StackGenVis: Alignment of Data, Algorithms, and Models for Stacking Ensemble Learning Using Performance Metrics https://doi.org/10.1109/TVCG.2020.3030352
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StackGenVis/frontend/node_modules/bitmap-sdf/index.js

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'use strict'
var clamp = require('clamp')
module.exports = calcSDF
var INF = 1e20;
function calcSDF(src, options) {
if (!options) options = {}
var cutoff = options.cutoff == null ? 0.25 : options.cutoff
var radius = options.radius == null ? 8 : options.radius
var channel = options.channel || 0
var w, h, size, data, intData, stride, ctx, canvas, imgData, i, l
// handle image container
if (ArrayBuffer.isView(src) || Array.isArray(src)) {
if (!options.width || !options.height) throw Error('For raw data width and height should be provided by options')
w = options.width, h = options.height
data = src
if (!options.stride) stride = Math.floor(src.length / w / h)
else stride = options.stride
}
else {
if (window.HTMLCanvasElement && src instanceof window.HTMLCanvasElement) {
canvas = src
ctx = canvas.getContext('2d')
w = canvas.width, h = canvas.height
imgData = ctx.getImageData(0, 0, w, h)
data = imgData.data
stride = 4
}
else if (window.CanvasRenderingContext2D && src instanceof window.CanvasRenderingContext2D) {
canvas = src.canvas
ctx = src
w = canvas.width, h = canvas.height
imgData = ctx.getImageData(0, 0, w, h)
data = imgData.data
stride = 4
}
else if (window.ImageData && src instanceof window.ImageData) {
imgData = src
w = src.width, h = src.height
data = imgData.data
stride = 4
}
}
size = Math.max(w, h)
//convert int data to floats
if ((window.Uint8ClampedArray && data instanceof window.Uint8ClampedArray) || (window.Uint8Array && data instanceof window.Uint8Array)) {
intData = data
data = Array(w*h)
for (i = 0, l = intData.length; i < l; i++) {
data[i] = intData[i*stride + channel] / 255
}
}
else {
if (stride !== 1) throw Error('Raw data can have only 1 value per pixel')
}
// temporary arrays for the distance transform
var gridOuter = Array(w * h)
var gridInner = Array(w * h)
var f = Array(size)
var d = Array(size)
var z = Array(size + 1)
var v = Array(size)
for (i = 0, l = w * h; i < l; i++) {
var a = data[i]
gridOuter[i] = a === 1 ? 0 : a === 0 ? INF : Math.pow(Math.max(0, 0.5 - a), 2)
gridInner[i] = a === 1 ? INF : a === 0 ? 0 : Math.pow(Math.max(0, a - 0.5), 2)
}
edt(gridOuter, w, h, f, d, v, z)
edt(gridInner, w, h, f, d, v, z)
var dist = window.Float32Array ? new Float32Array(w * h) : new Array(w * h)
for (i = 0, l = w*h; i < l; i++) {
dist[i] = clamp(1 - ( (gridOuter[i] - gridInner[i]) / radius + cutoff), 0, 1)
}
return dist
}
// 2D Euclidean distance transform by Felzenszwalb & Huttenlocher https://cs.brown.edu/~pff/dt/
function edt(data, width, height, f, d, v, z) {
for (var x = 0; x < width; x++) {
for (var y = 0; y < height; y++) {
f[y] = data[y * width + x]
}
edt1d(f, d, v, z, height)
for (y = 0; y < height; y++) {
data[y * width + x] = d[y]
}
}
for (y = 0; y < height; y++) {
for (x = 0; x < width; x++) {
f[x] = data[y * width + x]
}
edt1d(f, d, v, z, width)
for (x = 0; x < width; x++) {
data[y * width + x] = Math.sqrt(d[x])
}
}
}
// 1D squared distance transform
function edt1d(f, d, v, z, n) {
v[0] = 0;
z[0] = -INF
z[1] = +INF
for (var q = 1, k = 0; q < n; q++) {
var s = ((f[q] + q * q) - (f[v[k]] + v[k] * v[k])) / (2 * q - 2 * v[k])
while (s <= z[k]) {
k--
s = ((f[q] + q * q) - (f[v[k]] + v[k] * v[k])) / (2 * q - 2 * v[k])
}
k++
v[k] = q
z[k] = s
z[k + 1] = +INF
}
for (q = 0, k = 0; q < n; q++) {
while (z[k + 1] < q) k++
d[q] = (q - v[k]) * (q - v[k]) + f[v[k]]
}
}