I'm developing an app that has a painting feature. The user can paint on an image that is initially made of only pure black and pure white pixels. Later, after the user has finished painting, I need to do some processing on that image based on the colors of each pixel.
However, I realized that by the time I processed the image, the pixels weren't purely black/white anymore, but there were lots of greys in between, even if the user didn't paint anything. I wrote some code to check it and found out there were over 250 different colors on the image, while I was expecting only two (black and white). I suspect canvas is messing with my colors somehow, but I can't figure out why.
I hosted a demo on GitHub, showcasing the problem.
The image
This is the image. It is visibly made of only black and white pixels, but if you want to check by yourself you can use this website. It's source code is available on GitHub and I used it as a reference for my own color counting implementation.
My code
Here is the code where I load the image and count the unique colors. You can get the full source here.
class AppComponent {
/* ... */
// Rendering the image
ngAfterViewInit() {
this.context = this.canvas.nativeElement.getContext('2d');
const image = new Image();
image.src = 'assets/image.png';
image.onload = () => {
if (!this.context) return;
this.context.globalCompositeOperation = 'source-over';
this.context.drawImage(image, 0, 0, this.width, this.height);
};
}
// Counting unique colors
calculate() {
const imageData = this.context?.getImageData(0, 0, this.width, this.height);
const data = imageData?.data || [];
const uniqueColors = new Set();
for (let i = 0; i < data?.length; i += 4) {
const [red, green, blue, alpha] = data.slice(i, i + 4);
const color = `rgba(${red}, ${green}, ${blue}, ${alpha})`;
uniqueColors.add(color);
}
this.uniqueColors = String(uniqueColors.size);
}
This is the implementation from the other site:
function countPixels(data) {
const colorCounts = {};
for(let index = 0; index < data.length; index += 4) {
const rgba = `rgba(${data[index]}, ${data[index + 1]}, ${data[index + 2]}, ${(data[index + 3] / 255)})`;
if (rgba in colorCounts) {
colorCounts[rgba] += 1;
} else {
colorCounts[rgba] = 1;
}
}
return colorCounts;
}
As you can see, besides the implementations being similar, they output very different results - my site says I have 256 unique colors, while the other says there's only two. I also tried to just copy and paste the implementation but I got the same 256. That's why I imagine the problem is in my canvas, but I can't figure out what's going on.
You are scaling your image, and since you didn't tell which interpolation algorithm to use, a default smoothing one is being used.
This will make all the pixels that were on fixed boundaries and should now span on multiple pixels to be "mixed" with their white neighbors and produce shades of gray.
There is an imageSmoothingEnabled property that tells the browser to use a closest-neighbor algorithm, which will improve the situation, but even then you may not have a perfect result:
const canvas = document.querySelector("canvas");
const width = canvas.width = innerWidth;
const height = canvas.height = innerHeight;
const ctx = canvas.getContext("2d");
const img = new Image();
img.crossOrigin = "anonymous";
img.src = "https://raw.githubusercontent.com/ajsaraujo/unique-color-count-mre/master/src/assets/image.png";
img.decode().then(() => {
ctx.imageSmoothingEnabled = false;
ctx.drawImage(img, 0, 0, width, height);
const data = ctx.getImageData(0, 0, width, height).data;
const pixels = new Set(new Uint32Array(data.buffer));
console.log(pixels.size);
});
<canvas></canvas>
So the best would be to not scale your image, or to do so in a computer friendly fashion (using a factor that is a multiple of 2).
I am trying to draw the following image to a canvas but it appears blurry despite defining the size of the canvas. As you can see below, the image is crisp and clear whereas on the canvas, it is blurry and pixelated.
and here is how it looks (the left one being the original and the right one being the drawn-on canvas and blurry.)
What am I doing wrong?
console.log('Hello world')
var c = document.getElementById('canvas')
var ctx = c.getContext('2d')
var playerImg = new Image()
// http://i.imgur.com/ruZv0dl.png sees a CLEAR, CRISP image
playerImg.src = 'http://i.imgur.com/ruZv0dl.png'
playerImg.width = 32
playerImg.height = 32
playerImg.onload = function() {
ctx.drawImage(playerImg, 0, 0, 32, 32);
};
#canvas {
background: #ABABAB;
position: relative;
height: 352px;
width: 512px;
z-index: 1;
}
<canvas id="canvas" height="352" width="521"></canvas>
The reason this is happening is because of Anti Aliasing.
