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p5.js Flood Fill (bucket tool) works slowly and wierd

So I wrote a flood fill function that works like a paint-app bucket tool: you click inside a closed shape and it'll fill with a color.
I have two problems with it:
performance - let's say my canvas is 600*600 (370,000 pixels) and I draw a big circle in it that for example has about 100K pixels in it, it can take about 40(!!!) seconds to fill this circle! thats insane!
A sqaure of exactly 10,000 pixels takes 0.4-0.5 seconds on average, but (I guess) since the sizes of the arrays used the program are growing so much, a sqaure 10 times the size takes about 100 times the length to fill.
there's something wierd about the filling. I'm not really sure how it happens but it's always leaving a few un-filled pixels. Not much at all, but it's really wierd.
My flood fill function uses 4 helper-functions: get and set pixel color, checking if it's a color to fill, and checking if that's a pixel that has been checked before.
Here are all the functions:
getPixelColor = (x, y) => {
let pixelColor = [];
for (let i = 0; i < pixDens; ++i) {
for (let j = 0; j < pixDens; ++j) {
index = 4 * ((y * pixDens + j) * width * pixDens + (x * pixDens + i));
pixelColor[0] = pixels[index];
pixelColor[1] = pixels[index + 1];
pixelColor[2] = pixels[index + 2];
pixelColor[3] = pixels[index + 3];
}
}
return pixelColor;
};
setPixelColor = (x, y, currentColor) => { //Remember to loadPixels() before using this function, and to updatePixels() after.
for (let i = 0; i < pixDens; ++i) {
for (let j = 0; j < pixDens; ++j) {
index = 4 * ((y * pixDens + j) * width * pixDens + (x * pixDens + i));
pixels[index] = currentColor[0];
pixels[index + 1] = currentColor[1];
pixels[index + 2] = currentColor[2];
pixels[index + 3] = currentColor[3];
}
}
}
isDuplicate = (posHistory, vector) => {
for (let i = 0; i < posHistory.length; ++i) {
if (posHistory[i].x === vector.x && posHistory[i].y === vector.y) {
return true;
}
}
return false;
}
compareColors = (firstColor, secondColor) => {
for (let i = 0; i < firstColor.length; ++i) {
if (firstColor[i] !== secondColor[i]) {
return false;
}
}
return true;
}
floodFill = () => {
loadPixels();
let x = floor(mouseX);
let y = floor(mouseY);
let startingColor = getPixelColor(x, y);
if (compareColors(startingColor, currentColor)) {
return false;
}
let pos = [];
pos.push(createVector(x, y));
let posHistory = [];
posHistory.push(createVector(x, y));
while (pos.length > 0) {
x = pos[0].x;
y = pos[0].y;
pos.shift();
if (x <= width && x >= 0 && y <= height && y >= 0) {
setPixelColor(x, y, currentColor);
let xMinus = createVector(x - 1, y);
if (!isDuplicate(posHistory, xMinus) && compareColors(getPixelColor(xMinus.x, xMinus.y), startingColor)) {
pos.push(xMinus);
posHistory.push(xMinus);
}
let xPlus = createVector(x + 1, y);
if (!isDuplicate(posHistory, xPlus) && compareColors(getPixelColor(xPlus.x, xPlus.y), startingColor)) {
pos.push(xPlus);
posHistory.push(xPlus);
}
let yMinus = createVector(x, y - 1);
if (!isDuplicate(posHistory, yMinus) && compareColors(getPixelColor(yMinus.x, yMinus.y), startingColor)) {
pos.push(yMinus);
posHistory.push(yMinus);
}
let yPlus = createVector(x, y + 1);
if (!isDuplicate(posHistory, yPlus) && compareColors(getPixelColor(yPlus.x, yPlus.y), startingColor)) {
pos.push(yPlus);
posHistory.push(yPlus);
}
}
}
updatePixels();
}
I would really apprciate it if someone could help me solve the problems with the functions.
Thank you very much!!
EDIT: So I updated my flood fill function itself and removed an array of colors that I never used. this array was pretty large and a few push() and a shift() methods called on it on pretty much every run.
UNFORTUNATLY, the execution time is 99.9% the same for small shapes (for example, a fill of 10,000 takes the same 0.5 seconds, but large fills, like 100,000 pixels now takes about 30 seconds and not 40, so that's a step in the right direction.
I guess that RAM usage is down as well since it was a pretty large array but I didn't measured it.
The wierd problem where it leaves un-filled pixels behind is still here as well.

A little suggestion:
You don't actually have to use the posHistory array to determine whether to set color. If the current pixel has the same color as startingColor then set color, otherwise don't set. This would have the same effect.
The posHistory array would grow larger and larger during execution. As a result, a lot of work has to be done just to determine whether to fill a single pixel. I think this might be the reason behind your code running slowly.
As for the "weird thing":
This also happened to me before. I think that's because the unfilled pixels do not have the same color as startingColor. Say you draw a black shape on a white background, you would expect to see some gray pixels (close to white) between the black and white parts somewhere. These pixels play the role of smoothing the shape.

