diff --git a/hunks/adhoc/correlate.js b/hunks/adhoc/correlate.js
index 6aa9be975cb3d14bae1d867edf2836ab87e298eb..601ebbf317d68bd6db748dcf137f6283d8adf812 100644
--- a/hunks/adhoc/correlate.js
+++ b/hunks/adhoc/correlate.js
@@ -11,158 +11,149 @@ import {
State
} from '../hunks.js'
-let computedResult = null
-let running = false
-
-let correlateGrayscale = (a, b, x, y) => {
- // score a against b, starting b from x, y
- // a, b are float64 arrays, pixel-wise
- let sumA = 0
- let sumB = 0
- let sumDot = 0
- // i will be index within the flat-af array under a,
- for(let i = 0; i < a.length; i ++){
- for(let j = 0; j < a[0].length; j ++){
- sumA += a[i][j]
- sumB += b[i + x][j + y]
- sumDot += a[i][j] * b[i + x][j + y]
+function worker(){
+ 'use strict'
+ let correlateGrayscale = (a, b, x, y) => {
+ // score a against b, starting b from x, y
+ // a, b are float64 arrays, pixel-wise
+ let sumA = 0
+ let sumB = 0
+ let sumDot = 0
+ // i will be index within the flat-af array under a,
+ for (let i = 0; i < a.length; i++) {
+ for (let j = 0; j < a[0].length; j++) {
+ sumA += a[i][j]
+ sumB += b[i + x][j + y]
+ sumDot += a[i][j] * b[i + x][j + y]
+ }
}
+ // I'm still not convinced this is a really great filter
+ return sumDot / Math.sqrt((sumB * sumB) * (sumA * sumA))
}
- // I'm still not convinced this is a really great filter
- return sumDot / Math.sqrt((sumB * sumB)*(sumA * sumA))
-}
-let correlateRBGA = (a, b, x, y) => {
- let ai, bi
- let sumA = 0
- let sumB = 0
- let sumDot = 0
- for(let i = 0; i < a.length; i ++){
- for(let j = 0; j < a[0].length; j ++){
- ai = a[i][j]
- bi = b[i + x][j + y]
- // have two vals,
- a[i][j] // has [0-3] rgba
- b[i + x][j + y] // has [0-3] rgba
- // so ? per pixel, this is like:
- let pdb = ai[0] * bi[0] + ai[1] * bi[1] + ai[2] * bi[2]
- let pas = ai[0] + ai[1] + ai[2]
- let pbs = bi[0] + bi[1] + bi[2]
- let pps = pdb / Math.sqrt(pas * pas * pbs * pbs) // per pixel score,
- sumA += pas
- sumB += pbs
- sumDot += pdb
+ let correlateRBGA = (a, b, x, y) => {
+ let ai, bi
+ let sumA = 0
+ let sumB = 0
+ let sumDot = 0
+ for (let i = 0; i < a.length; i++) {
+ for (let j = 0; j < a[0].length; j++) {
+ ai = a[i][j]
+ bi = b[i + x][j + y]
+ // have two vals,
+ a[i][j] // has [0-3] rgba
+ b[i + x][j + y] // has [0-3] rgba
+ // so ? per pixel, this is like:
+ let pdb = ai[0] * bi[0] + ai[1] * bi[1] + ai[2] * bi[2]
+ let pas = ai[0] + ai[1] + ai[2]
+ let pbs = bi[0] + bi[1] + bi[2]
+ let pps = pdb / Math.sqrt(pas * pas * pbs * pbs) // per pixel score,
+ sumA += pas
+ sumB += pbs
+ sumDot += pdb
+ }
}
+ return sumDot / Math.sqrt(sumA * sumA * sumB * sumB)
}
- return sumDot / Math.sqrt(sumA * sumA * sumB * sumB)
-}
-
-let delay = (time) => {
- return new Promise(resolve =>{
- setTimeout(() => {
- resolve()
- }, time)
- })
-}
-let packGrayscale = (imgD) => {
- // make an md array shaped like[x][y]
- let arr = []
- for(let x = 0; x < imgD.width; x ++){
- arr.push(new Float64Array(imgD.height))
- }
- let p = 0
