简单易用的Python摄像头工具包，支持本地显示和网页显示，提供丰富的图像处理效果
Project description
AIToolkit Camera - 简易摄像头工具包
aitoolkit_cam 是一个针对Python的简单易用的摄像头工具包，让摄像头开发变得轻松简单。无论您是教育工作者还是学生，都可以通过几行代码轻松实现摄像头功能。
核心特点
- 🌟 简单易用：几行代码即可启动摄像头和网页服务
- 🌐 网页实时查看：支持通过浏览器远程查看摄像头画面
- 🔄 迭代器接口：兼容Python迭代器，可在for循环中使用
- 🖼️ 图像处理：支持基础图像处理功能
- 🔌 资源管理：自动释放摄像头资源
安装方法
pip install aitoolkit-cam
基础用法
简单示例
from aitoolkit_cam import Camera
# 创建摄像头对象
cam = Camera()
cam.web_enabled = True # 启用网页服务
# 启动摄像头
cam.start()
# 获取访问地址
url = cam.get_web_url()
print(f"访问地址: {url}")
print("请在浏览器中访问上述地址")
try:
# 循环获取视频帧并在网页显示
for frame in cam:
cam.cv_show(frame, "web")
except KeyboardInterrupt:
print("正在退出...")
finally:
# 释放资源
cam.stop()
Jupyter Notebook中使用
from aitoolkit_cam import Camera
import threading
import time
# 全局变量
cam = None
running = True
def stop_camera():
"""停止摄像头"""
global cam, running
running = False
if cam:
print("正在停止摄像头...")
cam.stop()
time.sleep(0.5)
print("摄像头已停止")
return "摄像头已停止，资源已释放"
def camera_loop(camera):
"""摄像头循环"""
global running
try:
for frame in camera:
if not running:
break
camera.cv_show(frame, "web")
except Exception as e:
print(f"摄像头循环错误: {e}")
finally:
if running:
running = False
camera.stop()
def start_camera():
"""启动摄像头"""
global cam, running
# 如果已有运行实例，先停止
if cam and running:
stop_camera()
running = True
# 创建摄像头对象
cam = Camera()
cam.web_enabled = True
# 启动摄像头
print("正在启动摄像头和网页服务...")
start_time = time.time()
cam.start()
# 获取地址
url = cam.get_web_url()
print(f"启动耗时: {time.time() - start_time:.2f}秒")
print(f"访问地址: {url}")
# 在后台线程中运行摄像头循环
thread = threading.Thread(target=camera_loop, args=(cam,), daemon=True)
thread.start()
print("摄像头已在后台运行")
print("使用 stop_camera() 函数停止摄像头")
return url
# 使用方法：
# 1. 运行 start_camera() 启动摄像头
# 2. 使用返回的URL访问摄像头画面
# 3. 完成后运行 stop_camera() 释放资源
高级用法
使用反向代理解决端口变化问题
当需要在前端页面或其他应用中嵌入摄像头画面时，可以使用反向代理保持URL稳定：
1. 安装Nginx
# 在Ubuntu/Debian上
sudo apt install nginx
# 在CentOS/RHEL上
sudo yum install nginx
# 在Windows上可以下载安装包
# http://nginx.org/en/download.html
2. 配置Nginx反向代理
创建或编辑Nginx配置文件（例如/etc/nginx/conf.d/camera.conf）：
server {
listen 80;
server_name your_server_name; # 修改为您的服务器名称或IP
location /camera/ {
proxy_pass http://localhost:8000/;
proxy_set_header Host $host;
proxy_set_header X-Real-IP $remote_addr;
proxy_set_header X-Forwarded-For $proxy_add_x_forwarded_for;
proxy_set_header X-Forwarded-Proto $scheme;
# WebSocket支持
proxy_http_version 1.1;
proxy_set_header Upgrade $http_upgrade;
proxy_set_header Connection "upgrade";
# 超时设置
proxy_read_timeout 86400;
}
}
3. 启动摄像头服务和Nginx
# 重启Nginx应用配置
sudo systemctl restart nginx
# 启动摄像头服务（端口固定为8000）
python -c "
from aitoolkit_cam import Camera
cam = Camera()
cam.web_enabled = True
cam.port = 8000
cam.start()
print(f'摄像头服务已启动: {cam.get_web_url()}')
input('按Enter键退出...')
cam.stop()
"
现在可以通过 http://your_server_name/camera/ 访问摄像头，无论底层摄像头服务端口如何变化。
在前端页面中嵌入摄像头画面
使用反向代理后，可以在HTML页面中嵌入摄像头画面：
<!DOCTYPE html>
<html>
<head>
<title>摄像头画面</title>
<style>
.camera-container {
width: 640px;
height: 480px;
margin: 0 auto;
border: 1px solid #ccc;
overflow: hidden;
}
.camera-feed {
width: 100%;
height: 100%;
object-fit: contain;
}
</style>
</head>
<body>
<div class="camera-container">
<img src="http://your_server_name/camera/video" class="camera-feed" alt="摄像头画面">
</div>
</body>
</html>
进阶功能
图像处理
from aitoolkit_cam import Camera, apply_effect
# 创建摄像头对象
cam = Camera()
cam.web_enabled = True
cam.start()
# 应用图像效果
for frame in cam:
# 应用灰度效果
processed = apply_effect(frame, "grayscale")
cam.cv_show(processed, "web")
使用上下文管理器
from aitoolkit_cam import Camera
# 使用with语句自动管理资源
with Camera() as cam:
cam.web_enabled = True
cam.start()
url = cam.get_web_url()
print(f"访问地址: {url}")
# 处理10帧后退出
count = 0
for frame in cam:
cam.cv_show(frame, "web")
count += 1
if count >= 10:
break
# with语句结束后自动释放资源
开发者信息
- 作者：[您的名字]
- 版本：0.2.2
- 许可证：MIT
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欢迎提交问题和贡献代码以改进这个项目！
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