pip install opencv-python selenium pillow numpy

import cv2
import numpy as np
from PIL import Image

def preprocess_image(img_path):
    """圖像預處理：灰度化+高斯模糊+邊緣檢測"""
    img = cv2.imread(img_path)
    # 灰度化
    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
    # 高斯模糊降噪
    blur = cv2.GaussianBlur(gray, (5, 5), 0)
    # Canny邊緣檢測
    edge = cv2.Canny(blur, 50, 150)
    return edge

def get_gap_offset(bg_path, gap_bg_path):
    """
    計算滑塊需要滑動的偏移距離
    :param bg_path: 完整背景圖路徑
    :param gap_bg_path: 帶缺口背景圖路徑
    :return: 缺口橫向偏移像素值
    """
    bg_edge = preprocess_image(bg_path)
    gap_edge = preprocess_image(gap_bg_path)
    
    # 圖像差值運算，提取缺口輪廓
    diff = cv2.absdiff(bg_edge, gap_edge)
    # 尋找輪廓
    contours, _ = cv2.findContours(diff, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
    
    for contour in contours:
        # 篩選符合滑塊尺寸的輪廓
        x, y, w, h = cv2.boundingRect(contour)
        if 30 < w < 100 and 30 < h < 100:
            return x  # 返回缺口橫向起始坐標，即滑動偏移量
    return 0

import random
import time

def get_slide_track(distance):
    """
    生成真人滑動軌跡
    :param distance: 需要滑動的總像素距離
    :return: 每一步滑動的像素偏移列表
    """
    track_list = []
    current = 0
    # 初始加速階段（前70%距離）
    mid = distance * 0.7
    # 加速度、初始速度隨機化，模擬不同人操作習慣
    v = 0
    a = random.uniform(2, 4)
    while current < mid:
        v += a
        track_list.append(round(v))
        current += round(v)
    
    # 末端減速階段（后30%距離）
    a = -random.uniform(1, 3)
    while current < distance:
        v += a
        if v < 0.5:
            v = 0.5
        track_list.append(round(v))
        current += round(v)
    
    # 誤差修正：補足最后距離，避免偏移
    last = sum(track_list) - distance
    if last > 0:
        track_list[-1] -= last
    elif last < 0:
        track_list[-1] += abs(last)
    
    # 加入隨機微小停頓節(jié)點
    pause_pos = random.randint(3, len(track_list)-2)
    track_list.insert(pause_pos, 0)
    return track_list

from selenium import webdriver
from selenium.webdriver.common.action_chains import ActionChains
from selenium.webdriver.chrome.options import Options

def crack_slide_captcha(url):
    # 配置瀏覽器參數(shù)，規(guī)避自動化檢測
    chrome_options = Options()
    chrome_options.add_experimental_option("excludeSwitches", ["enable-automation"])
    chrome_options.add_experimental_option("useAutomationExtension", False)
    driver = webdriver.Chrome(options=chrome_options)
    driver.maximize_window()
    driver.get(url)
    time.sleep(2)

    try:
        # 1. 定位驗證碼元素，下載背景圖、缺口圖（根據(jù)實際頁面DOM修改選擇器）
        # 此處為通用示例，需根據(jù)目標網(wǎng)站調(diào)整圖片元素獲取邏輯
        bg_img = driver.find_element("css selector", "div.bg-img")
        gap_img = driver.find_element("css selector", "div.gap-img")
        
        # 截圖保存圖片
        bg_img.screenshot("bg.png")
        gap_img.screenshot("gap.png")
        time.sleep(1)

        # 2. 計算滑動偏移量
        offset = get_gap_offset("bg.png", "gap.png")
        # 適配頁面縮放比例
        scale = driver.execute_script("return window.devicePixelRatio")
        real_offset = offset / scale

        # 3. 獲取滑動軌跡
        track = get_slide_track(real_offset)

        # 4. 模擬真人拖拽
        slider = driver.find_element("css selector", "div.slider-btn")
        ActionChains(driver).click_and_hold(slider).perform()
        time.sleep(random.uniform(0.1, 0.2))  # 按住滑塊短暫停頓
        
        # 分段滑動
        for step in track:
            ActionChains(driver).move_by_offset(xoffset=step, yoffset=0).perform()
            time.sleep(random.uniform(0.005, 0.01))
        
        # 末端微調(diào)+短暫停頓，模擬真人對齊操作
        time.sleep(random.uniform(0.2, 0.4))
        ActionChains(driver).release().perform()
        time.sleep(3)

        print("滑塊驗證執(zhí)行完成，可查看結(jié)果")
    except Exception as e:
        print(f"破解失敗：{str(e)}")
    finally:
        # driver.quit()  # 調(diào)試階段注釋，查看頁面結(jié)果

if __name__ == "__main__":
    # 替換為目標帶滑塊驗證碼的頁面地址
    target_url = "https://xxx.com/login"
    crack_slide_captcha(target_url)