环境安装

安装python需要的依赖包
cv2 安装可以参考这里：https://javapub.blog.csdn.net/article/details/123656345
安装webdriver -> chrome
下载对应版本，放在本地 D:\anaconda3\Scripts 目录下
https://registry.npmmirror.com/binary.html?path=chromedriver
效果展示

GIF效果：https://tva2.sinaimg.cn/large/007F3CC8ly1h0ku3yh9g5g31ex0pfwus.gif
cv2使用参考：https://blog.csdn.net/RNGuzi/article/details/90034485
注意：测试时慢点刷，容易封IP。
源码

对源码加了大量注释
测试网站：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'  # 测试网站
        self.wait = WebDriverWait(self.browser, 20)
        self.browser.get(self.url)

    # 保存俩张图片
    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)  # 读取图片 image_name应该是变量
        img = cv2.medianBlur(image, 5)  # 中值滤波，去除黑色边际中可能含有的噪声干扰。去噪。
        b = cv2.threshold(img, 15, 255, cv2.THRESH_BINARY)  # 调整裁剪效果，二值化处理。
        binary_image = b[1]  # 二值图--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)  # 左边界
        right = max(edges_x)  # 右边界
        width = right - left  # 宽度
        bottom = min(edges_y)  # 底部
        top = max(edges_y)  # 顶部
        height = top - bottom  # 高度
        pre1_picture = image[left:left + width, bottom:bottom + height]  # 图片截取
        return pre1_picture  # 返回图片数据

    # 匹配比对俩图距离
    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) # 图片格式转换为灰度图片 
        # cv2.imshow('template', template)
        # cv2.waitKey(0)
        res = cv2.matchTemplate(img_gray, template, cv2.TM_CCOEFF_NORMED) # 匹配模式，匹配图片
        run = 1

        # 使用二分法查找阈值的精确值
        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()    

    # 计算出先加速、后加速的数组
    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):
        """
        根据偏移量获取移动轨迹
        :param distance: 偏移量
        :return: 移动轨迹
        """
        # 移动轨迹
        track = []
        # 当前位移
        current = 0
        # 减速阈值
        mid = distance * 4 / 5
        # 计算间隔
        t = 0.2
        # 初速度
        v = 0

        while current < distance:
            if current < mid:
                # 加速度为正 2
                a = 4
            else:
                # 加速度为负 3
                a = -3
            # 初速度 v0
            v0 = v
            # 当前速度 v = v0 + at
            v = v0 + a * t
            # 移动距离 x = v0t + 1/2 * a * t^2
            move = v0 * t + 1 / 2 * a * t * t
            # 当前位移
            current += move
            # 加入轨迹
            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

# 发生意外，请留言。https://javapub.blog.csdn.net/article/details/123730597
if __name__ == '__main__':
    xp = 320  # 验证码的像素-长
    target = 'target.jpg'  # 临时保存的图片名
    template = 'template.png'  # 临时保存的图片名

    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()
原文地址：https://cloud.tencent.com/developer/article/1964164
python入门教程 - 滑块实战

环境安装

效果展示

源码

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