Python自动化解决滑块验证码的最佳实践

from selenium import webdriver
from selenium.webdriver.common.action_chains import ActionChains
import time
import random

def human_drag(driver, slider, distance):
    """模拟人类拖动行为"""
    action = ActionChains(driver)
    
    # 点击并按住滑块
    action.click_and_hold(slider).perform()
    
    # 分解移动轨迹
    remaining = distance
    while remaining > 0:
        # 随机步长（5-15像素）
        span = random.randint(5, 15)
        if span > remaining:
            span = remaining
        
        # 随机垂直偏移（模拟手抖）
        y_offset = random.randint(-2, 2)
        
        # 执行移动
        action.move_by_offset(span, y_offset).perform()
        remaining -= span
        
        # 随机停顿（0.1-0.3秒）
        time.sleep(random.uniform(0.1, 0.3))
    
    # 释放滑块
    action.release().perform()

# 使用示例
driver = webdriver.Chrome()
driver.get("https://example.com/login")

slider = driver.find_element_by_css_selector(".slider")
human_drag(driver, slider, distance=180)

import cv2
import numpy as np

def detect_gap(bg_path, slider_path):
    """使用OpenCV识别缺口位置"""
    # 读取图片
    bg = cv2.imread(bg_path)  # 背景图
    tp = cv2.imread(slider_path)  # 缺口图
    
    # 灰度化处理
    bg_gray = cv2.cvtColor(bg, cv2.COLOR_BGR2GRAY)
    tp_gray = cv2.cvtColor(tp, cv2.COLOR_BGR2GRAY)
    
    # 边缘检测
    bg_edge = cv2.Canny(bg_gray, 100, 200)
    tp_edge = cv2.Canny(tp_gray, 100, 200)
    
    # 模板匹配
    res = cv2.matchTemplate(bg_edge, tp_edge, cv2.TM_CCOEFF_NORMED)
    min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(res)
    
    return max_loc[0]  # 返回缺口x坐标

# 使用示例
gap_pos = detect_gap("background.png", "slider.png")
print(f"需要滑动的距离：{gap_pos}px")

import torch
from PIL import Image

# 加载预训练模型
model = torch.hub.load('ultralytics/yolov5', 'custom', path='best.pt')  

def detect_with_yolo(img_path):
    """使用YOLO检测缺口"""
    img = Image.open(img_path)
    results = model(img)
    
    # 解析检测结果
    predictions = results.pandas().xyxy[0]
    if len(predictions) > 0:
        x1 = predictions.iloc[0]['xmin']
        x2 = predictions.iloc[0]['xmax']
        return (x1 + x2) / 2  # 返回缺口中心位置
    return None

# 使用示例
gap_center = detect_with_yolo("captcha.png")