Operations on canavs image in js frozen browser

I wrote js script which performs various operations (eg. summing up photos with a constant, square root, moving, applying filters) on the pictures in the canvas. But for large images (eg. 2000x200 pixels), the script frozen/crashes the browser (tested on Firefox), in addition, everything takes a long time.
function get_pixel (x, y, canvas)
{
var ctx = canvas.getContext("2d");
var imgData = ctx.getImageData(x, y, 1, 1);
return imgData.data;
}
function set_pixel (x, y, canvas, red, green, blue, alpha)
{
var ctx = canvas.getContext('2d');
var imgData = ctx.getImageData(0, 0, canvas.width, canvas.height),
pxData = imgData.data,
length = pxData.length;
var i = (x + y * canvas.width) * 4;
pxData[i] = red;
pxData[i + 1] = green;
pxData[i + 2] = blue;
pxData[i + 3] = alpha;
ctx.putImageData (imgData, 0, 0);
}
function sum (number, canvas1, canvas2)
{
show_button_normalization (false);
asyncLoop(
{
length : 5,
functionToLoop : function(loop, i){
setTimeout(function(){
asyncLoop(
{
length : 5,
functionToLoop : function(loop, i){
setTimeout(function(){
var pixel1 = get_pixel (i, j, canvas1);
var pixel2;
if (canvas2 != null)
{
pixel2 = get_pixel (i, j, canvas2);
}
else
{
pixel2 = new Array(4);
pixel2[0] = number;
pixel2[1] = number;
pixel2[2] = number;
pixel2[3] = number;
}
var pixel = new Array(4);
pixel[0] = parseInt (parseInt (pixel1[0]*0.5) + parseInt (pixel2[0]*0.5));
pixel[1] = parseInt (parseInt (pixel1[1]*0.5) + parseInt (pixel2[1]*0.5));
pixel[2] = parseInt (parseInt (pixel1[2]*0.5) + parseInt (pixel2[2]*0.5));
pixel[3] = parseInt (parseInt (pixel1[3]*0.5) + parseInt (pixel2[3]*0.5));
set_pixel (i, j, image1_a, pixel[0], pixel[1], pixel[2], pixel[3]);
loop();
},1000);
},
});
loop();
},1000);
},
});
/*for (var i=0; i<canvas1.width; i++)
{
for (var j=0; j<canvas1.height; j++)
{
var pixel1 = get_pixel (i, j, canvas1);
var pixel2;
if (canvas2 != null)
{
pixel2 = get_pixel (i, j, canvas2);
}
else
{
pixel2 = new Array(4);
pixel2[0] = number;
pixel2[1] = number;
pixel2[2] = number;
pixel2[3] = number;
}
var pixel = new Array(4);
pixel[0] = parseInt (parseInt (pixel1[0]*0.5) + parseInt (pixel2[0]*0.5));
pixel[1] = parseInt (parseInt (pixel1[1]*0.5) + parseInt (pixel2[1]*0.5));
pixel[2] = parseInt (parseInt (pixel1[2]*0.5) + parseInt (pixel2[2]*0.5));
pixel[3] = parseInt (parseInt (pixel1[3]*0.5) + parseInt (pixel2[3]*0.5));
set_pixel (i, j, image1_a, pixel[0], pixel[1], pixel[2], pixel[3]);
}
}*/
}
Is it possible to fix it?

