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本项目的人脸识别是基于业内领先的C++开源库dlib中的深度学习模型,用Labeled Faces in the Wild人脸数据集进行测试,有高达99.38%的准确率。但对小孩和亚洲人脸的识别准确率尚待提升。
本模型改编自GitHub开源项目: https://github.com/ageitgey/face_recognition 。
本模型基于 ServiceBoot微服务引擎 进行服务化封装,参见: 《CubeAI模型开发指南》
$ sh pip-install-reqs.sh
$ serviceboot start
或
$ python3 run_model_server.py
一键式本地容器化部署和运行,参见: 《CubeAI模型独立部署指南》 或 CubeAI Docker Builder
本模型服务可一键发布至 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": "add_face"
"args": {
"name": <姓名>,
"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": 1(添加人脸成功)|0(图像中无人脸或有多余一个人脸)|-1(已经存在相似人脸)
}
API接口2:
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": [<识别结果>, <gps信息>, <带姓名标注的base64编码图像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:
{
"status": "ok"|"err",
"value": [<识别结果>, <gps信息>, <带姓名标注的base64编码图像URL>]
}
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:
{
"status": "ok"|"err",
"value": [<识别结果>, <gps信息>, <带姓名标注的base64编码图像URL>]
}
dlib人脸识别
TypeScript Python HTML Shell Dockerfile other
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