Python实现新版正方系统滑动验证码识别

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_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_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()

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