Process pixels together!!
Looking at the code I would say that Firefox crashing and/or taking a long time is not a surprise at all. An image that is 2000 by 2000 pixels has 4 million pixels. I don't know what asyncLoop does but to me it looks like you are using timers to set groups of 5 pixels at a time. This is horrifically inefficient.
Problems with your code
Even looking at the commented code (which I assume is an alternative approch) you are processing the pixels with way to much overhead.
The array pixel you get from the function getPixel which returns the pixel array that is part of the object getImageData returns. If you look at the details of getImageData and te return object imageData you will see that the array is a typed array of type Uint8ClampedArray
That means most of the code you use to mix the pixels is redundant as that is done by javascript automatically when it assigns a number to any typed array.
pixel[0] = parseInt (parseInt (pixel1[0]*0.5) + parseInt (pixel2[0]*0.5));
Will be much quicker if you use
pixel[0] = (pixel1[0] + pixel2[0]) * 0.5; // a * n + b * n is the same as ( a+ b) *n
// with one less multiplication.
Standard simple image processing
But even then using a function call for each pixel adds a massive overhead to the basic operation you are performing. You should fetch all the pixels in one go and process them as two flat arrays.
Your sum function should look more like
function sum (number, canvas1, canvas2){
var i, data, ctx, imgData, imgData1, data1;
ctx = canvas1.getContext("2d");
imgData = ctx.getImageData(0, 0, canvas1.width, canvas1.height);
data = imgData.data; // get the array of pixels
if(canvas2 === null){
i = data.length;
number *= 0.5; // pre calculate number
while(i-- > 0){
data[i] = data[i] * 0.5 + number;
}
}else{
if(canvas1.width !== canvas2.width || canvas1.height !== canvas2.height){
throw new RangeError("Canvas size miss-match, can not process data as requested.");
}
data1 = canvas2.getContext("2d").getImageData(0,0,canvas2.width, canvas2.height).data
i = data.length;
while(i-- > 0){
data[i] = (data[i] + data1[i]) * 0.5;
}
}
ctx.setImageData(imgData,0,0); // put the new pixels back to the canvas
}
Bit math is quicker
You can improve on that if you use a bit of bit manipulation. Using a 32 bit typed array you can divide then add four 8 bit values in parallel (4* approx quicker for pixel calculations).
Note that this method will round down by one value a little more often than it should. ie Math.floor(199 * 233) === 216 is true while the method below will return 215. This can be corrected for by using the bottom bit of both inputs to add to the result. This completely eliminates the rounding error but the processing cost in my view is not worth the improvement. I have included the fix as commented code.
Note this method will only work for a / n + b / m where n and m are equal to 2^p and p is an integer > 0 and < 7 (in other words only if n and m are 2,4,8,16,32,64,127) and you must mask out the bottom p bits for a and b
Example performs C = C * 0.5 + C1 * 0.5 when C and C1 represent each R,G,B,A channel for canvas1 and canvas2
function sum (number, canvas1, canvas2){
var i, data, ctx, imgData, data32, data32A;
// this number is used to remove the bottom bit of each color channel
// The bottom bit is redundant as divide by 2 removes it
const botBitMask = 0b11111110111111101111111011111110;
// mask for rounding error (not used in this example)
// const botBitMaskA = 0b00000001000000010000000100000001;
ctx = canvas1.getContext("2d");
imgData = ctx.getImageData(0, 0, canvas1.width, canvas1.height);
data32 = new Uint32Array(imgData.data.buffer);
i = data32.length; // get the length that is 1/4 the size
if(canvas2 === null){
number >>= 1; // divide by 2
// fill to the 4 channels RGBA
number = (number << 24) + (number << 16) + (number << 8) + number;
// get reference to the 32bit version of the pixel data
while(i-- > 0){
// Remove bottom bit of each channel and then divide each channel by 2 using zero fill right shift (>>>) then add to number
data32[i] = ((data32[i] & botBitMask) >>> 1) + number;
}
}else{
if(canvas1.width !== canvas2.width || canvas1.height !== canvas2.height){
throw new RangeError("Canvas size miss-match, can not process data as requested.");
}
data32A = new Uint32Array(canvas2.getContext("2d").getImageData(0,0,canvas2.width, canvas2.height).data.buffer);
i = data32.length;
while(i-- > 0){
// for fixing rounding error include the following line removing the second one. Do the same for the above loop but optimise for number
// data32[i] = (((data32[i] & botBitMask) >>> 1) + ((data32A[i] & botBitMask) >>> 1)) | ((data32[i] & botBitMaskA) | (data32A[i] & botBitMaskA))
data32[i] = ((data32[i] & botBitMask) >>> 1) + ((data32A[i] & botBitMask) >>> 1);
}
}
ctx.setImageData(imgData,0,0); // put the new pixels back to the canvas
}
With all that you should not have any major problems. Though you will still have the page blocked while the image is being processed (depending on the machine and the image size it may take up to a second or 2)
Other solutions.
If you want to stop the image processing from blocking the page you can use a web worker and just send the data to them to process synchronously. You can find out how to do that but just searching stackOverflow.
Or use WebGL to process the images.
And you have one final option. The canvas api uses the GPU to do all its rendering and if you understand the way blending and compositing works you can do a surprising amount of maths using the canvas.
For example you can multiply all pixels RGBA channels with a value 0-1 using the following.
// multiplies all channels in source canvas by val and returns the resulting canvas
// returns the can2 the result of each pixel
// R *= val;
// G *= val;
// B *= val;
// A *= val;
function multiplyPixels(val, source)
var sctx = source.getContext("2d");
// need two working canvas. I create them here but if you are doing this
// many times you should create them once and reuse them
var can1 = document.createElement("canvas");
var can2 = document.createElement("canvas");
can1.width = can2.width = source.width;
can1.height= can2.height = source.height;
var ctx1 = can1.getContext("2d");
var ctx2 = can2.getContext("2d");
var chanMult = Math.round(255 * val);
// clamp it to 0-255 inclusive
chanMult = chanMult < 0 ? 0 : chanMult > 255 ? 255 : chanMult;
ctx1.drawImage(source,0,0); // copy the source
// multiply all RGB pixels by val
ctx1.fillStyle = "rgba(" + chanMult + "," + chanMult + "," + chanMult + ",1)";
ctx1.globalCompositeOperation = "multiply";
ctx1.fillRect(0, 0, source.width, source.height);
// now multiply the alpha channel by val. Clamp it to 0-1
ctx2.globalAlpha = val < 0 ? 0 : val > 1 ? 1 : val;
ctx2.drawImage(can1,0,0);
return can2;
}
There are quite a few composite operation that you can use in combination to do multiplication, addition, subtraction and division. Note though the accuracy is a little less than 8bits as addition and subtraction requires weighted values to compensate for the blending's (automatic) multiplication. Also the alpha channel must be handled separately from the RGB channels using globalAlpha and the compositing operations.
Realtime
The processing you are doing is very simple and a 2000 by 2000 pixel image can easily be processed in realtime. WebGl filter is an example of using webGL to do image processing. Though the filter system is not modular and the code is very old school it is a good backbone for webGL filters and offers much higher quality results because it uses floating point RGBA values.

