Perception 0.8.4

Perception provides flexible, well-documented, and comprehensively tested tooling for perceptual hashing research, development, and production use.

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 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.

• Detecting child sexual abuse material
• Deduplicating media
• Benchmarking 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

- To do a (close to) comprehensive check before committing code, you can use `make precommit`.

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.

- [dedupe](https://github.com/dedupeio/dedupe)
- [imagededup](https://idealo.github.io/imagededup/)
- [ImageHash](https://github.com/JohannesBuchner/imagehash)
- [PhotoHash](https://github.com/bunchesofdonald/photohash)