Function postprocess

paddleocr-js/packages/core/src/models/det.ts:419–466

(
  context: DetRunContext,
  fullOutput: Tensor,
  preps: DetPreprocessResult[],
  params: InternalDetParams
)

}

function postprocess(
  context: DetRunContext,
  fullOutput: Tensor,
  preps: DetPreprocessResult[],
  params: InternalDetParams
): InternalDetBatchItem[] {
  const { cv, ort, config } = context;
  const od = fullOutput.dims;
  if (od.length !== 3 && od.length !== 4) {
    throw new Error(`Unexpected det output dims: [${od.join(", ")}]`);
  }
  const ohFull = od.length === 4 ? od[2] : od[1];
  const owFull = od.length === 4 ? od[3] : od[2];
  const nOut = od.length === 4 ? od[0] : preps.length === 1 ? 1 : od[0];
  if (nOut !== preps.length) {
    throw new Error(
      `Detection batch output N=${String(nOut)} does not match input batch ${String(preps.length)}`
    );
  }

  const maxH = Math.max(...preps.map((p) => p.dstH));
  const maxW = Math.max(...preps.map((p) => p.dstW));

  const items: InternalDetBatchItem[] = [];
  for (let i = 0; i < preps.length; i += 1) {
    const prep = preps[i];
    const { cropOh, cropOw } = detFeatureCropDims(prep.dstH, prep.dstW, maxH, maxW, ohFull, owFull);
    const planeTensor = sliceBatchedDetOutputPlane(
      ort,
      fullOutput,
      i,
      cropOh,
      cropOw,
      ohFull,
      owFull
    );
    const boxes = decodeDetOutput(
      { cv, config },
      planeTensor,
      prep,
      params.thresh,
      params.boxThresh,
      params.unclipRatio
    );
    items.push({ prep, boxes });
  }
  return items;
}

function decodeDetOutput(
  context: { cv: OpenCv; config: DetModelConfig },