- for(let y = 0; y < imgD.height; y ++){
- for(let x = 0; x < imgD.width; x ++){
- // grayscale all values as we load them up:
- arr[x][y] = (imgD.data[p] + imgD.data[p + 1] + imgD.data[p + 2]) / 3 // + imgD.data[p + 3]) / 4;
- p += 4;
+ let packGrayscale = (imgD) => {
+ // make an md array shaped like[x][y]
+ let arr = []
+ for (let x = 0; x < imgD.width; x++) {
+ arr.push(new Float64Array(imgD.height))
+ }
+ let p = 0
+ for (let y = 0; y < imgD.height; y++) {
+ for (let x = 0; x < imgD.width; x++) {
+ // grayscale all values as we load them up:
+ arr[x][y] = (imgD.data[p] + imgD.data[p + 1] + imgD.data[p + 2]) / 3 // + imgD.data[p + 3]) / 4;
+ p += 4;
+ }
}
+ return arr
}
- return arr
-}
-let packRBGA = (imgD) => {
- let arr = []
- for(let x = 0; x< imgD.width; x ++){
- arr.push([])
- for(let y = 0; y < imgD.height; y ++){
- arr[x][y] = new Float64Array(4)
+ let packRBGA = (imgD) => {
+ let arr = []
+ for (let x = 0; x < imgD.width; x++) {
+ arr.push([])
+ for (let y = 0; y < imgD.height; y++) {
+ arr[x][y] = new Float64Array(4)
+ }
}
- }
- let p = 0
- for(let y = 0; y < imgD.height; y ++){
- for(let x = 0; x < imgD.width; x ++){
- arr[x][y][0] = imgD.data[p ++]
- arr[x][y][1] = imgD.data[p ++]
- arr[x][y][2] = imgD.data[p ++]
- arr[x][y][3] = imgD.data[p ++]
+ let p = 0
+ for (let y = 0; y < imgD.height; y++) {
+ for (let x = 0; x < imgD.width; x++) {
+ arr[x][y][0] = imgD.data[p++]
+ arr[x][y][1] = imgD.data[p++]
+ arr[x][y][2] = imgD.data[p++]
+ arr[x][y][3] = imgD.data[p++]
+ }
}
+ return arr
}
- return arr
-}
-async function run(a, b){
- running = true
- // a is img to search for, b is img to search within. both are ImageData types
- let resX = b.width - a.width
- let resY = b.height - a.height
- let numruns = resX * resY
- console.log('numruns', numruns)
- // the move now is to make an md array of these values,
- let bArr = packRBGA(b) //packGrayscale(b)
- let aArr = packRBGA(a) //packGrayscale(a)
- // ok, results array like[x][y]
- let result = []
- for(let x = 0; x < resX; x ++){
- result.push(new Float64Array(resY))
- }
- // now fill,
- for(let x = 0; x < resX; x ++){
- for(let y = 0; y < resY; y ++){
- result[x][y] = correlateRBGA(aArr, bArr, x, y)
+ function run(a, b) {
+ // a is img to search for, b is img to search within. both are ImageData types
+ let resX = b.width - a.width
+ let resY = b.height - a.height
+ let numruns = resX * resY
+ // the move now is to make an md array of these values,
+ let bArr = packRBGA(b) //packGrayscale(b)
+ let aArr = packRBGA(a) //packGrayscale(a)
+ // ok, results array like[x][y]
+ let result = []
+ for (let x = 0; x < resX; x++) {
+ result.push(new Float64Array(resY))
}
- await delay(0) // to avoid blocking,
- }
- console.log('run complete')
- // make image from the result,
- let max = -Infinity
- let min = Infinity
- let mp = {x: 0, y: 0}
- for(let x = 0; x < resX; x ++){
- for(let y = 0; y < resY; y ++){
- if(result[x][y] > max){
- max = result[x][y]
- mp.x = x
- mp.y = y
