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- ## -*- coding: utf-8 -*-
- # !/usr/bin/env python
- # 余弦相似度计算
- import time
-
- import cv2
- from PIL import Image
- from numpy import average, dot, linalg
- # 对图片进行统一化处理
- def get_thum(image, size=(64, 64), greyscale=False):
- # 利用image对图像大小重新设置, Image.ANTIALIAS为高质量的
- image = image.resize(size, Image.ANTIALIAS)
- if greyscale:
- # 将图片转换为L模式，其为灰度图，其每个像素用8个bit表示
- image = image.convert('L')
- return image
- # 计算图片的余弦距离
- def image_similarity_vectors_via_numpy(image1, image2):
- image1 = get_thum(image1)
- image2 = get_thum(image2)
- images = [image1, image2]
- vectors = []
- norms = []
- for image in images:
- vector = []
- for pixel_tuple in image.getdata():
- vector.append(average(pixel_tuple))
- vectors.append(vector)
- # linalg=linear（线性）+algebra（代数），norm则表示范数
- # 求图片的范数
- norms.append(linalg.norm(vector, 2))
- a, b = vectors
- a_norm, b_norm = norms
- # dot返回的是点积，对二维数组（矩阵）进行计算
- res = dot(a / a_norm, b / b_norm)
- return res
-
-
- #差值感知算法
- def dHash(image):
- #缩放9*8
- image=cv2.resize(image,(9,8),interpolation=cv2.INTER_CUBIC)
- #转换灰度图
- image=cv2.cvtColor(image,cv2.COLOR_BGR2GRAY)
- # print(image.shape)
- hash=[]
- #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 image[i,j]>image[i,j+1]:
- hash.append(1)
- else:
- hash.append(0)
- return hash
-
- #计算汉明距离
- def Hamming_distance(hash1,hash2):
- num = 0
- for index in range(len(hash1)):
- if hash1[index] != hash2[index]:
- num += 1
- return num
-
- if __name__ == '__main__':
- # 把图片表示成一个向量，通过计算向量之间的余弦距离来表征两张图片的相似度 0.9760
- image1 = cv2.imread('../image/1.jpg')
- image2 = cv2.imread('../image/2.jpg')
- # image3 = cv2.imread('image/AI2.jpg')
- # image4 = cv2.imread('image/AI3.jpg')
- # image5 = cv2.imread('image/AI4.jpg')
- # image6 = cv2.imread('a.jpg')
- # image7 = cv2.imread('AI.jpg')
- hash1 = dHash(image1)
- hash2 = dHash(image2)
- # hash3 = dHash(image3)
- # hash4 = dHash(image4)
- # hash5 = dHash(image5)
- # hash6 = dHash(image6)
- # hash7 = dHash(image7)
- start = time.time()
- dist = Hamming_distance(hash1, hash2)
- #将距离转化为相似度
- similarity = 1 - dist * 1.0 / 64
- # print(dist)
- print(similarity, time.time() - start)
- # cosin = image_similarity_vectors_via_numpy(image1, image2)
- # print('图片余弦相似度', cosin)
- # cosin1 = image_similarity_vectors_via_numpy(image1, image3)
- # print('图片余弦相似度', cosin1)
- # cosin2 = image_similarity_vectors_via_numpy(image1, image4)
- # print('图片余弦相似度', cosin2)
- # cosin3 = image_similarity_vectors_via_numpy(image1, image5)
- # print('图片余弦相似度', cosin3)
- # cosin4 = image_similarity_vectors_via_numpy(image5, image6)
- # print('图片余弦相似度', cosin4)
- # cosin5 = image_similarity_vectors_via_numpy(image1, image6)
- # print('图片余弦相似度', cosin5)
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