- #Amazon chime sdk js npm how to#
- #Amazon chime sdk js npm movie#
- #Amazon chime sdk js npm pro#
- #Amazon chime sdk js npm code#
#Amazon chime sdk js npm how to#
We will also show you how to deploy the Amazon Chime SDK meeting chat demo app as a quick way to try out the features of the Amazon Chime SDK on your own. In this blog, we will extend the existing Amazon Chime SDK messaging demo chat app to incorporate instant meetings allowing all the members of a messaging channel to join a meeting that includes audio, video, and persistent chat. Since then, we have heard from customers asking how they could combine the two in order to have a shared audio, video, and messaging experience. In previous posts, we showed customers how to deploy a demo chat app and how to deploy a demo meeting app. DemoĪ demo implementing the simplified version of the center stage introduced here is available in the following repository.The Amazon Chime SDK is a set of real-time communications components that developers can use to quickly add messaging, audio, video, and screen sharing capabilities to their applications. The implementation is also available in the demo repository described below, so please refer to that for more concrete details. private targetCanvas: HTMLCanvasElement = document.createElement('canvas') private targetCanvasCtx: CanvasRenderingContext2D = ('2d')! private canvasVideoFrameBuffer = new CanvasVideoFrameBuffer(this.targetCanvas) private manager = new BlazefaceWorkerManager() // => įor details on how to register VideoFrameProcessor with the Video Processor API, please refer to the official documentation. In this case, we want to run the program in asm on the webworker, so we set the backendType to asm and processOnLocal to false (3). config and params configure the behavior of the manager (2). Initialize the various members to be used in this method. VideoFrameProcessor requires the implementation of a process method to process the video. The above npm module provides methods for detecting faces and calculating the trimming position, so it is very easy to implement using these methods. To implement this functionality in your application, you will need to implement the VideoFrameProcessor of the Video Processor API. The processing time per frame is displayed in the upper left corner, and you can see that Wasm is processing faster. In addition, try switching between WebGL and Wasm in the “backend-selector” pull-down menu. The left side is the original image, and the right side is the image with the face cut out with the simplified center frame. Select “tracking” from the “application mode” pull-down menu on the right side of the demo page.
The demo provided here can be made to behave in the same way as the center stage.
#Amazon chime sdk js npm code#
The source code and a demo of this module are also available, so if you are interested, please check the repository. I have published the npm module to run BlazeFace on WebWorker, so let’s use it this time to skip write troublesome codes. In order to run BlazeFace with WebWorker, it is necessary to implement a bit troublesome codes. (4) The trimmed video is sent to Amazon Chime’s server. The internal process of the Video Processor API (VideoFrameProcessor) requests processing from the Main Thread to the WebWorker, which locates the user’s face using BalzeFace and responds to the Main Thread. (2) Call the Video Processor API process in the Amazon Chime SDK for Javascript demo. In the figure below, the detected face is marked with a red frame, but a simplified center stage will crop to an area slightly larger than the red frame. This model can detect the position of a face in real time in a camera image, as shown in the figure below. BlazeFaceįace detection uses machine learning models, and Mediapipe offers a lightweight and highly accurate model called BlazeFace. Unlike the original center stage, this functionality doesn’t use camera functions such as zoom and focus, so its speed and image quality are not as good as the original, but the advantage is that it does not require any special hardware in the browser. Specifically, this function will detect faces from the webcam video and crop the area around them in the Amazon Chime SDK for Javascript application. In this article, we will try to create a simplified version of a center stage functionality using an ordinary webcam and a machine learning model.
#Amazon chime sdk js npm movie#
The sample movie captured with Center Stage is shown this page on gizmodo(Japanese). I don’t have detail information about center stage, but I assume that this center stage utilizes ipad’s camera functions, such as zooming and focusing to get the video data. Zoom and FaceTime offer the functionality with center stage( Zoom, Facetime).
#Amazon chime sdk js npm pro#
Center Stage is a feature of the recent ipad pro camera that recognizes and automatically follows the user’s face.
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