perception provides flexible, well-documented, and comprehensively tested tooling for perceptual hashing research, development, and production use. See the documentation for details.
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.
pip install perception
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)See below for end-to-end examples for common use cases for perceptual hashes.
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)
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)