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Teachable Machine Pose Model

<button type=“button” onclick=“init()”>Start</button>

  // More API functions here:
  // https://github.com/googlecreativelab/teachablemachine-community/tree/master/libraries/pose

  // the link to your model provided by Teachable Machine export panel
  const URL = "./my_model/";
  let model, webcam, ctx, labelContainer, maxPredictions;

  async function init() {
      const modelURL = URL + "model.json";
      const metadataURL = URL + "metadata.json";

      // load the model and metadata
      // Refer to tmImage.loadFromFiles() in the API to support files from a file picker
      // Note: the pose library adds a tmPose object to your window (window.tmPose)
      model = await tmPose.load(modelURL, metadataURL);
      maxPredictions = model.getTotalClasses();

      // Convenience function to setup a webcam
      const size = 200;
      const flip = true; // whether to flip the webcam
      webcam = new tmPose.Webcam(size, size, flip); // width, height, flip
      await webcam.setup(); // request access to the webcam
      await webcam.play();
      window.requestAnimationFrame(loop);

      // append/get elements to the DOM
      const canvas = document.getElementById("canvas");
      canvas.width = size; canvas.height = size;
      ctx = canvas.getContext("2d");
      labelContainer = document.getElementById("label-container");
      for (let i = 0; i < maxPredictions; i++) { // and class labels
          labelContainer.appendChild(document.createElement("div"));
      }
  }

  async function loop(timestamp) {
      webcam.update(); // update the webcam frame
      await predict();
      window.requestAnimationFrame(loop);
  }

  async function predict() {
      // Prediction #1: run input through posenet
      // estimatePose can take in an image, video or canvas html element
      const { pose, posenetOutput } = await model.estimatePose(webcam.canvas);
      // Prediction 2: run input through teachable machine classification model
      const prediction = await model.predict(posenetOutput);

      for (let i = 0; i < maxPredictions; i++) {
          const classPrediction =
              prediction[i].className + ": " + prediction[i].probability.toFixed(2);
          labelContainer.childNodes[i].innerHTML = classPrediction;
      }

      // finally draw the poses
      drawPose(pose);
  }

  function drawPose(pose) {
      if (webcam.canvas) {
          ctx.drawImage(webcam.canvas, 0, 0);
          // draw the keypoints and skeleton
          if (pose) {
              const minPartConfidence = 0.5;
              tmPose.drawKeypoints(pose.keypoints, minPartConfidence, ctx);
              tmPose.drawSkeleton(pose.keypoints, minPartConfidence, ctx);
          }
      }
  }

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