Fatigue Driving Detection Based on Dlib

Overview

Fatigue-Driving-Detection-Based-on-Dlib

项目概述

项目版本V1.0

V1.0版本仅是软件部分的图像处理, 暂不涉及硬件控制. 硬件控制见V2.0版本(暂未发布)

本项目为本科毕业设计的相关工作, 于2020.05.01完工, 于2021.11.25整理发布(因为太忙还是太懒呢?)

本项目仅是作为理论分析的简单验证工具和毕业设计的实践训练, 在运行过程中的误差很大,其效果远远达不到实际应用的要求.

由于当时刚接触相关内容, 并且再次整理时已经间隔了一年半, 因此肯定存在设计和代码上的缺陷和漏洞, 欢迎大家积极交流

演示视频(00:20s - 00:60s):
https://www.bilibili.com/video/BV1By4y1k7PY?spm_id_from=333.999.0.0

博客地址: 会放到CSDN上(暂未发布)

###项目版本V2.0更新预告: 由树莓派进行人脸位置的检测, 获取位置后控制两个舵机形成二自由度云台追踪人脸
加入了PID舵机控制 + socket通信 + 视频流输出 + 视频堆栈防延迟
演示视频(00:00s - 00:20s):
https://www.bilibili.com/video/BV1By4y1k7PY?spm_id_from=333.999.0.0

#项目文件说明

capture_path: 所有驾驶人的全景图像 (仅采集, 未使用)
face_path: 所有驾驶人的人脸区域图像, 用于身份识别的训练
test_video: 测试视频所存放的文件夹
aspect_ratio_estimation.py: 计算EAR 和 MAR的程序
dlib-19.7.0-cp36-cp36m-win_amd64.whl: dlib的安装文件 drivers_img_acquire.py: 获取驾驶人全景图像和人脸区域的程序
Eigen_Face_Recognizer.py: 特征脸识别器文件, 用特征脸识别不同驾驶人身份(效果并不好, 仅作为理论分析)
get_everybody_EARandMAR_standard.py: 得到每个驾驶人的EAR和MAR基准
haarcascade_eye.xml: 用于检测人眼睛位置的Haar级联分类器文件
haarcascade_frontalface_alt.xml: 用于检测人脸部位置的Harr级联分类器文件
head_posture_estimation.py: 头部姿态估计文件
main.py: 主函数, 用于处理拍摄好的视频图像
shape_predictor_68_face_landmarks.dat: Dlib数据库文件\

如何运行该项目

在运行项目之前, 应确保你有用于测试的视频文件. 本项目中提供了一个视频例程(driving.mp4)

必须执行: 首先运行 drivers_img_acquire.py 文件, 输入当前驾驶人的名字英文缩写,获取不同驾驶人的两类图像

  • 获取的第一类图像为 摄像头全景图像, 默认存放于 './capture_path/{your name}'
  • 获取的第二类图像为 驾驶人人脸区域图像, 默认存放于 './face_path/{your name}'

其次运行main.py程序即可.

如何安装必要的依赖库

必要的库有: Dlib. 其依赖于 cmake 和 boost

  • 1.activate {your env}
  • 2.pip install cmake
  • 3.pip install boost
  • 4.cd 进入存放dlib-19.7.0-cp36-cp36m-win_amd64.whl的文件夹下(已准备好)
  • 5.pip install dlib-19.7.0-cp36-cp36m-win_amd64.whl
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