A flexible and efficient cross process distributed edge computing engine.
Project description
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.register_queue("cam")
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.run()
node_cam_show.py
from edgehub import Node
import cv2
class CamGetNode(Node):
def before_run(self):
self.register_queue("cam")
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.run()
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