pyphashml 0.1.0

Python package to compute image perceptual hashes. The perceptual hash is based on the mobilenetv2 tensorflow image classification model. It is condensed down to 32-bytes per image. Distance between two hashes gives some measure of image similarity, where distance(a,b) == 0 means idential images and similar images give lower distance.

Usage

Run unit tests

python -m unittest tests.test_phashml

Build the package

cd pyPhashML
python -m build .

Install binary

pip install pyphashml-0.0.1-py3-none-any.whl

Calculate perceptual hash for two images and calculate distance:

from pyphashml.phashml import phashmlctx

x = phashmlctx.imghash("/path/to/imgfile.jpg")
y = phashmlctx.imghash("/path/to/imgfile2.jpg")
d = phashmlctx.hamming_distance(x, y)

x,y are bitstring objects. d is an integer value >= 0.

Submit perceptual hashes for a directory of images to ImageScoutPro server

python -m pyphashml.imgscoutclient --dir /path/to/img/files --key mykey --host 127.0.0.1 --port 6379 --db 0

Compare two images by their perceptual hashes

python3 -m pyphashml.imgcmp /path/to/img/file.jpg /path/to/img/file2.jpg

Reqires

bitstring numpy tensorflow >=1.14,<2 redis (for the imgscoutclient command to submit to imgscout server)