edgehub 0.0.4

A flexible and efficient cross process distributed edge computing engine.

edgehub

一款灵活高效的，跨进程分布式边缘计算引擎。

安装方式

pip3 install edgehub

使用示例

示例1

该示例由一个Master节点和两个Node节点组成。

Master负责消息传递，不负责运算。

两个Node，一个Node节点负责实时读入摄像头视频流，另一个Node节点负责展示前一个Node读到的视频流。

本示例需要安装python-opencv: pip3 install opencv-python

master.py

from edgehub import Master

if __name__ == '__main__':
    SERVER_IP = '127.0.0.1'
    SERVER_PROT = 9010
    m = Master("master", address=(SERVER_IP, SERVER_PROT), authkey=b'z', log_level="DEBUG")
    
    server = m.get_server()
    server.serve_forever()

node_cam_read.py

from edgehub import Node
import cv2


class CameraNode(Node):
    stream = Node
    camera_url = 1

    def before_run(self):
        self.stream = cv2.VideoCapture(self.camera_url)

    def on_queue_process(self, queue_name):
        (grabbed, frame) = self.stream.read()
        self.put(frame, queue_name)


if __name__ == '__main__':
    SERVER_IP = '127.0.0.1'
    SERVER_PROT = 9010
    n = CameraNode("cap_put", address=(SERVER_IP, SERVER_PROT), authkey=b'z')
    n.register_queue("cam")
    n.run()

node_cam_show.py

from edgehub import Node
import cv2


class CamGetNode(Node):
    def on_queue_process(self, queue_name):
        item = self.get(queue_name)
        cv2.imshow("queue_name", item)
        key = cv2.waitKey(1) & 0xFF
        if key == ord("q"):
            raise StopIteration()

        # 为了确保队列不积压 导致延迟递增  每次执行完后清空当前队列，这样每次处理时就会取到最新图片
        self.clear(queue_name)


if __name__ == '__main__':
    SERVER_IP = '127.0.0.1'
    SERVER_PROT = 9010
    n = CamGetNode(name="cam_get", address=(SERVER_IP, SERVER_PROT), authkey=b'z')
    n.register_queue("cam")
    n.run()