# -*- coding: utf-8 -*-
"""
Created on 2024-11-05 
---------
@summary:  感知哈希算法（Perceptual hash algorithm）
          参考: https://github.com/nivance/image-similarity?tab=readme-ov-file
---------
"""
from functools import reduce
from pathlib import Path

from PIL import Image


def avhash(im):
    if not isinstance(im, Image.Image):
        im = Image.open(im)

    im = im.resize((8, 8), Image.Resampling.LANCZOS).convert('L')
    avg = sum(im.getdata()) / 64.
    return reduce(lambda x, y: x | (y[1] << y[0]),
                  enumerate(map(lambda i: 0 if i < avg else 1, im.getdata())),
                  0)


def hamming(h1, h2):
    h, d = 0, h1 ^ h2
    while d:
        h += 1
        d &= d - 1
    return h


if __name__ == '__main__':
    # im, wd = './cache/image/0x100a0.jpg', './cache/image'
    #
    # h = avhash(im)
    #
    # seq = []
    # images = Path('./cache/image').iterdir()
    # for f in images:
    #     print(f.name)
    #     seq.append((f, hamming(avhash(f), h)))

    # for f, ham in sorted(seq, key=lambda i: i[1]):
    #     print("{}\t{}".format(ham, f))

    im1 = 'cache/0x100af.jpg'
    im2 = 'image/0x10075.jpg'
    h = avhash(im1)
    print(hamming(avhash(im2), h))