目前有個想法，就是將UI截圖與自動化截圖進行對比。不一致的情況下提示錯誤

截圖對比方法有：

import cv2

import numpy as np

# 均值哈希算法

def aHash(img):

# 縮放為8*8

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

# 轉(zhuǎn)換為灰度圖

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

# s為像素和初值為0，hash_str為hash值初值為''

s = 0

hash_str = ''

# 遍歷累加求像素和

for i in range(8):

for j in range(8):

s = s + gray[i, j]

# 求平均灰度

avg = s / 64

# 灰度大于平均值為1相反為0生成圖片的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):

# 縮放8*8

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

# 轉(zhuǎn)換灰度圖

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

hash_str = ''

# 每行前一個像素大于后一個像素為1，相反為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):

# 縮放32*32

img = cv2.resize(img, (32, 32)) # , interpolation=cv2.INTER_CUBIC

# 轉(zhuǎn)換為灰度圖

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

# 將灰度圖轉(zhuǎn)為浮點型，再進行dct變換

dct = cv2.dct(np.float32(gray))

# opencv實現(xiàn)的掩碼操作

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

# 通過得到RGB每個通道的直方圖來計算相似度

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

# 將圖像resize后，分離為RGB三個通道，再計算每個通道的相似值

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

# 計算單通道的直方圖的相似值

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])

# 計算直方圖的重合度

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值對比

def cmpHash(hash1, hash2):

n = 0

# hash長度不同則返回-1代表傳參出錯

if len(hash1)!=len(hash2):

return -1

# 遍歷判斷

for i in range(len(hash1)):

# 不相等則n計數(shù)+1，n最終為相似度

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('三直方圖算法相似度：', n)

由于截圖對比要求較高，我選擇差值哈希算法。

具體截圖代碼如下

對比代碼

結(jié)果：

總結(jié)

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