final.js

const data = require('./model_output_bgr.json');
const util = require('util');

/* function reshapeTo3D(flatArray) {
  const height = 13;
  const width = 13;
  const channels = 45;
  const result = [];

  let index = 0;
  for (let h = 0; h < height; h++) {
    const row = [];
    for (let w = 0; w < width; w++) {
      const cell = flatArray.slice(index, index + channels);
      row.push(cell);
      index += channels;
    }
    result.push(row);
  }

  return result;
} */
function reshapeTo3D(flatArray, dim1 = 13, dim2 = 13, dim3 = 45) {
  const result = new Array(dim1);
  let offset = 0;
  for (let i = 0; i < dim1; i++) {
    result[i] = new Array(dim2);
    for (let j = 0; j < dim2; j++) {
      result[i][j] = flatArray.slice(offset, offset + dim3);
      offset += dim3;
    }
  }
  return result;
}

// Helper function to apply sigmoid

function sigmoid(x) {
  return 1 / (1 + Math.exp(-x));
}

// Helper function for softmax
function softmax(logits) {
  const maxLogit = Math.max(...logits);
  const exps = logits.map(x => Math.exp(x - maxLogit));
  const sumExps = exps.reduce((a, b) => a + b, 0);
  return exps.map(e => e / sumExps);
}

// Extract bounding boxes and class probabilities
function extractBB(outputs, anchors, numClasses) {
  const boxes = [];
  const classProbs = [];

  for (let i = 0; i < 13; i++) {
    for (let j = 0; j < 13; j++) {
      const cell = outputs[i][j]; // [45]
      for (let b = 0; b < 3; b++) {
        const offset = b * (5 + numClasses);
        const tx = cell[offset];
        const ty = cell[offset + 1];
        const tw = cell[offset + 2];
        const th = cell[offset + 3];
        const tc = cell[offset + 4];

        const classLogits = cell.slice(offset + 5, offset + 5 + numClasses);
        const objectness = sigmoid(tc);
        const classProbabilities = softmax(classLogits).map(
          p => p * objectness,
        );

        const cx = (j + sigmoid(tx)) / 13;
        const cy = (i + sigmoid(ty)) / 13;
        const bw = (Math.exp(tw) * anchors[b][0]) / 13;
        const bh = (Math.exp(th) * anchors[b][1]) / 13;

        const x = cx - bw / 2;
        const y = cy - bh / 2;

        boxes.push([x, y, bw, bh]);
        classProbs.push(classProbabilities);
      }
    }
  }

  return [boxes, classProbs];
}

// Non-maximum suppression
function nonMaximumSuppression(
  boxes,
  probs,
  maxDetections,
  iouThreshold = 0.5,
) {
  const selectedBoxes = [];
  const selectedClasses = [];
  const selectedProbs = [];

  const candidates = [];

  for (let i = 0; i < probs.length; i++) {
    const maxProb = Math.max(...probs[i]);
    const classId = probs[i].indexOf(maxProb);
    candidates.push({index: i, classId, prob: maxProb});
  }

  candidates.sort((a, b) => b.prob - a.prob);

  const used = new Set();

  for (const candidate of candidates) {
    if (used.has(candidate.index)) continue;
    const currentBox = boxes[candidate.index];
    const currentClass = candidate.classId;

    let shouldSelect = true;

    for (let i = 0; i < selectedBoxes.length; i++) {
      if (selectedClasses[i] === currentClass) {
        const iouVal = iou(currentBox, selectedBoxes[i]);
        if (iouVal > iouThreshold) {
          shouldSelect = false;
          break;
        }
      }
    }

    if (shouldSelect) {
      selectedBoxes.push(currentBox);
      selectedClasses.push(currentClass);
      selectedProbs.push(candidate.prob);
      used.add(candidate.index);
    }

    if (selectedBoxes.length >= maxDetections) break;
  }

  return [selectedBoxes, selectedClasses, selectedProbs];
}

// IoU between two boxes
function iou(box1, box2) {
  const [x1, y1, w1, h1] = box1;
  const [x2, y2, w2, h2] = box2;

  const xmin1 = x1;
  const ymin1 = y1;
  const xmax1 = x1 + w1;
  const ymax1 = y1 + h1;

  const xmin2 = x2;
  const ymin2 = y2;
  const xmax2 = x2 + w2;
  const ymax2 = y2 + h2;

  const interXmin = Math.max(xmin1, xmin2);
  const interYmin = Math.max(ymin1, ymin2);
  const interXmax = Math.min(xmax1, xmax2);
  const interYmax = Math.min(ymax1, ymax2);

  const interArea =
    Math.max(0, interXmax - interXmin) * Math.max(0, interYmax - interYmin);
  const box1Area = w1 * h1;
  const box2Area = w2 * h2;
  const unionArea = box1Area + box2Area - interArea;

  return interArea / unionArea;
}

// Final postprocess
function postprocess(output3D, anchors, labels, probThreshold, maxDetections) {
  const numClasses = labels.length;
  const [boxes, classProbs] = extractBB(output3D, anchors, numClasses);

  const maxProbs = classProbs.map(p => Math.max(...p));
  const indices = maxProbs
    .map((prob, idx) => ({prob, idx}))
    .filter(x => x.prob > probThreshold)
    .sort((a, b) => b.prob - a.prob)
    .map(x => x.idx);

  const filteredBoxes = indices.map(i => boxes[i]);
  const filteredProbs = indices.map(i => classProbs[i]);

  const [selectedBoxes, selectedClasses, selectedProbs] = nonMaximumSuppression(
    filteredBoxes,
    filteredProbs,
    maxDetections,
  );

  return selectedBoxes.map((box, i) => ({
    probability: parseFloat(selectedProbs[i].toFixed(8)),
    tagId: selectedClasses[i],
    tagName: labels[selectedClasses[i]],
    boundingBox: {
      left: parseFloat(box[0].toFixed(8)),
      top: parseFloat(box[1].toFixed(8)),
      width: parseFloat(box[2].toFixed(8)),
      height: parseFloat(box[3].toFixed(8)),
    },
  }));
}

const flatOutput = [
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];

const ANCHORS = [
  [0.573, 0.677],
  [1.87, 2.06],
  [3.34, 5.47],
  [7.88, 3.53],
  [9.77, 9.17],
];
const LABELS = ['oropharynx', 'tonsil region a', 'tonsil region b', 'uvula'];
/* const flatArray = Object.keys(data)
  .sort((a, b) => parseInt(a) - parseInt(b)) // ensure correct order
  .map(k => data[k]); */
const output3D = reshapeTo3D(data);
// Print full output without truncation
// Disable truncation for console output

//console.log(util.inspect(output3D, {maxArrayLength: null, depth: null}));
console.dir(output3D, {maxArrayLength: 20000000, depth: 2000000, colors: true});
//console.log(output3D);

//console.log(data); // [13][13][45]
const results = postprocess(output3D, ANCHORS, LABELS, 0.1, 10);

//console.log(results);