loretoparisi/gist:c147ca437ab9d5e163b7

## gistfile1.txt

<html>
  <head>
    <!-- adapted from http://charlesleifer.com/blog/using-python-and-k-means-to-find-the-dominant-colors-in-images/ -->
    <script type="text/javascript">
      function euclidean(p1, p2) {
        var s = 0;
        for (var i = 0, l = p1.length; i < l; i++) {
          s += Math.pow(p1[i] - p2[i], 2)
        }
        return Math.sqrt(s);
      }

      function calculateCenter(points, n) {
        var vals = []
          , plen = 0;
        for (var i = 0; i < n; i++) { vals.push(0); }
        for (var i = 0, l = points.length; i < l; i++) {
          plen++;
          for (var j = 0; j < n; j++) {
            vals[j] += points[i][j];
          }
        }
        for (var i = 0; i < n; i++) {
          vals[i] = vals[i] / plen;
        }
        return vals;
      }

      function kmeans(points, k, min_diff) {
        plen = points.length;
        clusters = [];
        seen = [];
        while (clusters.length < k) {
          idx = parseInt(Math.random() * plen);
          found = false;
          for (var i = 0; i < seen.length; i++ ) {
            if (idx === seen[i]) {
              found = true;
              break;
            }
          }
          if (!found) {
            seen.push(idx);
            clusters.push([points[idx], [points[idx]]]);
          }
        }

        while (true) {
          plists = [];
          for (var i = 0; i < k; i++) {
            plists.push([]);
          }

          for (var j = 0; j < plen; j++) {
            var p = points[j]
             , smallest_distance = 10000000
             , idx = 0;
            for (var i = 0; i < k; i++) {
              var distance = euclidean(p, clusters[i][0]);
              if (distance < smallest_distance) {
                smallest_distance = distance;
                idx = i;
              }
            }
            plists[idx].push(p);
          }

          var diff = 0;
          for (var i = 0; i < k; i++) {
            var old = clusters[i]
              , list = plists[i]
              , center = calculateCenter(plists[i], 3)
              , new_cluster = [center, (plists[i])]
              , dist = euclidean(old[0], center);
            clusters[i] = new_cluster
            diff = diff > dist ? diff : dist;
          }
          if (diff < min_diff) {
            break;
          }
        }
        return clusters;
      }

      function rgbToHex(rgb) {
        function th(i) {
          var h = parseInt(i).toString(16);
          return h.length == 1 ? '0'+h : h;
        }
        return '#' + th(rgb[0]) + th(rgb[1]) + th(rgb[2]);
      }

      function process_image(img, ctx) {
        var points = [];
        ctx.drawImage(img, 0, 0, 200, 200);
        data = ctx.getImageData(0, 0, 200, 200).data;
        for (var i = 0, l = data.length; i < l;  i += 4) {
          var r = data[i]
            , g = data[i+1]
            , b = data[i+2];
          points.push([r, g, b]);
        }
        var results = kmeans(points, 3, 1)
         , hex = [];
        for (var i = 0; i < results.length; i++) {
          hex.push(rgbToHex(results[i][0]));
        }
        return hex;
      }

      function analyze(img_elem) {
        var ctx = document.getElementById('canvas').getContext('2d')
          , img = new Image();
        img.onload = function() {
          var results = document.getElementById('results');
          results.innerHTML = 'Waiting...';
          var colors = process_image(img, ctx)
            , p1 = document.getElementById('c1')
            , p2 = document.getElementById('c2')
            , p3 = document.getElementById('c3');
          p1.style.backgroundColor = colors[0];
          p2.style.backgroundColor = colors[1];
          p3.style.backgroundColor = colors[2];
          results.innerHTML = 'Done';
        }
        img.src = img_elem.src;
      }
    </script>
  </head>
  <body>
    <h3>Click an image below to analyze its colors</h3>
    <canvas id="canvas" style="display: none;" width="200" height="200"></canvas>
    <div>
      <p id="results">Results will be displayed here</p>
      <span id="c1">&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;</span>
      <span id="c2">&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;</span>
      <span id="c3">&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;</span>
    </div>
    <div>
      <img onclick="javascript:analyze(this);" src="akira_800x800.jpg" style="width: 250px;" />
      <img onclick="javascript:analyze(this);" src="akira-cycle-2_800x800.png" style="width: 250px;" />
      <img onclick="javascript:analyze(this);" src="akira-neo-tokyo-6_800x800.png" style="width: 250px;" />
      <img onclick="javascript:analyze(this);" src="akira-neo-tokyo-7_800x800.png" style="width: 250px;" />
    </div>
  </body>
</html>

