Bootstrap Profile Avatar card with Blur


	<div class="container">
	<div class="row">
		<h1>
        Dynamically blurring avatar images using Canvas.
        </h1>
	</div>
    <div class="row">
        <div class="col-sm-3">
            <div class="card">
                <canvas class="header-bg" width="250" height="70" id="header-blur"></canvas>
                <div class="avatar">
                    <img src="" alt="">
                </div>
                <div class="content">
                    <p>Web Developer <br>
                       More description here</p>
                    <p><button type="button" class="btn btn-default">Contact</button></p>
                </div>
            </div>
        </div>
        <div class="col-sm-3">
            <div class="card">
                <canvas class="header-bg" width="250" height="70" id="header-blur"></canvas>
                <div class="avatar">
                    <img src="" alt="">
                </div>
                <div class="content">
                    <p>Web Developer <br>
                       More description here</p>
                    <p><button type="button" class="btn btn-default">Contact</button></p>
                </div>
            </div>
        </div>
        <div class="col-sm-3">
            <div class="card">
                <canvas class="header-bg" width="250" height="70" id="header-blur"></canvas>
                <div class="avatar">
                    <img src="" alt="">
                </div>
                <div class="content">
                    <p>Web Developer <br>
                       More description here</p>
                    <p><button type="button" class="btn btn-default">Contact</button></p>
                </div>
            </div>
        </div>
    </div>
</div>

<img class="src-image" src="data:image/jpeg;base64,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">
<img class="src-image" src="data:image/jpeg;base64,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">
<img class="src-image" src="data:image/jpeg;base64,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">

.src-image {
  display: none;
}

.card {
  overflow: hidden;
  position: relative;
  border: 1px solid #CCC;
  border-radius: 8px;
  text-align: center;
  padding: 0;
  background-color: #284c79;
  color: rgb(136, 172, 217);
}

.card .header-bg {
  /* This stretches the canvas across the entire hero unit */
  position: absolute;
  top: 0;
  left: 0;
  width: 100%;
  height: 70px;
  border-bottom: 1px #FFF solid;
  /* This positions the canvas under the text */
  z-index: 1;
}
.card .avatar {
  position: relative;
  margin-top: 15px;
  z-index: 100;
}

.card .avatar img {
  width: 100px;
  height: 100px;
  -webkit-border-radius: 50%;
  -moz-border-radius: 50%;
  border-radius: 50%;
  border: 5px solid rgba(0,0,30,0.8);
}

    

/*

This Snippet is using a modified Stack Blur js lib for blurring the header images.
I could not use hosted images because Canvas cannot work with cross domain images. If you want to use hosted images make sure they are on the same domain.
Have fun!

*/

/*

StackBlur - a fast almost Gaussian Blur For Canvas

Version:     0.5
Author:		Mario Klingemann
Contact: 	[email protected]
Website:	http://www.quasimondo.com/StackBlurForCanvas
Twitter:	@quasimondo

In case you find this class useful - especially in commercial projects -
I am not totally unhappy for a small donation to my PayPal account
[email protected]

Or support me on flattr: 
https://flattr.com/thing/72791/StackBlur-a-fast-almost-Gaussian-Blur-Effect-for-CanvasJavascript

Copyright (c) 2010 Mario Klingemann

Permission is hereby granted, free of charge, to any person
obtaining a copy of this software and associated documentation
files (the "Software"), to deal in the Software without
restriction, including without limitation the rights to use,
copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the
Software is furnished to do so, subject to the following
conditions:

