目前有个想法，就是将UI截图与自动化截图进行对比。不一致的情况下提示错误

截图对比方法有：

import cv2

import numpy as np

# 均值哈希算法

def aHash(img):

# 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_str为hash值初值为''

s = 0

hash_str = ''

# 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_str + '1'

else:

hash_str = hash_str + '0'

return hash_str

# 差值感知算法

def dHash(img):

# 缩放8*8

img = cv2.resize(img, (9, 8))

# 转换灰度图

gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

hash_str = ''

# 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_str + '1'

else:

hash_str = hash_str + '0'

return hash_str

# 感知哈希算法(pHash)

def pHash(img):

# 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

# 通过得到RGB每个通道的直方图来计算相似度

def classify_hist_with_split(image1, image2, size=(256, 256)):

# 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 += calculate(im1, im2)

sub_data = sub_data / 3

return sub_data

# 计算单通道的直方图的相似值

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])

# 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

# Hash值对比

def cmpHash(hash1, hash2):

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

img1 = cv2.imread('openpic/x1y2.png') # 11--- 16 ----13 ---- 0.43

img2 = cv2.imread('openpic/x2y4.png')

img1 = cv2.imread('openpic/x3y5.png') # 10----11 ----8------0.25

img2 = cv2.imread('openpic/x9y1.png')

img1 = cv2.imread('openpic/x1y2.png') # 6------5 ----2--------0.84

img2 = cv2.imread('openpic/x2y6.png')

img1 = cv2.imread('openpic/t1.png') # 14------19---10--------0.70

img2 = cv2.imread('openpic/t2.png')

img1 = cv2.imread('openpic/t1.png') # 39------33---18--------0.58

img2 = cv2.imread('openpic/t3.png')

hash1 = aHash(img1)

hash2 = aHash(img2)

n = cmpHash(hash1, hash2)

print('均值哈希算法相似度：', n)

hash1 = dHash(img1)

hash2 = dHash(img2)

n = cmpHash(hash1, hash2)

print('差值哈希算法相似度：', n)

hash1 = pHash(img1)

hash2 = pHash(img2)

n = cmpHash(hash1, hash2)

print('感知哈希算法相似度：', n)

n = classify_hist_with_split(img1, img2)

print('三直方图算法相似度：', n)

由于截图对比要求较高，我选择差值哈希算法。

具体截图代码如下

对比代码

结果：