python 图片相似算法

1、安装

pip install opencv_python

2、具体代码（借鉴）

import cv2
import numpy as np


# 均值哈希算法
def aHash(img):
    # 缩放为8*8
    img = cv2.resize(img, (8, 8), interpolation=cv2.INTER_CUBIC)
    # 转换为灰度图
    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
    # s为像素和初值为0，hash_str为hash值初值为''
    s = 0
    hash_str = ''
    # 遍历累加求像素和
    for i in range(8):
        for j in range(8):
            s = s + gray[i, j]
    # 求平均灰度
    avg = s / 64
    # 灰度大于平均值为1相反为0生成图片的hash值
    for i in range(8):
        for j in range(8):
            if gray[i, j] > avg:
                hash_str = hash_str + '1'
            else:
                hash_str = hash_str + '0'
    return hash_str


# 差值感知算法
def dHash(img):
    #缩放8*8
    img = cv2.resize(img, (9, 8), interpolation=cv2.INTER_CUBIC)
    # 转换灰度图
    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
    hash_str = ''
    # 每行前一个像素大于后一个像素为1，相反为0，生成哈希
    for i in range(8):
        for j in range(8):
            if gray[i, j] > gray[i, j+1]:
                hash_str = hash_str+'1'
            else:
                hash_str = hash_str+'0'
    return hash_str


# Hash值对比
def cmpHash(hash1, hash2):
    n = 0
    # hash长度不同则返回-1代表传参出错
    if len(hash1) != len(hash2):
        return -1
    # 遍历判断
    for i in range(len(hash1)):
        # 不相等则n计数+1，n最终为相似度
        if hash1[i] != hash2[i]:
            n = n+1
    return n


if __name__ == '__main__':
    img1 = cv2.imread('./imgs/4.jpg')
    img2 = cv2.imread('./imgs/5.jpg')
    # hash1 = aHash(img1)
    # hash2 = aHash(img2)
    # print(hash1, type(hash1))
    # print(hash2, type(hash2))
    # n = cmpHash(hash1, hash2)
    # print('均值哈希算法相似度：'+ str(n))
    #
    hash1 = dHash(img1)
    hash2 = dHash(img2)
    print(hash1)
    print(hash2)
    n = cmpHash(hash1, hash2)
    print('差值哈希算法相似度：'+ str(n))