python UI自動化截圖對比

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：

https://www.cnblogs.com/dcb3688/p/4610660.html

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
    # 灰度大於平均值為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))
    # 轉換灰度圖
    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
    hash_str = ''
    # 每行前一個像素大於后一個像素為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

    # 轉換為灰度圖
    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
    # 將灰度圖轉為浮點型，再進行dct變換
    dct = cv2.dct(np.float32(gray))
    # opencv實現的掩碼操作
    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+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)

　由於截圖對比要求較高，我選擇差值哈希算法。

具體截圖代碼如下

 

 對比代碼

 

 結果：