python JD sliding verification code type 2 code, type 1 low probability pass

When python beginners challenge JD's slide verification code at the beginning, it's a bit difficult. I've also studied it for a long time. It's not only the problem of pictures but also the problem of codes. There are also various problems that beginners encounter (all of which can make people tangle for half a day), such as less parentheses, less typing:, no indentation, etc. In the end, it's still a little bit of a change based on the code of the God to pass with low probability
Needless to say, there are two types of codes. The first type uses the complete original map, and the second type uses the notch original map (gray and binary processing). The final result is: the first type of code can't pass all the time because of the track problem, of course, the distance location is quite different from the actual situation; the second type of original code can't pass because of the track problem, I slightly added two Line code, the last low probability pass (if you are lucky, you can pass 2-3 times) (more than 10 times, it is recommended to exit and try again) [I think it may be the reason for the big error between the distance position and the actual situation], go to the code directly below.
First type code:
Reprint and reference link are, if there is any leakage, please inform:
https: // / qq_42709514 / article / details / 81975918

from PIL import Image
from selenium import webdriver
from selenium.webdriver.common.action_chains import ActionChains
import time,random
#Supplementary judgment module
from import expected_conditions as EC     #Judgement element
from import By
from import WebDriverWait
def get_image(browser):
    bigimg_element = browser.find_element_by_xpath('//Div / div [@ class = "jdjrv bigimg"] ') (no height may require a little reaction time, time.sleep(2)
    bigsize = bigimg_element.size       #invalid syns will appear if there is less than one bracket in the previous line
    biglocation = bigimg_element.location
    bigleft = biglocation['x']
    bigtop = biglocation['y']
    bigright = bigleft + bigsize['width']
    bigbottom = bigtop + bigsize['height']
    print(bigsize)                        #In this paper, the purpose of multiple print is to verify whether the program can run normally and get some parameter values for judgment (such as gap distance print(loc))
    print(bigleft, bigtop, bigright, bigbottom)
    snap_obj ='D:\quekou.png')
    print(snap_obj.size)              #For verification
    img_obj = snap_obj.crop((bigleft, bigtop, bigright, bigbottom))     #For what?
    image = img_obj     #If the location is image = get image (browser), it will cause a loop
    ad = match_source(image)
    visualstack = get_diff_location(ad, image)
    loc = visualstack
    track_list = get_track7(loc)
    return img_obj
def match_source(image):
    imagea ='C:/Users/Administrator/Desktop/jingdongimg/1 (1).png')
    imageb ='C:/Users/Administrator/Desktop/jingdongimg/1 (2).png')
    imagec ='C:/Users/Administrator/Desktop/jingdongimg/1 (3).png')
    imaged ='C:/Users/Administrator/Desktop/jingdongimg/1 (4).png')
    imagee ='C:/Users/Administrator/Desktop/jingdongimg/1 (5).png')
    imagef ='C:/Users/Administrator/Desktop/jingdongimg/1 (6).png')
    imageg ='C:/Users/Administrator/Desktop/jingdongimg/1 (7).png')
    imageh ='C:/Users/Administrator/Desktop/jingdongimg/1 (8).png')
    imagei ='C:/Users/Administrator/Desktop/jingdongimg/1 (9).png')
    imagej ='C:/Users/Administrator/Desktop/jingdongimg/1 (10).png')
    list = [imagea, imageb, imagec, imaged,imagee,imagef,imageg,imageh,imagei,imagej]
    # list = [imagea, imageb, imagec]
    #Search pixel difference, traverse the original image
    for i in list:
        # The original drawing of my computer is the same as that of the slide corresponding to the notch drawing. The original drawing of the vertical coordinate is 88px larger than that of the notch drawing, which can be modified according to the actual situation
        pixel1 = image.getpixel((200,100 ))
        pixel2 = i.getpixel((200, 100))
        # pixel[0] represents R value, pixel[1] represents G value, pixel[2] represents B value
        if abs(pixel1[0] - pixel2[0]) < 5:
            return i
    return image
# Get gap position 1 (get gap position 1: use the method of setting threshold; get gap position 2 use the method of comparing RGB, but for JD, the difference between the two methods is quite large, which may also be the reason why I set it)
def get_diff_location(image1,image2):
    left = 43
    for i in range(left, image1.size[0]):
        for j in range(image1.size[1]):
            if not is_pixel_equal(image1, image2, i, j):
                left = i
                return left
    return left
def is_pixel_equal(image1,image2, x, y):
       //Determine whether two pixels are the same
       :param image1: Picture 1
       :param image2: Picture 2
       :param x: position x
       :param y: position y
       :return: Whether the pixels are the same
    # Take the pixels of two pictures
    pix1 = image1.load()[x, y]
    pix2 = image2.load()[x, y]
    threshold = 60
    if (abs(pix1[0] - pix2[0] < threshold) and abs(pix1[1] - pix2[1] < threshold) and abs(
            pix1[2] - pix2[2] < threshold)):
        return True
        return False
# Get gap position 2
# def get_diff_location(image1,image2):
#     (40270) (10100), the slider size is 38 * 38, the length of the image search area is (40270), and the height is (10100), which can be modified according to the actual situation
#     for i in range(40,270):
#         for j in range(10,100):
#             #Traversing the original image and notch image prime value to find the notch location
#             if is_similar(image1,image2,i,j)==False:
#                return i
#     return -1
# # Compare RGB value to gap position
# def is_similar(image1,image2,x,y):
#     # pixel1=image1.getpixel((x, y))     
#     pixel1 = image1.getpixel((x, y))
#     pixel2=image2.getpixel((x, y))
#     # There may be errors in the screenshot pixels, 50 as the tolerance range (refer to Titan's, no change)
#     if abs(pixel1[0]-pixel2[0])>=50 and abs(pixel1[1]-pixel2[1])>=50 and abs(pixel1[2]-pixel2[2])>=50:
#         return False
#     return True
def get_track7(distance):(Refer to the God's, no change)
    #  """
    # Calculate the moving track according to the offset and manual operation simulation
    # : param distance: offset
    # : return: move track
    # """
    # Moving trajectory
    # Current displacement
    current = 0
     # Deceleration threshold
    mid = distance * 4 / 5
     # time interval
    t = 0.2
    # Initial velocity
    v = 0
    while current < distance:
        if current < mid:
             a = random.uniform(7, 9)
            a = -(random.uniform(12.5, 13.5))
        v0 = v+1
        v = v0 + a * t
        x = v0 * t + 1 / 2 * a * t * t
        current += x

