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https://github.com/benhoyt/dhash

Python library to calculate the difference hash (perceptual hash) for a given image, useful for detecting duplicates
https://github.com/benhoyt/dhash

difference-hash hash image image-processing perceptual-hashing python

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Python library to calculate the difference hash (perceptual hash) for a given image, useful for detecting duplicates

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dhash

=====



.. image:: https://img.shields.io/pypi/v/dhash.svg

   :target: https://pypi.org/project/dhash/

   :alt: dhash on PyPI (Python Package Index)



.. image:: https://github.com/benhoyt/dhash/actions/workflows/ci.yml/badge.svg

   :target: https://github.com/benhoyt/dhash/actions/workflows/ci.yml

   :alt: GitHub Actions Tests



dhash is a Python library that generates a "difference hash" for a given image

-- a `perceptual hash`_ based on Neal Krawetz's dHash algorithm in `this

"Hacker Factor" blog entry`_.



The library is `on the Python Package Index (PyPI)`_ and works on both Python

3 and Python 2.7. To install it, fire up a command prompt, activate your

virtual environment if you're using one, and type:



::



    pip install dhash



The algorithm to create a difference hash is very simple:



* Convert the image to grayscale

* Downsize it to a 9x9 thumbnail (size=8 means an 8+1 by 8+1 image)

* Produce a 64-bit "row hash": a 1 bit means the pixel intensity is increasing

  in the x direction, 0 means it's decreasing

* Do the same to produce a 64-bit "column hash" in the y direction

* Combine the two values to produce the final 128-bit hash value



The library defaults to producing a size 8 dhash, but you can override this

easily by passing ``size=N`` as a keyword argument to most functions. For

example, you can produce a more accurate (but slower to work with) dhash of

512 bits by specifying ``size=16``.



I've found that dhash is great for detecting near duplicates (we

found dupes using a size 8 dhash with a maximum delta of 2

bits). But because of the simplicity of the algorithm, it's not great at

finding similar images or duplicate-but-cropped images -- you'd need a more

sophisticated image fingerprint if you want that. However, the dhash is good

for finding exact duplicates and near duplicates, for example, the same image

with slightly altered lighting, a few pixels of cropping, or very light

photoshopping.



To use the dhash library, you need either the `wand`_ ImageMagick binding or

the `Pillow (PIL)`_ library installed. Pick one and stick with it -- they will

produce slightly different dhash values due to differences in their grayscale

conversion and resizing algorithms.



If you have both libraries installed, dhash will use wand by default. To

override this and force use of Pillow/PIL, call ``dhash.force_pil()`` before

using the library.



To produce a dhash value using wand:



.. code:: python



    import dhash

    import wand.image



    with wand.image.Image(filename='dhash-test.jpg') as image:

        row, col = dhash.dhash_row_col(image)

    print(dhash.format_hex(row, col))



To produce a dhash value using Pillow:



.. code:: python



    import dhash

    from PIL import Image



    image = Image.open('dhash-test.jpg')

    row, col = dhash.dhash_row_col(image)

    print(dhash.format_hex(row, col))



If you have your own library to convert an image to grayscale and downsize it

to 9x9 (or 17x17 for size=16), you can pass ``dhash_row_col()`` a list of

integer pixel intensities (for example, from 0 to 255). For example:



.. code:: python



    >>> import dhash

    >>> row, col = dhash.dhash_row_col([0,0,1,1,1, 0,1,1,3,4, 0,1,6,6,7, 7,7,7,7,9, 8,7,7,8,9], size=4)

    >>> format(row, '016b')

    '0100101111010001'

    >>> format(col, '016b')

    '0101001111111001'



To produce the hash value as a 128-bit integer directly, use

``dhash_int(image, size=N)``. To format the hash value in various ways, use

the ``format_*`` functions:



.. code:: python



    >>> row, col = (13962536140006260880, 9510476289765573406)

    >>> dhash.format_bytes(row, col)

    b'\xc1\xc4\xe4\xa4\x84\xa0\x80\x90\x83\xfb\xff\xcc\x00@\x83\x1e'

    >>> dhash.format_hex(row, col)

    'c1c4e4a484a0809083fbffcc0040831e'



To compute the number of bits different (hamming distance) between two

hashes, you can use the ``get_num_bits_different(hash1, hash2)`` helper

function:



.. code:: python



    >>> import dhash

    >>> dhash.get_num_bits_different(0x4bd1, 0x5bd2)

    3



You can also use dhash to generate the difference hash for a specific image

from the command line:



::



    $ python -m dhash dhash-test.jpg

    c1c4e4a484a0809083fbffcc0040831e



    $ python -m dhash --format=decimal dhash-test.jpg

    13962536140006260880 9510476289765573406



    # show the 8x8 row and column grids

    $ python -m dhash --format=matrix dhash-test.jpg

    * * . . . . . * 

    * * . . . * . . 

    * * * . . * . . 

    * . * . . * . . 

    * . . . . * . . 

    * . * . . . . . 

    * . . . . . . . 

    * . . * . . . . 



    * . . . . . * * 

    * * * * * . * * 

    * * * * * * * * 

    * * . . * * . . 

    . . . . . . . . 

    . * . . . . . . 

    * . . . . . * * 

    . . . * * * * . 



    # compute the bit delta between two images

    $ python -m dhash dhash-test.jpg similar.jpg

    1 bit differs out of 128 (0.8%)



Read the code in `dhash.py`_ for more details – it's pretty small!



dhash was written by `Ben Hoyt`_ and is licensed with a

permissive MIT license (see `LICENSE.txt`_).



.. _perceptual hash: https://en.wikipedia.org/wiki/Perceptual_hashing

.. _on the Python Package Index (PyPI): https://pypi.python.org/pypi/dhash

.. _this "Hacker Factor" blog entry: http://www.hackerfactor.com/blog/index.php?/archives/529-Kind-of-Like-That.html

.. _wand: https://pypi.python.org/pypi/Wand

.. _Pillow (PIL): https://pypi.python.org/pypi/Pillow

.. _dhash.py: https://github.com/benhoyt/dhash/blob/master/dhash.py

.. _Ben Hoyt: http://benhoyt.com/

.. _LICENSE.txt: https://github.com/benhoyt/dhash/blob/master/LICENSE.txt