human/dist/human-nobundle.cjs.map

{
  "version": 3,
  "sources": ["../src/facemesh/blazeface.js", "../src/facemesh/keypoints.js", "../src/facemesh/box.js", "../src/facemesh/util.js", "../src/facemesh/pipeline.js", "../src/facemesh/uvcoords.js", "../src/facemesh/triangulation.js", "../src/facemesh/facemesh.js", "../src/ssrnet/ssrnet.js", "../src/emotion/emotion.js", "../src/posenet/modelBase.js", "../src/posenet/modelMobileNet.js", "../src/posenet/heapSort.js", "../src/posenet/buildParts.js", "../src/posenet/keypoints.js", "../src/posenet/vectors.js", "../src/posenet/decodePose.js", "../src/posenet/decodeMultiple.js", "../src/posenet/util.js", "../src/posenet/modelPoseNet.js", "../src/posenet/posenet.js", "../src/handpose/box.js", "../src/handpose/handdetector.js", "../src/handpose/keypoints.js", "../src/handpose/util.js", "../src/handpose/pipeline.js", "../src/handpose/handpose.js", "../src/config.js", "../src/index.js"],
  "sourcesContent": ["const tf = require('@tensorflow/tfjs');\n\nconst ANCHORS_CONFIG = {\n  strides: [8, 16],\n  anchors: [2, 6],\n};\n\nconst NUM_LANDMARKS = 6;\nfunction generateAnchors(width, height, outputSpec) {\n  const anchors = [];\n  for (let i = 0; i < outputSpec.strides.length; i++) {\n    const stride = outputSpec.strides[i];\n    const gridRows = Math.floor((height + stride - 1) / stride);\n    const gridCols = Math.floor((width + stride - 1) / stride);\n    const anchorsNum = outputSpec.anchors[i];\n    for (let gridY = 0; gridY < gridRows; gridY++) {\n      const anchorY = stride * (gridY + 0.5);\n      for (let gridX = 0; gridX < gridCols; gridX++) {\n        const anchorX = stride * (gridX + 0.5);\n        for (let n = 0; n < anchorsNum; n++) {\n          anchors.push([anchorX, anchorY]);\n        }\n      }\n    }\n  }\n  return anchors;\n}\n\nconst disposeBox = (box) => {\n  box.startEndTensor.dispose();\n  box.startPoint.dispose();\n  box.endPoint.dispose();\n};\n\nconst createBox = (startEndTensor) => ({\n  startEndTensor,\n  startPoint: tf.slice(startEndTensor, [0, 0], [-1, 2]),\n  endPoint: tf.slice(startEndTensor, [0, 2], [-1, 2]),\n});\n\nconst scaleBox = (box, factors) => {\n  const starts = tf.mul(box.startPoint, factors);\n  const ends = tf.mul(box.endPoint, factors);\n  const newCoordinates = tf.concat2d([starts, ends], 1);\n  return createBox(newCoordinates);\n};\n\nfunction decodeBounds(boxOutputs, anchors, inputSize) {\n  const boxStarts = tf.slice(boxOutputs, [0, 1], [-1, 2]);\n  const centers = tf.add(boxStarts, anchors);\n  const boxSizes = tf.slice(boxOutputs, [0, 3], [-1, 2]);\n  const boxSizesNormalized = tf.div(boxSizes, inputSize);\n  const centersNormalized = tf.div(centers, inputSize);\n  const halfBoxSize = tf.div(boxSizesNormalized, 2);\n  const starts = tf.sub(centersNormalized, halfBoxSize);\n  const ends = tf.add(centersNormalized, halfBoxSize);\n  const startNormalized = tf.mul(starts, inputSize);\n  const endNormalized = tf.mul(ends, inputSize);\n  const concatAxis = 1;\n  return tf.concat2d([startNormalized, endNormalized], concatAxis);\n}\n\nfunction scaleBoxFromPrediction(face, scaleFactor) {\n  return tf.tidy(() => {\n    const box = face['box'] ? face['box'] : face;\n    return scaleBox(box, scaleFactor).startEndTensor.squeeze();\n  });\n}\n\nclass BlazeFaceModel {\n  constructor(model, config) {\n    this.blazeFaceModel = model;\n    this.width = config.detector.inputSize;\n    this.height = config.detector.inputSize;\n    this.maxFaces = config.detector.maxFaces;\n    this.anchorsData = generateAnchors(config.detector.inputSize, config.detector.inputSize, ANCHORS_CONFIG);\n    this.anchors = tf.tensor2d(this.anchorsData);\n    this.inputSizeData = [config.detector.inputSize, config.detector.inputSize];\n    this.inputSize = tf.tensor1d([config.detector.inputSize, config.detector.inputSize]);\n    this.iouThreshold = config.detector.iouThreshold;\n    this.scoreThreshold = config.detector.scoreThreshold;\n  }\n\n  async getBoundingBoxes(inputImage) {\n    const [detectedOutputs, boxes, scores] = tf.tidy(() => {\n      const resizedImage = inputImage.resizeBilinear([this.width, this.height]);\n      const normalizedImage = tf.mul(tf.sub(resizedImage.div(255), 0.5), 2);\n      const batchedPrediction = this.blazeFaceModel.predict(normalizedImage);\n      const prediction = batchedPrediction.squeeze();\n      const decodedBounds = decodeBounds(prediction, this.anchors, this.inputSize);\n      const logits = tf.slice(prediction, [0, 0], [-1, 1]);\n      const scoresOut = tf.sigmoid(logits).squeeze();\n      return [prediction, decodedBounds, scoresOut];\n    });\n\n    const boxIndicesTensor = await tf.image.nonMaxSuppressionAsync(boxes, scores, this.maxFaces, this.iouThreshold, this.scoreThreshold);\n    const boxIndices = await boxIndicesTensor.array();\n    boxIndicesTensor.dispose();\n    let boundingBoxes = boxIndices.map((boxIndex) => tf.slice(boxes, [boxIndex, 0], [1, -1]));\n    boundingBoxes = await Promise.all(boundingBoxes.map(async (boundingBox) => {\n     
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