[log]brew install mongodb

本文详细介绍了如何在MacOS环境下通过Homebrew安装并配置MongoDB数据库服务,包括设置环境变量、创建数据目录、修改配置文件以及启动MongoDB服务的步骤。
brew install mongodb


A CA file has been bootstrapped using certificates from the SystemRoots
keychain. To add additional certificates (e.g. the certificates added in
the System keychain), place .pem files in
  /usr/local/etc/openssl/certs

and run
  /usr/local/opt/openssl/bin/c_rehash

openssl is keg-only, which means it was not symlinked into /usr/local,
because Apple has deprecated use of OpenSSL in favor of its own TLS and crypto libraries.

If you need to have openssl first in your PATH run:
  echo 'export PATH="/usr/local/opt/openssl/bin:$PATH"' >> ~/.bash_profile

For compilers to find openssl you may need to set:
  export LDFLAGS="-L/usr/local/opt/openssl/lib"
  export CPPFLAGS="-I/usr/local/opt/openssl/include"



A CA file has been bootstrapped using certificates from the SystemRoots
keychain. To add additional certificates (e.g. the certificates added in
the System keychain), place .pem files in
  /usr/local/etc/openssl/certs

and run
  /usr/local/opt/openssl/bin/c_rehash

openssl is keg-only, which means it was not symlinked into /usr/local,
because Apple has deprecated use of OpenSSL in favor of its own TLS and crypto libraries.

If you need to have openssl first in your PATH run:
  echo 'export PATH="/usr/local/opt/openssl/bin:$PATH"' >> ~/.bash_profile

For compilers to find openssl you may need to set:
  export LDFLAGS="-L/usr/local/opt/openssl/lib"
  export CPPFLAGS="-I/usr/local/opt/openssl/include"

==> mongodb
To have launchd start mongodb now and restart at login:
  brew services start mongodb  # 启动
Or, if you don't want/need a background service you can just run:
  mongod --config /usr/local/etc/mongod.conf





~ $ sudo mkdir -p /data/db
Password:
~ $ sudo chown -R $USER /data/db
~ $ nano /usr/local/etc/mongod.con



pgrep mongod  # 退出


~ $ brew services start mongodb
==> Successfully started `mongodb` (label: homebrew.mxcl.mongodb)


~ $ mongo
MongoDB shell version v4.0.3
connecting to: mongodb://127.0.0.1:27017
Implicit session: session { "id" : UUID("c2d0607c-1788-428d-9844-fb77192dce48") }
MongoDB server version: 4.0.3
Welcome to the MongoDB shell.
For interactive help, type "help".
For more comprehensive documentation, see
	http://docs.mongodb.org/

这个是参考,?

https://www.jianshu.com/p/d929436a4b7c

本指南详细阐述基于Python编程语言结合OpenCV计算机视觉库构建实时眼部状态分析系统的技术流程。该系统能够准确识别眼部区域,并对眨眼动作与持续闭眼状态进行判别。OpenCV作为功能强大的图像处理工具库,配合Python简洁的语法特性与丰富的第三方模块支持,为开发此类视觉应用提供了理想环境。 在环境配置阶段,除基础Python运行环境外,还需安装OpenCV核心模块与dlib机器学习库。dlib库内置的HOG(方向梯度直方图)特征检测算法在面部特征定位方面表现卓越。 技术实现包含以下关键环节: - 面部区域检测:采用预训练的Haar级联分类器或HOG特征检测器完成初始人脸定位,为后续眼部分析建立基础坐标系 - 眼部精确定位:基于已识别的人脸区域,运用dlib提供的面部特征点预测模型准确标定双眼位置坐标 - 眼睑轮廓分析:通过OpenCV的轮廓提取算法精确勾勒眼睑边缘形态,为状态判别提供几何特征依据 - 眨眼动作识别:通过连续帧序列分析眼睑开合度变化,建立动态阈值模型判断瞬时闭合动作 - 持续闭眼检测:设定更严格的状态持续时间与闭合程度双重标准,准确识别长时间闭眼行为 - 实时处理架构:构建视频流处理管线,通过帧捕获、特征分析、状态判断的循环流程实现实时监控 完整的技术文档应包含模块化代码实现、依赖库安装指引、参数调优指南及常见问题解决方案。示例代码需具备完整的错误处理机制与性能优化建议,涵盖图像预处理、光照补偿等实际应用中的关键技术点。 掌握该技术体系不仅有助于深入理解计算机视觉原理,更为疲劳驾驶预警、医疗监护等实际应用场景提供了可靠的技术基础。后续优化方向可包括多模态特征融合、深度学习模型集成等进阶研究领域。 资源来源于网络分享,仅用于学习交流使用,请勿用于商业,如有侵权请联系我删除!
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