The above copyright notice and this permission notice shall be
included in all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES
OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND
NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT
HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY,
WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR
OTHER DEALINGS IN THE SOFTWARE.
*/

var mul_table = [
        512,512,456,512,328,456,335,512,405,328,271,456,388,335,292,512,
        454,405,364,328,298,271,496,456,420,388,360,335,312,292,273,512,
        482,454,428,405,383,364,345,328,312,298,284,271,259,496,475,456,
        437,420,404,388,374,360,347,335,323,312,302,292,282,273,265,512,
        497,482,468,454,441,428,417,405,394,383,373,364,354,345,337,328,
        320,312,305,298,291,284,278,271,265,259,507,496,485,475,465,456,
        446,437,428,420,412,404,396,388,381,374,367,360,354,347,341,335,
        329,323,318,312,307,302,297,292,287,282,278,273,269,265,261,512,
        505,497,489,482,475,468,461,454,447,441,435,428,422,417,411,405,
        399,394,389,383,378,373,368,364,359,354,350,345,341,337,332,328,
        324,320,316,312,309,305,301,298,294,291,287,284,281,278,274,271,
        268,265,262,259,257,507,501,496,491,485,480,475,470,465,460,456,
        451,446,442,437,433,428,424,420,416,412,408,404,400,396,392,388,
        385,381,377,374,370,367,363,360,357,354,350,347,344,341,338,335,
        332,329,326,323,320,318,315,312,310,307,304,302,299,297,294,292,
        289,287,285,282,280,278,275,273,271,269,267,265,263,261,259];
        
   
var shg_table = [
	     9, 11, 12, 13, 13, 14, 14, 15, 15, 15, 15, 16, 16, 16, 16, 17, 
		17, 17, 17, 17, 17, 17, 18, 18, 18, 18, 18, 18, 18, 18, 18, 19, 
		19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 20, 20, 20,
		20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 21,
		21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21,
		21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 22, 22, 22, 22, 22, 22, 
		22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22,
		22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 23, 
		23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23,
		23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23,
		23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 
		23, 23, 23, 23, 23, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 
		24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24,
		24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24,
		24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24,
		24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24 ];


function stackBlurCanvasRGBA( canvas, top_x, top_y, width, height, radius )
{
	if ( isNaN(radius) || radius < 1 ) return;
	radius |= 0;
	
	var context = canvas.getContext("2d");
	var imageData;
	
	try {
	  try {
		imageData = context.getImageData( top_x, top_y, width, height );
	  } catch(e) {
	  
		// NOTE: this part is supposedly only needed if you want to work with local files
		// so it might be okay to remove the whole try/catch block and just use
		// imageData = context.getImageData( top_x, top_y, width, height );
		try {
			netscape.security.PrivilegeManager.enablePrivilege("UniversalBrowserRead");
			imageData = context.getImageData( top_x, top_y, width, height );
		} catch(e) {
			alert("Cannot access local image");
			throw new Error("unable to access local image data: " + e);
			return;
		}
	  }
	} catch(e) {
	  alert("Cannot access image");
	  throw new Error("unable to access image data: " + e);
	}
			
	var pixels = imageData.data;
			
	var x, y, i, p, yp, yi, yw, r_sum, g_sum, b_sum, a_sum, 
	r_out_sum, g_out_sum, b_out_sum, a_out_sum,
	r_in_sum, g_in_sum, b_in_sum, a_in_sum, 
	pr, pg, pb, pa, rbs;
			
	var div = radius + radius + 1;
	var w4 = width << 2;
	var widthMinus1  = width - 1;
	var heightMinus1 = height - 1;
	var radiusPlus1  = radius + 1;
	var sumFactor = radiusPlus1 * ( radiusPlus1 + 1 ) / 2;
	
	var stackStart = new BlurStack();
	var stack = stackStart;
	for ( i = 1; i < div; i++ )
	{
		stack = stack.next = new BlurStack();
		if ( i == radiusPlus1 ) var stackEnd = stack;
	}
	stack.next = stackStart;
	var stackIn = null;
	var stackOut = null;
	
	yw = yi = 0;
	
	var mul_sum = mul_table[radius];
	var shg_sum = shg_table[radius];
	
	for ( y = 0; y < height; y++ )
	{
		r_in_sum = g_in_sum = b_in_sum = a_in_sum = r_sum = g_sum = b_sum = a_sum = 0;
		
