Posted by and 's . BlazePose is a high-fidelity body pose model designed specifically to support challenging domains like yoga, fitness and dance. It can detect 33 keypoints, extending the 17 keypoint topology of the original they can be used to unlock various domain-specific applications like without special hardware. With today’s release we enable developers to use the same models on the web that are powering unlocking the same great performance across all devices.
The new TensorFlow.js pose-detection API supports two runtimes: TensorFlow.js and MediaPipe. TensorFlow.js provides the flexibility and wider adoption of JavaScript, optimized for several backends including WebGL (GPU), WASM (CPU), and Node. MediaPipe capitalizes on WASM with GPU accelerated processing and provides faster out-of-the-box inference speed. The MediaPipe runtime currently lacks Node and iOS Safari support, but we'll be adding the support soon.
To use BlazePose with the new pose-detection API, you have to first decide whether to use the TensorFlow.js runtime or MediaPipe runtime. To understand the advantages of each runtime, check the performance and loading times section later in this document for further details. For each runtime, you can use either script tag or NPM for installation. Using TensorFlow.js runtime: Using MediaPipe runtime: Once the package is installed, you only need to follow the few steps below to start using it. There are three variants of the model: lite, full, and heavy. The model accuracy increases from lite to heavy, while the inference speed decreases and memory footprint increases. The heavy variant is intended for applications that require high accuracy, while the lite variant is intended for latency-critical applications. The full variant is a balanced option, which is also the default option here. Using TensorFlow.js runtime: Using MediaPipe runtime: You can also choose the lite or the heavy variant by setting the Each pose contains 33 keypoints, with absolute x, y coordinates, confidence score and name: Refer to our ReadMe () for more details about the API. As you begin to play and develop with BlazePose, we would appreciate your . If you make something using this model, tag it with ,. For pose estimation, we utilize our . Using a detector, this pipeline first locates the pose region-of-interest (ROI) within the frame. The tracker subsequently predicts all 33 pose keypoints from this ROI. Note that for video use cases, the detector is run only on the first frame. For subsequent frames we derive the ROI from the previous frame’s pose keypoints as discussed below. Since the and ML Kit. For detailed information, read the . TensorFlow.js continuously seeks opportunities to bring the latest and fastest runtime for browsers. To achieve the best performance for this BlazePose model, in addition to the TensorFlow.js runtime (w/ WebGL backend) we further integrated with the MediaPipe runtime via the , which also powers Google products such as . You can switch the model type and runtime live in the demo UI to see what works best for your device. Loading times: Bundle size can affect initial page loading experience, such as . Also, if your application is currently using TensorFlow.js, you don’t need to load those packages again, models will share the same TensorFlow.js runtime. Choose the runtime that best suits your latency and bundle size requirements. A summary of loading times and bundle sizes is provided below: Bundle Size gzipped + minified Average Loading Time WiFi: download speed 100Mbps MediaPipe Runtime Initial Page Load 22.1KB 0.04s Initial Detector Creation: Runtime 1.57MB Lite model 10.6MB 1.91s Full model 14MB 1.91s Heavy model 34.9MB 4.82s TensorFlow.js Runtime Initial Page Load 162.6KB 0.07s Initial Detector Creation: Lite model 10.4MB 1.91s Full model 13.8MB 1.91s Heavy model 34.7MB 4.82s Bundle size and loading time analysis for MediaPipe and TF.js runtime. The loading time is estimated based on a simulated WiFi network with 100Mbps download speed and includes time from request sent to content downloaded, see what is included in more detail 3D pose. We also plan to speed up the TensorFlow.js WebGL backend to make model execution even faster. This will be achieved through repeated benchmarking and backend optimization, such as operator fusion. We will also bring Node.js support in the near future. We would like to acknowledge our colleagues, who participated in creating BlazePose GHUM 3D: , , , along with those involved with the TensorFlow.js pose-detection API: , . Thematisch verwandte Begriffe: High, Fidelity, Pose, Tracking · 6 Treffer Videos werden geladen ... Beiträge werden geladen ... Videos werden geladen ... Beiträge werden geladen ... Videos werden geladen ... Beiträge werden geladen ... Videos werden geladen ... Beiträge werden geladen ... Videos werden geladen ...
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Reiche interessante Links, Zero-Days oder Debatten ein. Die Community entscheidet per Upvote über die Veröffentlichung.BlazePose can track 33 keypoints across a variety complex poses in real-time. Installation
<script src="https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-core"></script>
<script src="https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-converter"></script>
<script src="https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-backend-webgl"></script>
<script src="https://cdn.jsdelivr.net/npm/@tensorflow-models/pose-detection"></script>yarn add @tensorflow/tfjs-core, @tensorflow/tfjs-converter
yarn add @tensorflow/tfjs-backend-webgl
yarn add @tensorflow-models/pose-detection<script src="https://cdn.jsdelivr.net/npm/@mediapipe/pose"></script>
<script src="https://cdn.jsdelivr.net/npm/@tensorflow-models/pose-detection"></script>yarn add @mediapipe/pose
yarn add @tensorflow-models/pose-detectionTry it yourself!
// Import TFJS runtime with side effects.
import '@tensorflow/tfjs-backend-webgl';
import * as poseDetection from '@tensorflow-models/pose-detection';
// Create a detector.
const detector = await poseDetection.createDetector(poseDetection.SupportedModels.BlazePose, {runtime: 'tfjs'});// Import MediaPipe runtime with side effects.
import '@mediapipe/pose';
import * as poseDetection from '@tensorflow-models/pose-detection';
// Create a detector.
const detector = await poseDetection.createDetector(poseDetection.SupportedModels.BlazePose, {runtime: 'mediapipe'});modelType field, as shown below: // Create a detector.
const detector = await poseDetection.createDetector(poseDetection.SupportedModels.BlazePose, {runtime, modelType:'lite'});// Pass in a video stream to the model to detect poses.
const video = document.getElementById('video');
const poses = await detector.estimatePoses(video);console.log(poses[0].keypoints);
// Outputs:
// [
// {x: 230, y: 220, score: 0.9, name: "nose"},
// {x: 212, y: 190, score: 0.8, name: "left_eye_inner"},
// ...
// ]BlazePose Browser Performance
Acknowledgements
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