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GeezerVison is showing github.com — GitHub - tensorflower/seetaFace6Python: 简单、快速搞定人脸识别应用,觉得有帮助,给个start吧!

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asserts
asserts
 
 
seetaface
seetaface
 
 
face_age_demo.py
face_age_demo.py
 
 
face_anti_spoofing_demo.py
face_anti_spoofing_demo.py
 
 
face_detect_demo.py
face_detect_demo.py
 
 
face_gender_demo.py
face_gender_demo.py
 
 
face_landmark_demo.py
face_landmark_demo.py
 
 
face_pose_ex_demo.py
face_pose_ex_demo.py
 
 
face_recon_demo.py
face_recon_demo.py
 
 
face_track_demo.py
face_track_demo.py
 
 
readme.md
readme.md
 
 

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seetaFace6 python api(10分钟搞定商业级别人脸识别应用)

1. 简介

项目基于SeetaFace6 封装的python接口,使用简便,性能与原始c++模块基本一致, 模块上SeetaFace6 相较于SeetaFace2 上训练样本更多,准确率更高,提供的功能和识别模型也更多 接口封装上,放弃了使用 pybind11 封装python 接口,接口函数完全纯 c 接口,使用 ctypes 调用,解除不同版本python使用上的限制 10分钟搞定是夸张说法,但本次项目基本涵盖了普通商用人脸识别所需的大部分功能,并且使用简单。

支持 windows/linux 下的所有python 版本

  python3.5及以下版本的python需要将seetaface/back_py目录下的 api.py 和 face_struct.py 两个文件放到setaface目录下,替换setaface目录下的api.py 和 face_struct.py 文件

2.下载模型(已下载则忽略)

百度网盘:https://pan.baidu.com/share/init?surl=LlXe2-YsUxQMe-MLzhQ2Aw 提取码:ngne
将下载的所有 *.csta 模型文件 放入 seetaFace6Python/seetaface/model 目录下

3. 运行示例

3.1 运行依赖

当前需要机器同时支持以下几个指令集 AVX | SSE | FMA ,请先确认机器是否同时支持它们

    示例依赖 opencv ,安装opencv(若已有cv2模块则忽略)

    pip install -i https://pypi.tuna.tsinghua.edu.cn/simple opencv-python

3.2 演示demo

  linux下则需要添加库路径

    临时:
        ubuntu:
            export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:${seetaFace6Python目录路径}/seetaface/lib/ubuntu
        centos:
            export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:${seetaFace6Python目录路径}/seetaface/lib/centos
    永久:
        ubuntu:
            sudo echo  ${seetaFace6Python目录路径}/seetaface/lib/ubuntu  > /etc/ld.so.conf.d/seetaface6.conf
        centos:
            sudo echo  ${seetaFace6Python目录路径}/seetaface/lib/centos  > /etc/ld.so.conf.d/seetaface6.conf
        
        sudo ldconfig
    
    进入 seetaFace6Python 目录下,有各种基本功能的使用demo

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