	<html>
	<head>
	<!-- adapted from http://charlesleifer.com/blog/using-python-and-k-means-to-find-the-dominant-colors-in-images/ -->
	<script type="text/javascript">
	function euclidean(p1, p2) {
	var s = 0;
	for (var i = 0, l = p1.length; i < l; i++) {
	s += Math.pow(p1[i] - p2[i], 2)
	}
	return Math.sqrt(s);
	}

	function calculateCenter(points, n) {
	var vals = []
	, plen = 0;
	for (var i = 0; i < n; i++) { vals.push(0); }
	for (var i = 0, l = points.length; i < l; i++) {
	plen++;
	for (var j = 0; j < n; j++) {
	vals[j] += points[i][j];
	}
	}
	for (var i = 0; i < n; i++) {
	vals[i] = vals[i] / plen;
	}
	return vals;
	}

	function kmeans(points, k, min_diff) {
	plen = points.length;
	clusters = [];
	seen = [];
	while (clusters.length < k) {
	idx = parseInt(Math.random() * plen);
	found = false;
	for (var i = 0; i < seen.length; i++ ) {
	if (idx === seen[i]) {
	found = true;
	break;
	}
	}
	if (!found) {
	seen.push(idx);
	clusters.push([points[idx], [points[idx]]]);
	}
	}

	while (true) {
	plists = [];
	for (var i = 0; i < k; i++) {
	plists.push([]);
	}

	for (var j = 0; j < plen; j++) {
	var p = points[j]
	, smallest_distance = 10000000
	, idx = 0;
	for (var i = 0; i < k; i++) {
	var distance = euclidean(p, clusters[i][0]);
	if (distance < smallest_distance) {
	smallest_distance = distance;
	idx = i;
	}
	}
	plists[idx].push(p);
	}

	var diff = 0;
	for (var i = 0; i < k; i++) {
	var old = clusters[i]
	, list = plists[i]
	, center = calculateCenter(plists[i], 3)
	, new_cluster = [center, (plists[i])]
	, dist = euclidean(old[0], center);
	clusters[i] = new_cluster
	diff = diff > dist ? diff : dist;
	}
	if (diff < min_diff) {
	break;
	}
	}
	return clusters;
	}

	function rgbToHex(rgb) {
	function th(i) {
	var h = parseInt(i).toString(16);
	return h.length == 1 ? '0'+h : h;
	}
	return '#' + th(rgb[0]) + th(rgb[1]) + th(rgb[2]);
	}

	function process_image(img, ctx) {
	var points = [];
	ctx.drawImage(img, 0, 0, 200, 200);
	data = ctx.getImageData(0, 0, 200, 200).data;
	for (var i = 0, l = data.length; i < l; i += 4) {
	var r = data[i]
	, g = data[i+1]
	, b = data[i+2];
	points.push([r, g, b]);
	}
	var results = kmeans(points, 3, 1)
	, hex = [];
	for (var i = 0; i < results.length; i++) {
	hex.push(rgbToHex(results[i][0]));
	}
	return hex;
	}

	function analyze(img_elem) {
	var ctx = document.getElementById('canvas').getContext('2d')
	, img = new Image();
	img.onload = function() {
	var results = document.getElementById('results');
	results.innerHTML = 'Waiting...';
	var colors = process_image(img, ctx)
	, p1 = document.getElementById('c1')
	, p2 = document.getElementById('c2')
	, p3 = document.getElementById('c3');
	p1.style.backgroundColor = colors[0];
	p2.style.backgroundColor = colors[1];
	p3.style.backgroundColor = colors[2];
	results.innerHTML = 'Done';
	}
	img.src = img_elem.src;
	}
	</script>
	</head>
	<body>
	<h3>Click an image below to analyze its colors</h3>
	<canvas id="canvas" style="display: none;" width="200" height="200"></canvas>
	<div>
	<p id="results">Results will be displayed here</p>
	<span id="c1">        </span>
	<span id="c2">        </span>
	<span id="c3">        </span>
	</div>
	<div>
	<img onclick="javascript:analyze(this);" src="akira_800x800.jpg" style="width: 250px;" />
	<img onclick="javascript:analyze(this);" src="akira-cycle-2_800x800.png" style="width: 250px;" />
	<img onclick="javascript:analyze(this);" src="akira-neo-tokyo-6_800x800.png" style="width: 250px;" />
	<img onclick="javascript:analyze(this);" src="akira-neo-tokyo-7_800x800.png" style="width: 250px;" />
	</div>
	</body>
	</html>