text: /**
   * @license
   * Copyright 2018 Google Inc. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licnses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  // import * as tf from '@tensorflow/tfjs-core';
  // import * as posenet from '../src';
  
  const color = 'aqua';
  const lineWidth = 2;
  
  function toTuple({ y, x }) {
    return [y, x];
  }
  
  /**
   * Draws a line on a canvas, i.e. a joint
   */
  function drawSegment([ay, ax], [by, bx], color, scale, ctx) {
    ctx.beginPath();
    ctx.moveTo(ax * scale, ay * scale);
    ctx.lineTo(bx * scale, by * scale);
    ctx.lineWidth = lineWidth;
    ctx.strokeStyle = color;
    ctx.stroke();
  }
  
  /**
   * Draws a pose skeleton by looking up all adjacent keypoints/joints
   */
  function drawSkeleton(keypoints, minConfidence, ctx, scale = 1) {
    const adjacentKeyPoints = posenet.getAdjacentKeyPoints(
      keypoints, minConfidence);
  
    adjacentKeyPoints.forEach((keypoints) => {
      drawSegment(toTuple(keypoints[0].position),
        toTuple(keypoints[1].position), color, scale, ctx);
    });
  }
  
  /**
   * Draw pose keypoints onto a canvas
   */
  function drawKeypoints(keypoints, minConfidence, ctx, scale = 1) {
    for (let i = 0; i < keypoints.length; i++) {
      const keypoint = keypoints[i];
  
      if (keypoint.score < minConfidence) {
        continue;
      }
  
      const { y, x } = keypoint.position;
      ctx.beginPath();
      ctx.arc(x * scale, y * scale, 3, 0, 2 * Math.PI);
      ctx.fillStyle = color;
      ctx.fill();
    }
  }
  
  /**
   * Draw the bounding box of a pose. For example, for a whole person standing
   * in an image, the bounding box will begin at the nose and extend to one of
   * ankles
   */
  function drawBoundingBox(keypoints, ctx) {
    const boundingBox = posenet.getBoundingBox(keypoints);
  
    ctx.rect(boundingBox.minX, boundingBox.minY,
      boundingBox.maxX - boundingBox.minX, boundingBox.maxY - boundingBox.minY);
  
    ctx.stroke();
  }
  
  /**
   * Converts an arary of pixel data into an ImageData object
   */
  async function renderToCanvas(a, ctx) {
    const [height, width] = a.shape;
    const imageData = new ImageData(width, height);
  
    const data = await a.data();
  
    for (let i = 0; i < height * width; ++i) {
      const j = i * 4;
      const k = i * 3;
  
      imageData.data[j + 0] = data[k + 0];
      imageData.data[j + 1] = data[k + 1];
      imageData.data[j + 2] = data[k + 2];
      imageData.data[j + 3] = 255;
    }
  
    ctx.putImageData(imageData, 0, 0);
  }
  
  /**
   * Draw an image on a canvas
   */
  function renderImageToCanvas(image, size, canvas) {
    canvas.width = size[0];
    canvas.height = size[1];
    const ctx = canvas.getContext('2d');
  
    ctx.drawImage(image, 0, 0);
  }
  
  /**
   * Draw heatmap values, one of the model outputs, on to the canvas
   * Read our blog post for a description of PoseNet's heatmap outputs
   * https://medium.com/tensorflow/real-time-human-pose-estimation-in-the-browser-with-tensorflow-js-7dd0bc881cd5
   */
  function drawHeatMapValues(heatMapValues, outputStride, canvas) {
    const ctx = canvas.getContext('2d');
    const radius = 5;
    const scaledValues = heatMapValues.mul(tf.scalar(outputStride, 'int32'));
  
    drawPoints(ctx, scaledValues, radius, color);
  }
  
  /**
   * Used by the drawHeatMapValues method to draw heatmap points on to
   * the canvas
   */
  function drawPoints(ctx, points, radius, color) {
    const data = points.buffer().values;
  
    for (let i = 0; i < data.length; i += 2) {
      const pointY = data[i];
      const pointX = data[i + 1];
  
      if (pointX !== 0 && pointY !== 0) {
        ctx.beginPath();
        ctx.arc(pointX, pointY, radius, 0, 2 * Math.PI);
        ctx.fillStyle = color;
        ctx.fill();
      }
    }
  }
  
  /**
   * Draw offset vector values, one of the model outputs, on to the canvas
   * Read our blog post for a description of PoseNet's offset vector outputs
   * https://medium.com/tensorflow/real-time-human-pose-estimation-in-the-browser-with-tensorflow-js-7dd0bc881cd5
   */
  function drawOffsetVectors(
    heatMapValues, offsets, outputStride, scale = 1, ctx) {
    const offsetPoints = posenet.singlePose.getOffsetPoints(
      heatMapValues, outputStride, offsets);
  
    const heatmapData = heatMapValues.buffer().values;
    const offsetPointsData = offsetPoints.buffer().values;
  
    for (let i = 0; i < heatmapData.length; i += 2) {
      const heatmapY = heatmapData[i] * outputStride;
      const heatmapX = heatmapData[i + 1] * outputStride;
      const offsetPointY = offsetPointsData[i];
      const offsetPointX = offsetPointsData[i + 1];
  
      drawSegment([heatmapY, heatmapX], [offsetPointY, offsetPointX],
        color, scale, ctx);
    }
  }
  