python实现腾讯滑块验证码识别_Python

腾讯滑块验证码识别,识别凹槽的x轴位置，mock滑块的加速度。该项目公开API，提供识别和加速度模拟部分，第二部分模拟滑动进行识别返回数据请求

项目地址：https://github.com/zhaojunlike/python-tecent-slider-crack

安装python环境

参考：https://janikarhunen.fi/how-to-install-python-3-6-1-on-centos-7

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									sudo yum install https://centos7.iuscommunity.org/ius-release.rpm

									sudo yum install python36u

									python3.6 -V

									sudo yum install python36u-pip

									sudo yum install python36u-devel

创建环境 Creating a virtualenv

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									python3.6 -m venv venv

									. venv/bin/activate

									pip install [package_name]

									# 安装依赖

									pip install -r requirements.txt

daemonize 运行

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									# 参考 https://www.digitalocean.com/community/tutorials/how-to-serve-flask-applications-with-uswgi-and-nginx-on-ubuntu-18-04

									# Install the latest stable release:

									pip install uwsgi

									# ... or if you want to install the latest LTS (long term support) release,

									pip install https://projects.unbit.it/downloads/uwsgi-lts.tar.gz

									# 创建ln 

									cp captcha.service /etc/systemd/system/captcha.service

									systemctl enable captcha.service

									systemctl start captcha.service

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									uwsgi --ini /usr/local/nginx/html/myblog/uwsgiconfig.ini

									#后台运行

									uwsgi --ini /usr/local/nginx/html/myblog/uwsgiconfig.ini --daemonize /usr/local/nginx/html/myblog/myblog.out

nginx做代理

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									location /tx/ {

									    add_header Access-Control-Allow-Origin *;

									    include        uwsgi_params;

									    uwsgi_pass     127.0.0.1:8008;

									}

访问api

请求图片识别和加速度模拟

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									http://127.0.0.1:5000/tx/image

									POST /tx/image HTTP/1.1

									Host:host

									Content-Type: application/json

									Accept: */*

									Cache-Control: no-cache

									Accept-Encoding: gzip, deflate

									Content-Length: 1055

									Connection: keep-alive

									cache-control: no-cache

									{

									    "url": "图片的地址"

									}

									返回数据

									{

									    "data": {

									        "list": [],//模拟的点

									        "url": "",//图片地址

									        "x": 515,// x轴的偏移量

									    },

									    "message": "解析成功"

									}

模拟浏览器移动

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									const slider = {width: 680, point: 0, move: 0, steps: 0, posX: 0};//原本的高度

									//开始计算移动的距离

									slider.point = bgSize.width / slider.width * x;

									slider.move = handle.x + slider.point - 5;

									slider.steps = Math.random() * 100 / 30 + 100;

									slider.posX = handle.x + handle.width / 2;

									logger.info(`开始识别和移动滑块`, slider);

									//滑块的位置

									await page.mouse.move(slider.posX, handle.y + handle.height / 3, {steps: slider.steps});

									await page.mouse.down();

									let val = handle.x;

									for (let i = 0; i < traces.length; i++) {

									    val += bgSize.width / slider.width * (traces[i]);//缩放距离

									    slider.move = val;

									    if (val <= slider.posX) continue;

									    await page.mouse.move(slider.move, handle.y + handle.height / 2 + 5);

									}

									await page.waitFor(100);

									await page.mouse.up();