A package for image hashing

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Project description

perception

perception provides flexible, well-documented, and comprehensively tested tooling for perceptual hashing research, development, and production use. See the documentation for details.

Background

perception was initially developed at Thorn as part of our work to eliminate child sexual abuse material from the internet. For more information on the issue, check out our CEO's TED talk.

Getting Started

Installation

pip install opencv-python perception

Hashing

Hashing with different functions is simple with perception.

from perception import hashers

file1, file2 = 'test1.jpg', 'test2.jpg'
hasher = hashers.PHash()
hash1, hash2 = hasher.compute(file1), hasher.compute(file2)
distance = hasher.compute_distance(hash1, hash2)

Examples

See below for end-to-end examples for common use cases for perceptual hashes.

Supported Hashing Algorithms

perception currently ships with:

pHash (DCT hash) (perception.hashers.PHash)
Facebook's PDQ Hash (perception.hashers.PDQ)
dHash (difference hash) (perception.hashers.DHash)
aHash (average hash) (perception.hashers.AverageHash)
Marr-Hildreth (perception.hashers.MarrHildreth)
Color Moment (perception.hashers.ColorMoment)
Block Mean (perception.hashers.BlockMean)
wHash (wavelet hash) (perception.hashers.WaveletHash)

Contributing

To work on the project, start by doing the following.

# Install local dependencies for
# code completion, etc.
make init

# Build the Docker container to run
# tests and such.
make build

Running make init will update this repository's dependencies and preserve the results in Pipfile.lock. We strongly recommend including changes to the lock alongside any other changes that may have been made.
You can get a JupyterLab server running to experiment with using make lab-server.
To do a (close to) comprehensive check before committing code, you can use make precommit.
To view the documentation, use make documentation-server.

To implement new features, please first file an issue proposing your change for discussion.

To report problems, please file an issue with sample code, expected results, actual results, and a complete traceback.

Alternatives

There are other packages worth checking out to see if they meet your needs for perceptual hashing. Here are some examples.

Project details

These details have not been verified by PyPI

Project links

Homepage

Release history Release notifications | RSS feed

0.7.7

Sep 24, 2024

0.7.6

Sep 23, 2024

0.7.5

Sep 13, 2024

0.7.4

Sep 13, 2024

0.7.2

Apr 10, 2024

0.7.2a6 pre-release

Apr 9, 2024

0.7.2a5 pre-release

Apr 9, 2024

0.7.0

Mar 28, 2024

0.6.8

Mar 21, 2023

0.6.7

Mar 21, 2023

0.6.6

Oct 3, 2022

0.6.5

Aug 10, 2022

0.6.4

Jul 12, 2022

0.6.3

Jul 11, 2022

This version

0.6.1

Jul 7, 2022

0.5.3

Apr 11, 2022

0.5.2

Jun 4, 2021

0.5.1

Jan 22, 2021

0.5.0

Jan 19, 2021

0.4.9

Jan 11, 2021

0.4.9rc3 pre-release

Jan 11, 2021

0.4.8

Jan 5, 2021

0.4.7

Dec 18, 2020

0.4.6

Dec 15, 2020

0.4.5

Dec 4, 2020

0.4.4

Nov 21, 2020

0.4.3

Nov 21, 2020

0.4.2

Oct 27, 2020

0.4.1

Oct 23, 2020

0.4.0

Oct 17, 2020

0.3.15

Sep 9, 2020

0.3.14

Sep 1, 2020

0.3.13

Jul 16, 2020

0.3.12

Jun 29, 2020

0.3.11

Jun 26, 2020

0.3.10

Jun 23, 2020

0.3.9

Jun 16, 2020

0.3.8

Jun 9, 2020

0.3.7

Jun 3, 2020

0.3.6

Jun 2, 2020

0.3.5

Jun 1, 2020

0.3.4

May 29, 2020

0.3.3

May 19, 2020

0.3.2

May 4, 2020

0.3.1

May 2, 2020

0.3.0

Apr 29, 2020

0.2.1

Dec 20, 2019

0.2.0

Dec 20, 2019

0.1.0

Nov 4, 2019

0.0.0

Sep 9, 2019

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Uploaded via: twine/4.0.1 CPython/3.7.13

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Uploaded via: twine/4.0.1 CPython/3.7.13

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Uploaded via: twine/4.0.1 CPython/3.7.13

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Algorithm	Hash digest
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Uploaded via: twine/4.0.1 CPython/3.7.13

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Algorithm	Hash digest
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