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pointFinderDemo.html
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<html>
<head>
<title>SurfaceAugmenter</title>
<style> </style>
<script type="text/javascript" src="libs/NumJS/NumJS.js"></script>
<script type="text/javascript">NumJS.loader_html("libs/NumJS/")</script>
<script type="text/javascript" src="src/image.js"></script>
<script type="text/javascript" src="src/helpers.js"></script>
<script type="text/javascript" src="src/lines.js"></script>
<script type="text/javascript" src="libs/pixastic/pixastic.core.js"></script>
<script type="text/javascript" src="libs/pixastic/actions/histogram.js"></script>
<script type="text/javascript">
var img;
var imgWidth, imgHeight;
var imgScale = 1.0;
var points = [];
var pixels, bwPixels, maskPixels;
var maskInitBlack = 50;
var maskLine = 200;
var maskOutBlack = 100;
function setup()
{
document.getElementById('imageFile').addEventListener('change', handleImageSelect, false);
document.getElementById('canvas').addEventListener('click', handleCanvasClick, false);
// enable dnd on canvas to load images
var holder = document.getElementById('canvas');
holder.ondragover = function () { this.className = 'hover'; return false; };
holder.ondragend = function () { this.className = ''; return false; };
holder.ondrop = function (e) {
this.className = ''; // what for?
e.preventDefault();
var file = e.dataTransfer.files[0];
asyncLoadImageFromFile(file);
};
asyncLoadImageFromURL("test1.png");
}
function handleImageSelect(evt) {
pixels = undefined;
var files = evt.target.files; // FileList object
var file = files[0];
asyncLoadImageFromFile(file);
}
function handleCanvasClick(evt) {
var canvas = document.getElementById('canvas');
var context = canvas.getContext('2d');
if (!pixels) {
pixels = context.getImageData(0,0, canvas.width, canvas.height);
bwPixels = RGBA2A(pixels, context);
bwPixelsSobolev = createImageDataA(canvas.width, canvas.height);
applyKernelAlphaOnPixels(bwPixels, dxx, dyy, bwPixelsSobolev);
maskPixels = createImageDataA(canvas.width, canvas.height);
APixelsFill(maskPixels, 0);
}
var coords = canvas.relMouseCoords(evt);
var x = coords.x/imgScale;
var y = coords.y/imgScale;
if (true || evt.ctrlKey || evt.button == 1)
{
x = Math.round(x);
y = Math.round(y);
var whiteSum = 255;
var whiteVarsum = 0;
var whiteN = 1;
var blackSum = 0;
var blackVarsum = 0;
var blackN = 1;
var br = 0.0;
fill4(x, y, function(x, y, state) {
if (x >= 0 && x < imgWidth && y >= 0 && y < imgHeight) {
var whiteAvg = whiteSum/whiteN;
var blackAvg = blackSum/blackN;
var whiteVar = whiteVarsum/whiteN;
var blackVar = blackVarsum/blackN;
var idx = x+imgWidth*y;
var idx4 = 4*idx;
var mask = maskPixels.data[idx];
if (mask == maskInitBlack || mask == maskLine || mask == maskOutBlack) {
return false;
}
var val = bwPixelsSobolev.data[idx];
br += 0.03;
//pixels.data[idx4] = br;
//pixels.data[idx4+1] = br-255;
//pixels.data[idx4+2] = br-512;
if (state == maskInitBlack)
{
if (val > (0.5*blackAvg+0.5*whiteAvg)) {
//blackSum += val; blackVarsum += Math.abs(val-blackAvg); blackN++;
whiteSum += val; whiteVarsum += Math.abs(val-whiteAvg); whiteN++;
maskPixels.data[idx] = maskLine;
//pixels.data[idx4] = 255;
//pixels.data[idx4+1] = 0;
//pixels.data[idx4+2] = 0;
return maskLine;
}
blackSum += val; blackVarsum += Math.abs(val-blackAvg); blackN++;
//whiteSum += val; whiteVarsum += Math.abs(val-whiteAvg); whiteN++;
maskPixels.data[idx] = maskInitBlack;
//pixels.data[idx4] = 0;
//pixels.data[idx4+1] = 255;
//pixels.data[idx4+2] = 0;
return maskInitBlack;
}
else if (state == maskLine)
{
if (val < (0.7*blackAvg+0.3*whiteAvg)) {
//pixels.data[idx4] = 255;
//pixels.data[idx4+1] = 255;
//pixels.data[idx4+2] = 0;
maskPixels.data[idx] = maskOutBlack;
return maskOutBlack;
}
//blackSum += val; blackVarsum += Math.abs(val-blackAvg); blackN++;
whiteSum += val; whiteVarsum += Math.abs(val-whiteAvg); whiteN++;
maskPixels.data[idx] = maskLine;
//pixels.data[idx4] = 0;
//pixels.data[idx4+1] = 0;
//pixels.data[idx4+2] = 255;
return maskLine;
}
}
return false;
}, maskInitBlack);
applyMaskAlphaOnPixels(bwPixelsSobolev, maskPixels, bwPixelsSobolev)
var bestGroup = findLineCandidates(pixels, maskPixels, bwPixelsSobolev);
for (var i = 0; i < bestGroup.length; i++) {
improveLine(bestGroup[i], bwPixelsSobolev, 4, 0.05);
}
for (var i = 0; i < bestGroup.length-2; i++) {
var x1 = intersectLines(bestGroup[i], bestGroup[i+1]);
var x2 = intersectLines(bestGroup[i+2], bestGroup[(i+3)%bestGroup.length]);
drawLine2(x1, x2, pixels, 0, 0, 255);
}
for (var i = 0; i < bestGroup.length; i++) {
drawLine2(bestGroup[i].p1, bestGroup[i].p2, pixels, 0, 255, 0);
