PUCK - Perceptually-Uniform Color Map Kit Script

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      //VERSION=3


/*
Perceptually-Uniform Colormap Kit
author: Keenan Ganz (ganzk@rpi.edu)
September 2020
*/

/*
Reference white values for D65 illuminant,
secondary observer.
*/
var REF_X = 95.0489
var REF_Y = 100.0
var REF_Z = 108.8840

function percent_between(min, max, x){
  return (x - min) / (max - min)
}

function clip(min, max, x) {
  return Math.min(max, Math.max(min, x))
}

function rgb2lab(rgb) {
  /*
  Convert rgb coordinates to CIELAB coordinates via XYZ. 
  Expects normalized RGB values.

  Arithmetic from easyrgb.com
  */
  let [r, g, b] = rgb

  function to_linear(val) {
    if (val > 0.04045)
      return Math.pow((val + 0.055) / 1.055, 2.4)
    else
      return val / 12.92
  }

  let r_lin = to_linear(r) * 100
  let g_lin = to_linear(g) * 100
  let b_lin = to_linear(b) * 100

  let x = r_lin * 0.4124 + g_lin * 0.3576 + b_lin * 0.1805
  let y = r_lin * 0.2126 + g_lin * 0.7152 + b_lin * 0.0722
  let z = r_lin * 0.0193 + g_lin * 0.1192 + b_lin * 0.9505

  let x_std = x / REF_X
  let y_std = y / REF_Y
  let z_std = z / REF_Z

  function std_prep(val) {
    if (val > 0.008856)
      return Math.pow(val, 1.0 / 3.0)
    else
      return val * 7.787 + (16.0 / 116.0)
  }
  let L = 116.0 * (std_prep(y_std) - 16.0 / 116.0)
  let a = 500.0 * (std_prep(x_std) - std_prep(y_std))
  b = 200.0 * (std_prep(y_std) - std_prep(z_std))

  return [L, a, b]
}

function lab2rgb(Lab) {
  /*
  Convert CIELAB coordinates to RGB coordinates.

  Arithmetic from easyrgb.com
  */
  let [L, a, b] = Lab
  let var_y = (L + 16.0) / 116.0
  let var_x = (a / 500.0) + var_y
  let var_z = var_y - (b / 200.0)

  function undo_std_prep(val) {
    if (Math.pow(val, 3.0) > 0.008856)
      return Math.pow(val, 3.0)
    else
      return (val - (16.0 / 116.0)) / 7.787
  }
  var_y = undo_std_prep(var_y)
  var_x = undo_std_prep(var_x)
  var_z = undo_std_prep(var_z)

  let x = var_x * REF_X / 100
  let y = var_y * REF_Y / 100
  let z = var_z * REF_Z / 100

  let var_r = x * 3.2406 + y * -1.5372 + z * -0.4986
  let var_g = x * -0.9689 + y * 1.8758 + z * 0.0415
  let var_b = x * 0.0557 + y * -0.2040 + z * 1.0570

  function undo_linear(val) {
    if (val > 0.0031308)
      return 1.055 * Math.pow(val, (1.0 / 2.4)) - 0.055
    else
      return val * 12.92
  }

  let r = Math.max(undo_linear(var_r), 0)
  let g = Math.max(undo_linear(var_g), 0)
  b = Math.max(undo_linear(var_b), 0)

  // mapping isn't perfect, constrain to [0, 1]

  return [clip(0, 1, r), clip(0, 1, g), clip(0, 1, b)]
}

class Colormap {
  /*
  Base class for making perceptually uniform color maps. Using this class on its own
  simply maps RGB coordinates to LAB space and linearly interpolates inbetween values.

  Use SequentialColorMap, DivergentColorMap, etc. for more specific use cases.
  */
  constructor(color_anchors, data_anchors, remap=true, uniform=false) {
    if (color_anchors.length < 1)
      throw "ColorMap requires at least one color."
    if (color_anchors.length != data_anchors.length)
      throw "Color and data anchors must be of same length."
    // verify that the data array is sorted low to high

    for (let i = 1; i < data_anchors.length; i++) {
      if (data_anchors[i] < data_anchors[i - 1])
        throw "Data anchors array must be sorted."
    }
    // map incoming rgb coordinates into LAB space

    this.data_anchors = data_anchors
    if (remap) this.color_anchors = color_anchors.map(rgb2lab)
    else this.color_anchors = color_anchors
    // do the lightness correction, if desired, and then check

    // if the correction moved colors outside of RGB space

    if (uniform) { this._lightness_correction() } 
  }

  _lightness_correction() {return}

  get_color(data_value) {
    // return edge values if data value is oob

    if (data_value <= this.data_anchors[0])
      return lab2rgb(this.color_anchors[0])
    else if (data_value >= this.data_anchors[this.data_anchors.length - 1])
      return lab2rgb(this.color_anchors[this.color_anchors.length-1])

    return lab2rgb(colorBlend(data_value, this.data_anchors, this.color_anchors))
  }
}

class LinearColormap extends Colormap {
  /*
  Simple linear ramp color map class. Set uniform to true
  in the constructor to enforce constant lightness.
  */
  constructor(color_anchors, data_anchors, uniform=true) {
    super(color_anchors, data_anchors, true, uniform)
  }

  _lightness_correction() {
    // get overall change in lightness

    let L0 = this.color_anchors[0][0]
    let Lp = this.color_anchors[this.color_anchors.length-1][0]
    let dL = Lp - L0