		r_out_sum = radiusPlus1 * ( pr = pixels[yi] );
		g_out_sum = radiusPlus1 * ( pg = pixels[yi+1] );
		b_out_sum = radiusPlus1 * ( pb = pixels[yi+2] );
		a_out_sum = radiusPlus1 * ( pa = pixels[yi+3] );
		
		r_sum += sumFactor * pr;
		g_sum += sumFactor * pg;
		b_sum += sumFactor * pb;
		a_sum += sumFactor * pa;
		
		stack = stackStart;
		
		for( i = 0; i < radiusPlus1; i++ )
		{
			stack.r = pr;
			stack.g = pg;
			stack.b = pb;
			stack.a = pa;
			stack = stack.next;
		}
		
		for( i = 1; i < radiusPlus1; i++ )
		{
			p = yi + (( widthMinus1 < i ? widthMinus1 : i ) << 2 );
			r_sum += ( stack.r = ( pr = pixels[p])) * ( rbs = radiusPlus1 - i );
			g_sum += ( stack.g = ( pg = pixels[p+1])) * rbs;
			b_sum += ( stack.b = ( pb = pixels[p+2])) * rbs;
			a_sum += ( stack.a = ( pa = pixels[p+3])) * rbs;
			
			r_in_sum += pr;
			g_in_sum += pg;
			b_in_sum += pb;
			a_in_sum += pa;
			
			stack = stack.next;
		}
		
		
		stackIn = stackStart;
		stackOut = stackEnd;
		for ( x = 0; x < width; x++ )
		{
			pixels[yi+3] = pa = (a_sum * mul_sum) >> shg_sum;
			if ( pa != 0 )
			{
				pa = 255 / pa;
				pixels[yi]   = ((r_sum * mul_sum) >> shg_sum) * pa;
				pixels[yi+1] = ((g_sum * mul_sum) >> shg_sum) * pa;
				pixels[yi+2] = ((b_sum * mul_sum) >> shg_sum) * pa;
			} else {
				pixels[yi] = pixels[yi+1] = pixels[yi+2] = 0;
			}
			
			r_sum -= r_out_sum;
			g_sum -= g_out_sum;
			b_sum -= b_out_sum;
			a_sum -= a_out_sum;
			
			r_out_sum -= stackIn.r;
			g_out_sum -= stackIn.g;
			b_out_sum -= stackIn.b;
			a_out_sum -= stackIn.a;
			
			p =  ( yw + ( ( p = x + radius + 1 ) < widthMinus1 ? p : widthMinus1 ) ) << 2;
			
			r_in_sum += ( stackIn.r = pixels[p]);
			g_in_sum += ( stackIn.g = pixels[p+1]);
			b_in_sum += ( stackIn.b = pixels[p+2]);
			a_in_sum += ( stackIn.a = pixels[p+3]);
			
			r_sum += r_in_sum;
			g_sum += g_in_sum;
			b_sum += b_in_sum;
			a_sum += a_in_sum;
			
			stackIn = stackIn.next;
			
			r_out_sum += ( pr = stackOut.r );
			g_out_sum += ( pg = stackOut.g );
			b_out_sum += ( pb = stackOut.b );
			a_out_sum += ( pa = stackOut.a );
			
			r_in_sum -= pr;
			g_in_sum -= pg;
			b_in_sum -= pb;
			a_in_sum -= pa;
			
			stackOut = stackOut.next;

			yi += 4;
		}
		yw += width;
	}

	
	for ( x = 0; x < width; x++ )
	{
		g_in_sum = b_in_sum = a_in_sum = r_in_sum = g_sum = b_sum = a_sum = r_sum = 0;
		
		yi = x << 2;
		r_out_sum = radiusPlus1 * ( pr = pixels[yi]);
		g_out_sum = radiusPlus1 * ( pg = pixels[yi+1]);
		b_out_sum = radiusPlus1 * ( pb = pixels[yi+2]);
		a_out_sum = radiusPlus1 * ( pa = pixels[yi+3]);
		
		r_sum += sumFactor * pr;
		g_sum += sumFactor * pg;
		b_sum += sumFactor * pb;
		a_sum += sumFactor * pa;
		
