I have an image like this:

and like to find a specific color in the image (#ffa400 / #ffffff) and get their percentage occurrence value (orange maybe: 75%, white: 25%)

NOTE: Keep in mind that I don't like to get the average color (in this case: orange) like Color Thief does, but I'D like to find a SPECIFIC color.

Below is a snippet, so this is all that I got so far, but the code doesn't seem working & my idea also doesn't seem like an efficient & fast way.

function extract_colors(img) {
    var canvas = document.createElement("canvas");
    var c = canvas.getContext('2d');
    c.width = canvas.width = img.width;
    c.height = canvas.height = img.height;
    c.clearRect(0, 0, c.width, c.height);
    c.drawImage(img, 0, 0, img.width, img.height);
    return getColors(c);

}

function getColors(c) {
    var col, colors = {};
    var pixels, r, g, b, a;
    r = g = b = a = 0;
    pixels = c.getImageData(0, 0, c.width, c.height);
    for (var i = 0, data = pixels.data; i < data.length; i + = 4) {
        r = data[i];
        g = data[i + 1];
        b = data[i + 2];
        a = data[i + 3];
        if (a < (255 / 2))
            continue;
        col = rgbToHex(r, g, b);
        if (col == 'ffa400') { // find color #ffa400
            if (!colors[col])
                colors[col] = 0;
            colors[col] + +;
        }
    }
    return colors;
}


function rgbToHex(r, g, b) {
    return ((r << 16) | (g << 8) | b).toString(16);
}

var img = document.getElementById('img');
out.innerHTML = extract_colors(img)

<img id='img' width=100 src='.png'>

<p id='out'>...</p>

I have an image like this:

and like to find a specific color in the image (#ffa400 / #ffffff) and get their percentage occurrence value (orange maybe: 75%, white: 25%)

NOTE: Keep in mind that I don't like to get the average color (in this case: orange) like Color Thief does, but I'D like to find a SPECIFIC color.

Below is a snippet, so this is all that I got so far, but the code doesn't seem working & my idea also doesn't seem like an efficient & fast way.

function extract_colors(img) {
    var canvas = document.createElement("canvas");
    var c = canvas.getContext('2d');
    c.width = canvas.width = img.width;
    c.height = canvas.height = img.height;
    c.clearRect(0, 0, c.width, c.height);
    c.drawImage(img, 0, 0, img.width, img.height);
    return getColors(c);

}

function getColors(c) {
    var col, colors = {};
    var pixels, r, g, b, a;
    r = g = b = a = 0;
    pixels = c.getImageData(0, 0, c.width, c.height);
    for (var i = 0, data = pixels.data; i < data.length; i + = 4) {
        r = data[i];
        g = data[i + 1];
        b = data[i + 2];
        a = data[i + 3];
        if (a < (255 / 2))
            continue;
        col = rgbToHex(r, g, b);
        if (col == 'ffa400') { // find color #ffa400
            if (!colors[col])
                colors[col] = 0;
            colors[col] + +;
        }
    }
    return colors;
}


function rgbToHex(r, g, b) {
    return ((r << 16) | (g << 8) | b).toString(16);
}

var img = document.getElementById('img');
out.innerHTML = extract_colors(img)

<img id='img' width=100 src='https://i.sstatic/EPDlQ.png'>

<p id='out'>...</p>

EDIT 1.0

I'm trying to use a code like this, but it doesn't seems to work either.

var orangeMatches=0, whiteMatches=0
        Jimp.read("https://i.sstatic/EPDlQ.png").then(function (image) {
            image.scan(0, 0, image.bitmap.width, image.bitmap.height, function (x, y, idx) {
              var red   = this.bitmap.data[ idx + 0 ];
              var green = this.bitmap.data[ idx + 1 ];
              var blue  = this.bitmap.data[ idx + 2 ];
              var alpha = this.bitmap.data[ idx + 3 ];

              if (red == 255 && green == 164 && blue == 0 && alpha == 1){
                orangeMatches++;
              }
              if (red == 255 && green == 255 && blue == 255 && alpha == 1){
                whiteMatches++;
              }
          });
          console.log(orangeMatches, whiteMatches)
        });

Edit 2.0

I'd like to have some kind of tolerant value (10%) so that there is not every color to be counted, but the colors (e.g #ffa400 #ffa410) that are matching together are counted together.

