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There are multiple ways to solve the problem of running near-real-time analysis on video streams.We will start by outlining three approaches in increasing levels of sophistication.In our final "producer-consumer" system, we have a producer thread that looks very similar to our previous infinite loop.However, instead of consuming analysis results as soon as they are available, the producer simply puts the tasks into a queue to keep track of them.By doing this, we can visualize the detected face immediately, and then update the emotions later once the API call returns.

As with all the Cognitive Services, Developers developing with our APIs and samples are required to follow the "Developer Code of Conduct for Microsoft Cognitive Services." The image, voice, video or text understanding capabilities of Video Frame Analyzer uses Microsoft Cognitive Services.

Our maximum frame-rate is limited by the latency of the API calls.

While a simple single-threaded loop makes sense for a lightweight client-side algorithm, it doesn't fit well with the latency involved in cloud API calls.

In this guide, you learned how to run near-real-time analysis on live video streams using the Face, Computer Vision, and Emotion APIs, and how you can use our sample code to get started.

You can get started building your app with free API keys at the Microsoft Cognitive Services sign-up page.

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