Find regions of similar color in image

I've been working on this problem for some time now with little promising results. I am trying to split up an image into connected regions of similar color. (basically split a list of all the pixels into multiple groups (each group containing the coordinates of the pixels that belong to it and share a similar color).
For example:
http://unsplash.com/photos/SoC1ex6sI4w/
In this image the dark clouds at the top would probably fall into one group. Some of the grey rock on the mountain in another, and some of the orange grass in another. The snow would be another - the red of the backpack - etc.
I'm trying to design an algorithm that will be both accurate and efficient (it needs to run in a matter of ms on midrange laptop grade hardware)
Below is what I have tried:
Using a connected component based algorithm to go through every pixel from top left scanning every line of pixels from left to right (and comparing the current pixel to the top pixel and left pixel). Using the CIEDE2000 color difference formula if the pixel at the top or left was within a certain range then it would be considered "similar" and part of the group.
This sort of worked - but the problem is it relies on color regions having sharp edges - if any color groups are connected by a soft gradient it will travel down that gradient and continue to "join" the pixels as the difference between the individual pixels being compared is small enough to be considered "similar".
To try to fix this I chose to set every visited pixel's color to the color of most "similar" adjacent pixel (either top or left). If there are no similar pixels than it retains it's original color. This somewhat fixes the issue of more blurred boundaries or soft edges because the first color of a new group will be "carried" along as the algorithm progresses and eventually the difference between that color and the current compared color will exceed the "similarity" threashold and no longer be part of that group.
Hopefully this is making sense. The problem is neither of these options are really working. On the image above what is returned are not clean groups but noisy fragmented groups that is not what I am looking for.
I'm not looking for code specifically - but more ideas as to how an algorithm could be structured to successfully combat this problem. Does anyone have ideas about this?
Thanks!

