duplicate_images 0.8.4

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.

Official home page: https://github.com/lene/DuplicateImages

Development page: https://gitlab.com/lilacashes/DuplicateImages

PyPI page: https://pypi.org/project/duplicate-images

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 --hash-db hashes.json

Supported image formats

• JPEG and PNG (tested quite thoroughly)
• HEIC (experimental support, tested cursorily only)
• All other formats supported by the pillow Python Imaging Library should work, but are not specifically tested.

Image comparison algorithms

Use the --algorithm option to select how equal images are found. The default algorithm is phash.

ahash, colorhash, dhash, dhash_vertical, phash, phash_simple, whash: seven 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.

Image similarity threshold configuration

Use the --hash-size parameter to tune the precision of the hashing algorithms. For the colorhash algorithm the hash size is interpreted as the number of bin bits and defaults to 3. For all other algorithms the hash size defaults to 8. For whash it must be a power of 2.

Use the --max-distance parameter to tune how close images should be to be considered duplicates. The argument is a positive integer. Its value is highly dependent on the algorithm used and the nature of the images compared, so the best value for your use case can oly be found through experimentation.

NOTE: using the --max-distance parameter slows down the comparison considerably with large image collections, making the runtime complexity go from O(N) to O(N²). If you want to scan collections with at least thousands of images, it is highly recommended to tune the desired similarity threshold with the --hash-size parameter alone, if that is at all possible.

Pre-storing and using image hashes to speed up computation

Use the --hash-db ${FILE}.json or --hash-db ${FILE}.pickle option to store image hashes in the file $FILE in JSON or Pickle format 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.

Actions for matching image pairs

Use the --on-equal option to select what to do to pairs of equal images. The default action is print.

• delete-first or d1: deletes the first of the two files
• delete-second or d2: 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 (deprecated by exec)
• xv: launches the xv image viewer to compare the two files (deprecated by exec)
• print: prints the two files
• print_inline: like print but without newline
• quote: prints the two files quoted for POSIX shells
• quote_inline: like quote but without newline
• exec: executes a command (see --exec argument)
• none: does nothing.

The --exec argument allows calling another program when the --on-equal exec option is given. You can pass a command line string like --exec "program {1} {2}" where {1} and {2} are replaced by the matching pair files.

Examples:

• --exec "open -a Preview -W {1} {2}": Opens the files in MacOS Preview app and waits for it.

Parallel execution

Use the --parallel option to utilize all free cores on your system for calculating image hashes.

Serial execution

find-dups can also use an alternative algorithm which is O(N²) in the number of images. Use the --serial option to use this alternative algorithm.

Progress bar 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
• --quiet decreases the log level by 1 for each time it is called; --debug and --quiet cancel each other out

Development notes

Needs Python3, Pillow imaging library and pillow-heif HEIF plugin 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

There is a job in GitLab CI for publishing to pypi.org that runs as soon as a new tag is added, which happens automatically whenever a MR is merged. The tag is the same as the version in the pyproject.toml file. For every MR it needs to be ensured that the version is not the same as an already existing tag.

To publish the package on PyPI manually:

$ 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 here that username and password are the same on PyPI and TestPyPI)

Updating GitHub mirror

GitHub is set up as a push mirror in GitLab CI, but mirroring is flaky at the time and may not succeed.

To push to the GitHub repository manually (assuming the GitHub repository is set up as remote github):

$ git checkout master
$ git fetch
$ git pull --rebase
$ git tag  # to check that the latest tag is present
$ git push --tags github master 

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).

Contributors

• Lene Preuss (https://github.com/lene): primary developer
• Mike Reiche (https://github.com/mreiche): support for arbitrary actions, speedups
• https://github.com/beijingjazzpanda: bug fix