📝 FuncaptchaServer

基于🔗 Siamese-Pytorch 训练的Funcaptcha类型的3D_Rollball_Animals图片识别服务器

🔍️ 识别率

• 识别率在95%以上, 但是由于图片的多样性, 仍然会有一定的失败率, 请issue反馈, 提供图片.

• Funcaptcha其他类型欢迎PR.

🐅 项目介绍

⬇️ 部署相关

• 📡 依赖安装:

pip install -r requirements.txt

• 📡 运行:

python main.py

🖼️ 使用示例

• 📡 示例curl命令:

curl --location --request POST 'http://127.0.0.1:8181/createTask' \
--header 'Content-Type: application/json' \
--data-raw '{
    "clientKey": "your_key",
    "task": {
        "type": "FunCaptchaClassification",
        "image": "data:image/jpeg;base64,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        "question": "4_3d_rollball_animals"
    }
}'

• 📡 响应示例:

 {
  "errorCode": "",
  "errorId": 0,
  "solution": {
    "objects": [
      4
    ]
  },
  "status": "ready",
  "taskId": "bb11d056130b5e41f3d870edfa21c6a4"
}

• 📡 识别说明:

errorId: 0 表示识别成功
objects: 对应识别结果
*从0开始计数，"objects": [4] 表示识别结果为序列4，与手指对应