This package conatains a KVS producer binding in Python to upload H.264 ES from DeepLens cameras and NUUO NVRs.
This is the back end code base to interface with devices, VMS and AWS/KVS.
Production code must not hard-code AWS credentials but use execution roles instead.
Install from NECLA-ML anaconda repo:
conda install -c necla-ml ml-ws
- RTSP streaming pipeline with GStreamer and RTCP time sync
import logging from time import time from datetime import datetime from ml.gst import RTSPPipeline, MESSAGE_TYPE, RTSP_CONFIG logging.basicConfig( format='%(asctime)s %(levelname)-8s %(message)s', level=logging.INFO, datefmt='%Y-%m-%d %H:%M:%S') # pipeline config cfg = RTSP_CONFIG( location = 'rtsp://server_address:554/path', latency = 5000, protocols = 'tcp', # tcp | udp | http | udp_mcast | unknown | tls encoding = 'H264', # h264 | h265 encoding_device = 'cpu', # cpu | gpu framerate = 10, scale = (720, 1280) # (H, W) ) # init pipeline pipeline = RTSPPipeline(cfg) # start pipeline pipeline.start() frame_idx = 0 try: while True: msg_type, message = pipeline.read() if msg_type == MESSAGE_TYPE.FRAME: frame = message.data timestamp = message.timestamp duration = message.duration timestamp_frame = datetime.utcfromtimestamp(timestamp).strftime('%m-%d-%Y %H:%M:%S') timestamp_now = datetime.utcfromtimestamp(time()).strftime('%m-%d-%Y %H:%M:%S') logging.info(f'Frame: {frame_idx} | Current: {timestamp_now} | Frame: {timestamp_frame}') frame_idx +=1 elif msg_type == MESSAGE_TYPE.EOS: raise message elif msg_type == MESSAGE_TYPE.ERROR: raise message else: print('Unknown message type') break except KeyboardInterrupt: logging.info('Stopping stream...') except Exception as e: logging.error(f'Error: {e}') pipeline.close()
After logged in to the Deeplens cam, enable port 8883 for Greengrass:
sudo ufw allow 8883/tcp
Grant permission to access /dev/video*:
sudo usermod -a -G video aws_cam
The streaming upload is provided by the producer SDK from Amazon. The local development instructions builds the SDK for Python bindings.
For better streaming performance, it is recommended to configure the camera encoder settings beforehand. For example, reducing the GOP seems to reduce the rendering delay on Amazon KVS console. The DeepLens camera encoder settings can be configured by the following command:
sudo /opt/awscam/camera/installed/bin/mxuvc --ch 1 [resolution | framerate | gop | bitrate | framesize | ...] sudo /opt/awscam/camera/installed/bin/mxuvc --ch 1 resolution 1280 720 sudo /opt/awscam/camera/installed/bin/mxuvc --ch 1 framerate 15 sudo /opt/awscam/camera/installed/bin/mxuvc --ch 1 gop 15 sudo /opt/awscam/camera/installed/bin/mxuvc --ch 1 bitrate 2000000
In practice, continuous streaming should be deployed as a system service to start on boot as follows. The user may be specified and the stream name is automatically captured from the device serial number. The service deployment should be done on the device.
make deploy SVC=deeplens
To manually control the service:
make [start | stop | restart | status | log]
See examples in tests/ for the API usage.
The Python binding wraps and simplifies the KVS SDK APIs for streaming. The following code segment shows the setup to upload a video stream for 60s:
from ws.streaming import Producer streamer = Producer(resolution=720, fps=15, gop=15, bitrate=2000000) streamer.connect('MyKVStream') streamer.upload(60) streamer.disconnect()
To utilize GPUs and compile CUDA modules, additional GPU packages are necessary:
cudatoolkitas a dependency ofpytorchshould have been installedcudatoolkit-devrequires extra space >=16GB for installation
To contribute to this project, follow the development flow:
-
Fork this repo in the beginning
-
Uninstall WS/ML through
conda remove --force ml ws -
Install/Build dependencies:
conda install -c necla-ml ml kvs-producer-sdk -
Switch to the
devbranch for development and testing followed by merge back tomainmake pull # Pull submodules recursively make dev-setup # Switch to dev branch and build the package for local installation git commit ... # Check in modified files git push # Push to the dev branch on the repo make merge # Merge back to the main branch and make a pull request afterwards