三直方圖算法：

import cv2
def calculate(image1, image2):
    hist1 = cv2.calcHist([image1], [0], None, [256], [0.0, 255.0])
    hist2 = cv2.calcHist([image2], [0], None, [256], [0.0, 255.0])
    # 計算直方圖的重合度
    degree = 0
    for i in range(len(hist1)):
        if hist1[i] != hist2[i]:
            degree = degree + (1 - abs(hist1[i] - hist2[i]) / max(hist1[i], hist2[i]))
        else:
            degree = degree + 1
    degree = degree / len(hist1)
    return degree
def classify_hist_with_split(image1, image2, size=(256, 256)):
    # 將圖像resize后，分離為RGB三個通道，再計算每個通道的相似值
    image1 = cv2.resize(image1, size)
    image2 = cv2.resize(image2, size)
    sub_image1 = cv2.split(image1)
    sub_image2 = cv2.split(image2)
    sub_data = 0
    for im1, im2 in zip(sub_image1, sub_image2):
        sub_data += calculate(im1, im2)
    sub_data = sub_data / 3
    return sub_data
img = cv2.imread('test_1.jpg')
print(classify_hist_with_split(img, img, size=(256, 256)))

均值哈希算法：

import cv2
#均值哈希算法
def aHash(img):
    #縮放為8*8
    img=cv2.resize(img,(8,8))
    #轉換為灰度圖
    gray=cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
    #s為像素和初值為0，hash_str為hash值初值為''
    s=0
    hash_str=''
    #遍歷累加求像素和
    for i in range(8):
        for j in range(8):
            s=s+gray[i,j]
    #求平均灰度
    avg=s/64
    #灰度大于平均值為1相反為0生成圖片的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
img = cv2.imread('test_1.jpg')
print(aHash(img))
print(len(aHash(img)))

差值哈希算法：

import cv2

def dHash(img):
    #縮放8*8
    img=cv2.resize(img,(9,8))
    #轉換灰度圖
    gray=cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
    hash_str=''
    #每行前一個像素大于后一個像素為1，相反為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
img = cv2.imread('test_1.jpg')
print(dHash(img))
print(len(dHash(img)))

感知哈希算法：

import cv2
import numpy as np
def pHash(img):
    #縮放32*32
    img = cv2.resize(img, (32, 32))   # , interpolation=cv2.INTER_CUBIC

    # 轉換為灰度圖
    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
    # 將灰度圖轉為浮點型，再進行dct變換
    dct = cv2.dct(np.float32(gray))
    #opencv實現的掩碼操作
    dct_roi = dct[0:8, 0:8]
    hash = []
    avreage = np.mean(dct_roi)
    for i in range(dct_roi.shape[0]):
        for j in range(dct_roi.shape[1]):
            if dct_roi[i, j] > avreage:
                hash.append(1)
            else:
                hash.append(0)
    return hash
img = cv2.imread('test_1.jpg')
print(pHash(img))

直方圖灰度算法：

import cv2
# 計算單通道的直方圖的相似值
def calculate(image1, image2):
    hist1 = cv2.calcHist([image1], [0], None, [256], [0.0, 255.0])
    hist2 = cv2.calcHist([image2], [0], None, [256], [0.0, 255.0])
    # 計算直方圖的重合度
    degree = 0
    for i in range(len(hist1)):
        if hist1[i] != hist2[i]:
            degree = degree + (1 - abs(hist1[i] - hist2[i]) / max(hist1[i], hist2[i]))
        else:
            degree = degree + 1
    degree = degree / len(hist1)
    return degree
img = cv2.imread('test_1.jpg')
print(calculate(img,img))