文末源碼，閱讀大約2.8分鐘

傻瓜式教程 - 體驗滑塊，提供練習場景及源碼。

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• 環境安裝
• 效果展示
• 源碼

環境安裝

安裝python需要的依賴包

cv2 安裝可以參考這裡：https://javapub.blog.csdn.net/article/details/123656345

安裝webdriver -> chrome

下載對應版本，放在本地 D:\anaconda3\Scripts 目錄下

https://registry.npmmirror.com/binary.html?path=chromedriver

效果展示

GIF效果：https://tva2.sinaimg.cn/large/007F3CC8ly1h0ku3yh9g5g31ex0pfwus.gif

cv2使用參考：https://blog.csdn.net/RNG_uzi_/article/details/90034485

註意：測試時慢點刷，容易封IP。

源碼

有問題可以留言探討，公眾號：JavaPub

對源碼加了大量註釋

測試網站：http://app.miit-eidc.org.cn/miitxxgk/gonggao/xxgk/queryCpParamPage?dataTag=Z&gid=U3119671&pc=303

import os
import cv2
import time
import random
import requests
import numpy as np
from PIL import Image
from io import BytesIO
from selenium import webdriver
from selenium.webdriver.common.by import By
from selenium.webdriver import ActionChains
from selenium.webdriver.support.wait import WebDriverWait
from selenium.webdriver.support import expected_conditions as EC

class CrackSlider():
    def __init__(self):
        # self.browser = webdriver.Edge()
        self.browser = webdriver.Chrome()
        self.s2 = r'//*[@id="captcha_div"]/div/div[1]/div/div[1]/img[1]'
        self.s3 = r'//*[@id="captcha_div"]/div/div[1]/div/div[1]/img[2]'
        self.url = 'http://app.miit-eidc.org.cn/miitxxgk/gonggao/xxgk/queryCpParamPage?dataTag=Z&gid=U3119671&pc=303'  # 測試網站
        self.wait = WebDriverWait(self.browser, 20)
        self.browser.get(self.url)

    # 保存倆張圖片
    def get_img(self, target, template, xp):
        time.sleep(3)
        target_link = self.browser.find_element_by_xpath(self.s2).get_attribute("src")
        template_link = self.browser.find_element_by_xpath(self.s3).get_attribute("src")
        target_img = Image.open(BytesIO(requests.get(target_link).content))
        template_img = Image.open(BytesIO(requests.get(template_link).content))
        target_img.save(target)
        template_img.save(template)
        size_loc = target_img.size
        print('size_loc[0]-----\n')
        print(size_loc[0])
        zoom = xp / int(size_loc[0])  # 耦合像素
        print('zoom-----\n')
        print(zoom)
        return zoom

    def change_size(self, file):
        image = cv2.imread(file, 1)  # 讀取圖片 image_name應該是變數
        img = cv2.medianBlur(image, 5)  # 中值濾波，去除黑色邊際中可能含有的雜訊干擾。去噪。
        b = cv2.threshold(img, 15, 255, cv2.THRESH_BINARY)  # 調整裁剪效果，二值化處理。
        binary_image = b[1]  # 二值圖--具有三通道
        binary_image = cv2.cvtColor(binary_image, cv2.COLOR_BGR2GRAY)
        x, y = binary_image.shape
        edges_x = []
        edges_y = []
        for i in range(x):
            for j in range(y):
                if binary_image[i][j] == 255:
                    edges_x.append(i)
                    edges_y.append(j)

        left = min(edges_x)  # 左邊界
        right = max(edges_x)  # 右邊界
        width = right - left  # 寬度
        bottom = min(edges_y)  # 底部
        top = max(edges_y)  # 頂部
        height = top - bottom  # 高度
        pre1_picture = image[left:left + width, bottom:bottom + height]  # 圖片截取
        return pre1_picture  # 返回圖片數據

    # 匹配比對倆圖距離
    def match(self, target, template):
        img_gray = cv2.imread(target, 0)
        img_rgb = self.change_size(template)
        template = cv2.cvtColor(img_rgb, cv2.COLOR_BGR2GRAY) # 圖片格式轉換為灰度圖片 
        # cv2.imshow('template', template)
        # cv2.waitKey(0)
        res = cv2.matchTemplate(img_gray, template, cv2.TM_CCOEFF_NORMED) # 匹配模式，匹配圖片
        run = 1

