duplicate_images 0.5.1

Finds equal or similar images in a directory containing (many) image files

Finding Duplicate Images

Finds equal or similar images in a directory containing (many) image files.

Usage

Installing:

$ pip install duplicate_images

Printing the help screen:

$ find-dups -h

Quick test run:

$ find-dups $IMAGE_ROOT 

Typical usage:

$ find-dups $IMAGE_ROOT \
    --parallel --progress \
    --algorithm phash --on-equal print \
    --hash-db hashes.pickle

Image comparison algorithms

Use the --algorithm option to select how equal images are found.

ahash, colorhash, dhash, phash, whash: five different image hashing algorithms. See https://pypi.org/project/ImageHash for an introduction on image hashing and https://tech.okcupid.com/evaluating-perceptual-image-hashes-okcupid for some gory details which image hashing algorithm performs best in which situation. For a start I recommend using phash, and only evaluating the other algorithms if phash does not perform satisfactorily in your use case.

Actions for matching image pairs

Use the --on-equal option to select what to do to pairs of equal images.

• delete-first: deletes the first of the two files
• delete-second: deletes the second of the two files
• delete-bigger or d>: deletes the file with the bigger size
• delete-smaller or d<: deletes the file with the smaller size
• eog: launches the eog image viewer to compare the two files
• xv: launches the xv image viewer to compare the two files
• print: prints the two files
• quote: prints the two files with quotes around each
• none: does nothing. The default action is print.

Parallel execution

Use the --parallel option to utilize all free cores on your system.

Progress and verbosity control

• --progress prints a progress bar each for the process of reading the images and the process of finding duplicates among the scanned image
• --debug prints debugging output

Pre-storing and using image hashes to speed up computation

Use the --hash-db $PICKLE_FILE option to store image hashes in the file $PICKLE_FILE and read image hashes from that file if they are already present there. This avoids having to compute the image hashes anew at every run and can significantly speed up run times.

Development notes

Needs Python3 and Pillow imaging library to run, additionally Wand for the test suite.

Uses Poetry for dependency management.

Installation

From source:

$ git clone https://gitlab.com/lilacashes/DuplicateImages.git
$ cd DuplicateImages
$ pip3 install poetry
$ poetry install

Running

$ poetry run find-dups $PICTURE_DIR

or

$ poetry run find-dups -h

for a list of all possible options.

Test suite

Running it all:

$ poetry run pytest
$ poetry run mypy duplicate_images tests
$ poetry run flake8
$ poetry run pylint duplicate_images tests

or simply

$ .git_hooks/pre-push

Setting the test suite to be run before every push:

$ cd .git/hooks
$ ln -s ../../.git_hooks/pre-push .

Publishing

$ poetry config repositories.testpypi https://test.pypi.org/legacy/
$ poetry build
$ poetry publish --username $PYPI_USER --password $PYPI_PASSWORD --repository testpypi && \
  poetry publish --username $PYPI_USER --password $PYPI_PASSWORD

(obviously assuming that username and password are the same on PyPI and TestPyPI)

Profiling

CPU time

To show the top functions by time spent, including called functions:

$ poetry run python -m cProfile -s tottime ./duplicate_images/duplicate.py \ 
    --algorithm $ALGORITHM --action-equal none $IMAGE_DIR 2>&1 | head -n 15

or, to show the top functions by time spent in the function alone:

$ poetry run python -m cProfile -s cumtime ./duplicate_images/duplicate.py \ 
    --algorithm $ALGORITHM --action-equal none $IMAGE_DIR 2>&1 | head -n 15

Memory usage

$ poetry run fil-profile run ./duplicate_images/duplicate.py \
    --algorithm $ALGORITHM --action-equal none $IMAGE_DIR 2>&1

This will open a browser window showing the functions using the most memory (see https://pypi.org/project/filprofiler for more details).