Python实现新版正方系统滑动验证码识别

目录

• 步骤一：点击数据分析
• 步骤二：滑动验证码图像分析，计算滑动距离x值
• 步骤三：生成提交参数

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")
 
 www.cppcns.com
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 t编程客栈ime
from io import BytesIO
 
from PIL import Image
 
 
class ZfCapt编程客栈chaRecognit(object):
    def __init__(self, img_path):
        self.img = Image.open(img_path)
 
    def _get_xy(self):
        # 计算 x,y 值
        def _is_dividing_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_dividing_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_payhttp://www.cppcns.comload(self):
        base_x = 950
        X, Y = self._get_xy()
        payloads = [{"x": base_x + random.randint(5, 20), 编程客栈"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()

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