• javascript
• jquery
• html
• image

1 Answer 1

A few notes

The main color in your picture is actually ffa500, not ffa400. There are in fact no occurances of ffa400 at all.

Also keep in mind that this is an anti-aliased bitmap graphic. Visually we see only two colors, but to smoothen the transition from one color to another a set of "in-between-colors" are generated. In other words, the sum of ffffff and ffa500 will not be exactly 100%. (This is why the output from my function below will display a lot more colors than the two in question)

Solution 1

The basic idea of this solution is to get every color in the picture, store these in one mon object where the occurrence of each color is counted. When this object is returned, we can later count the occurrence of some specific color from the output.

This will return something like:

{
    ffa500: 7802,
    ffa501: 4,
    ffa502: 2,
    ...,
    ffffff: 1919,
    total: 10000
}

Now you can access the occurences of a given color this way:

var imageData = extract_colors(img),
    white = imageData.ffffff,
    total = imageData.total;

And to display how many percent of the image a given color covers, do something like this (toFixed() is simply to limit the value to two decimals):

whitePct = (white / total * 100).toFixed(2);

Working sample

function getColors(ctx) {

		// Get the canvas data
    var pixels = ctx.getImageData(0, 0, ctx.width, ctx.height),
        data = pixels.data,
        
        // Set up our output object to collect color data
        output = {};
    
    // For each color we encounter, check the
    // output object. If the color already exists
    // there, simply increase its counted value.
    // If it does not, create a new key.
    for (var i = 0; i < data.length; i+=4) {
        var r = data[i],
            g = data[i + 1],
            b = data[i + 2],
            col = rgbToHex(r, g, b);

        if( output[col] )
        	output[col]++
        else
        	output[col] = 1
    }
    
    // Count total
    var total = 0;
    for(var key in output) {
    	total = total + parseInt(output[key])
    }
    output.total = total;
    
    // Return the color data as an object
    return output;
}

// Our elements
var img = document.getElementById('img'),
	  out = document.getElementById('out');

// Retrieve the image data
var imageData = extract_colors(img),

    // Count our given colors
    white = imageData.ffffff,
    orange = imageData.ffa500,
    total = imageData.total,

    // Calculate percentage value
    whitePct = (white / total * 100).toFixed(2),
    orangePct = (orange / total * 100).toFixed(2);
  
out.innerHTML = `White: ${whitePct}%<br/>Orange: ${orangePct}%`;

// See the console for all colors identified
console.log(extract_colors(img))

// ----- These functions are left untouched ----- \\

function extract_colors(img) {
    var canvas = document.createElement("canvas");
    var c = canvas.getContext('2d');
    c.width = canvas.width = img.width;
    c.height = canvas.height = img.height;
    c.clearRect(0, 0, c.width, c.height);
    c.drawImage(img, 0, 0, img.width, img.height);
    return getColors(c);
}

function rgbToHex(r, g, b) {
    return ((r << 16) | (g << 8) | b).toString(16);
}

p, img {
 margin: 10px 5px;
 float: left
}

<!-- Image converted to base64 to keep it locally availible -->
<img id='img' width=100 src='data:image/png;base64,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'>
<p id='out'>...</p>

Solution 2

(Consider this solution a draft. It might need some tweaking if to be used for a live website, but at least it should work!)

