python查找相似图片

pip install nanopq

Ks 是8的倍数，需要比数据维度小

向量长度需要是M的倍数
默认按照距离排序，非常不错


import datetime
import heapq

import os

import cv2
import nanopq
import numpy as np


import nanopq
import numpy as np
import shutil


def dHash(gray,height):
    #缩放8*8
    # gray=cv2.resize(img,(64,63),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(height):
        for j in range(height):
            if gray[i,j]>gray[i,j+1]:
                hash_str=hash_str+'1'
            else:
                hash_str=hash_str+'0'
    return hash_str

def pq_dis():
    N, D = 10000, 128
    X = np.random.random((N, D)).astype(np.float32)  # 10,000 128-dim vectors
    query = np.random.random((D,)).astype(np.float32)  # a 128-dim vector

    # Instantiate with M=8 sub-spaces
    pq = nanopq.PQ(M=8,Ks=256)

    # Train with the top 1000 vectors
    pq.fit(X[:1000])

    # Encode to PQ-codes
    X_code = pq.encode(X)  # (10000, 8) with dtype=np.uint8

    time1=datetime.datetime.now()
    # Results: create a distance table online, and compute Asymmetric Distance to each PQ-code
    dists = pq.dtable(query).adist(X_code)

    nsmallestList = heapq.nsmallest(5, dists)
    print(nsmallestList)
    indexs=[dists.tolist().index(i) for i in nsmallestList]
    print(indexs)
    print(dists[indexs])
    print("time",(datetime.datetime.now()-time1).microseconds)

if __name__ == '__main__':
    path=r"C:\Users\Administrator\Documents\Tencent Files\441648051\FileRecv\pic_error"
    files=os.listdir(path)

    datas=[]
    for file in files:
        img_1=cv2.imread(path+"/"+file,0)
        img1 = cv2.resize(img_1, (65, 64), interpolation=cv2.INTER_LINEAR)
        dhash=dHash(img1,64)
        data= list(map(int,dhash))
        datas.append(data)
    datas=np.asarray(datas,dtype=np.float32)
    N=len(datas)
    D=64*64

    query =datas[0]# np.random.random((D,)).astype(np.float32)  # a 128-dim vector

    # Instantiate with M=8 sub-spaces
    pq = nanopq.PQ(M=8,Ks=48)

    # Train with the top 1000 vectors
    pq.fit(datas)

    # Encode to PQ-codes
    X_code = pq.encode(datas)  # (10000, 8) with dtype=np.uint8

    time1=datetime.datetime.now()
    # Results: create a distance table online, and compute Asymmetric Distance to each PQ-code
    dists = pq.dtable(query).adist(X_code)

    nsmallestList = heapq.nsmallest(54, dists)
    print(nsmallestList)
    indexs=[dists.tolist().index(i) for i in nsmallestList]
    print(indexs)
    for i in indexs:
        print(files[i])
        shutil.copy(path+"/"+files[i], "out/"+str(dists[i])+"_"+files[i])
    print("time",(datetime.datetime.now()-time1).microseconds)