Simply set the imageSmoothingEnabled to false like so
context.imageSmoothingEnabled = false;
Here is a jsFiddle verson
jsFiddle : https://jsfiddle.net/mt8sk9cb/
var c = document.getElementById('canvas')
var ctx = c.getContext('2d')
var playerImg = new Image()
// http://i.imgur.com/ruZv0dl.png sees a CLEAR, CRISP image
playerImg.src = 'http://i.imgur.com/ruZv0dl.png'
playerImg.onload = function() {
ctx.imageSmoothingEnabled = false;
ctx.drawImage(playerImg, 0, 0, 256, 256);
};
Your problem is that your css constraints of canvas{width:512}vs the canvas property width=521will make your browser resample the whole canvas.
To avoid it, remove those css declarations.
var c = document.getElementById('canvas')
var ctx = c.getContext('2d')
var playerImg = new Image()
// http://i.imgur.com/ruZv0dl.png sees a CLEAR, CRISP image
playerImg.src = 'http://i.imgur.com/ruZv0dl.png'
playerImg.width = 32
playerImg.height = 32
playerImg.onload = function() {
ctx.drawImage(playerImg, 0, 0, 32, 32);
};
#canvas {
background: #ABABAB;
position: relative;
z-index: 1;
}
<canvas id="canvas" height="352" width="521"></canvas>
Also, if you were resampling the image (from 32x32 to some other size), #canvas' solution would have been the way to go.
As I encountered this older post for some of my issues, here's even more additional insight to blurry images to layer atop the 'imageSmoothingEnabled' solution.
This is more specifically for the use case of monitor specific rendering and only some people will have encountered this issue if they have been trying to render retina quality graphics into their canvas with disappointing results.
Essentially, high density monitors means your canvas needs to accommodate that extra density of pixels. If you do nothing, your canvas will only render enough pixel information into its context to account for a pixel ratio of 1.
So for many modern monitors who have ratios > 1, you should change your canvas context to account for that extra information but keep your canvas the normal width and height.
To do this you simply set the rendering context width and height to: target width and height * window.devicePixelRatio.
canvas.width = target width * window.devicePixelRatio;
canvas.height = target height * window.devicePixelRatio;
Then you set the style of the canvas to size the canvas in normal dimensions:
canvas.style.width = `${target width}px`;
canvas.style.height = `${target height}px`;
Last you render the image at the maximum context size the image allows. In some cases (such as images rendering svg), you can still get a better image quality by rendering the image at pixelRatio sized dimensions:
ctx.drawImage(
img, 0, 0,
img.width * window.devicePixelRatio,
img.height * window.devicePixelRatio
);
So to show off this phenomenon I made a fiddle. You will NOT see a difference in canvas quality if you are on a pixelRatio monitor close to 1.
https://jsfiddle.net/ufjm50p9/2/
In addition to #canvas answer.
context.imageSmoothingEnabled = false;
Works perfect. But in my case changing size of canvas resetting this property back to true.
window.addEventListener('resize', function(e){
context.imageSmoothingEnabled = false;
}, false)
The following code works for me:
img.onload = function () {
canvas.width = img.width;
canvas.height = img.height;
context.drawImage(img, 0, 0, img.width, img.height, 0, 0, img.width, img.height);
};
img.src = e.target.result; // your src
Simple tip: draw with .5 in x and y position. like drawImage(, 0.5, 0.5) :D There you get crisp edges :D
Here is my code. I created imageData 2D array in javascript. After I put all pixels into the this array, I want to create image and put these values into the image.
var imageData = new Array(width);
var image = new Image;
for (var i = 0; i < width; i++) {
imageData[i] = new Array(height);
}
image.src = imageData; //Here is the problem. I want to do that.
To create an image from array you can do this:
var width = 400,
height = 400,
buffer = new Uint8ClampedArray(width * height * 4); // have enough bytes
The * 4 at the end represent RGBA which we need to be compatible with canvas.
