Package: py-image-dedup
py-image-deduppy-image-dedupis a tool to sort out or remove duplicates within a photo library.
Unlike most other solutions,py-image-dedupintentionally uses an approximate image comparison to also detect
duplicates of images that slightly differ in resolution, color or other minor details.It is build uponImage-Matcha very popular library to compute
a pHash for an image and store the result in an ElasticSearch backend for very high scalability.How it worksPhase 1 - Database cleanupIn the first phase the elasticsearch backend is checked against the
current filesystem state, cleaning up database entries of files that
no longer exist. This will speed up queries made lateron.Phase 2 - Counting filesAlthough not necessary for the deduplication process it is very convenient
to have some kind of progress indication while the deduplication process
is at work. To be able to provide that, available files must be counted beforehand.Phase 3 - Analysing filesIn this phase every image file is analysed. This means generating a signature (pHash)
to quickly compare it to other images and adding other metadata of the image
to the elasticsearch backend that is used in the next phase.This phase is quite CPU intensive and the first run take take quite
some time. Using as much threads as feasible (using the-tparameter)
is advised to get the best performance.Since we might already have a previous version of this file in the database
before analysing a given file the file modification time is compared to the
given one. If the database content seems to be still correct the signature
for this file willnotbe recalculated. Because of this, subsequent
runs will be much faster. There still has to happen some file access though,
so it is probably limited by that.Phase 4 - Finding duplicatesEvery file is now processed again - but only by means of querying the
database backend for similar images (within the givenmax_dist).
If there are images found that match the similarity criteria they are considered
duplicate candidates. All candidates are then ordered by the following
criteria (in this exact order):pixel count (more is better)EXIF data (more exif data is better)file size (bigger is better)file modification time (newer is better)distance (lower is better)filename contains "copy" (False is better)filename length (longer is better) - (for "edited" versions)parent folder path length (shorter is better)score (higher is better)The first candidate in the resulting list is considered to be the best
available version of all candidates.Phase 5 - Moving/Deleting duplicatesAll but the best version of duplicate candidates identified in the previous
phase are now deleted from the file system (if you didn't specify--dry-runof course).Ifduplicates_target_directoryis set, the specified folder will be used as
a root directory to move duplicates to, instead of deleting them, replicating their original
folder structure.Phase 6 - Removing empty folders (Optional)In the last phase, folders that are empty due to the deduplication
process are deleted, cleaning up the directory structure (if turned on in configuration).How to useInstallInstallpy-image-dedupusing pip:pip3installpy-image-dedupConfigurationpy-image-dedupusescontainer-app-confto provide configuration via a YAML file as well as ENV variables which
generates a reference config on startup. Have a look at thedocumentation about itSeepy_image_dedup_reference.yamlfor an example in this repo.Setup elasticsearch backendSince this library is based onImage-Matchyou need a running elasticsearch instance for efficient storing and
querying of image signatures.Elasticsearch versionThis library requires elasticsearch version 5 or later. Sadly theImage-Matchlibrary still
specifies version 2, soa fork of the original projectis used instead. This fork is maintained by me, and any contributions
are very much appreciated.Set up the indexpy-image-dedupuses a single index (calledimagesby default).
When configured, this index will be created automatically for you.Command line usagepy-image-dedupcan be used from the command line like this:py-image-dedupdeduplicate--helpHave a look at the help output to see how you can customize it.DaemonCAUTION!This feature is still very much a work in progress.Alwayshave a backup of your data!py-image-deduphas a built in daemon that allows you to continuously
monitor your source directories and deduplicate them on the fly.When running the daemon (and enabled in configuration) a prometheus reporter
is used to allow you to gather some statistical insights.py-image-dedupdaemonDry runTo analyze images and get an overview of what images would be deleted
be sure to make a dry run first.py-image-dedupdeduplicate--dry-runFreeBSDIf you want to run this on a FreeBSD host make sure you have an up
to date release that is able to install ports.SinceImage-Matchdoes a lot of
math it relies onnumpyandscipy. To get those working on FreeBSD
you have to install them as a port:pkginstallpkgconf
pkginstallpy38-numpy
pkginstallpy27-scipyFor.pngsupport you also need to installpkginstallpngI still ran into issues after installing all these and just threw those
two in the mix and it finally worked:pkginstallfreetype
pkginstallpy27-matplotlib# this has a LOT of dependenciesEncoding issuesWhen using the python libraryclickon FreeBSD you might run into
encoding issues. To mitigate this change your locale fromANSIItoUTF-8if possible.This can be achieved f.ex. by creating a file~/.login_confwith the following content:me:\
	:charset=ISO-8859-1:\
	:lang=de_DE.UTF-8:DockerTo runpy-image-dedupusing docker you can use themarkusressel/py-image-dedupimage from DockerHub:sudo docker run -t \
    -p 8000:8000 \
    -v /where/the/original/photolibrary/is/located:/data/in \
    -v /where/duplicates/should/be/moved/to:/data/out \
    -e PY_IMAGE_DEDUP_DRY_RUN=False \
    -e PY_IMAGE_DEDUP_ANALYSIS_SOURCE_DIRECTORIES=/data/in/ \
    -e PY_IMAGE_DEDUP_ANALYSIS_RECURSIVE=True \
    -e PY_IMAGE_DEDUP_ANALYSIS_ACROSS_DIRS=True \
    -e PY_IMAGE_DEDUP_ANALYSIS_FILE_EXTENSIONS=.png,.jpg,.jpeg \
    -e PY_IMAGE_DEDUP_ANALYSIS_THREADS=8 \
    -e PY_IMAGE_DEDUP_ANALYSIS_USE_EXIF_DATA=True \
    -e PY_IMAGE_DEDUP_DEDUPLICATION_DUPLICATES_TARGET_DIRECTORY=/data/out/ \
    -e PY_IMAGE_DEDUP_ELASTICSEARCH_AUTO_CREATE_INDEX=True \
    -e PY_IMAGE_DEDUP_ELASTICSEARCH_HOST=elasticsearch \
    -e PY_IMAGE_DEDUP_ELASTICSEARCH_PORT=9200 \
    -e PY_IMAGE_DEDUP_ELASTICSEARCH_INDEX=images \
    -e PY_IMAGE_DEDUP_ELASTICSEARCH_AUTO_CREATE_INDEX=True \
    -e PY_IMAGE_DEDUP_ELASTICSEARCH_MAX_DISTANCE=0.1 \
    -e PY_IMAGE_DEDUP_REMOVE_EMPTY_FOLDERS=False \
    -e PY_IMAGE_DEDUP_STATS_ENABLED=True \
    -e PY_IMAGE_DEDUP_STATS_PORT=8000 \
    markusressel/py-image-dedup:latestSince an elasticsearch instance is required too, you can
also use thedocker-compose.ymlfile included in this repo which will
set up a single-node elasticsearch cluster too:sudodocker-composeupUID and GIDTo runpy-image-dedupinside the container using a specific user id
and group id you can use the env variablesPUID=1000andPGID=1000.ContributingGitHub is for social coding: if you want to write code, I encourage contributions through pull requests from forks
of this repository. Create GitHub tickets for bugs and new features and comment on the ones that you are interested in.Licensepy-image-dedup by Markus Ressel
Copyright (C) 2018  Markus Ressel

This program is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.

This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
GNU General Public License for more details.

You should have received a copy of the GNU General Public License
along with this program.  If not, see <http://www.gnu.org/licenses/>.