Get average color of image via Javascript

Not sure this is possible, but looking to write a script that would return the average hex or rgb value for an image. I know it can be done in AS but looking to do it in JavaScript.

AFAIK, the only way to do this is with <canvas/>...


Note, this will only work with images on the same domain and in browsers that support HTML5 canvas:

function getAverageRGB(imgEl) {

    var blockSize = 5, // only visit every 5 pixels
        defaultRGB = {r:0,g:0,b:0}, // for non-supporting envs
        canvas = document.createElement('canvas'),
        context = canvas.getContext && canvas.getContext('2d'),
        data, width, height,
        i = -4,
        rgb = {r:0,g:0,b:0},
        count = 0;

    if (!context) {
        return defaultRGB;

    height = canvas.height = imgEl.naturalHeight || imgEl.offsetHeight || imgEl.height;
    width = canvas.width = imgEl.naturalWidth || imgEl.offsetWidth || imgEl.width;

    context.drawImage(imgEl, 0, 0);

    try {
        data = context.getImageData(0, 0, width, height);
    } catch(e) {
        /* security error, img on diff domain */
        return defaultRGB;

    length =;

    while ( (i += blockSize * 4) < length ) {
        rgb.r +=[i];
        rgb.g +=[i+1];
        rgb.b +=[i+2];

    // ~~ used to floor values
    rgb.r = ~~(rgb.r/count);
    rgb.g = ~~(rgb.g/count);
    rgb.b = ~~(rgb.b/count);

    return rgb;


For IE, check out excanvas.

Figured I'd post a project I recently came across to get dominant color:

Color Thief

A script for grabbing the dominant color or a representative color palette from an image. Uses javascript and canvas.

The other solutions mentioning and suggesting dominant color never really answer the question in proper context ("in javascript"). Hopefully this project will help those who want to do just that.

"Dominant Color" is tricky. What you want to do is compare the distance between each pixel and every other pixel in color space (Euclidean Distance), and then find the pixel whose color is closest to every other color. That pixel is the dominant color. The average color will usually be mud.

I wish I had MathML in here to show you Euclidean Distance. Google it.

I have accomplished the above execution in RGB color space using PHP/GD here:

This however is very computationally expensive. It will crash your system on large images, and will definitely crash your browser if you try it in the client. I have been working on refactoring my execution to: - store results in a lookup table for future use in the iteration over each pixel. - to divide large images into grids of 20px 20px for localized dominance. - to use the euclidean distance between x1y1 and x1y2 to figure out the distance between x1y1 and x1y3.

Please let me know if you make progress on this front. I would be happy to see it. I will do the same.

Canvas is definitely the best way to do this in the client. SVG is not, SVG is vector based. After I get the execution down, the next thing I want to do is get this running in the canvas (maybe with a webworker for each pixel's overall distance calculation).

Another thing to think about is that RGB is not a good color space for doing this in, because the euclidean distance between colors in RGB space is not very close to the visual distance. A better color space for doing this might be LUV, but I have not found a good library for this, or any algorythims for converting RGB to LUV.

An entirely different approach would be to sort your colors in a rainbow, and build a histogram with tolerance to account for varying shades of a color. I have not tried this, because sorting colors in a rainbow is hard, and so are color histograms. I might try this next. Again, let me know if you make any progress here.

First: it can be done without HTML5 Canvas or SVG.
Actually, someone just managed to generate client-side PNG files using JavaScript, without canvas or SVG, using the data URI scheme.

Second: you might actually not need Canvas, SVG or any of the above at all.
If you only need to process images on the client side, without modifying them, all this is not needed.

You can get the source address from the img tag on the page, make an XHR request for it - it will most probably come from the browser cache - and process it as a byte stream from Javascript.
You will need a good understanding of the image format. (The above generator is partially based on libpng sources and might provide a good starting point.)

I would say via the HTML canvas tag.

You can find here a post by @Georg talking about a small code by the Opera dev :

// Get the CanvasPixelArray from the given coordinates and dimensions.
var imgd = context.getImageData(x, y, width, height);
var pix =;

// Loop over each pixel and invert the color.
for (var i = 0, n = pix.length; i < n; i += 4) {
    pix[i  ] = 255 - pix[i  ]; // red
    pix[i+1] = 255 - pix[i+1]; // green
    pix[i+2] = 255 - pix[i+2]; // blue
    // i+3 is alpha (the fourth element)

// Draw the ImageData at the given (x,y) coordinates.
context.putImageData(imgd, x, y);

This invert the image by using the R, G and B value of each pixel. You could easily store the RGB values, then round up the Red, Green and Blue arrays, and finally converting them back into an HEX code.

I recently came across a jQuery plugin which does what I originally wanted in regards to getting a dominiate color from an image.

Javascript does not have access to an image's individual pixel color data. At least, not maybe until html5 ... at which point it stands to reason that you'll be able to draw an image to a canvas, and then inspect the canvas (maybe, I've never done it myself).

All-In-One Solution

I would personally combine Color Thief along with this modified version of Name that Color to obtain a more-than-sufficient array of dominant color results for images.


Consider the following image:

enter image description here

You can use the following code to extract image data relating to the dominant color:

let color_thief = new ColorThief();
let sample_image = new Image();

sample_image.onload = () => {
  let result ='#' + color_thief.getColor(sample_image).map(x => {
    const hex = x.toString(16);
    return hex.length === 1 ? '0' + hex : hex;


  console.log(result[0]); // #f0c420     : Dominant HEX/RGB value of closest match
  console.log(result[1]); // Moon Yellow : Dominant specific color name of closest match
  console.log(result[2]); // #ffff00     : Dominant HEX/RGB value of shade of closest match
  console.log(result[3]); // Yellow      : Dominant color name of shade of closest match
  console.log(result[4]); // false       : True if exact color match

sample_image.crossOrigin = 'anonymous';
sample_image.src = document.getElementById('sample-image').src;

This is about "Color Quantization" that has several approachs like MMCQ (Modified Median Cut Quantization) or OQ (Octree Quantization). Different approach use K-Means to obtain clusters of colors.

I have putted all together here, since I was finding a solution for tvOS where there is a subset of XHTML, that has no <canvas/> element:

Generate the Dominant Colors for an RGB image with XMLHttpRequest

As pointed out in other answers, often what you really want the dominant color as opposed to the average color which tends to be brown. I wrote a script that gets the most common color and posted it on this gist

There is a online tool that helps you to find the average or the dominant color of image.You just have to upload a image from your computer and then click on the image. It gives the average color in HEX , RGB and HSV. It also find the color shades matching that color to choose from. I have used it multiple times.

Less accurate but fastest way to get average color of the image with datauri support:

function get_average_rgb(img) {
    var context = document.createElement('canvas').getContext('2d');
    if (typeof img == 'string') {
        var src = img;
        img = new Image;
        img.setAttribute('crossOrigin', ''); 
        img.src = src;
    context.imageSmoothingEnabled = true;
    context.drawImage(img, 0, 0, 1, 1);
    return context.getImageData(1, 1, 1, 1).data.slice(0,3);