Fill the buffer with some data, for example:
for(var y = 0; y < height; y++) {
for(var x = 0; x < width; x++) {
var pos = (y * width + x) * 4; // position in buffer based on x and y
buffer[pos ] = ...; // some R value [0, 255]
buffer[pos+1] = ...; // some G value
buffer[pos+2] = ...; // some B value
buffer[pos+3] = 255; // set alpha channel
}
}
When filled use the buffer as source for canvas:
// create off-screen canvas element
var canvas = document.createElement('canvas'),
ctx = canvas.getContext('2d');
canvas.width = width;
canvas.height = height;
// create imageData object
var idata = ctx.createImageData(width, height);
// set our buffer as source
idata.data.set(buffer);
// update canvas with new data
ctx.putImageData(idata, 0, 0);
Note that you could use the imageData buffer (here: idata.data) instead of creating your own. Creating your own is really only useful if you use for example floating point values or get the buffer from some other source - setting the buffer as above will take care of clipping and rounding the values for you though.
Now the data in your custom array is copied to the canvas buffer. Next step is to create an image file:
var dataUri = canvas.toDataURL(); // produces a PNG file
Now you can use the data-uri as source for an image:
image.onload = imageLoaded; // optional callback function
image.src = dataUri
You can't make an image.src like that.
A valid dataURL is a base64 encoded string with a type prefix--not an array.
The image data array associated with a canvas is an Uint8ClampedArray--not a normal javascript array.
Here's one way to create a pixel array that you can manipulate:
A Demo: http://jsfiddle.net/m1erickson/956kC/
// create an offscreen canvas
var canvas=document.createElement("canvas");
var ctx=canvas.getContext("2d");
// size the canvas to your desired image
canvas.width=40;
canvas.height=30;
// get the imageData and pixel array from the canvas
var imgData=ctx.getImageData(0,0,40,30);
var data=imgData.data;
// manipulate some pixel elements
for(var i=0;i<data.length;i+=4){
data[i]=255; // set every red pixel element to 255
data[i+3]=255; // make this pixel opaque
}
// put the modified pixels back on the canvas
ctx.putImageData(imgData,0,0);
// create a new img object
var image=new Image();
// set the img.src to the canvas data url
image.src=canvas.toDataURL();
// append the new img object to the page
document.body.appendChild(image);
Create a canvas element of the right size and get its 2D rendering context. You don't have to add this canvas to the document. Use the context to create an ImageData object. Copy the values from your array into the ImageData object. In your case, it might be more efficient to populate the ImageData object in the first place, instead of a separate array. Use the context's putImageData to draw the pixel data. Then, depending on the specific requirements of "Creating image," you might need to serialize the canvas into a data uri, so that you can fill in an img element's src.
There's probably a real simple explanation for this, but I'm struggling really hard to find it. I'm trying to implement a crop picture feature and I'm getting an undesired border on the right and bottom sides of the picture, as seen here: http://imgur.com/Xjtk39K. I think I've narrowed it down to something having to do with the first image translation that happens when I crop the picture. Here's the applicable code (the doubling of the cropped image size in submitPhotoUpload is due to some unknown halving of the picture height in my code. Advice for that would be welcome too):
cropPhoto: function(evt){
var formType = App.UploadPhotoView.whichForm;
var canvas = document.getElementById("crop-canvas-preview-"+formType);
var context = canvas.getContext("2d");
if(this.canvasHidden){
canvas.style.display = 'inline';
App.UploadPhotoView.canvasHidden = false;
}
var x1 = App.UploadPhotoView.x1;
var y1 = App.UploadPhotoView.y1;
var width = App.UploadPhotoView.width;
var previewSize = $("#upload-div-"+formType).height() * 0.25;
var resizeX = App.UploadPhotoView.scalingFactorX;
var resizeY = App.UploadPhotoView.scalingFactorY;
var img = document.getElementById("preview-photo-"+formType);
context.clearRect(0, 0, previewSize, previewSize);
context.drawImage(img, x1*resizeX, y1*resizeY, width*resizeX,
width*resizeY, 0, 0, previewSize*2, previewSize);
},
submitPhotoUpload: function(){
var formType = "pic";
if ($('#preview-photo-'+formType).attr('src') != '#') {
var uploadImage = new Image();
uploadImage.src = document.getElementById('crop-canvas-preview-'+formType).toDataURL();
var canvas = document.createElement('canvas');
canvas.width = uploadImage.width * 3;
canvas.height = uploadImage.height * 6;
canvas.getContext('2d').drawImage(uploadImage, 0, 0, canvas.width, canvas.height);
var newPhoto = App.Photo.createRecord({
filename: canvas.toDataURL(),
location: this.get('location'),
description: this.get('description')
});
this.get('store').commit();
resetPhotoUpload(formType);
}else{
this.set('submissionFailed', true);
}
},
The reason is that you are using fractional values with the clearing and clipping.