You could convert from RGB to HSL to make it easier to calculate the distance between the colors. I'm setting the color difference tolerance in the line:
if (color_distance(original_pixels[i], group_headers[j]) < 0.3) {...}
If you change 0.3, you can get different results.
See it working.
Please, let me know if it helps.
function hsl_to_rgb(h, s, l) {
// from http://stackoverflow.com/questions/2353211/hsl-to-rgb-color-conversion
var r, g, b;
if (s == 0) {
r = g = b = l; // achromatic
} else {
var hue2rgb = function hue2rgb(p, q, t) {
if (t < 0) t += 1;
if (t > 1) t -= 1;
if (t < 1 / 6) return p + (q - p) * 6 * t;
if (t < 1 / 2) return q;
if (t < 2 / 3) return p + (q - p) * (2 / 3 - t) * 6;
return p;
}
var q = l < 0.5 ? l * (1 + s) : l + s - l * s;
var p = 2 * l - q;
r = hue2rgb(p, q, h + 1 / 3);
g = hue2rgb(p, q, h);
b = hue2rgb(p, q, h - 1 / 3);
}
return [Math.round(r * 255), Math.round(g * 255), Math.round(b * 255)];
}
function rgb_to_hsl(r, g, b) {
// from http://stackoverflow.com/questions/2353211/hsl-to-rgb-color-conversion
r /= 255, g /= 255, b /= 255;
var max = Math.max(r, g, b),
min = Math.min(r, g, b);
var h, s, l = (max + min) / 2;
if (max == min) {
h = s = 0; // achromatic
} else {
var d = max - min;
s = l > 0.5 ? d / (2 - max - min) : d / (max + min);
switch (max) {
case r:
h = (g - b) / d + (g < b ? 6 : 0);
break;
case g:
h = (b - r) / d + 2;
break;
case b:
h = (r - g) / d + 4;
break;
}
h /= 6;
}
return [h, s, l];
}
function color_distance(v1, v2) {
// from http://stackoverflow.com/a/13587077/1204332
var i,
d = 0;
for (i = 0; i < v1.length; i++) {
d += (v1[i] - v2[i]) * (v1[i] - v2[i]);
}
return Math.sqrt(d);
};
function round_to_groups(group_nr, x) {
var divisor = 255 / group_nr;
return Math.ceil(x / divisor) * divisor;
};
function pixel_data_to_key(pixel_data) {
return pixel_data[0].toString() + '-' + pixel_data[1].toString() + '-' + pixel_data[2].toString();
}
function posterize(context, image_data, palette) {
for (var i = 0; i < image_data.data.length; i += 4) {
rgb = image_data.data.slice(i, i + 3);
hsl = rgb_to_hsl(rgb[0], rgb[1], rgb[2]);
key = pixel_data_to_key(hsl);
if (key in palette) {
new_hsl = palette[key];
new_rgb = hsl_to_rgb(new_hsl[0], new_hsl[1], new_hsl[2]);
rgb = hsl_to_rgb(hsl);
image_data.data[i] = new_rgb[0];
image_data.data[i + 1] = new_rgb[1];
image_data.data[i + 2] = new_rgb[2];
}
}
context.putImageData(image_data, 0, 0);
}
function draw(img) {
var canvas = document.getElementById('canvas');
var context = canvas.getContext('2d');
context.drawImage(img, 0, 0, canvas.width, canvas.height);
img.style.display = 'none';
var image_data = context.getImageData(0, 0, canvas.width, canvas.height);
var data = image_data.data;
context.drawImage(target_image, 0, 0, canvas.width, canvas.height);
data = context.getImageData(0, 0, canvas.width, canvas.height).data;
original_pixels = [];
for (i = 0; i < data.length; i += 4) {
rgb = data.slice(i, i + 3);
hsl = rgb_to_hsl(rgb[0], rgb[1], rgb[2]);
original_pixels.push(hsl);
}
group_headers = [];
groups = {};
for (i = 0; i < original_pixels.length; i += 1) {
if (group_headers.length == 0) {
group_headers.push(original_pixels[i]);
}
group_found = false;
for (j = 0; j < group_headers.length; j += 1) {
// if a similar color was already observed
if (color_distance(original_pixels[i], group_headers[j]) < 0.3) {
group_found = true;
if (!(pixel_data_to_key(original_pixels[i]) in groups)) {
groups[pixel_data_to_key(original_pixels[i])] = group_headers[j];
}
}
if (group_found) {
break;
}
}
if (!group_found) {
if (group_headers.indexOf(original_pixels[i]) == -1) {
group_headers.push(original_pixels[i]);
}
if (!(pixel_data_to_key(original_pixels[i]) in groups)) {
groups[pixel_data_to_key(original_pixels[i])] = original_pixels[i];
}
}
}
posterize(context, image_data, groups)
}
var target_image = new Image();
target_image.crossOrigin = "";
target_image.onload = function() {
draw(target_image)
};
target_image.src = "http://i.imgur.com/zRzdADA.jpg";
canvas {
width: 300px;
height: 200px;
}
<canvas id="canvas"></canvas>

You can use "Mean Shift Filtering" algorithm to do the same.
Here's an example.
You will have to determine function parameters heuristically.
And here's the wrapper for the same in node.js
npm Wrapper for meanshift algorithm
Hope this helps!

The process you are trying to complete is called Image Segmentation and it's a well studied area in computer vision, with hundreds of different algorithms and implementations.
The algorithm you mentioned should work for simple images, however for real world images such as the one you linked to, you will probably need a more sophisticated algorithm, maybe even one that is domain specific (are all of your images contains a view?).
I have little experience in Node.js, however from Googling a bit I found the GraphicsMagic library, which as a segment function that might do the job (haven't verified).
In any case, I would try looking for "Image segmentation" libraries, and if possible, not limit myself only to Node.js implementations, as this language is not the common practice for writing vision applications, as opposed to C++ / Java / Python.

I would try a different aproach. Check out this description of how a flood fill algorithm could work:
Create an array to hold information about already colored coordinates.
Create a work list array to hold coordinates that must be looked at. Put the start position in it.
When the work list is empty, we are done.
Remove one pair of coordinates from the work list.
If those coordinates are already in our array of colored pixels, go back to step 3.
Color the pixel at the current coordinates and add the coordinates to the array of colored pixels.
Add the coordinates of each adjacent pixel whose color is the same as the starting pixel’s original color to the work list.
Return to step 3.
The "search approach" is superior because it does not only search from left to right, but in all directions.

