# coding: utf-8
# @Author: 一棵柚子树
# @time: 2022/3/26 0:28
import time
import numpy
import cv2
from PIL import Image
def calculate(image1, image2):
hist1 = cv2.calcHist([image1], [0], None, [256], [0, 256])
hist2 = cv2.calcHist([image2], [0], None, [256], [0, 256])
# 计算直方图的重合度
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)
print(degree)
return degree
def picture_similarity(image1, image2, size=(256, 256)):
image1 = Image.open(image1)
image2 = Image.open(image2)
# 将图像resize后，分离为RGB三个通道，再计算每个通道的相似值
image1 = cv2.cvtColor(numpy.asarray(image1), cv2.COLOR_RGB2BGR)
image2 = cv2.cvtColor(numpy.asarray(image2), cv2.COLOR_RGB2BGR)
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)
print(f"长度：{len(sub_image1)}")
sub_data = sub_data / len(sub_image1)
return int(sub_data*100)
if __name__ == '__main__':
img1_path = "desktop.png"
img2_path = "desktop2.png"
result1 = picture_similarity(img1_path, img2_path)
print(f"相似度为：{result1}")
