97 lines
3.3 KiB
Python
97 lines
3.3 KiB
Python
## -*- coding: utf-8 -*-
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# !/usr/bin/env python
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# 余弦相似度计算
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import time
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import cv2
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from PIL import Image
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from numpy import average, dot, linalg
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# 对图片进行统一化处理
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def get_thum(image, size=(64, 64), greyscale=False):
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# 利用image对图像大小重新设置, Image.ANTIALIAS为高质量的
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image = image.resize(size, Image.ANTIALIAS)
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if greyscale:
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# 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
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image = image.convert('L')
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return image
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# 计算图片的余弦距离
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def image_similarity_vectors_via_numpy(image1, image2):
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image1 = get_thum(image1)
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image2 = get_thum(image2)
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images = [image1, image2]
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vectors = []
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norms = []
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for image in images:
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vector = []
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for pixel_tuple in image.getdata():
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vector.append(average(pixel_tuple))
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vectors.append(vector)
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# linalg=linear（线性）+algebra（代数），norm则表示范数
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# 求图片的范数
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norms.append(linalg.norm(vector, 2))
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a, b = vectors
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a_norm, b_norm = norms
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# dot返回的是点积，对二维数组（矩阵）进行计算
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res = dot(a / a_norm, b / b_norm)
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return res
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#差值感知算法
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def dHash(image):
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#缩放9*8
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image=cv2.resize(image,(9,8),interpolation=cv2.INTER_CUBIC)
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#转换灰度图
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image=cv2.cvtColor(image,cv2.COLOR_BGR2GRAY)
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# print(image.shape)
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hash=[]
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#每行前一个像素大于后一个像素为1，相反为0，生成哈希
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for i in range(8):
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for j in range(8):
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if image[i,j]>image[i,j+1]:
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hash.append(1)
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else:
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hash.append(0)
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return hash
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#计算汉明距离
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def Hamming_distance(hash1,hash2):
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num = 0
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for index in range(len(hash1)):
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if hash1[index] != hash2[index]:
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num += 1
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return num
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if __name__ == '__main__':
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# 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
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image1 = cv2.imread('../image/1.jpg')
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image2 = cv2.imread('../image/2.jpg')
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# image3 = cv2.imread('image/AI2.jpg')
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# image4 = cv2.imread('image/AI3.jpg')
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# image5 = cv2.imread('image/AI4.jpg')
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# image6 = cv2.imread('a.jpg')
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# image7 = cv2.imread('AI.jpg')
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hash1 = dHash(image1)
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hash2 = dHash(image2)
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# hash3 = dHash(image3)
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# hash4 = dHash(image4)
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# hash5 = dHash(image5)
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# hash6 = dHash(image6)
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# hash7 = dHash(image7)
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start = time.time()
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dist = Hamming_distance(hash1, hash2)
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#将距离转化为相似度
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similarity = 1 - dist * 1.0 / 64
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# print(dist)
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print(similarity, time.time() - start)
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# cosin = image_similarity_vectors_via_numpy(image1, image2)
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# print('图片余弦相似度', cosin)
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# cosin1 = image_similarity_vectors_via_numpy(image1, image3)
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# print('图片余弦相似度', cosin1)
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# cosin2 = image_similarity_vectors_via_numpy(image1, image4)
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# print('图片余弦相似度', cosin2)
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# cosin3 = image_similarity_vectors_via_numpy(image1, image5)
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# print('图片余弦相似度', cosin3)
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# cosin4 = image_similarity_vectors_via_numpy(image5, image6)
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# print('图片余弦相似度', cosin4)
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# cosin5 = image_similarity_vectors_via_numpy(image1, image6)
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# print('图片余弦相似度', cosin5) |