    // make the lightness values monotonically change

    for (let i = 1; i < this.color_anchors.length - 1; i++) {
      let percent_interval = percent_between(
        this.data_anchors[this.data_anchors.length - 1],
        this.data_anchors[0],
        this.data_anchors[i]
      )
      this.color_anchors[i][0] = L0 + (dL * percent_interval)
    }
  }
};

class DivergentColormap extends Colormap {
  /*
  Color map that reaches a max/min lightness at its center and changes
  monotonically toward the edges of the data with equal lightness
  at each edge. If you don't want the lightness correction, just use
  LinearColormap with uniform=false.
  */
  constructor(color_anchors, data_anchors) {
    if (color_anchors.length % 2 != 1)
      throw "DivergentColorMap must have an odd number of anchors."
    super(color_anchors, data_anchors, true, true)
  }

  _lightness_correction() {
    // L0 is the mean lightness of the edge colors

    // Lp is the lightness of the center color

    let len = this.color_anchors.length
    let L0 = (this.color_anchors[0][0] + this.color_anchors[len - 1][0]) / 2
    // set the edge colors lightness to the mean

    this.color_anchors[len - 1][0] = this.color_anchors[0][0] = L0

    // Calculate intermediate lightness values

    let Lp = this.color_anchors[Math.floor(len / 2)][0]
    let dL = Lp - L0

    for (let i = 1; i < Math.floor(len / 2); i++) {
      let left_percent_interval = percent_between(
        this.data_anchors[Math.floor(len / 2)],
        this.data_anchors[0],
        this.data_anchors[i]
      )
      // Invert right % since lightness trends the other way now

      let right_percent_interval = 1 - percent_between(
        this.data_anchors[len - 1],
        this.data_anchors[Math.floor(len / 2)],
        this.data_anchors[len - 1 - i]
      )
      this.color_anchors[i][0] = L0 + (dL * left_percent_interval)
      this.color_anchors[len - 1 - i][0] = L0 + (dL * right_percent_interval)
    }
  }
}

class IsoluminantColormap extends Colormap {
  /*
  Color map that enforces constant lightness. Not particularly pretty on its own,
  but can be overlaid on relief shading to visualize data on top of topography.
  */
  constructor(color_anchors, data_anchors) {
    super(color_anchors, data_anchors, true, true)
  }
  _lightness_correction() {
    // get the mean lightness of all colors

    let L_sum = 0
    for (let i = 0; i < this.color_anchors.length; i++) {
      L_sum += this.color_anchors[i][0]
    }
    let L_avg = L_sum / this.color_anchors.length

    // set all anchors to have lightness of this value

    for (let i = 0; i < this.color_anchors.length; i++) {
      this.color_anchors[i][0] = L_avg
    }
  }
}

/*
Example usage: Masked NDVI on a white-green
color map.
*/

var map = [
  [217/255, 229/255, 206/255],
  [12/255, 22/255, 3/255],
]

var data = [-0.1, 1] 

var cmap = new LinearColormap(map, data);

function setup() {
  return {
    input: ["B02", "B03", "B04", "B08", "dataMask"],
    output: {
      bands: 3
    }
  };
}

function trueColor(sample){
  return [sample.B04 * 2.5, sample.B03 * 2.5, sample.B02 * 2.5]
}

function evaluatePixel(sample) {
  let ndvi = (sample.B08 - sample.B04) / (sample.B04 + sample.B08)
  if (ndvi > -.1) {return cmap.get_color(ndvi)}
  else {return trueColor(sample)}
}

Evaluate and visualize

• EO Browser

General description of the script

A set of visualization utilities that produce beautiful images designed for human perception from single-channel data (NDVI, spectral angle, etc). Generate quality figures that are true to the data.

Good visualizations depend on good color maps. However, it is very easy to unintentionally create data artifacts or obscure trends unless care is taken. For example, many have written about the problems with rainbow color maps (e.g. Borland and Taylor 2007 and Kovesi 2015, see references). Yet, rainbows remain prevalent in scientific publications (and occasionally on SentinelHub).

SentinelHub custom scripts have limited support for custom color maps: colorBlend and ColorRampVisualizer. As a result, many users end up hardcoding their colors. Also, these functions use RGB colors instead of a color gamut designed around human perception. The goal of this project is to create tools for custom script developers to generate useful and perceptually uniform color maps. It provides visualization classes for users familiar with CIELAB color space and works to correct colors in RGB space to be perceptually uniform. The underlying mathematics is used by many scientific plotting applications, such as matplotlib, bokeh, chroma.js, and others.

Details of the script

How the Script Works

This script supports 4 flavors of color map in addition to functions that inter-convert between the CIELAB, XYZ, and RGB color spaces in pure JavaScript. In each case, the user simply provides two arrays, one for anchor colors and one for data values, and the class will generate the correct color for data values calculated by the script. For example, to generate a brown-green color map for NDVI values in the range [0, 1], simply use:

var ndvi_map = [ // https://www.google.com/search?q=rgb+color+picker [66/255, 50/255, 28/255], [0, 209/255, 3/255], ] var data = [0, 1] var cmap = new LinearColormap(ndvi_map, data);

function setup() { return { input: [“B04”, “B08”, “dataMask”], output: { bands: 3 } } }

function evaluatePixel(sample) { let ndvi = (sample.B08 - sample.B04) / (sample.B08 + sample.B04) return cmap.get_color(ndvi) }

In addition to LinearColormap, there is DivergentColormap, IsoluminantColormap, and the base class Colormap. The base class expects colors in CIELAB color space and simply calls colorBlend to get in-between colors before converting back to RGB space. All other classes expect colors in RGB space, map them onto CIELAB space, and apply a lightness correction specific to the class. The corrections for each class are:

• LinearColormap: Lightness monotonically changes across the whole map. Your st