You might look at k-means clustering.
http://docs.opencv.org/3.0-beta/modules/core/doc/clustering.html

Creating a colorwheel with javascript

I'm trying to figure out a way to create a colorwheel similar to this:, in JS. The colorwheel should have ~4096(*) elements the size of a single pixel, with their color set via a CSS background rule.
I know this is not how you're supposed to create a colorpicker, and that normally you should never have so many single-pixel DOM elements for anything. You don't need to tell this to me or try to figure out a different way to accomplish this.
I'd also be interested in having each of the pixel-sized elements be left-aligned, instead of for example absolutely-positioned.
(x): 4096 is the number of all shorthand HEX codes (#XXX), but the colorwheel doesn't have monochrome values, except for white. So the actual number of unique colors would be 4081(?)
This is the code I've managed to come up with (pretty much nothing):
var p = ['0', '1', '2', '3', '4', '5', '6', '7', '8', '9', 'a', 'b', 'c', 'd', 'e', 'f'];
for(var i = 0; i < p.length; i++)
{
for(var j = 0; j < p.length; j++)
{
for(var k = 0; k < p.length; k++)
{
document.write('<div style="background:#' + p[i] + p[j] + p[k] +'"></div>');
}
}
}
And is the result I get (zoomed 10x), with the following CSS:
div
{
float: left;
width: 10px;
height: 10px;
}
As you can see, it's pretty far from what I want. So any help would be greatly appreciated, since I'm quite lost on how to accomplish this. I've got the colors, but I don't know how to arrange them in a wheel.
EDIT:
Unless someone happens to give me a pretty much complete solution to this, it seems that this is a bit above my skill-level, at the moment. So I'm willing to settle for something that (I assume) would be easier to implement.
Basically another form of output that would be acceptable, would be something like this:

I had previously posted an answer that relied upon the browser converting a colour from the HSL colour-space to the RGB one. Unfortunately, this was an approach that while simple, didn't produce the images shown.
In order to correctly produce the desired outputs, a far easier method is to instead utilise the HSV colour model - one which is quite similar to the HSL one.
When we use the correct colour-space, determining the correct colour for any given pixel is a simple matter of interpolating 3 values - all of which change linearly (the amount of change remains constant. 0 at one end, 1 at the other end will mean 0.5 at a point half-way between them)
First, lets look at your desired outputs and how our HSV inputs change with respect to X and Y coordinates. We'll start of with the easier to visualise and create - the flat strip.
Flat strip
We can observe the following about this image:
The hue ranges from 0 at the left edge to 360 at the right edge.
The sat ranges from 0 at the top edge to 1, half-way between the top
& bottom edges. Beyond the point it reaches 1, it's clamped to 1.
The val ranges from 0 half-way between the top and bottom to 1 at
the bottom. Before the point it is 0, it's clamped to 0.
Now, let's look at the wheel representation of the same picture.
Colour wheel
If you look closely, you'll see that the strip, when wrapped into a circle will produce the colour-wheel. The centre of the wheel corresponds to the top-edge of the strip and the outer edge corresponds to the bottom edge.
This is also how we can show that the original wheel you showed is a somewhat innacurate representation of the colour-space, since it has the red on the left edge. Basically, your image has been flipped horizontally. ;)
Okay, that then shows how the images are related to the HSV colour-space. Next, we really need to be able to create them on the fly. This is fairly straight-forward now we've the plan for how to go about it.
Once this is done, we'll end up with 2 canvases - these were the images I used for the annotations. From there, there's a couple of ways you could go about it.
You could: allow the user to pick any colour they like, before returning to them the closest colour from the set of short-hand hex values.
Or you could: back-up a little, only setting colours on the canvas to those which are in the same set of short-hand values.
One will take longer to calculate the chosen colour, while the other will take longer to calculate the initial images.
I have left that part of the implementation up to you, instead opting to: eschew the idea of so many DOM elements, using just 2 canvas instead and also, to simply pick the colour exactly as chosen, based off the code I linked to # MDN.
function newEl(tag){return document.createElement(tag)}
window.addEventListener('load', onDocLoaded, false);
function onDocLoaded(evt)
{
var strip = makeCanvas();
strip.addEventListener('mousemove', pick);
document.body.appendChild( strip );
var wheel = makeWheel(256);
wheel.addEventListener('mousemove', pick);
document.body.appendChild( wheel );
}
var hsv2rgb = function(hsv) {
var h = hsv.hue, s = hsv.sat, v = hsv.val;
var rgb, i, data = [];
if (s === 0) {
rgb = [v,v,v];
} else {
h = h / 60;
i = Math.floor(h);
data = [v*(1-s), v*(1-s*(h-i)), v*(1-s*(1-(h-i)))];
switch(i) {
case 0:
rgb = [v, data[2], data[0]];
break;
case 1:
rgb = [data[1], v, data[0]];
break;
case 2:
rgb = [data[0], v, data[2]];
break;
case 3:
rgb = [data[0], data[1], v];
break;
case 4:
rgb = [data[2], data[0], v];
break;
default:
rgb = [v, data[0], data[1]];
break;
}
}
return rgb;
};
function clamp(min, max, val)
{
if (val < min) return min;
if (val > max) return max;
return val;
}
function makeCanvas()
{
var can, ctx;
can = newEl('canvas');
ctx = can.getContext('2d');
can.width = 360;
can.height = 100;
var span = newEl('span');
var imgData = ctx.getImageData(0,0,360,100);
var xPos, yPos, index;
var height=imgData.height, width=imgData.width;
for (yPos=0; yPos<height; yPos++)
{
for (xPos=0; xPos<width; xPos++)
{
// this is the point at which the S & V values reach
// the peaks or start to change. 2 means height/2
// so a divisor of 3 would mean the 'break-points'
// were at the 1/3 and 2/3 positions
// while a divisor of 4 would imply 1/4 and 3/4
//
// Have a look at the generated images using the eye-
// dropper tool of an image program (Gimp, Photoshop,
// etc) that allows you to choose the HSV colour
// model, to get a better idea of what I'm saying
// here.
var divisor = 2;
var hue = xPos;
var sat = clamp(0, 1, yPos / (height/divisor) );
var val = clamp(0, 1, (height-yPos) / (height/divisor) );
var rgb = hsv2rgb( {hue:hue, sat:sat, val:val} );
index = 4 * (xPos + yPos*360);
imgData.data[ index + 0 ] = rgb[0] * 255; // r
imgData.data[ index + 1 ] = rgb[1] * 255; // g
imgData.data[ index + 2 ] = rgb[2] * 255; // b
imgData.data[ index + 3 ] = 255; // a
}
}
ctx.putImageData(imgData, 0, 0);
return can;
}
// see the comment in the above function about the divisor. I've
// hard-coded it here, to 2
// diameter/2 corresponds to the max-height of a strip image
function makeWheel(diameter)
{
var can = newEl('canvas');
var ctx = can.getContext('2d');
can.width = diameter;
can.height = diameter;
var imgData = ctx.getImageData(0,0,diameter,diameter);
var maxRange = diameter / 2;
for (var y=0; y<diameter; y++)
{
for (var x=0; x<diameter; x++)
{
var xPos = x - (diameter/2);
var yPos = (diameter-y) - (diameter/2);
var polar = pos2polar( {x:xPos, y:yPos} );
var sat = clamp(0,1,polar.len / ((maxRange/2)));
var val = clamp(0,1, (maxRange-polar.len) / (maxRange/2) );
var rgb = hsv2rgb( {hue:polar.ang, sat:sat, val:val} );
var index = 4 * (x + y*diameter);
imgData.data[index + 0] = rgb[0]*255;
imgData.data[index + 1] = rgb[1]*255;
imgData.data[index + 2] = rgb[2]*255;
imgData.data[index + 3] = 255;
}
}
ctx.putImageData(imgData, 0,0);
return can;
}
function deg2rad(deg)
{
return (deg / 360) * ( 2 * Math.PI );
}
function rad2deg(rad)
{
return (rad / (Math.PI * 2)) * 360;
}
function pos2polar(inPos)
{
var vecLen = Math.sqrt( inPos.x*inPos.x + inPos.y*inPos.y );
var something = Math.atan2(inPos.y,inPos.x);
while (something < 0)
something += 2*Math.PI;
return { ang: rad2deg(something), len: vecLen };
}
function pick(event)
{
var can = this;
var ctx = can.getContext('2d');
var color = document.getElementById('color');
var x = event.layerX;
var y = event.layerY;
var pixel = ctx.getImageData(x, y, 1, 1);
var data = pixel.data;
var rgba = 'rgba(' + data[0] + ',' + data[1] +
',' + data[2] + ',' + (data[3] / 255) + ')';
color.style.background = rgba;
color.textContent = rgba;
}
canvas
{
border: solid 1px red;
}
<div id="color" style="width: 200px; height: 50px; float: left;"></div>

