

(defun images-similar-p (image-name-1 image-name-2)

    (let ((hash1 (perceptual-hashes:ahash image-name-1))

          (hash2 (perceptual-hashes:ahash image-name-2)))

        (< (perceptual-hashes:hamming-distance hash1 hash2)

           +threshold+)))

~~~~~