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用来检测图片中的人脸是否为来自认证设备端的近距离裸拍活体人脸对象,可广泛应用在人脸实时采集场景,满足人脸注册认证的真实性和安全性要求,活体判断的前置条件是图像中有人脸。
参考自: https://modelscope.cn/models/damo/cv_manual_face-liveness_flir/summary
本模型基于 ServiceBoot微服务引擎 开发,参见: 《CubeAI模型开发指南》 。
本模型可发布至 CubeAI智立方平台 进行共享和部署,参见: 《CubeAI模型发布指南》 。
本模型还可直接基于git源代码在本机进行部署和运行,参见: 《CubeAI模型独立部署指南》 或 CubeAI Docker Builder 。
更多CubeAI模型样例请参见: 《CubeAI模型示范库》 。
测试和演示本模型时,如果需要用到视频流媒体服务,其环境搭建可参见:
本模型提供了4个API接口:
API接口1:
API端点: /api/data
HTTP方法: POST
HTTP请求体:
{
"action": "predict",
"args": {
"img": <压缩图像的base64编码字符串(或其Data URL表示)>
}
}
HTTP响应体:
{
"status": "ok"|"err",
"value": [<results>, <base64编码压缩图像URL>]
}
API接口2:
API端点: /api/data
HTTP方法: POST
HTTP请求体:
{
"action": "predict_video",
"args": {
"url": <云端视频流媒体URL, 例如: rtmp://localhost/live/ch1>
}
}
HTTP响应体:
{
"status": "ok"|"err",
"value": <(流媒体当前帧图像)AI处理结果的base64编码压缩图像URL>
}
API接口3:
API端点: /api/stream/predict
HTTP方法: POST
HTTP请求体: <二进制编码的压缩图像字节流>
HTTP响应体: 同API接口1
API接口4:
API端点: /api/file/predict
HTTP方法: POST
HTTP请求体: <用于HTTP文件上传的XHR格式请求体>
HTTP响应体: 同API接口1
No Description
Python Shell Dockerfile Text other
Dear OpenI User
Thank you for your continuous support to the Openl Qizhi Community AI Collaboration Platform. In order to protect your usage rights and ensure network security, we updated the Openl Qizhi Community AI Collaboration Platform Usage Agreement in January 2024. The updated agreement specifies that users are prohibited from using intranet penetration tools. After you click "Agree and continue", you can continue to use our services. Thank you for your cooperation and understanding.
For more agreement content, please refer to the《Openl Qizhi Community AI Collaboration Platform Usage Agreement》