The problem is that you are mapping a three-axis system (R, G, B) on a two-axis system (x,y). In the colorwheel example the three axis are drawn like a circle, and it is very difficult to create this using only x and y coördinates, especially when the requirement is that the pixels (div's) should be left-aligned.
The best approach to accomplish this is to create a three-dimensional array with the color values, map this onto a two-dimensional array and then loop this two-dimensional array from left to right and top-down.
Like this:
var p = ['0', '1', '2', '3', '4', '5', '6', '7', '8', '9', 'a', 'b', 'c', 'd', 'e', 'f'];
for(var i = 0; i < p.length; i++)
{
for(var j = 0; j < p.length; j++)
{
for(var k = 0; k < p.length; k++)
{
var x = i - k * 0.5;
var y = j - k * 0.25;
background[x][y] = "#" + p[i] + p[j] + p[k];
}
}
}
for (var l = 0; l < maxX; l++)
{
for (var m = 0; m < maxY; m++)
{
document.write('<div style="background:#' + background[x][y] +'"></div>');
}
}
This is not a complete answer, just a possible first step to solve this. Maybe this can help you to create a good solution.

Reading MNIST dataset with javascript/node.js

I'm trying to decode the dataset from this source: http://yann.lecun.com/exdb/mnist/
There is a description of the "very simple" IDX file type in the bottom, but I cannot figure it out.
What I'm trying to achieve is something like:
var imagesFileBuffer = fs.readFileSync(__dirname + '/train-images-idx3-ubyte');
var labelFileBuffer = fs.readFileSync(__dirname + '/train-labels-idx1-ubyte');
var pixelValues = {};
Do magic
pixelValues are now like:
// {
// "0": [0,0,200,190,79,0... for all 784 pixels ... ],
// "4": [0,0,200,190,79,0... for all 784 pixels ... ],
etc for all image entries in the dataset. I've tried to figure out the structure of the binary files, but failed.