        if 0.6 < current - distance < 1:
            x = x - 0.53
            tracks.append(round(x, 2))

        elif 1 < current - distance < 1.5:
            x = x - 1.4
            tracks.append(round(x, 2))
        elif 1.5 < current - distance < 3:
            x = x - 1.8
            tracks.append(round(x, 2))

            tracks.append(round(x, 2))
    print(tracks, sum(tracks))
    return tracks
def slide(browser,track_list):
    for track in track_list:
    ActionChains(browser).move_by_offset(xoffset=1, yoffset=0).perform()
    #Try after failure
        failure = WebDriverWait(browser, 20,5).until(EC.text_to_be_present_in_element((By.CLASS_NAME, 'JDJRV-slide-center'), 'Slide right to complete the puzzle'))		#(with reference to the great God, this part is changed to Jingdong)
        #Failure = webdriverwait (browser, 5). Until (EC. Text \
        print('Verify success')
        return None
    if failure:
        # self.crack_slider(browser)
browser = webdriver.Firefox()
browser.find_element_by_link_text("Hello, please log in").click()
elem=browser.find_element_by_class_name("login-tab.login-tab-r").click() #u #This will work, but not the next line.
username='******'   #Input account
passwd='******'     #Input password
elem=browser.find_element_by_id("loginname") #u
elem=browser.find_element_by_id("nloginpwd") #p
elem=browser.find_element_by_id("loginsubmit")#.submit()    #Different web pages find different by id or class name
# Calling function

