Mqtt 协议实现在线视频会议

GA666666 2023-12-21 AM 723℃ 0条

简介
该教程介绍如何使用 Python 和 MQTT 实现一个简单的在线视频会议系统,其中包括推送端和订阅端。推送端通过摄像头捕获视频帧,使用 MQTT 将图像数据实时推送到指定主题。订阅端订阅相同的 MQTT 主题,接收图像并在本地显示,同时计算并显示帧率。

推送端

  1. 准备环境
    确保已经安装必要的 Python 库:

pip install opencv-python numpy paho-mqtt
1

  1. 推送端代码

    pusher.py

    import base64
    import time
    import multiprocessing
    import paho.mqtt.client as mqtt
    import numpy as np
    import cv2

MQTT配置

mqtt_broker = "localhost"
mqtt_port = 1883
mqtt_topic = "your_mqtt_topic"

初始化MQTT客户端

client = mqtt.Client()
client.connect(mqtt_broker, mqtt_port, 60)

def read_and_publish(output_queue):

cap = cv2.VideoCapture(2)
try:
    while True:
        ret, frame = cap.read()
        if ret:
            _, img_encoded = cv2.imencode('.jpg', frame)
            img_base64 = base64.b64encode(img_encoded.tobytes()).decode('utf-8')
            # 将base64图像放入队列
            output_queue.put(img_base64)
except Exception as e:
    print(f"Error: {e}")
finally:
    # Release the capture object when done
    cap.release()

def publish_to_mqtt(input_queue):

while True:
    if not input_queue.empty():
        img_base64 = input_queue.get()
        # 发布到MQTT通道
        client.publish(mqtt_topic, img_base64)

def main():

# 使用队列在主进程和子进程之间传递数据
output_queue = multiprocessing.Queue()
input_queue = multiprocessing.Queue()

# 启动子进程
process_read_publish = multiprocessing.Process(target=read_and_publish, args=(output_queue,))
process_mqtt_publish = multiprocessing.Process(target=publish_to_mqtt, args=(input_queue,))

# 启动子进程
process_read_publish.start()
process_mqtt_publish.start()

while True:
    start = time.time()

    # 从队列中获取数据
    while not output_queue.empty():
        img_base64 = output_queue.get()
        # 将数据放入用于MQTT发布的队列
        input_queue.put(img_base64)

    end = time.time()
    print(end - start)

# 不要忘记在结束时释放资源
process_read_publish.terminate()
process_mqtt_publish.terminate()
process_read_publish.join()
process_mqtt_publish.join()

if name == "__main__":

main()

订阅端

  1. 准备环境
    确保已经安装必要的 Python 库:

pip install opencv-python numpy paho-mqtt
1

  1. 订阅端代码

    subscriber.py

    import time
    import cv2
    import numpy as np
    import base64
    import paho.mqtt.client as mqtt
    import concurrent.futures
    from multiprocessing import Process, Queue

MQTT配置

mqtt_broker = "localhost"
mqtt_port = 1883
mqtt_topic = "your_mqtt_topic"

共享变量,使用multiprocessing.Value来确保同步

global_fps = Value('f', 0)
start_time = time.time()
end_time = time.time()

def display_fps(frame):

global start_time
global end_time
end_time = time.time()
elapsed_time = end_time - start_time
print(end_time)
global_fps.value = 1 / elapsed_time

# 在图像上显示帧率
cv2.putText(frame, f"FPS: {global_fps.value:.2f}", (10, 30), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2, cv2.LINE_AA)
start_time = end_time

def image_receiver(output_queue):

# 初始化MQTT客户端
client = mqtt.Client()
client.connect(mqtt_broker, mqtt_port, 60)

# 设置消息接收回调
def on_message(client, userdata, msg):
    global start_time
    try:
        # 解码base64图像
        start_time = time.time()
        img_base64 = msg.payload.decode('utf-8')
        img_decoded = base64.b64decode(img_base64)
        img_np = np.frombuffer(img_decoded, dtype=np.uint8)
        img = cv2.imdecode(img_np, cv2.IMREAD_COLOR)

        display_fps(img, start_time)

        # 将图像放入队列
        output_queue.put(img)

    except Exception as e:
        print(f"Error: {e}")

client.on_message = on_message

# 订阅MQTT主题
client.subscribe(mqtt_topic)

# 开始循环,等待消息
client.loop_forever()

def main():

# 使用队列在主进程和子进程之间传递数据
output_queue = Queue()

# 创建子进程
receiver_process = Process(target=image_receiver, args=(output_queue,))
receiver_process.start()

while True:
    start = time.time()

    # 从队列中获取图像
    while not output_queue.empty():
        img = output_queue.get()

        # 显示图像
        cv2.imshow('Received Image', img)
        cv2.waitKey(1)

    end = time.time()
    print(end - start)

# 等待子进程结束
receiver_process.join()

if name == "__main__":

main()

在推送端运行 pusher.py。
在订阅端运行 subscriber.py。
现在,您应该能够在订阅端实时接收并显示推送端的视频流,同时计算并显示帧率

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