I realized there would be duplicate keys in my structure of the pixelValues object, so I made an array of objects of it instaed. The following code will create the structure I'm after:
var dataFileBuffer = fs.readFileSync(__dirname + '/train-images-idx3-ubyte');
var labelFileBuffer = fs.readFileSync(__dirname + '/train-labels-idx1-ubyte');
var pixelValues = [];
// It would be nice with a checker instead of a hard coded 60000 limit here
for (var image = 0; image <= 59999; image++) {
var pixels = [];
for (var x = 0; x <= 27; x++) {
for (var y = 0; y <= 27; y++) {
pixels.push(dataFileBuffer[(image * 28 * 28) + (x + (y * 28)) + 15]);
}
}
var imageData = {};
imageData[JSON.stringify(labelFileBuffer[image + 8])] = pixels;
pixelValues.push(imageData);
}
The structure of pixelValues is now something like this:
[
{5: [28,0,0,0,0,0,0,0,0,0...]},
{0: [0,0,0,0,0,0,0,0,0,0...]},
...
]
There are 28x28=784 pixel values, all varying from 0 to 255.
To render the pixels, use my for loops like I did above, rendering the first pixel in the upper left corner, then working towards the right.

Just a small improvement:
for (var image = 0; image <= 59999; image++) {
with 60000 there is an "entry" with null's at the very end of your pixelValues.
EDIT:
I got a little obsessed with details because I wanted to convert the MNIST dataset back to real and separate image files. So I have found more mistakes in your code.
it is definitely +16 because you have to skip the 16 Bytes of header data. This little mistake is reflected in your answer where the first pixel value of the first digit (with is a 5) is '28'. Which is actually the value that tells how many columns the image has - not the first pixel of the image.
Your nested for loops has to be turned inside-out to get you the right pixel order - asuming you will "rebuild" your image from the upper left corner down to the lower right corner. With your code the image will be flipped along the axis that goes from the upper left to the lower right corner.
So your code should be:
var dataFileBuffer = fs.readFileSync(__dirname + '/train-images-idx3-ubyte');
var labelFileBuffer = fs.readFileSync(__dirname + '/train-labels-idx1-ubyte');
var pixelValues = [];
// It would be nice with a checker instead of a hard coded 60000 limit here
for (var image = 0; image <= 59999; image++) {
var pixels = [];
for (var y = 0; y <= 27; y++) {
for (var x = 0; x <= 27; x++) {
pixels.push(dataFileBuffer[(image * 28 * 28) + (x + (y * 28)) + 16]);
}
}
var imageData = {};
imageData[JSON.stringify(labelFileBuffer[image + 8])] = pixels;
pixelValues.push(imageData);
}
Those little details wouldn't be an issue if you stay consistent and use those extracted data to -for example- train neural networks, because you will do the same with the testing dataset. But if you want to take that MNIST trained neural network and try to verify it with real life hand written digits, you will get bad results because the real images are not flipped.

Hopefully this helps someone out, I have added the ability to save the images to a png. Please note you will need to have an images directory
var fs = require('fs');
const {createCanvas} = require('canvas');
function readMNIST(start, end)
{
var dataFileBuffer = fs.readFileSync(__dirname + '\\test_images_10k.idx3-ubyte');
var labelFileBuffer = fs.readFileSync(__dirname + '\\test_labels_10k.idx1-ubyte');
var pixelValues = [];
for (var image = start; image < end; image++)
{
var pixels = [];
for (var y = 0; y <= 27; y++)
{
for (var x = 0; x <= 27; x++)
{
pixels.push(dataFileBuffer[(image * 28 * 28) + (x + (y * 28)) + 16]);
}
}
var imageData = {};
imageData["index"] = image;
imageData["label"] = labelFileBuffer[image + 8];
imageData["pixels"] = pixels;
pixelValues.push(imageData);
}
return pixelValues;
}
function saveMNIST(start, end)
{
const canvas = createCanvas(28, 28);
const ctx = canvas.getContext('2d');
var pixelValues = readMNIST(start, end);
pixelValues.forEach(function(image)
{
ctx.clearRect(0, 0, canvas.width, canvas.height);
for (var y = 0; y <= 27; y++)
{
for (var x = 0; x <= 27; x++)
{
var pixel = image.pixels[x + (y * 28)];
var colour = 255 - pixel;
ctx.fillStyle = `rgb(${colour}, ${colour}, ${colour})`;
ctx.fillRect(x, y, 1, 1);
}
}
const buffer = canvas.toBuffer('image/png')
fs.writeFileSync(__dirname + `\\images\\image${image.index}-${image.label}.png`, buffer)
})
}
saveMNIST(0, 5);