Python3破解滑块验证码的完整方案

pip install opencv-python selenium pillow numpy

import cv2
import numpy as np
from PIL import Image

def preprocess_image(img_path):
    """图像预处理：灰度化+高斯模糊+边缘检测"""
    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边缘检测
    edge = cv2.Canny(blur, 50, 150)
    return edge

def get_gap_offset(bg_path, gap_bg_path):
    """
    计算滑块需要滑动的偏移距离
    :param bg_path: 完整背景图路径
    :param gap_bg_path: 带缺口背景图路径
    :return: 缺口横向偏移像素值
    """
    bg_edge = preprocess_image(bg_path)
    gap_edge = preprocess_image(gap_bg_path)
    
    # 图像差值运算，提取缺口轮廓
    diff = cv2.absdiff(bg_edge, gap_edge)
    # 寻找轮廓
    contours, _ = cv2.findContours(diff, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
    
    for contour in contours:
        # 筛选符合滑块尺寸的轮廓
        x, y, w, h = cv2.boundingRect(contour)
        if 30 < w < 100 and 30 < h < 100:
            return x  # 返回缺口横向起始坐标，即滑动偏移量
    return 0

import random
import time

def get_slide_track(distance):
    """
    生成真人滑动轨迹
    :param distance: 需要滑动的总像素距离
    :return: 每一步滑动的像素偏移列表
    """
    track_list = []
    current = 0
    # 初始加速阶段（前70%距离）
    mid = distance * 0.7
    # 加速度、初始速度随机化，模拟不同人操作习惯
    v = 0
    a = random.uniform(2, 4)
    while current < mid:
        v += a
        track_list.append(round(v))
        current += round(v)
    
    # 末端减速阶段（后30%距离）
    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)
    
    # 误差修正：补足最后距离，避免偏移
    last = sum(track_list) - distance
    if last > 0:
        track_list[-1] -= last
    elif last < 0:
        track_list[-1] += abs(last)
    
    # 加入随机微小停顿节点
    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):
    # 配置浏览器参数，规避自动化检测
    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. 定位验证码元素，下载背景图、缺口图（根据实际页面DOM修改选择器）
        # 此处为通用示例，需根据目标网站调整图片元素获取逻辑
        bg_img = driver.find_element("css selector", "div.bg-img")
        gap_img = driver.find_element("css selector", "div.gap-img")
        
        # 截图保存图片
        bg_img.screenshot("bg.png")
        gap_img.screenshot("gap.png")
        time.sleep(1)

        # 2. 计算滑动偏移量
        offset = get_gap_offset("bg.png", "gap.png")
        # 适配页面缩放比例
        scale = driver.execute_script("return window.devicePixelRatio")
        real_offset = offset / scale

        # 3. 获取滑动轨迹
        track = get_slide_track(real_offset)

        # 4. 模拟真人拖拽
        slider = driver.find_element("css selector", "div.slider-btn")
        ActionChains(driver).click_and_hold(slider).perform()
        time.sleep(random.uniform(0.1, 0.2))  # 按住滑块短暂停顿
        
        # 分段滑动
        for step in track:
            ActionChains(driver).move_by_offset(xoffset=step, yoffset=0).perform()
            time.sleep(random.uniform(0.005, 0.01))
        
        # 末端微调+短暂停顿，模拟真人对齐操作
        time.sleep(random.uniform(0.2, 0.4))
        ActionChains(driver).release().perform()
        time.sleep(3)

        print("滑块验证执行完成，可查看结果")
    except Exception as e:
        print(f"破解失败：{str(e)}")
    finally:
        # driver.quit()  # 调试阶段注释，查看页面结果

if __name__ == "__main__":
    # 替换为目标带滑块验证码的页面地址
    target_url = "https://xxx.com/login"
    crack_slide_captcha(target_url)