+ // now fill,
+ for (let x = 0; x < resX; x++) {
+ for (let y = 0; y < resY; y++) {
+ result[x][y] = correlateRBGA(aArr, bArr, x, y)
}
- if(result[x][y] < min) min = result[x][y]
}
- }
- console.log('max, min', max, min)
- console.log('mp', mp)
- // now we want to unwrap this into an imagedata type,
- // filling back in grayscale type
- let imdBuffer = new Uint8ClampedArray(resX * resY * 4)
- let lc = 0
- let dt = 0
- for(let y = 0; y < resY; y ++){
- for(let x = 0; x < resX; x ++){
- // stretch to full range,
- dt = (result[x][y] - min) * (255 / (max - min))
- imdBuffer[lc ++] = dt
- imdBuffer[lc ++] = dt
- imdBuffer[lc ++] = dt
- imdBuffer[lc ++] = 255
+ // make image from the result,
+ let max = -Infinity
+ let min = Infinity
+ let mp = { x: 0, y: 0 }
+ for (let x = 0; x < resX; x++) {
+ for (let y = 0; y < resY; y++) {
+ if (result[x][y] > max) {
+ max = result[x][y]
+ mp.x = x
+ mp.y = y
+ }
+ if (result[x][y] < min) min = result[x][y]
+ }
+ }
+ // now we want to unwrap this into an imagedata type,
+ // filling back in grayscale type
+ let imdBuffer = new Uint8ClampedArray(resX * resY * 4)
+ let lc = 0
+ let dt = 0
+ for (let y = 0; y < resY; y++) {
+ for (let x = 0; x < resX; x++) {
+ // stretch to full range,
+ dt = (result[x][y] - min) * (255 / (max - min))
+ imdBuffer[lc++] = dt
+ imdBuffer[lc++] = dt
+ imdBuffer[lc++] = dt
+ imdBuffer[lc++] = 255
+ }
}
+ let imgRes = new ImageData(imdBuffer, resX, resY)
+ self.postMessage(imgRes)
+ }
+
+ onmessage = (evt) => {
+ run(evt.data.a, evt.data.b)
}
- let imgRes = new ImageData(imdBuffer, resX, resY)
- console.log('imgRes', imgRes)
- running = false
- computedResult = imgRes
}
-export default function Correlate(){
+let computedResult = null
+let running = false
+
+export default function Correlate() {
Hunkify(this)
let imgIn = this.input('ImageData', 'frame')
@@ -180,7 +171,19 @@ export default function Correlate(){
this.dom = $('<div>').get(0)
+ let webWorker
+
this.init = () => {
+ // startup the worker:
+ let bleb = new Blob(['('+worker.toString()+'())'])
+ let url = window.URL.createObjectURL(bleb)
+ webWorker = new Worker(url)
+ webWorker.addEventListener('message', (evt) => {
+ running = false
+ if(!resOut.io()){
+ resOut.put(evt.data)
+ }
+ })
}
this.onload = () => {
@@ -189,7 +192,13 @@ export default function Correlate(){
}
this.loop = () => {
- if(imgIn.io() && !running){
+ if (imgIn.io() && !running) {
+ /*
+ imgIn.get()
+ running = true
+ console.log("POSTING")
+ webWorker.postMessage('msg')
+ */
// ok, the image data:
let img = imgIn.get()
// to scale this thing, we draw it into a canvas (virtual ... not attached to page)
@@ -208,10 +217,8 @@ export default function Correlate(){
let a = ctxB.getImageData(b.width / 2, b.height / 2, 50, 50)
// and write that out, to debug ...
ctxA.putImageData(a, 0, 0)
- run(a, b)
- }
- if(!resOut.io() && computedResult !== null){
- resOut.put(computedResult)
+ webWorker.postMessage({a: a, b: b})
+ running = true
}
}
}