用Python实现滑动验证码的方法和代码是什么-群英

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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()

现在大家对于用Python实现滑动验证码的方法和代码是什么的内容应该都有一定的认识了吧，希望这篇能对大家有所帮助。最后，想要了解更多，欢迎关注群英网络，群英网络将为大家推送更多相关的文章。