基于python实现新版正方系统滑动验证码识别

　　这篇文章主要介绍了基于python实现新版正方系统滑动验证码识别算法和方案，文中示例代码对我们的学习和工作有一定的帮助，感兴趣的可以了解一下
　　Python实现新版正方系统滑动验证码识别算法和方案
　　步骤一：点击数据分析
　　点击滑动按钮，将发送一个请求到 /zfcaptchaLogin
　　请求内容＂type＂: ＂verify＂ ＂rtk＂: ＂6cfab177-afb2-434e-bacf-06840c12e7af＂ ＂time＂: ＂1624611806948＂ ＂mt＂: ＂W3sieCI6OTY1LCJ5IjoxNjksInQiOjE2MjQ2MTE4MDY4Njh9LHsieCI6OTY1LCJ5IjoxNjksInQiOjE2MjQ2MTE4MDY5NDh9XQ==＂ ＂instanceId＂: ＂zfcaptchaLogin＂ ＂extend＂: ＂eyJhcHBOYW1lIjoiTmV0c2NhcGUiLCJ1c2VyQWdlbnQiOiJNb3ppbGxhLzUuMCAoTWFjaW50b3NoOyBJbnRlbCBNYWMgT1MgWCAxMF8xNV83KSBBcHBsZVdlYktpdC81MzcuMzYgKEtIVE1MLCBsaWtlIEdlY2tvKSBDaHJvbWUvOTEuMC40NDcyLjEwNiBTYWZhcmkvNTM3LjM2IiwiYXBwVmVyc2lvbiI6IjUuMCAoTWFjaW50b3NoOyBJbnRlbCBNYWMgT1MgWCAxMF8xNV83KSBBcHBsZVdlYktpdC81MzcuMzYgKEtIVE1MLCBsaWtlIEdlY2tvKSBDaHJvbWUvOTEuMC40NDcyLjEwNiBTYWZhcmkvNTM3LjM2In0=＂
　　通过 base64 解密 mt和 extend 得出解密的数值# mt [{＂x＂:965,＂y＂:169,＂t＂:1624611806868},{＂x＂:965,＂y＂:169,＂t＂:1624611806948}] # extend {＂appName＂:＂Netscape＂,＂userAgent＂:＂Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/91.0.4472.106 Safari/537.36＂,＂appVersion＂:＂5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/91.0.4472.106 Safari/537.36＂}
　　mt 为用户的点击行为，x为X轴上的值，y为Y轴上的值，t为时间戳。通过大量点击分析，发现x值最小值为 950，得出950 为 X轴的起点，y值随机无固定值。
　　extend 为请求头部内容步骤二：滑动验证码图像分析，计算滑动距离x值
　　将图像灰度化，通过getpixel可以获取图像某一点的颜色值， 颜色值越高代表图像越浅，所以寻找纵向连续50个像素点均是 getpixel(x+1, y) > getpixel(x, y)（X轴=x 比 X轴=x+1 颜色浅）
　　并扫描图像，当x=130、扫描高度=50时，的颜色比x+1时深。
　　from PIL import Image import matplotlib.pyplot as plt import numpy as np    scanf_height= 50 # 扫描的高度 img = Image.open(＂zfcaptchaLogin.png＂)       def contrast(imgl, x, y,scanf_height):     # 黄框颜色值比红框颜色值浅的个数     count = 0     for i in range(scanf_height):         if imgl.getpixel((x+1, y+i)) > imgl.getpixel((x, y+i)):             count += 1     # 当 count = scanf_height, 代表黄条区域 整体 红条区域 颜色值浅，则是验证码框位置     return count       def scanf(img):     imgx, imgy = img.size     imgl = img.convert(＂L＂) # 图像灰度化     plt.yticks([])     plt.xticks([i for i in range(0, imgx, 25)])     plt.imshow(img)     plt.pause(0.5)     for y in range(0, imgy-scanf_height, 10):         plt.pause(0.01)         plt.clf()         plt.yticks([])         plt.xticks([i for i in range(0, imgx, 25)])         plt.imshow(imgl, cmap=plt.cm.gray)         for x in range(1, imgx-1, 1):             plt.pause(0.0001)             plt.plot([x-1,x-1], [y, y+scanf_height], color=＂white＂)             plt.plot([x,x], [y, y+scanf_height], color=＂red＂)             plt.plot([x+1,x+1], [y, y+scanf_height], color=＂yellow＂)             count = contrast(imgl, x,y, scanf_height)             plt.title(＂count: {}＂.format(count) )                print(＂x,y=[{}, {}], 黄条区域值比红条区域颜色值浅的个数:{}＂.format(x,y, count))             if count == scanf_height:                 return       scanf(img) plt.show()
　　优化代码计算x，y值
　　import json import random import time from io import BytesIO    from PIL import Image       class ZfCaptchaRecognit(object):     def __init__(self, img_path):         self.img = Image.open(img_path)        def _get_xy(self):         # 计算 x,y 值         def _is_piding_line(img_l, x, y):             for n in range(50):                 # 寻找纵向连续50个像素点均是 X=x 比 X=x+1 颜色深                 if y + n >= img_l.size[1] or x >= img_l.size[0] - 1:                     return False                 if img_l.getpixel((x + 1, y + n)) - img_l.getpixel((x, y + n)) < 2:                     return False             return True            img_l = self.img.convert(＂L＂)         for x in range(img_l.size[0]):             for y in range(img_l.size[1]):                 if _is_piding_line(img_l, x, y):                     return (x, y)           def show_tag(self):         # 展示 切分点         X, Y = self._get_xy()         img2 = Image.new(＂RGB＂, self.img.size, (255, 255, 255))         for x in range(self.img.size[0]):             for y in range(self.img.size[1]):                 pix = self.img.getpixel((x, y))                 img2.putpixel((x, y), pix)                 if x == X or y == Y:                     img2.putpixel((x, y), 225)            img2.save(＂show_tag.png＂)         img2.show()       captcha = ZfCaptchaRecognit(＂zfcaptchaLogin.png＂) captcha.show_tag()步骤三：生成提交参数
　　通过 步骤一得出x值最小为950，y值无规律
　　则提交参数mt的大致格式数据是[{     ＂x＂:950+ 滑动距离 + 浮动值,  #  浮动值的范围通过分析提交参数得出在10~20内     ＂y＂:random.randint(150, 190),  # 无规律，暂定150到190范围内     ＂t＂:int(time.time() * 1000)},  # 时间戳  ...]
　　获取mt 参数import json import random import time from io import BytesIO    from PIL import Image       class ZfCaptchaRecognit(object):     def __init__(self, img_stream):         obj = BytesIO(img_stream)         self.img = Image.open(obj)        def _get_xy(self):         ...        def generate_payload(self):         base_x = 950         X, Y = self._get_xy()         payloads = [{＂x＂: base_x + random.randint(5, 20), ＂y＂: random.randint(150, 190), ＂t＂: int(time.time() * 1000)}]         for i in range(random.randint(15, 30)):             # 在上一个参数基础下浮动             last_payload = payloads[-1].copy()             payloads[0][＂x＂] += random.choice([0] * 8 + [1, -1] * 2 + [2, -2])             last_payload[＂t＂] += random.randint(1, 20)             last_payload[＂y＂] += random.choice([0] * 8 + [1, -1] * 2 + [2, -2])             payloads.append(last_payload)            payloads[-1][＂x＂] = base_x + random.randint(10, 20) + X         return json.dumps(payloads)    captcha = ZfCaptchaRecognit(＂zfcaptchaLogin.png＂) captcha. generate_payload()