The nerve arises in the pons, an area of the brainstem. Extracranial – the course of the nerve outside the cranium, through the face and neck.Intracranial – the course of the nerve through the cranial cavity, and the cranium itself.There are many branches, which transmit a combination of sensory, motor and parasympathetic fibres.Īnatomically, the course of the facial nerve can be divided into two parts: The course of the facial nerve is very complex. translate (x, y ) // rotate the canvas to the specified degrees this. save ( ) // move to the center of the canvas this. getContext ( '2d' ) async function initCamera ( ) = options // save the unrotated context of the canvas so we can restore it later // the alternative is to untranslate & unrotate after drawing this.
querySelector ( 'canvas' ) const ctx = canvasEl ?. querySelector ( 'video' ) const canvasEl = document. main.ts import * as poseDetection from import import './style.css' const videoEl = document.
Skeleton head draw install#
Let's install the TensorFlow.js dependencies so we can use it in our project. We decided to go with BlazePose because we found it to have good performance, and it provides additional tracking points that could be useful. The TensorFlow.js pose detections models can be found here: Īs you can see, they have 3 models to choose from, each of which has pros and cons. In addition to the pre-built models, you can train your own, but let's save that for another article! You can take a look at the available models here: There are loads of pre-built models that can achieve all sorts of tasks from object detection to speech recognition, and thankfully for us, pose detection. TensorFlow.js is a library that enables machine learning within the browser. Don't I need to be genius AI, machine learning guru to be able to do pose detection?" That's very humble of you but don't put yourself down like that! TensorFlow.js make all of this very clever AI stuff extremely accessible to any frontend developer! Ok, here comes the fun part! Now that our webcam is up and running and we have a way to draw over the top of the video feed, it's time to use AI to analyse the video feed and detect any poses skeletons within the video.Īt this point your might be thinking: "Jozef, I am just a lowly frontend developer. Pose detection with TensorFlow.js and BlazePose Hopefully, by this point, you should be seeing your face appearing on the page! Once all that's set up, it's time to add the stream to the video by assigning it to the video element's srcObject property.
By adding an onloadmetadata event listener to the video element that resolves our promise, we can ensure we don't use the video before it is ready. After assigning a video a media stream, it takes time to initialise everything and actually load the stream. Before we do that, we make a new promise that will allow us to wait for the video to completely load before resolving. The bigger the video, the more resource-intensive the pose detection will be, so 640x480 is a happy medium.Īfter getting the stream, we can then assign it to the video element we created earlier. In this case, we have chosen to limit it to 640x480 if possible so that the pose detection will be efficient. This tells the browser the ideal dimensions of the video we want to receive. We also specify width and height parameters.
On mobile devices, this should mean the selfie camera is selected by default. Specifying the facingMode property in the video object lets the browser know that it should choose the camera facing the user if there are multiple cameras. We pass an options object to the getUserMedia function. When we call the initCamera function, the user will be prompted to allow access to their webcam.
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