![]() ![]() For example, you can create a yolo inference application that serves an onnx model like this, and then wrap it up in a docker like this. All you need to do is to deploy a docker container that takes an image and spits out the inference. You can use Azure Machine learning yolo models, Intel OpenVino models, Azure Custom Vision models, or even Nvidia Deepstream models through the myriad of extensions available in Azure Video Analyzer. The first thing you need is the model you want to deploy and a service to go along with it. Your Azure subscription will be billed when the pipeline is in the active state. However, a pipeline can be active without data flowing through it (for example, the input video source goes offline). Upon deactivation, an active pipeline enters the “Deactivating” state and then “Inactive” state. Here it is.ĭata (live video) starts flowing through the pipeline when it reaches the “Active” state. One final concept that we need to understand before we jump into implementation is the actual state machine/pipeline lifecycle that is involved. The live pipeline can then be activated to enable the flow of data. A live pipeline enables you to provide values for parameters in a pipeline topology. This pipeline defines what nodes are used in the pipeline topology, and how they are connected within it. A pipeline enables you to define a blueprint of the pipeline topologies with parameters as placeholders for values. Sink nodes enable delivering the processing results to services and apps outside the pipeline topology.Īzure Video Analyzer on IoT Edge enables you to manage pipelines via two entities – “Pipeline Topology” and “Live Pipeline”.Processor nodes enable processing of media within the pipeline topology.Media in this context, conceptually, could be an audio stream, a video stream, a data stream, or a stream that has audio, video, and/or data combined together in a single stream. Source nodes enable capturing of media into the pipeline topology.The diagram below provides a graphical representation of a pipeline. Familiarize yourself with the terminology used in the process.īasically, a pipeline can have one or more of the following types of nodes: A pipeline consists of nodes that are connected to achieve the desired flow of data. An Azure Video Analyzer pipeline lets you define where input data should be captured from, how it should be processed, and where the results should be delivered. Not surprisingly, these state machines are called AVA ' Pipelines'. These worklfows are actually state machines (yes Automata!) that are provided by Azure to developers to do something like ETL on video data. Lying underneath all the shiny things are rigorous workflows that run 24x7 and that without AVA, can be any engineer's nightmare even on a good day. Here is a representation of how Azure envisions Enterprises to implement video analytics solutions at scale in the future. Get real-time analytics to create safer workspaces, optimal in-store experiences, and extract rich video insights with fully integrated, high-quality AI models. Again, it is highly encouraged to go through the first article for background and context.Īzure Video Analyzer builds on top of AI models, simplifying capture, orchestration, and playback for streaming and stored videos. Before proceeding it is assumed that you are aware of what we are doing (IVA), the architecture involved, met the prerequisites, and all the proper IoT edge modules are running along with the rtsp camera(s). Our goal is to build, ship, deploy computer vision models into the AI NVR and obviously see inference results coming out as events in a stream. In the first article I discussed how to setup a production-ready AI enabled Network Video Recorder using a cheap off-the-market intel 圆4 Ubuntu pc and Azure Video Analyzer. In the below sections we will expand upon our previous claim that AVA allows us to 'bring your own models' into the 'picture' very easily. This is the second in a series of articles ( part 1) which explore how to integrate Artificial Intelligence into a video processing infrastructure.
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