The idea here is to call extract_colors() with the colors we want to map as arguments. A mon object is built to hold the output, and this is populated with one object for each color. The color objects consists of a counter and an rgb value. Using the isNeighborColor() function, the rgb value is used to match not only the specific color but also any color close to it. Adjust the tolerance level as you want. (Ideally this would be another argument I guess).

Call the function specifying the image and what colors to count the occurrence of:

var imageData = extract_colors(img, 'ffffff', 'ffa500'),

This will output an object for each color:

{
  "ffffff": {
    "counter": 1979,
    "rgb": {
      "r": 255,
      "g": 255,
      "b": 255
    }
  },
  "ffa500": {
    "counter": 7837,
    "rgb": {
      "r": 255,
      "g": 165,
      "b": 0
    }
  },
  "total": 9816
}

What we want is to access the counter values:

var white = imageData.ffffff.counter;

Working sample

// Added a rest parameter for unlimited color input
function extract_colors(img, ...colors) {
    var canvas = document.createElement("canvas");
    var c = canvas.getContext('2d');
    c.width = canvas.width = img.width;
    c.height = canvas.height = img.height;
    c.clearRect(0, 0, c.width, c.height);
    c.drawImage(img, 0, 0, img.width, img.height);
    return getColors(c, ...colors);
}

// Unchanged
function rgbToHex(r, g, b) {
    return ((r << 16) | (g << 8) | b).toString(16);
}

// From: https://stackoverflow./a/5624139/2311559
function hexToRgb(hex) {
    var result = /^#?([a-f\d]{2})([a-f\d]{2})([a-f\d]{2})$/i.exec(hex);
    return result ? {
        r: parseInt(result[1], 16),
        g: parseInt(result[2], 16),
        b: parseInt(result[3], 16)
    } : null;
}

// From: https://stackoverflow./a/11506531/2311559
// Slightly modified
function isNeighborColor(color1, color2, tolerance) {
    tolerance = tolerance || 32;
    return Math.abs(color1.r - color2.r) <= tolerance
        && Math.abs(color1.g - color2.g) <= tolerance
        && Math.abs(color1.b - color2.b) <= tolerance;
}

function getColors(ctx, ...colors) {

		var pixels = ctx.getImageData(0, 0, ctx.width, ctx.height),
        data = pixels.data,
        output = {};
    
    // Build an 'output' object. This will hold one object for
    // each color we want to check. The color objects will 
    // each consist of a counter and an rgb value. The counter 
    // is used to count the occurrence of each color. The rgb 
    // value is used to match colors with a certain tolerance 
    // using the 'isNeighborColor()' function above.
    for (var i = 0; i < colors.length; i++) {
        output[colors[i]] = {
        	counter: 0,
          rgb: hexToRgb(colors[i])
        };
    }

    // For each pixel, match its color against the colors in our
    // 'output' object. Using the 'isNeighborColor()' function 
    // we will also match colors close to our input, given the 
    // tolerance defined.
    for (var i = 0; i < data.length; i+=4) {
        var r = data[i],
            g = data[i + 1],
            b = data[i + 2],
            colobj = {r, g, b},
            col = rgbToHex(r, g, b);

        Object.keys(output).map(function(objectKey, index) {
            var rgb = output[objectKey].rgb,
            		count = output[objectKey].counter;
            if(isNeighborColor(rgb, colobj)) {
            	output[objectKey].counter = count+1;
            }
        });

    }
    
    // Count total
    var total = 0;
    for(var key in output) {
    	total = total + parseInt(output[key].counter)
    }
    output.total = total;
    
    // Return the color data as an object
    return output;
}

// Our elements
var img = document.getElementById('img'),
	  out = document.getElementById('out');

// Retrieve the image data
var imageData = extract_colors(img,'ffffff','ffa500'),

    // Count our given colors
    white = imageData.ffffff.counter,
    orange = imageData.ffa500.counter,
    total = imageData.total,

