这里还有一个选择
它比其他答案复杂得多。:-O
但是通过这种方式,只需一个连接到相机,您就可以将同一流同时“分叉”到多个多进程、屏幕、将其重播为多播、将其写入磁盘等。
..当然,以防万一您需要类似的东西(否则您更喜欢较早的答案)
让我们创建两个独立的python程序:
(1)服务器程序(rtsp连接、解码)server.py
(2) 客户端程序(从共享内存中读取帧)client.py
服务器必须在客户端之前启动,即
python3 server.py
然后在另一个终端:
python3 client.py
这是代码:
(1)服务器.py
import time
from valkka.core import *
# YUV => RGB interpolation to the small size is done each 1000 milliseconds and passed on to the shmem ringbuffer
image_interval=1000
# define rgb image dimensions
width =1920//4
height =1080//4
# posix shared memory: identification tag and size of the ring buffer
shmem_name ="cam_example"
shmem_buffers =10
shmem_filter =RGBShmemFrameFilter(shmem_name, shmem_buffers, width, height)
sws_filter =SwScaleFrameFilter("sws_filter", width, height, shmem_filter)
interval_filter =TimeIntervalFrameFilter("interval_filter", image_interval, sws_filter)
avthread =AVThread("avthread",interval_filter)
av_in_filter =avthread.getFrameFilter()
livethread =LiveThread("livethread")
ctx =LiveConnectionContext(LiveConnectionType_rtsp, "rtsp://user:password@192.168.x.x", 1, av_in_filter)
avthread.startCall()
livethread.startCall()
avthread.decodingOnCall()
livethread.registerStreamCall(ctx)
livethread.playStreamCall(ctx)
# all those threads are written in cpp and they are running in the
# background. Sleep for 20 seconds - or do something else while
# the cpp threads are running and streaming video
time.sleep(20)
# stop threads
livethread.stopCall()
avthread.stopCall()
print("bye")
(2)客户端.py
import cv2
from valkka.api2 import ShmemRGBClient
width =1920//4
height =1080//4
# This identifies posix shared memory - must be same as in the server side
shmem_name ="cam_example"
# Size of the shmem ringbuffer - must be same as in the server side
shmem_buffers =10
client=ShmemRGBClient(
name =shmem_name,
n_ringbuffer =shmem_buffers,
width =width,
height =height,
mstimeout =1000, # client timeouts if nothing has been received in 1000 milliseconds
verbose =False
)
while True:
index, isize = client.pull()
if (index==None):
print("timeout")
else:
data =client.shmem_list[index][0:isize]
img =data.reshape((height,width,3))
img =cv2.GaussianBlur(img, (21, 21), 0)
cv2.imshow("valkka_opencv_demo",img)
cv2.waitKey(1)
如果您有兴趣,请在https://elsampsa.github.io/valkka-examples/中查看更多内容