Python如何实现正方系统滑动验证码识别

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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 Imageimport matplotlib.pyplot as pltimport 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 jsonimport randomimport timefrom 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 jsonimport randomimport timefrom 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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