//drawLine2A(bestGroup[i].p1, bestGroup[i].p2, bwPixelsSobolev, 0);
}
context.putImageData(pixels, 0,0);
var canvas2 = document.getElementById('canvas2');
canvas2.width = maskPixels.width;
canvas2.height = maskPixels.height;
var context2 = canvas2.getContext('2d');
var grayPixels = A2RGBA(maskPixels, context2);
context2.putImageData(grayPixels, 0,0);
var canvas3 = document.getElementById('canvas3');
canvas3.width = maskPixels.width;
canvas3.height = maskPixels.height;
var context3 = canvas3.getContext('2d');
var grayPixels = A2RGBA(bwPixelsSobolev, context3);
for (var i = 0; i < bestGroup.length; i++) {
drawLine2(bestGroup[i].p1, bestGroup[i].p2, grayPixels, 255, 0, 0);
}
context3.putImageData(grayPixels, 0,0);
}
else {
if (x >= 0 && x < imgWidth && y >= 0 && y < imgHeight)
points.push([x, y]);
context.beginPath();
context.arc(x, y, 3, 0, Math.PI*2, true);
context.closePath();
context.fill();
if (points.length == 2)
{
var sum = 0;
var n = 0;
var w = bwPixels.width;
xiaolinWuLineIterator(points[0][0],points[0][1], points[1][0], points[1][1], function(x, y, c) {
n += c;
sum += c* bwPixels.data[x+w*y];
pixels.data[4*(x+w*y)] = (1-c) * pixels.data[4*(x+w*y)] + c * 0;
pixels.data[4*(x+w*y)+1] = (1-c) * pixels.data[4*(x+w*y)+1] + c * 0;
pixels.data[4*(x+w*y)+2] = (1-c) * pixels.data[4*(x+w*y)+2] + c * 0;
//pixels.data[4*(x+w*y)+3] = 255;
});
alert("s: " + sum + " n: " + n + " avg: " + (sum/n));
/*
lineIterator(points[0][0],points[0][1], points[1][0], points[1][1], function(x, y) {
n++;
sum += bwPixels[x+w*y];
pixels.data[4*(x+w*y)] = 255 - pixels.data[4*(x+w*y)];
pixels.data[4*(x+w*y)+1] = 255 - pixels.data[4*(x+w*y)+1];canvas.width = img.width;
canvas.height = img.height;
pixels.data[4*(x+w*y)+2] = 255 - pixels.data[4*(x+w*y)+2];
});
//*/
//alert("s: " + sum + " n: " + n + " " + (sum/n));
context.putImageData(pixels, 0, 0);
//document.getElementById('outputText').value = "convert ..\\..\\web\\" + fileName + " -virtual-pixel transparent -distort Perspective \"" + s + "\" " + fileName + "out.png";
//handleLoad(img);
points = [];
}
}
}
function drawImage(img) {
var canvas = document.getElementById('canvas');
canvas.width = img.width;
canvas.height = img.height;
imgWidth = img.width;
imgHeight = img.height;
var context = canvas.getContext('2d');
context.drawImage(img, 0, 0, img.width, img.height);
}
function scaleImage(scale)
{
imgScale *= scale;
var canvas = document.getElementById('canvas');
canvas.style.width = "" + (imgWidth*imgScale) +"px";
canvas.style.height = "" + (imgHeight*imgScale) +"px";
//
//canvas.height = imgHeight;
}
var hist = {};
function handleTestClick()
{
var canvas = document.getElementById('canvas');
var context = canvas.getContext('2d');
//var pixels = context.getImageData(110,110,120,120);
var pixels = context.getImageData(0,0, canvas.width, canvas.height);
var bwPixels = RGBA2A(pixels, context);
var bwPixelsSobolev = RGBA2A(pixels, context);
var x, y;
applyKernelAlphaOnPixels(bwPixels, dxx, dyy, bwPixelsSobolev)
var grayPixels = A2RGBA(bwPixelsSobolev, context);
context.putImageData(grayPixels, 0,0);
return;
Pixastic.process(document.getElementById("canvas"), "histogram", {
average : false, paint:true,color:"rgba(255,255,255,0.5)",returnValue:hist
});
document.getElementById('debugText').value = hist.values;
}
function xffdlj() {
//pixels = context.getImageData(0,0, canvas.width, canvas.height);
//bwPixels = Filters.RGBA2A(pixels, context);
// applyKernelAlpha(pixels, kernel, x, y)
}
</script>
</head>
<body onload="setup()">
<b>This demo was superseeded by the final version of this: <a href="surfaceAugmenterBeta.html">The SurfaceAugmenter</a></b>
<hr>
Click inide the white paper to find the inner contour using an antialiased search in the radon transformed gradient image. the intersections of the lines are calculated with clifford's NumJS :) Next step: Perspective correction :) This is so much more fun than work...
<form name="controls" action="">
<input type="file" value="imageFile" id="imageFile" />
<input type="button" value="scale/1.2" onclick="scaleImage(1/1.2);"><input type="button" value="scale*1.2" onclick="scaleImage(1.2);">
<input type="button" value="Apply Sobel Operator" onclick="handleTestClick();">
</form>
<canvas id="canvas"></canvas>
<br>
<br>
the mask used for clipping the gradient for the line search
<br>
<canvas id="canvas2"></canvas>
<br>
<br>
the clipped magnitude of the gradient overlayed with the vectorized lines (this is used for the search so the vectorized lines should be very well aligned with the pixels)
<br>
<canvas id="canvas3"></canvas>
</body>
</html>