Python實(shí)現(xiàn)新版正方系統(tǒng)滑動驗(yàn)證碼識別
Python實(shí)現(xiàn)新版正方系統(tǒng)滑動驗(yàn)證碼識別算法和方案

步驟一:點(diǎn)擊數(shù)據(jù)分析
點(diǎn)擊滑動按鈕,將發(fā)送一個請求到 /zfcaptchaLogin
請求內(nèi)容
"type": "verify" "rtk": "6cfab177-afb2-434e-bacf-06840c12e7af" "time": "1624611806948" "mt": "W3sieCI6OTY1LCJ5IjoxNjksInQiOjE2MjQ2MTE4MDY4Njh9LHsieCI6OTY1LCJ5IjoxNjksInQiOjE2MjQ2MTE4MDY5NDh9XQ==" "instanceId": "zfcaptchaLogin" "extend": "eyJhcHBOYW1lIjoiTmV0c2NhcGUiLCJ1c2VyQWdlbnQiOiJNb3ppbGxhLzUuMCAoTWFjaW50b3NoOyBJbnRlbCBNYWMgT1MgWCAxMF8xNV83KSBBcHBsZVdlYktpdC81MzcuMzYgKEtIVE1MLCBsaWtlIEdlY2tvKSBDaHJvbWUvOTEuMC40NDcyLjEwNiBTYWZhcmkvNTM3LjM2IiwiYXBwVmVyc2lvbiI6IjUuMCAoTWFjaW50b3NoOyBJbnRlbCBNYWMgT1MgWCAxMF8xNV83KSBBcHBsZVdlYktpdC81MzcuMzYgKEtIVE1MLCBsaWtlIEdlY2tvKSBDaHJvbWUvOTEuMC40NDcyLjEwNiBTYWZhcmkvNTM3LjM2In0="
通過 base64 解密 mt和 extend 得出解密的數(shù)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 為用戶的點(diǎn)擊行為,x為X軸上的值,y為Y軸上的值,t為時間戳。通過大量點(diǎn)擊分析,發(fā)現(xiàn)x值最小值為 950,得出950 為 X軸的起點(diǎn),y值隨機(jī)無固定值。
extend 為請求頭部內(nèi)容
步驟二:滑動驗(yàn)證碼圖像分析,計(jì)算滑動距離x值
將圖像灰度化,通過getpixel可以獲取圖像某一點(diǎn)的顏色值, 顏色值越高代表圖像越淺,所以尋找縱向連續(xù)50個像素點(diǎn)均是 getpixel(x+1, y) > getpixel(x, y)(X軸=x 比 X軸=x+1 顏色淺)
并掃描圖像,當(dāng)x=130、掃描高度=50時,的顏色比x+1時深。

from PIL import Image
import matplotlib.pyplot as plt
import numpy as np
scanf_height= 50 # 掃描的高度
img = Image.open("zfcaptchaLogin.png")
def contrast(imgl, x, y,scanf_height):
# 黃框顏色值比紅框顏色值淺的個數(shù)
count = 0
for i in range(scanf_height):
if imgl.getpixel((x+1, y+i)) > imgl.getpixel((x, y+i)):
count += 1
# 當(dāng) count = scanf_height, 代表黃條區(qū)域 整體 紅條區(qū)域 顏色值淺,則是驗(yàn)證碼框位置
return count
def scanf(img):
imgx, imgy = img.size
imgl = img.convert('L') # 圖像灰度化
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=[{}, {}], 黃條區(qū)域值比紅條區(qū)域顏色值淺的個數(shù):{}".format(x,y, count))
if count == scanf_height:
return
scanf(img)
plt.show()
優(yōu)化代碼計(jì)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):
# 計(jì)算 x,y 值
def _is_dividing_line(img_l, x, y):
for n in range(50):
# 尋找縱向連續(xù)50個像素點(diǎn)均是 X=x 比 X=x+1 顏色深
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 切分點(diǎn)
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()
步驟三:生成提交參數(shù)
通過 步驟一得出x值最小為950,y值無規(guī)律
則提交參數(shù)mt的大致格式數(shù)據(jù)是
[{
"x":950+ 滑動距離 + 浮動值, # 浮動值的范圍通過分析提交參數(shù)得出在10~20內(nèi)
"y":random.randint(150, 190), # 無規(guī)律,暫定150到190范圍內(nèi)
"t":int(time.time() * 1000)}, # 時間戳
...]
獲取mt 參數(shù)
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)):
# 在上一個參數(shù)基礎(chǔ)下浮動
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()
以上就是Python實(shí)現(xiàn)新版正方系統(tǒng)滑動驗(yàn)證碼識別的詳細(xì)內(nèi)容,更多關(guān)于Python滑動驗(yàn)證碼識別的資料請關(guān)注腳本之家其它相關(guān)文章!
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