The clearRect call will clear on sub-pixel basis which means it can leave "half" pixel clipped.
The clip however cannot clip the image with sub-pixels as images are pixel based and will truncate the value to match an integer value, hence you get a small gap in the final result.
Luckily this is easy to get around by simply forcing the values to integer values:
ONLINE DEMO HERE
context.clearRect(0, 0, previewSize|0, previewSize|0);
context.drawImage(img, (x1*resizeX)|0, (y1*resizeY)|0, (width*resizeX)|0,
(width*resizeY)|0, 0, 0, previewSize|0, previewSize|0);
Of course, you might consider doing this outside the function call. If you need a more accurate rounding just add 0.5 to the values before doing as here, shifting 0 bits (which force a float number to integer) or use Math.round() instead.
Here is the result:
left: cleared and clipped using frational positions, right: using integer values
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/>...
DEMO V2: http://jsfiddle.net/xLF38/818/
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,
length,
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 = data.data.length;
while ( (i += blockSize * 4) < length ) {
++count;
rgb.r += data.data[i];
rgb.g += data.data[i+1];
rgb.b += data.data[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: https://gist.github.com/cf23f8bddb307ad4abd8
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.
Edit: You can simply create a PNG with document.getElementById("canvas").toDataURL("image/png", 1.0)
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.)
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(0, 0, 1, 1).data.slice(0,3);
}
console.log(get_average_rgb(document.querySelector('#img1')));
console.log(get_average_rgb(document.querySelector('#img2')));
<img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAAEAAAABCAYAAAAfFcSJAAAADUlEQVR42mNk+M/wHwAEBgIApD5fRAAAAABJRU5ErkJggg==" width="32" height="32" id="img1">
<img src="https://lh3.googleusercontent.com/a/AEdFTp4Wi1oebZlBCwFID8OZZuG0HLsL-xIxO5m2TNw=k-s32" id="img2" crossOrigin="anonymous">
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(0, 0, 1, 1).data.slice(0,3);
}
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 = imgd.data;
// 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.
This is #350D's answer but async (as some images may take time to load) and in typescript
async function get_average_rgb(src: string): Promise<Uint8ClampedArray> {
/* https://stackoverflow.com/questions/2541481/get-average-color-of-image-via-javascript */
return new Promise(resolve => {
let context = document.createElement('canvas').getContext('2d');
context!.imageSmoothingEnabled = true;
let img = new Image;
img.src = src;
img.crossOrigin = "";
img.onload = () => {
context!.drawImage(img, 0, 0, 1, 1);
resolve(context!.getImageData(0, 0, 1, 1).data.slice(0,3));
};
});
}
I recently came across a jQuery plugin which does what I originally wanted https://github.com/briangonzalez/jquery.adaptive-backgrounds.js in regards to getting a dominiate color from an image.
EDIT: Only after posting this, did I realize that #350D's answer does the exact same thing.
