You've already forked macports-ports
mirror of
https://github.com/macports/macports-ports.git
synced 2026-07-12 18:20:25 -07:00
Renee Otten
45 lines
1.8 KiB
Tcl
45 lines
1.8 KiB
Tcl
# -*- coding: utf-8; mode: tcl; tab-width: 4; indent-tabs-mode: nil; c-basic-offset: 4 -*- vim:fenc=utf-8:ft=tcl:et:sw=4:ts=4:sts=4
|
|
|
|
PortSystem 1.0
|
|
PortGroup python 1.0
|
|
|
|
name py-imagehash
|
|
python.rootname ImageHash
|
|
version 4.3.1
|
|
|
|
categories-append devel graphics
|
|
platforms {darwin any}
|
|
supported_archs noarch
|
|
license BSD
|
|
maintainers nomaintainer
|
|
|
|
description Perceptual Image Hashing Module
|
|
long_description Image hashes tell whether two images look nearly \
|
|
identical. This is different from cryptographic \
|
|
hashing algorithms (like MD5, SHA-1) where tiny \
|
|
changes in the image give completely different \
|
|
hashes. In image fingerprinting, we actually want \
|
|
our similar inputs to have similar output hashes \
|
|
as well. The image hash algorithms (average, \
|
|
perceptual, difference, wavelet) analyse the image \
|
|
structure on luminance (without color \
|
|
information). The color hash algorithm analyses \
|
|
the color distribution and black & gray fractions \
|
|
(without position information).
|
|
|
|
homepage https://github.com/JohannesBuchner/imagehash
|
|
|
|
checksums rmd160 46c6a282f7ebb9c3fb5256a318259699fdb9e817 \
|
|
sha256 7038d1b7f9e0585beb3dd8c0a956f02b95a346c0b5f24a9e8cc03ebadaf0aa70 \
|
|
size 296989
|
|
|
|
python.versions 310 311 312 313
|
|
|
|
if {${subport} ne ${name}} {
|
|
depends_run-append \
|
|
port:py${python.version}-numpy \
|
|
port:py${python.version}-Pillow \
|
|
port:py${python.version}-pywavelets \
|
|
port:py${python.version}-scipy
|
|
}
|