The Red Eye project is a service definition rapper around inexpensive cameras attached to inexpesive micro-controllers that take pretty good video.
The video streams are built using the GStreamer library. The camera native capabilities are used to extract as high quailty, realtime images possible.
The video streams will be consumed by one or more sinks, including but not limited to Files, cloud storage, High Def Display and Computer Vision libraries.
Multiple Live Streams
A really cool feature of the Raspberry Pi Camera, that we will be explioting is it’s ability to deliver multiple streams of video at the same time, such as different resolutions, snapshots and such.
High Def Live Viewing
The PiCamera has the capability of streaming multiple streams, we will take advantage of this by having the camera send a High Def stream for real time display and to be archived and replayed.
Low Definition To OpenCV
Computer Vision algorithms can benefit by working with lower resolution versions of live images. The algorithms calculate with a small number of pixels and go faster.
Snapshots
There are a number of reasons we may want to take an store snapshots to capture various events, we’ll get into that later, but for now the requirements we will need to take snapshots and save them.
Camera Control Inputs
A control channel is available to modify camera behavior during operations, which may include changing configuration, start and stop video streams, and so.
The control inputs can be the result of a REST Command, MQTT message or Websockets interface.
Streaming Video
The video streams are produced by the camera in H.264, so we need to do a little bit of work to get the video into multicast video streams.
Multicast RTP
The video streams produced by the video will need a little turning them into video streams of some sort. Our scheme is such that:
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Video streams may be recieved by more than one consumer
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We do not want Video producer to have to know who is recieving and when.. Multicast will just be sent as long as there is any consumer.
We will need to piece together the series of filters to recieve the video from Rasberry Pi, to gstreamer that will turn the video into UDP, RTP, RTSP multicast streams.
Video Consumers
The Video is streamed for live consumption as well as storage and retrieval. For this, high definition video is likely to be needed.
Video will also be consumed by computer vision algorithms, in this case, the lower resolution images help the algorithms plow through pixels much faster.
Snapshot can be extracted from a video stream.
Control Communications
The camera has a rich set of configuration options and modes that it can operate under. We are wrapping the configuration options, as well as control time commands like starting and stoping streams.
Additional commands may be set to start time lapse snapshots and quick auto-sequence shots. The control commands can be set off throught the following API channels:
- REST
- MQTT
- Websockets
All control channels flowing into a single Interface / API with the explicit goal to maintain consistency regardless of the channel used to communicate controls.
Tactile Controls
The GPIO pins can be used to trigger camera events, simple things like pushing a button to take a snapshot, or triggering a snapshot when a switch is flipped, for example.
Software Library Dependencies
- PiCamera to operate the camera
- Flask for REST and Web Interface
- Paho MQTT for Control Bus
- gstreamer to stream captured video
- raspberry Pi gstreamer src plugin
- gstreamer RTP/RTSP streaming
- Cloud Libraries to save data offline storage