python和opencv常用求图像相似度持续更新
1.目前使用方法有平均哈希，差值哈希，感知哈希，单通道直方图，多通道直方图，以及特征匹配structural_similarity
import os
import cv2
import numpy as np
from skimage.metrics import structural_similarity
class SSIM:
def __init__(self, image1, image2):
self.image1 = image1
self.image2 = image2
if os.path.exists(image1) and os.path.exists(image2):
self.img1 = cv2.imread(self.image1)
self.img2 = cv2.imread(self.image2)
else:
raise "{}或{}未找到".format(image1, image2)
@staticmethod
def aHash(img):
# 平均值哈希算法
# 缩放为8*8
img = cv2.resize(img, (8, 8))
# 转换为灰度图
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
# s为像素和初值为0，hash为hash值初值为''
s = 0
hash= ''
# 遍历累加求像素和
for i in range(8):
for j in range(8):
s = s + gray[i, j]
# 求平均灰度
avg = s / 64
# 灰度大于平均值为1相反为0生成图片的hash值
for i in range(8):
for j in range(8):
if gray[i, j] > avg:
hash_str = hash + '1'
else:
hash_str = hash + '0'
return hash_str
@staticmethod
def dHash(img):
# 差值哈希算法
# 缩放8*8
img = cv2.resize(img, (9, 8))
# 转换灰度图
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
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 gray[i, j] > gray[i, j + 1]:
hash_str = hash + '1'
else:
hash_str = hash + '0'
return hash_str
@staticmethod
def pHash(img):
# 感知哈希算法
# 缩放32*32
img = cv2.resize(img, (32, 32)) # , interpolation=cv2.INTER_CUBIC
# 转换为灰度图
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
# 将灰度图转为浮点型，再进行dct变换
dct = cv2.dct(np.float32(gray))
# opencv实现的掩码操作
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
@staticmethod
def histogram(image1, image2):
# 灰度直方图算法
# 计算单通道的直方图的相似值
hist1 = cv2.calcHist([image1], [0], None, [256], [0.0, 255.0])
hist2 = cv2.calcHist([image2], [0], None, [256], [0.0, 255.0])
# 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)
return degree
def Multiparty_histogram(self, image1, image2, size=(256, 256)):
# RGB每个通道的直方图相似度
# 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.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 += self.calculate(im1, im2)
sub_data = sub_data / 3
return sub_data
@staticmethod
def contrast_Hash(hash1, hash2):
# Hash值对比
# 算法中1和0顺序组合起来的即是图片的指纹hash。顺序不固定，但是比较的时候必须是相同的顺序。
# 对比两幅图的指纹，计算汉明距离，即两个64位的hash值有多少是不一样的，不同的位数越小，图片越相似
# 汉明距离：一组二进制数据变成另一组数据所需要的步骤，可以衡量两图的差异，汉明距离越小，则相似度越高。汉明距离为0，即两张图片完全一样
n = 0
# hash长度不同则返回-1代表传参出错
if len(hash1) != len(hash2):
return -1
# 遍历判断
for i in range(len(hash1)):
# 不相等则n计数+1，n最终为相似度
if hash1[i] != hash2[i]:
n = n + 1
return n
@staticmethod
def contrast_image(imageA, imageB):
"""
对比两张图片的相似度，相似度等于1 完美匹配
:param imageA:
:param imageB:
:return:
"""
imageA = cv2.imread(imageA)
imageB = cv2.imread(imageB)
grayA = cv2.cvtColor(imageA, cv2.COLOR_BGR2GRAY)
grayB = cv2.cvtColor(imageB, cv2.COLOR_BGR2GRAY)
# 计算两个灰度图像之间的结构相似度指数,相似度等于1完美匹配
(score, diff) = structural_similarity(grayA, grayB, full=True)
diff = (diff * 255).astype("uint8")
print("SSIM:{}".format(score))
return score, diff
def sum_Hash(self):
# 均值、差值、感知哈希算法三种算法值越小，则越相似,相同图片值为0
hash1 = self.aHash(self.img1)
hash2 = self.aHash(self.img2)
n1 = self.contrast_Hash(hash1, hash2)
print('均值哈希算法相似度aHash：', n1)
hash1 = self.dHash(self.img1)
hash2 = self.dHash(self.img2)
n2 = self.contrast_Hash(hash1, hash2)
print('差值哈希算法相似度dHash：', n2)
hash1 = self.pHash(self.img1)
hash2 = self.pHash(self.img2)
n3 = self.contrast_Hash(hash1, hash2)
print('感知哈希算法相似度pHash：', n3)
sum_Hash = np.mean([n1, n2, n3])
return sum_Hash
def histogram(self):
# 三直方图算法和单通道的直方图 0-1之间，值越大，越相似。 相同图片为1
n4 = self.Multiparty_histogram(self.img1, self.img2)
print('三直方图算法相似度：', n4)
n5 = self.histogram(self.img1, self.img2)
print("单通道的直方图", n5)
return np.mean([n4, n5])
if __name__ == "__main__":
ssim = SSIM(image1=r'\800.jpg', image2=r'\801.jpg')
ssim.sum_Hash()