这篇文章主要为大家展示了“Python如何实现正方系统滑动验证码识别”，内容简而易懂，条理清晰，希望能够帮助大家解决疑惑，下面让小编带领大家一起研究并学习一下“Python如何实现正方系统滑动验证码识别”这篇文章吧。
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()以上是“Python如何实现正方系统滑动验证码识别”这篇文章的所有内容，感谢各位的阅读！相信大家都有了一定的了解，希望分享的内容对大家有所帮助，如果还想学习更多知识，欢迎关注编程网行业资讯频道！