    // Calculate percentage value
    whitePct = (white / total * 100).toFixed(2),
    orangePct = (orange / total * 100).toFixed(2);

out.innerHTML = `White: ${whitePct}%<br/>Orange: ${orangePct}%`;

// Optional console output
console.log(extract_colors(img,'ffffff','ffa500'));

p, img {
 margin: 10px 5px;
 float: left
}

<!-- Image converted to base64 to keep it locally availible -->
<img id='img' width=100 src='data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAGQAAABkCAYAAABw4pVUAAAAAXNSR0IArs4c6QAAAARnQU1BAACxjwv8YQUAAAAJcEhZcwAADsQAAA7EAZUrDhsAAAAZdEVYdFNvZnR3YXJlAEFkb2JlIEltYWdlUmVhZHlxyWU8AAADImlUWHRYTUw6Y29tLmFkb2JlLnhtcAAAAAAAPD94cGFja2V0IGJlZ2luPSLvu78iIGlkPSJXNU0wTXBDZWhpSHpyZVN6TlRjemtjOWQiPz4gPHg6eG1wbWV0YSB4bWxuczp4PSJhZG9iZTpuczptZXRhLyIgeDp4bXB0az0iQWRvYmUgWE1QIENvcmUgNS4wLWMwNjAgNjEuMTM0Nzc3LCAyMDEwLzAyLzEyLTE3OjMyOjAwICAgICAgICAiPiA8cmRmOlJERiB4bWxuczpyZGY9Imh0dHA6Ly93d3cudzMub3JnLzE5OTkvMDIvMjItcmRmLXN5bnRheC1ucyMiPiA8cmRmOkRlc2NyaXB0aW9uIHJkZjphYm91dD0iIiB4bWxuczp4bXA9Imh0dHA6Ly9ucy5hZG9iZS5jb20veGFwLzEuMC8iIHhtbG5zOnhtcE1NPSJodHRwOi8vbnMuYWRvYmUuY29tL3hhcC8xLjAvbW0vIiB4bWxuczpzdFJlZj0iaHR0cDovL25zLmFkb2JlLmNvbS94YXAvMS4wL3NUeXBlL1Jlc291cmNlUmVmIyIgeG1wOkNyZWF0b3JUb29sPSJBZG9iZSBQaG90b3Nob3AgQ1M1IE1hY2ludG9zaCIgeG1wTU06SW5zdGFuY2VJRD0ieG1wLmlpZDo4QUUwNzk4N0I2QjIxMUUzQUQzMkIzMTA4REQ0Nzc1MiIgeG1wTU06RG9jdW1lbnRJRD0ieG1wLmRpZDo4QUUwNzk4OEI2QjIxMUUzQUQzMkIzMTA4REQ0Nzc1MiI+IDx4bXBNTTpEZXJpdmVkRnJvbSBzdFJlZjppbnN0YW5jZUlEPSJ4bXAuaWlkOkI1NDU5MjAyQjZCMTExRTNBRDMyQjMxMDhERDQ3NzUyIiBzdFJlZjpkb2N1bWVudElEPSJ4bXAuZGlkOjhBRTA3OTg2QjZCMjExRTNBRDMyQjMxMDhERDQ3NzUyIi8+IDwvcmRmOkRlc2NyaXB0aW9uPiA8L3JkZjpSREY+IDwveDp4bXBtZXRhPiA8P3hwYWNrZXQgZW5kPSJyIj8+UfPEQAAADrpJREFUeF7tXQmQFNUZ/th7ZXdhWZZdOWQFxIAQAdGIYEAQQSkTjYaoIakklknMYUw8EmJSJpUEjWV5xJiURNRK4lGmPDCeYDyjIqdyBLmRQ2DZZdkFlns339dvOoyT7jl2enua2f7Kn3nvzWx3v/97//V6euzU+ihaESIwyIm8hggIQkIChpCQgCEkJGAICQkYQkKSgfJQW45GpJ0QEuKGI5SDEVH76AlASxVQPBAo6tpupIR1iBNEQPkZQNkwKr8QqDidfbYrzjLvr74PePc6IJ/tTmbIK4SEOGEvZfxfgQFfM/1Y7HgdeGl8uxASuiwn5FLq3jNtJ1SOZjzJi3S8RUiIE6TrHXNN2wk5BfyHrLWDbwkJcYIIqVtr2m6orDGxxmNkNyHKhNqyihUXFEfi/W31BSEhSUOKPEAp7sNUlVlSW0jhn1nB2w09p4SEJIXDlP2UQTcAl28CJs4GGq13UoPc1vY4caTHucChSNtDZBch+yh53ZiyPgSMutOMVU8CBl8J7DHdpKFMK15gz2OhWMRXjwN79hDSRKk5j1axBuj3TTNmYxQLudISYz3JQhaybYVpu6F7b8/dVnYQotU/mYXcxNfo+2khsSisAM571gT5FjOUENLM0QTarpoQEuIIazV/YNpuOJHKG/TD1Px+Dk2q/v1IxwHdx3i+p5UdhMiXr7gLWMfYEQ+j7jEpbbJWos/WzzdtJ5T0Tf5YSSI7CJHitK+04GeMJautIWdwulfVUsmRbjzYe1S1b0caDihkXGJs95KU7CBEECFNO4GF003fDYWVwOjrTEaWCCKlMc4Hi09ksjAwJMQVXLD46Glg7QOm74bhMxiQR5h7HfEgQo6Q5CMq2x1QVMVznhISEhdllDe+b9puyO0MjCQpygbi1REi5NA2ZnEbTD8WucVAZ1qIh4E9+wjRjPKpoacGm74berJgPP17QHOk7wQdq3kLXeFy03dCMTMKO954gOwjRFA8aVwJLLvd9N0w4l6gK1e4m+uyLISyr87qOqKcxOdTjR5V7NlJiCBSFt0KHEiQUp3/hFG6m0JFyv5dpu2ELkPo+cojnfQRXEKkoHRWnfaiWqnp52gB8VA+HBj3oNlud4KKzsYlDNzaPnZA16E8V0UHsBAFSrmSdAKmttDruLqX3mb6bjjl60DN+c6uSxpqWEQr0maZAzqReW00ZjUhciEVXLln38EJMyNyW73JoAtl4c/jb4Hk0L+N+oOzVcrSdjOwH3YhRKjo41mmFTxCpBARMuw3wOCbgEtXAQMv4UrnWFvyfcUAzXLeDVSabpS4oGwQMObPgMJFdNaktrxVPIVXjfdskzF4hEhnQ38C9Jpi+p170cc/A1zDegAFRjmpTl7fSdj8Dl3X3abvhoHfpeti1hSbCitB2P6yaTtB15rK1n4cBIsQWUBxN2DIt00/GsXVwDQ6+bOo1OLeqbuxUsr7twC1b5q+Gyb9m7GHfi6adOtm1Rum7YRSVutZaSFa/af9mBM81fSdMOR64EIqdfjN9O3sy70lA7ke7QrPucLquqKAKewIpsvRCrZu584xbTd4tMkYHELko5UVDfuF1Y2Lkn7Amb8HrmD2U0LyZC3JrFC5rkPbgbcuNX03DOaiqDrnWNZlEZLgHnAV6xEPAntwCNFKH3Rj/GwmFt1GAJd/xOKOdUQuY43iT6JVKtLXPgtsZEEYD1MYc1BslCwtRQd6J/S6OHlrjYPgEMK5Y8mdwPNnAusTKCsW/a4Gpi7lymbsOULNudRwFqRYzXohLfFQnApcOI9EyzCU+VmB/VWNOqOadYwHgT04hAgsOawbTG9eCTzTP7HCopHPZGDUA8Bl66gcklrLMTdrketq4OfmX2v6bqi5ijFtmiFFhOx4zRp2ROmALIshNjRxKWzPemBWBZX2HU40hRSm9GTgovmML/oKT4l7DSHyFz3J+uZd03fDmb8zhZ8sa8sLZswJRVwQ3XjxacaR4BEiaPIKpLq3sWQm8Hh3YBWLtlQgFzKNS/uzzMZymAI53fjjYTF7dHxLPOEkk0Bo9Tc5HGT/JywmFwKbnuZn+6a9hXJ8PB8iA1HArrmQKSkVXDnOGk4aO+cBK+4FPmJsUnoqsm0ok6r5CuNFgri1YDqwnDXQxMdIDBOJnWtJJPPu5g20wo9JeIOpV2ThaeD4eWBHV6mgKZfQ70tU4FMaTQ07mDm9OMYQLGJkiTquKvMLmHn1/SIbLlj2K2Der+mayKa+ryWLkX+R2ImCoHYaOD6foJK1KDZMmgWczEIvV9pNAUt+ydV+HwluNGmwlKuUdeoaE5yjsWsx8DqTjDomG6r2bW2lqXg3HL+PtEmJcum9z2b98gNazVet4aSxl8r/gG5s5f0miZDVVLMYtOqPCJbOABbfYs6lKt8HZJYQu+gS2rriZCk6xklMUc/9I314infvtj5PV8TKvJYxQZqY9BcTU15lurvpOUOEYoNPyBwhigcllawHmAnlUqsixPbFtl9OFpqBfTPrnNtZOzDwp8rwGtYw79zIY+0155dVyJ35jMwQIsVpwlfs4KR7cJVyJW5kkG7cSleynMJxva+MRRlRsrrVTLThWMn6ZdxLQI+R7KRAzCcvAv+cYpUvqfLpFTJDiAJo9VhgAonIV7ERhXrm9LtISiMLw0+eYWbEtvy7XId8fSJF6X3bWmomAyPvALoM1TsJQDU8wQLwMBeFjy4qFpkhRMF4ONPIkbeavhtUsB1kJrRrGbCO8eE/rL5lOdr3EkHx4o9mJeJb+cFhrPZH3GMNu2IxM68lvzXHziD8J0RnU/wY+xBT1pgHa5KBUtVVKvL+RrLo71tqOUaWRI5WttPq1l5UHj+gr/z0/bIZi0bdAuDlz7PBWGaTnCH4T4hcSdHJwOQ5/5/zp4pD9Yw/L9CtfcjkgLL7fVbRJEmkyL3ZFbksSG5PxJx6CXA6g3f30XrH4E1maGseN8VihuE/IbKOcsaPL8S5JdoWHKEfbFzBqrsB2EayNzwI7GwypMi9KUEQMUqTCzjQ/xrzTZMGxqjHGWO6cjwAyAwhA7giRz9q+u2FVprEYZb0ypyWMzZspeJFjqxHM7YslSwVMLc9QKuyrSnDyExQ19aHpGdPkvMjpqejGEyZqpZ9hlfUjk5cu7JrZ9GCmCQc3sJr+NiMZzCrikVmCLEha1GKqisoZ5HYfRxT1EFMic8ATryQATbNrdN4aGZ6u5iLYR3rn3Y8TapInxAFS/vLANHQJBVYk4GuQOmsjqXXQvr4vF4s0MqBgd+gJHjeo6145WJg+/PmWjUHnT8aijlKg53S6nZCeoRoAhUjWIBNpVLllCOHyqNCt/4L2MxqOVlSoqHD2CSplpB7U2Ae+i2mypQyZmcFXehqNNhGHGZJP/tzPHbkmcQBJL7raYwt9uoiC7LQ+TcfSwh8QHqESFGDr2W28ifTj8aqmUwnWZBpG8ILiBzpSlmSeOhN96bfGyljCl1ORSr+pILNdFW6d99Kv6ljT6Sl6HixmEUmtKflU32S3mm0avT8XatmFINDTD+9XFW6UrkPbeZKQduYNs+7CXjtcmDORcDcy4CF1zPVfYtvJoH6NSSYZNjXeNThUaqjYt9feMC7m9YdxmWLCuQSeTiJuLTFdlWJoKuWK9QXFfS6fwNd5NPAMlbwL0wAHiJrc89x/+6VFtBuFpI6jpeLxgN4QEiSkKIVc3qyQi6jlJ7FypiuJv8kXkV3KoZlskhRzNDClDvUq9yUCNTf2gRGkyaF2pW5LCefHyxkfNj6HvAyXdLd/MA/SNCi6Rx7ldX6OspKVvevBKb2iEZ6MUQK08MuYx7mkWK4XXYHXcpPzSoWpEgp9+qo07VQ+/rWxoGdfI9yoJb9bWzzwC3U8sHtlDqSxPGDO/g+5SBdoU2STikyRIrasWKvfpFp/015KdDtbGZXc4/VH3p//JNAn5h9LrmsR+gnfYwh/hGiSRdwclMbObkUEv8WalHbIp8Smo9lTTzWvvUUFnh7GBPUbt7I1yazb6WZSekSXZ5eNSbLkJIF9TskIdbqZNF38Qp+1kfHLQvbvRTYyzgjd7WPhG2j69pPi7QJChAhPp2GkP/v3M9fMoRCxic94dT/amDYDGD0Y0yZmZFpgQQQ/hEi6EGbIGDXAr9nnjT8tZASFnFBwP4ttNRIO2DwjxApQL8xsmc10MQA3LzZ+HfrQcy2h7E2oYnZmp1hBQz+BXVBxbCIKaI2Cqv4KqEbK6pkv5rZTxcKP1BUwj7HrHG9Mg4UqFbxaP3cz3PohpSupcNmWTZ0Nold4MW2pSSlpTkkLY81gySHBOVS1FZScEIR3V8fHrs/C0wWlkVsdzmVpDn83qIT7uExuBb+d84OTUgysK/I7VUECurbImhcsUr3xktocfotq1KSJrI6D6BVDDGkPUw27Fu2+tsOmfZq4ko1tTWSSPQ5iRQlkZLtbRObDEHWpBkoHkhUb2onWJ9tYJW/4W3gg0eA16cDs6nsWayDZpKMFL9t6if8sxApsttIuh26nlSeiGozyJZ+YCwnasMqh2w1szis/9C4RSFgFuIPISKjUwEw4UX67rGcqDKrdkYrCcnRNdnXxWmKoI2PAq9MM48WRIY7nsuyKKcfyePkOnFp2sG6PSVfiQCDie4qWsJzC9p6b/sSbHf4Q4h8veB0I8tvtJIN+3oCCJ8MMUSyCAkJGHyMIYR+fS3T0P8/KowhEv5zpJmyz+xh+SF6nGHfJsoGI/py9sFaBnhzWUGEP2mvfQZ9+c2q3nxcolbNEzmfZaF7uSgaji1FvdXh6hBBZ/GRB1dY1mqaFgJGiE+nIaQEnS3TEk1GAKFLDBEgtB8h8t1yBcluKGZK7E3Mo2pkHu0XQ9b/HVh5F/2vfjg3yGjhfyRj2G1Aj7GRsQiyKqhnA7I6qIdICiEhAYMHhASlwMgOpEeIeCioYPwI8F5EOtB9FN0O9rF2SS+o6/aGHi0r7sG2rjyLoL03LbT61ZxjZMwHpEeIIFKyjItPQffejxsLCeE5wiwrYAgJCRhCQgKGkJCAISQkUAD+C1sX7Gj8A6l6AAAAAElFTkSuQmCC'>
<p id='out'>...</p>