# -*- coding: utf-8 -*- 

from uiautomator import Device 
import time
import os
import cv2
import numpy as np

class Test:
    def __init__(self,deviceid):
        self.device=Device(deviceid)
        self.deviceid=deviceid
        
    def click(self,x,y):
        self.device.click(x,y)
        
    def startActivity(self,activity):
        os.system(‘adb -s %s shell am start %s‘ %(self.deviceid,activity))
            
    #平均hash算法计算
    def classify_aHash(self,image1,image2): 
        image1 = cv2.resize(image1,(8,8)) 
        image2 = cv2.resize(image2,(8,8)) 
        gray1 = cv2.cvtColor(image1,cv2.COLOR_BGR2GRAY) 
        gray2 = cv2.cvtColor(image2,cv2.COLOR_BGR2GRAY) 
        hash1 = self.getHash(gray1) 
        hash2 = self.getHash(gray2) 
        return self.Hamming_distance(hash1,hash2) 
        
    #输入灰度值，返回hash
    def getHash(self,image): 
        avreage = np.mean(image) 
        hash = [] 
        for i in range(image.shape[0]): 
            for j in range(image.shape[1]): 
                if image[i,j] > avreage: 
                    hash.append(1) 
                else: 
                    hash.append(0) 
        return hash
        
    # 计算汉明距离 
    def Hamming_distance(self,hash1,hash2): 
        num = 0
        for index in range(len(hash1)): 
            if hash1[index] != hash2[index]: 
                num += 1
        return num 
    
    #使用adb命令截取手机页面图片
    def screenshot(self):
        time.sleep(8)
        os.system("adb shell /system/bin/screencap -p /sdcard/p2.png")     #（保存到SDCard）
        os.system("adb pull /sdcard/p2.png E:\Study\uiautomator\picture")    #（保存到电脑）
        
if __name__ == "__main__":
    w=Test(‘0123456789ABCDEF‘)   #记录手机串号，查看方式adb devices
    w.startActivity(‘com.android.mms/.ui.ConversationList‘)
    w.screenshot()
    img1 = cv2.imread("E:\\Study\\uiautomator\\picture\\p1.png")   #读取保存的图片，原始图片
    img2 = cv2.imread("E:\\Study\\uiautomator\\picture\\p2.png")    #读取与原始图相同使用screenshot方法截取的页面图片
    cv2.imshow("img1",img1)     #展示图片1
    cv2.imshow("img2",img2)     #展示图片2
    degree = w.classify_aHash(img1,img2)   #调用方法，对比两张图片是否相同，返回值0为相同，返回1为不同，值越小，返回值越小，相似度越高
    print degree
    cv2.waitKey(0)