# # --------
//Reprint and reference contents are as follows. Please inform us if there is any omission:
# # Original link:
# Original link:
# # # Original link:

Category 2 Code:
Reprint and reference link are, thank God:

import cv2
import time
import numpy as np
from selenium import webdriver
from urllib import request
from selenium.webdriver.common.action_chains import ActionChains
#Supplementary judgment module (the following modules are added on the basis of the God)
from import expected_conditions as EC     #Judgement element
from import By
from import WebDriverWait
def loadpage(browser,username, password):
    browser.get(' ', open the JD website
    browser.find_element_by_class_name("login-tab.login-tab-r").click()  # Click account password to log in
    browser.find_element_by_id("loginname").send_keys(username)  # u
    browser.find_element_by_id("nloginpwd").send_keys(password)  # p
    browser.find_element_by_id("loginsubmit").click()  # Click login
    while True:(On the basis of the big God code, we have modified some)
            failure = WebDriverWait(browser, 10, 2).until(EC.text_to_be_present_in_element((By.CLASS_NAME, 'JDJRV-slide-center'), 'Slide right to complete the puzzle'))
            print('Verify success')
            return  None
        if failure:
def getPic(browser):(Using the code of the God, basically unchanged)
    # Large image for finding login image
    s2 = r'//div/div[@class="JDJRV-bigimg"]/img'
    # Small slider to find login image
    s3 = r'//div/div[@class="JDJRV-smallimg"]/img'
    bigimg = browser.find_element_by_xpath(s2).get_attribute("src")
    smallimg = browser.find_element_by_xpath(s3).get_attribute("src")
    # print(smallimg + '\n')
    # print(bigimg)
    # Naming of large background map
    backimg = "backimg.png"
    # Slider naming
    slideimg = "slideimg.png"
    # Download background map and save it locally
    request.urlretrieve(bigimg, backimg)
    # Download slider to save locally
    request.urlretrieve(smallimg, slideimg)
    # Take pictures and grayscale them
    block = cv2.imread(slideimg, 0)
    template = cv2.imread(backimg, 0)
    # Binary image name     #High recognition rate of grayed image
    blockName = "block.jpg"
    templateName = "template.jpg"
    # Save the binary image
    cv2.imwrite(blockName, block)
    cv2.imwrite(templateName, template)
    block = cv2.imread(blockName)
    block = cv2.cvtColor(block, cv2.COLOR_RGB2GRAY)
    block = abs(255 - block)
    cv2.imwrite(blockName, block)
    block = cv2.imread(blockName)
    template = cv2.imread(templateName)
    # Get offset
    result = cv2.matchTemplate(block, template, cv2.TM_CCOEFF_NORMED)  # Find the location of the block in the template and return the result as a matrix, which is the matching result of each point
    x, y = np.unravel_index(result.argmax(), result.shape)
    print('Previous code can operate normally')
    # Get the slider (based on the God, change the point to simulate the human effect)
    element = browser.find_element_by_xpath(s3)
    #time.sleep()	#You can't add time.sleep to this line, because after a pause, JD basically thinks you're a robot
    ActionChains(browser).move_to_element_with_offset(to_element=element, xoffset=y*4/5, yoffset=0).perform()    #I changed y to y*4/5 on the basis of God
    time.sleep(0.2)     #First line added by myself
    ActionChains(browser).move_to_element_with_offset(to_element=element, xoffset=y -y*7/10, yoffset=0).perform()     #I added the second line to simulate human behavior
#Open the web page and enter the account password
browser = webdriver.Firefox()
loadpage(browser,"***"',"***")	#The second is to enter JD account number and the third is to enter JD password
# # *Reprint and reference

Jingdong verification code is very difficult for my little white. There are many things I don't understand about the above two types of codes, mainly referring to the code of God. To be honest, the passing rate of JD verification code is a little low (one is that the location is not accurate, the other is that it is doubted whether it is artificial operation), but I was satisfied to pass by chance at last. After several days of research, JD verification code is over. Thank you for your code.

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Tags: Selenium less Firefox Python

Posted on Tue, 10 Mar 2020 02:37:08 -0700 by gudfry