树莓派打卡
时间: 2025-05-19 15:11:44 浏览: 19
### 基于树莓派的打卡项目实现
#### 项目概述
基于树莓派的打卡项目通常涉及硬件与软件两部分的设计。硬件方面主要依赖摄像头模块用于人脸识别,而软件则通过Python编程语言结合OpenCV库来完成核心算法[^1]。
#### 软件环境搭建
为了在树莓派上运行打卡程序,需先配置好基础开发环境。如果使用的是无图形界面的操作系统(如Ubuntu Server),可以通过命令行工具安装必要的依赖项[^2]:
```bash
sudo apt update && sudo apt upgrade -y
sudo apt install python3-pip python3-opencv chromium-browser -y
pip3 install selenium opencv-python numpy
```
上述脚本完成了以下操作:
- 更新并升级系统的包管理器;
- 安装`python3-pip`作为Python包管理工具;
- 配置`opencv-python`以及科学计算所需的`numpy`库;
- 添加浏览器驱动支持以便后续可能扩展至网页自动化功能[^2]。
#### 功能模块分析
##### 主页面设计
主页面可以采用轻量级Web框架Flask构建,提供给用户访问入口的同时也便于远程监控设备状态。例如,在HTML模板文件夹下创建index.html定义布局样式,并由对应的路由函数渲染返回[^1]:
```html
<!-- templates/index.html -->
<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="UTF-8">
<title>Face Attendance System</title>
</head>
<body>
<h1>Welcome to Face Recognition Attendance Management Platform!</h1>
<p>Please enter the verification code below:</p>
<form action="/verify" method="post">
<input type="text" name="code"/>
<button type="submit">Submit Code</button>
</form>
</body>
</html>
```
##### 验证码识别逻辑
验证码验证环节可通过简单的哈希加密方式处理输入字符串并与预存密钥对比确认身份合法与否。此过程应尽可能简化交互流程减少延迟影响用户体验[^1]:
```python
from flask import Flask, request, render_template_string
app = Flask(__name__)
@app.route('/')
def index():
return render_template_string(open('templates/index.html').read())
@app.route('/verify', methods=['POST'])
def verify_code():
entered_code = request.form.get('code')
correct_key = 'your_secret_hashed_value'
if hash(entered_code) == int(correct_key):
# Assuming hashed value stored as integer form here.
return '<h2>Congratulations! You have been authenticated successfully.</h2>'
else:
return '<h2>Error: Invalid Verification Code Provided.</h2>', 403
if __name__ == '__main__':
app.run(host='0.0.0.0', port=5000)
```
##### 人脸采集与训练模型
利用OpenCV读取视频流捕获多张图片保存到指定路径形成样本集;之后调用LBPH脸部检测分类器进行学习生成xml权重文档供预测阶段加载应用[^1]:
```python
import cv2
import os
face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + "haarcascade_frontalface_default.xml")
cap = cv2.VideoCapture(0)
count = 0
while True:
ret, frame = cap.read()
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
faces = face_cascade.detectMultiScale(gray, scaleFactor=1.5, minNeighbors=5)
for (x,y,w,h) in faces:
roi_gray = gray[y:y+h, x:x+w]
img_item = f"user_{str(count)}.png"
cv2.imwrite(os.path.join('dataset/',img_item),roi_gray)
color = (255,0,0) #blue rectangle around detected area
stroke = 2
end_cord_x = x + w
end_cord_y = y + h
cv2.rectangle(frame,(x,y),(end_cord_x,end_cord_y),color,stroke)
count +=1
cv2.imshow('frame',frame)
k=cv2.waitKey(30)&0xff
if k==27 or count>=30:#press ESC key when done collecting enough samples
break
cap.release()
cv2.destroyAllWindows()
recognizer = cv2.face.LBPHFaceRecognizer_create()
images_path_list=os.listdir('./dataset/')
labels=[int(img.split('_')[1].split('.')[0])for img in images_path_list]
faces=[]
for image_name in images_path_list:
current_image=cv2.imread(f"./dataset/{image_name}",cv2.IMREAD_GRAYSCALE)
faces.append(current_image)
recognizer.train(faces,np.array(labels))
recognizer.save("trainer.yml")
print("[INFO] Training Completed Successfully!")
```
##### 数据推送机制
对于实时更新的需求,考虑借助WebSocket协议保持客户端和服务端之间持久连接从而即时传递最新记录详情[^1]。
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