        # 使用二分法查找閾值的精確值
        L = 0
        R = 1
        while run < 20:
            run += 1
            threshold = (R + L) / 2
            if threshold < 0:
                print('Error')
                return None
            loc = np.where(res >= threshold)
            if len(loc[1]) > 1:
                L += (R - L) / 2
            elif len(loc[1]) == 1:
                break
            elif len(loc[1]) < 1:
                R -= (R - L) / 2
        res = loc[1][0]
        print('match distance-----\n')
        print(res)
        return res

    def move_to_gap(self, tracks):
        slider = self.wait.until(EC.element_to_be_clickable((By.CLASS_NAME, 'yidun_slider')))
        ActionChains(self.browser).click_and_hold(slider).perform()
        #element = self.browser.find_element_by_xpath(self.s3)
        #ActionChains(self.browser).click_and_hold(on_element=element).perform()
        while tracks:
            x = tracks.pop(0)
            print('tracks.pop(0)-----\n')
            print(x)
            ActionChains(self.browser).move_by_offset(xoffset=x, yoffset=0).perform()
            #ActionChains(self.browser).move_to_element_with_offset(to_element=element, xoffset=x, yoffset=0).perform()
            #time.sleep(0.01)
        time.sleep(0.05)
        ActionChains(self.browser).release().perform()

    def move_to_gap1(self, distance):
        distance += 46
        time.sleep(1)
        element = self.browser.find_element_by_xpath(self.s3)
        ActionChains(self.browser).click_and_hold(on_element=element).perform()
        ActionChains(self.browser).move_to_element_with_offset(to_element=element, xoffset=distance, yoffset=0).perform()
        #ActionChains(self.browser).release().perform()
        time.sleep(1.38)
        ActionChains(self.browser).release(on_element=element).perform()

    def move_to_gap2(self, distance):
        element = self.browser.find_elements_by_class_name("yidun_slider")[0]
        action = ActionChains(self.browser)
        mouse_action = action.click_and_hold(on_element=element)
        distance += 11
        distance = int(distance * 32/33)
        move_steps = int(distance/4)
        for i in range(0,move_steps):
            mouse_action.move_by_offset(4,random.randint(-5,5)).perform()
        time.sleep(0.1)
        mouse_action.release().perform()    

    # 計算出先加速、後加速的數組
    def get_tracks(self, distance, seconds, ease_func):
        distance += 20
        tracks = [0]
        offsets = [0]
        for t in np.arange(0.0, seconds, 0.1):
            ease = ease_func
            print('ease-----\n')
            print(ease)
            offset = round(ease(t / seconds) * distance)
            print('offset-----\n')
            print(offset)
            tracks.append(offset - offsets[-1])
            print('offset - offsets[-1]-----\n')
            print(offset - offsets[-1])
            offsets.append(offset)
            print('offsets-----\n')
            print(offsets)
        tracks.extend([-3, -2, -3, -2, -2, -2, -2, -1, -0, -1, -1, -1])
        return tracks
    def get_tracks1(self,distance):
        """
        根據偏移量獲取移動軌跡
        :param distance: 偏移量
        :return: 移動軌跡
        """
        # 移動軌跡
        track = []
        # 當前位移
        current = 0
        # 減速閾值
        mid = distance * 4 / 5
        # 計算間隔
        t = 0.2
        # 初速度
        v = 0

        while current < distance:
            if current < mid:
                # 加速度為正 2
                a = 4
            else:
                # 加速度為負 3
                a = -3
            # 初速度 v0
            v0 = v
            # 當前速度 v = v0 + at
            v = v0 + a * t
            # 移動距離 x = v0t + 1/2 * a * t^2
            move = v0 * t + 1 / 2 * a * t * t
            # 當前位移
            current += move
            # 加入軌跡
            track.append(round(move))
        return track

    def ease_out_quart(self, x):
        res = 1 - pow(1 - x, 4)
        print('ease_out_quart-----\n')
        print(res)
        return res

# 發生意外，請留言。https://javapub.blog.csdn.net/article/details/123730597
if __name__ == '__main__':
    xp = 320  # 驗證碼的像素-長
    target = 'target.jpg'  # 臨時保存的圖片名
    template = 'template.png'  # 臨時保存的圖片名

    cs = CrackSlider()
    zoom = cs.get_img(target, template, xp)
    distance = cs.match(target, template)
    track = cs.get_tracks((distance + 7) * zoom, random.randint(2, 4), cs.ease_out_quart)
    #track = cs.get_tracks1(distance)
    #track = cs.get_tracks((distance + 7) * zoom, random.randint(1, 2), cs.ease_out_quart)
    cs.move_to_gap(track)
    #cs.move_to_gap1(distance)
    #cs.move_to_gap2(distance)
    time.sleep(2)
    #cs.browser.close()

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