@thi.ng/tensors/histogram.js

0D/1D/2D/3D/4D tensors with extensible polymorphic operations and customizable storage

import { cdf } from "./cdf.js";
import { defOpT } from "./defopt.js";
import { ensureShape } from "./errors.js";
import { findIndex } from "./find.js";
import { mulN } from "./muln.js";
import { tensor } from "./tensor.js";
const histogramUint = (src, depth, mask = (1 << depth) - 1, shift = 0) => {
  const histType = src.length < 256 ? "u8" : src.length < 65536 ? "u16" : "u32";
  const histogram = tensor(histType, [1 << depth]);
  if (src.dim > 1) src = src.reshape([src.length]);
  const {
    offset: oa,
    shape: [sa],
    stride: [ta],
    data: adata
  } = src;
  for (let i = 0; i < sa; i++)
    histogram.data[adata[oa + i * ta] >>> shift & mask]++;
  return histogram;
};
const equalizeHistogram = (out, src, depth, numSamples, threshold = 0) => {
  !out && (out = src);
  ensureShape(out, src.shape);
  const norm = mulN(tensor("f64", [src.length]), src, 1 / numSamples);
  const $cdf = cdf(null, norm);
  const lo = findIndex($cdf, (x) => x > threshold);
  if (lo < 0) return out.fill(0);
  const {
    offset: oc,
    shape: [sc],
    stride: [tc],
    data: cdata
  } = $cdf;
  const {
    offset: oo,
    stride: [to],
    data: odata
  } = out;
  const base = cdata[oc + lo * tc];
  const scale = (2 ** depth - 1) / (cdata[oc + (sc - 1) * tc] - base);
  for (let i = 0; i < sc; i++) {
    odata[oo + i * to] = Math.max(0, scale * (cdata[oc + i * tc] - base));
  }
  return out;
};
const applyLUT = (out, a, lut) => {
  !out && (out = a);
  ensureShape(out, a.shape);
  const {
    offset: ol,
    stride: [tl],
    data: ldata
  } = lut;
  return defOpT((x) => ldata[ol + x * tl])(out, a);
};
export {
  applyLUT,
  equalizeHistogram,
  histogramUint
};