		stack = stackStart;
		
		for( i = 0; i < radiusPlus1; i++ )
		{
			stack.r = pr;
			stack.g = pg;
			stack.b = pb;
			stack.a = pa;
			stack = stack.next;
		}
		
		yp = width;
		
		for( i = 1; i <= radius; i++ )
		{
			yi = ( yp + x ) << 2;
			
			r_sum += ( stack.r = ( pr = pixels[yi])) * ( rbs = radiusPlus1 - i );
			g_sum += ( stack.g = ( pg = pixels[yi+1])) * rbs;
			b_sum += ( stack.b = ( pb = pixels[yi+2])) * rbs;
			a_sum += ( stack.a = ( pa = pixels[yi+3])) * rbs;
		   
			r_in_sum += pr;
			g_in_sum += pg;
			b_in_sum += pb;
			a_in_sum += pa;
			
			stack = stack.next;
		
			if( i < heightMinus1 )
			{
				yp += width;
			}
		}
		
		yi = x;
		stackIn = stackStart;
		stackOut = stackEnd;
		for ( y = 0; y < height; y++ )
		{
			p = yi << 2;
			pixels[p+3] = pa = (a_sum * mul_sum) >> shg_sum;
			if ( pa > 0 )
			{
				pa = 255 / pa;
				pixels[p]   = ((r_sum * mul_sum) >> shg_sum ) * pa;
				pixels[p+1] = ((g_sum * mul_sum) >> shg_sum ) * pa;
				pixels[p+2] = ((b_sum * mul_sum) >> shg_sum ) * pa;
			} else {
				pixels[p] = pixels[p+1] = pixels[p+2] = 0;
			}
			
			r_sum -= r_out_sum;
			g_sum -= g_out_sum;
			b_sum -= b_out_sum;
			a_sum -= a_out_sum;
		   
			r_out_sum -= stackIn.r;
			g_out_sum -= stackIn.g;
			b_out_sum -= stackIn.b;
			a_out_sum -= stackIn.a;
			
			p = ( x + (( ( p = y + radiusPlus1) < heightMinus1 ? p : heightMinus1 ) * width )) << 2;
			
			r_sum += ( r_in_sum += ( stackIn.r = pixels[p]));
			g_sum += ( g_in_sum += ( stackIn.g = pixels[p+1]));
			b_sum += ( b_in_sum += ( stackIn.b = pixels[p+2]));
			a_sum += ( a_in_sum += ( stackIn.a = pixels[p+3]));
		   
			stackIn = stackIn.next;
			
			r_out_sum += ( pr = stackOut.r );
			g_out_sum += ( pg = stackOut.g );
			b_out_sum += ( pb = stackOut.b );
			a_out_sum += ( pa = stackOut.a );
			
			r_in_sum -= pr;
			g_in_sum -= pg;
			b_in_sum -= pb;
			a_in_sum -= pa;
			
			stackOut = stackOut.next;
			
			yi += width;
		}
	}
	
	context.putImageData( imageData, top_x, top_y );
	
}

function BlurStack()
{
	this.r = 0;
	this.g = 0;
	this.b = 0;
	this.a = 0;
	this.next = null;
}

$( document ).ready(function() {
  var BLUR_RADIUS = 40;
  var sourceImages = [];

  $('.src-image').each(function(){
    sourceImages.push($(this).attr('src'));
  });

  $('.avatar img').each(function(index){
    $(this).attr('src', sourceImages[index] );
  });

  var drawBlur = function(canvas, image) {
    var w = canvas.width;
    var h = canvas.height;
    var canvasContext = canvas.getContext('2d');
    canvasContext.drawImage(image, 0, 0, w, h);
    stackBlurCanvasRGBA(canvas, 0, 0, w, h, BLUR_RADIUS);
  }; 
    
  
  $('.card canvas').each(function(index){
    var canvas = $(this)[0];
    
    var image = new Image();
    image.src = sourceImages[index];
    
    image.onload = function() {
      drawBlur(canvas, image);
    }
  });
});