python3图⽚验证码识别种类最多第三⽅模块-MuggleOCR pip install muggle_ocr pip install muggle_ocr -i --trusted-host pypi.douban
案例1
导⼊包
import time
# 1. 导⼊包
import muggle_ocr
"""
焗油机使⽤预置模型，预置模型包含了[ModelType.OCR, ModelType.Captcha] 两种 其中 ModelType.OCR ⽤于识别普通印刷⽂本, ModelType.Captcha ⽤于识别4-6位简单英数验证码
"""
# 打开印刷⽂本图⽚
with open(r"test1.png", "rb") as f:
ocr_bytes = f.read()
# 打开验证码图⽚
with open(r"test2.jpg", "rb") as f:
captcha_bytes = f.read()
# 2. 初始化；model_type 可选: [ModelType.OCR, ModelType.Captcha]
sdk = muggle_ocr.SDK(model_type=muggle_ocr.ModelType.OCR)
# ModelType.Captcha 可识别光学印刷⽂本
for i in range(5):
st = time.time()
# 3. 调⽤预测函数
text = sdk.predict(image_bytes=ocr_bytes)
print(text, time.time() - st)
# ModelType.Captcha 可识别4-6位验证码
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sdk = muggle_ocr.SDK(model_type=muggle_ocr.ModelType.Captcha)
for i in range(5):
st = time.time()
# 3. 调⽤预测函数
text = sdk.predict(image_bytes=captcha_bytes)
print(text, time.time() - st)
"""
使⽤⾃定义模型自来水供水系统
⽀持基于 github/kerlomz/captcha_trainer 框架训练的模型
训练完成后，进⼊导出编译模型的[out]路径下, 把[graph]路径下的pb模型和[model]下的yaml配置⽂件放到同⼀路径下。
将 conf_path 参数指定为 yaml配置⽂件的绝对或项⽬相对路径即可，其他步骤⼀致，如下⽰例：
"""
with open(r"test3.jpg", "rb") as f:
b = f.read()
sdk = muggle_ocr.SDK(conf_path="./ocr.yaml")
text = sdk.predict(image_bytes=b)
案例2
import time
import muggle_ocr
import os
sdk = muggle_ocr.SDK(model_type=muggle_ocr.ModelType.OCR)
root_dir = r"./imgs"
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for i in os.listdir(root_dir):
n = os.path.join(root_dir, i)任一农
with open(n, "rb") as f:
b = f.read()
st = time.time()
text = sdk.predict(image_bytes=b)
print(i, text, time.time() - st)
3
import datetime
import time
import requests
import json
import base64
import muggle_ocr
import random
import warnings
warnings.filterwarnings("ignore")
def login_qufenqi():
sdk = muggle_ocr.SDK(model_type=muggle_ocr.ModelType.Captcha)
# sdk = muggle_ocr.SDK(model_type=muggle_ocr.ModelType.OCR)
# num_str = ''.join(str(random.choice(range(10))) for _ in range(7))
# 获取图⽚
url = "passport.qufenqi/verify/getimg?r=0.05915737242270325"
headers = {
"authority": "passport.qufenqi",
"method": "GET",
"path": "/verify/getimg?r=0.05915737242270325",
"scheme": "https",
"accept": "image/webp,image/apng,image/*,*/*;q=0.8",
"accept-encoding": "gzip, deflate, br",
"accept-language": "zh-CN,zh;q=0.9,en;q=0.8",
"referer": "passport.qufenqi/i/resetloginpass",
"sec-fetch-dest": "image",
"sec-fetch-mode": "no-cors",
"sec-fetch-site": "same-origin",
"user-agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/83.0.4103.61 Safari/537.36", }
response = (url, headers=headers, verify=False, timeout=10)
print("图⽚验证码",t)
with open('a.jpg', 'wb') as fw:
fw.t)
# 验证号码
code = sdk.t)
print(code)
url = "passport.qufenqi/i/resetloginpass/setaccount"
headers = {
"authority":"passport.qufenqi",
"method":"POST",
"path":"/i/resetloginpass/setaccount",
"scheme":"https",
高浓除砂器"accept":"*/*",
"accept-encoding":"gzip, deflate, br",
"accept-language":"zh-CN,zh;q=0.9,en;q=0.8",
"content-length":"31",
"content-type":"application/x-www-form-urlencoded; charset=UTF-8",
"origin":"passport.qufenqi",
"referer":"passport.qufenqi/i/resetloginpass",
"sec-fetch-dest":"empty",
"sec-fetch-mode":"cors",
"sec-fetch-site":"same-origin",
"user-agent":"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/83.0.4103.61 Safari/537.36", "x-requested-with":"XMLHttpRequest",
}
time.sleep(1)
data = {"mobile": "139********","imgcode": code} # input("输⼊验证码:")
print(data)
response = requests.post(url, headers=headers, data=data, verify=False, timeout=10)
print(json.))
if __name__ == '__main__':
# while True:
# time.sleep(1)
login_qufenqi()
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