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UniPercept-lituus

Perceptual image hashing and similarity search backed by the native UniPercept engine.

pip install UniPercept-lituus

The installed module is named unipercept:

import unipercept

left = unipercept.phash("left.jpg")
right = unipercept.phash("right.jpg")

print(unipercept.to_hex(left))
print(unipercept.hamming(left, right))
print(unipercept.similarity(left, right))

What it provides

  • aHash, dHash and fixed-point DCT pHash as 64-bit integers;
  • variable-size blockhash signatures;
  • decoding from memory or files through UniImage;
  • grayscale conversion and deterministic resize kernels;
  • a BK-tree for Hamming-radius lookup;
  • wheels containing the native UniPercept library.
tree = unipercept.BkTree()
tree.insert(100, left)
tree.insert(101, right)

for image_id, distance in tree.query(left, radius=5):
    print(image_id, distance)

For an already decoded RGB or RGBA buffer:

gray = unipercept.to_grayscale(rgb_bytes, width, height, channels=3)
small = gray.resize(32, 32)
signature = small.phash()

Perceptual hashes are similarity signatures, not cryptographic hashes. Use them to select candidates and perform an application-specific confirmation when false positives matter.

Building from source

An sdist build requires Nim and Nimble on PATH, plus a supported C compiler. From a repository checkout:

nimble pyLib
cd py
python -m pip install -e .
python -m pytest -q

python setup.py build_ext --inplace remains available as a direct extension rebuild command.

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