Surprisingly, this can be done in just 4 lines of code:
const canvas = document.getElementById("canvas"),
preview = document.getElementById("preview"),
ctx = canvas.getContext("2d");
canvas.width = 1;
canvas.height = 1;
preview.width = 400;
preview.height = 400;
function getDominantColor(imageObject) {
//draw the image to one pixel and let the browser find the dominant color
ctx.drawImage(imageObject, 0, 0, 1, 1);
//get pixel color
const i = ctx.getImageData(0, 0, 1, 1).data;
console.log(`rgba(${i[0]},${i[1]},${i[2]},${i[3]})`);
console.log("#" + ((1 << 24) + (i[0] << 16) + (i[1] << 8) + i[2]).toString(16).slice(1));
}
// vvv all of this is to just get the uploaded image vvv
const input = document.getElementById("input");
input.type = "file";
input.accept = "image/*";
input.onchange = event => {
const file = event.target.files[0];
const reader = new FileReader();
reader.onload = readerEvent => {
const image = new Image();
image.onload = function() {
//shows preview of uploaded image
preview.getContext("2d").drawImage(
image,
0,
0,
preview.width,
preview.height,
);
getDominantColor(image);
};
image.src = readerEvent.target.result;
};
reader.readAsDataURL(file, "UTF-8");
};
canvas {
width: 200px;
height: 200px;
outline: 1px solid #000000;
}
<canvas id="preview"></canvas>
<canvas id="canvas"></canvas>
<input id="input" type="file" />
How it works:
Create the canvas context
const context = document.createElement("canvas").getContext("2d");
This will draw the image to only one canvas pixel, making the browser find the dominant color for you.
context.drawImage(imageObject, 0, 0, 1, 1);
After that, just get the image data for the pixel:
const i = context.getImageData(0, 0, 1, 1).data;
Finally, convert to rgba or HEX:
const rgba = `rgba(${i[0]},${i[1]},${i[2]},${i[3]})`;
const HEX = "#" + ((1 << 24) + (i[0] << 16) + (i[1] << 8) + i[2]).toString(16).slice(1);
There is one problem with this method though, and that is that getImageData will sometimes throw errors Unable to get image data from canvas because the canvas has been tainted by cross-origin data., which is the reason you need to upload images in the demo instead of inputting a URL for example.
This method can also be used for pixelating images by increasing the width and height to draw the image.
This works on chrome but may not on other browsers.
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.
Example:
Consider the following image:
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 = ntc.name('#' + color_thief.getColor(sample_image).map(x => {
const hex = x.toString(16);
return hex.length === 1 ? '0' + hex : hex;
}).join(''));
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
To get the average (not the dominant) color of an image, create a tiny canvas of the scaled-down original image (of max size i.e: 10 px). Then loop the canvas area imageData to construct the average RGB:
const getAverageColor = (img) => {
const max = 10; // Max size (Higher num = better precision but slower)
const {naturalWidth: iw, naturalHeight: ih} = img;
const ctx = document.createElement`canvas`.getContext`2d`;
const sr = Math.min(max / iw, max / ih); // Scale ratio
const w = Math.ceil(iw * sr); // Width
const h = Math.ceil(ih * sr); // Height
const a = w * h; // Area
img.crossOrigin = 1;
ctx.canvas.width = w;
ctx.canvas.height = h;
ctx.drawImage(img, 0, 0, w, h);
const data = ctx.getImageData(0, 0, w, h).data;
let r = g = b = 0;
for (let i=0; i<data.length; i+=4) {
r += data[i];
g += data[i+1];
b += data[i+2];
}
r = ~~(r/a);
g = ~~(g/a);
b = ~~(b/a);
return {r, g, b};
};
const setBgFromAverage = (img) => {
img.addEventListener("load", () => {
const {r,g,b} = getAverageColor(img);
img.style.backgroundColor = `rgb(${r},${g},${b})`;
});
};
document.querySelectorAll('.thumb').forEach(setBgFromAverage);
.thumbs { display: flex; flex-wrap: wrap; gap: 0.5rem; }
.thumb { height: 100px; padding: 20px; }
<div class="thumbs">
<img class="thumb" alt="image" src="https://i.imgur.com/22BrBjx.jpeg">
<img class="thumb" alt="image" src="https://i.imgur.com/MR0dUpw.png">
<img class="thumb" alt="image" src="https://i.imgur.com/o7lpiDR.png">
<img class="thumb" alt="image" src="https://i.imgur.com/egYvHp6.jpeg">
<img class="thumb" alt="image" src="https://i.imgur.com/62EAzOY.jpeg">
<img class="thumb" alt="image" src="https://i.imgur.com/3VxBMeF.jpeg">
</div>
There is a online tool pickimagecolor.com 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.