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getuserbarcode

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An advanced barcode-scanner written in JavaScript

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import Cluster2 from './cluster'; import ArrayHelper from './array_helper'; const vec2 = { clone: require('gl-vec2/clone'), }; const vec3 = { clone: require('gl-vec3/clone'), }; /** * @param x x-coordinate * @param y y-coordinate * @return ImageReference {x,y} Coordinate */ export function imageRef(x, y) { var that = { x: x, y: y, toVec2: function() { return vec2.clone([this.x, this.y]); }, toVec3: function() { return vec3.clone([this.x, this.y, 1]); }, round: function() { this.x = this.x > 0.0 ? Math.floor(this.x + 0.5) : Math.floor(this.x - 0.5); this.y = this.y > 0.0 ? Math.floor(this.y + 0.5) : Math.floor(this.y - 0.5); return this; } }; return that; }; /** * Computes an integral image of a given grayscale image. * @param imageDataContainer {ImageDataContainer} the image to be integrated */ export function computeIntegralImage2(imageWrapper, integralWrapper) { var imageData = imageWrapper.data; var width = imageWrapper.size.x; var height = imageWrapper.size.y; var integralImageData = integralWrapper.data; var sum = 0, posA = 0, posB = 0, posC = 0, posD = 0, x, y; // sum up first column posB = width; sum = 0; for ( y = 1; y < height; y++) { sum += imageData[posA]; integralImageData[posB] += sum; posA += width; posB += width; } posA = 0; posB = 1; sum = 0; for ( x = 1; x < width; x++) { sum += imageData[posA]; integralImageData[posB] += sum; posA++; posB++; } for ( y = 1; y < height; y++) { posA = y * width + 1; posB = (y - 1) * width + 1; posC = y * width; posD = (y - 1) * width; for ( x = 1; x < width; x++) { integralImageData[posA] += imageData[posA] + integralImageData[posB] + integralImageData[posC] - integralImageData[posD]; posA++; posB++; posC++; posD++; } } }; export function computeIntegralImage(imageWrapper, integralWrapper) { var imageData = imageWrapper.data; var width = imageWrapper.size.x; var height = imageWrapper.size.y; var integralImageData = integralWrapper.data; var sum = 0; // sum up first row for (var i = 0; i < width; i++) { sum += imageData[i]; integralImageData[i] = sum; } for (var v = 1; v < height; v++) { sum = 0; for (var u = 0; u < width; u++) { sum += imageData[v * width + u]; integralImageData[((v) * width) + u] = sum + integralImageData[(v - 1) * width + u]; } } }; export function thresholdImage(imageWrapper, threshold, targetWrapper) { if (!targetWrapper) { targetWrapper = imageWrapper; } var imageData = imageWrapper.data, length = imageData.length, targetData = targetWrapper.data; while (length--) { targetData[length] = imageData[length] < threshold ? 1 : 0; } }; export function computeHistogram(imageWrapper, bitsPerPixel) { if (!bitsPerPixel) { bitsPerPixel = 8; } var imageData = imageWrapper.data, length = imageData.length, bitShift = 8 - bitsPerPixel, bucketCnt = 1 << bitsPerPixel, hist = new Int32Array(bucketCnt); while (length--) { hist[imageData[length] >> bitShift]++; } return hist; }; export function sharpenLine(line) { var i, length = line.length, left = line[0], center = line[1], right; for (i = 1; i < length - 1; i++) { right = line[i + 1]; // -1 4 -1 kernel line[i - 1] = (((center * 2) - left - right)) & 255; left = center; center = right; } return line; }; export function determineOtsuThreshold(imageWrapper, bitsPerPixel) { if (!bitsPerPixel) { bitsPerPixel = 8; } var hist, threshold, bitShift = 8 - bitsPerPixel; function px(init, end) { var sum = 0, i; for ( i = init; i <= end; i++) { sum += hist[i]; } return sum; } function mx(init, end) { var i, sum = 0; for ( i = init; i <= end; i++) { sum += i * hist[i]; } return sum; } function determineThreshold() { var vet = [0], p1, p2, p12, k, m1, m2, m12, max = (1 << bitsPerPixel) - 1; hist = computeHistogram(imageWrapper, bitsPerPixel); for ( k = 1; k < max; k++) { p1 = px(0, k); p2 = px(k + 1, max); p12 = p1 * p2; if (p12 === 0) { p12 = 1; } m1 = mx(0, k) * p2; m2 = mx(k + 1, max) * p1; m12 = m1 - m2; vet[k] = m12 * m12 / p12; } return ArrayHelper.maxIndex(vet); } threshold = determineThreshold(); return threshold << bitShift; }; export function otsuThreshold(imageWrapper, targetWrapper) { var threshold = determineOtsuThreshold(imageWrapper); thresholdImage(imageWrapper, threshold, targetWrapper); return threshold; }; // local thresholding export function computeBinaryImage(imageWrapper, integralWrapper, targetWrapper) { computeIntegralImage(imageWrapper, integralWrapper); if (!targetWrapper) { targetWrapper = imageWrapper; } var imageData = imageWrapper.data; var targetData = targetWrapper.data; var width = imageWrapper.size.x; var height = imageWrapper.size.y; var integralImageData = integralWrapper.data; var sum = 0, v, u, kernel = 3, A, B, C, D, avg, size = (kernel * 2 + 1) * (kernel * 2 + 1); // clear out top & bottom-border for ( v = 0; v <= kernel; v++) { for ( u = 0; u < width; u++) { targetData[((v) * width) + u] = 0; targetData[(((height - 1) - v) * width) + u] = 0; } } // clear out left & right border for ( v = kernel; v < height - kernel; v++) { for ( u = 0; u <= kernel; u++) { targetData[((v) * width) + u] = 0; targetData[((v) * width) + (width - 1 - u)] = 0; } } for ( v = kernel + 1; v < height - kernel - 1; v++) { for ( u = kernel + 1; u < width - kernel; u++) { A = integralImageData[(v - kernel - 1) * width + (u - kernel - 1)]; B = integralImageData[(v - kernel - 1) * width + (u + kernel)]; C = integralImageData[(v + kernel) * width + (u - kernel - 1)]; D = integralImageData[(v + kernel) * width + (u + kernel)]; sum = D - C - B + A; avg = sum / (size); targetData[v * width + u] = imageData[v * width + u] > (avg + 5) ? 0 : 1; } } }; export function cluster(points, threshold, property) { var i, k, cluster, point, clusters = []; if (!property) { property = "rad"; } function addToCluster(newPoint) { var found = false; for ( k = 0; k < clusters.length; k++) { cluster = clusters[k]; if (cluster.fits(newPoint)) { cluster.add(newPoint); found = true; } } return found; } // iterate over each cloud for ( i = 0; i < points.length; i++) { point = Cluster2.createPoint(points[i], i, property); if (!addToCluster(point)) { clusters.push(Cluster2.create(point, threshold)); } } return clusters; }; export const Tracer = { trace: function(points, vec) { var iteration, maxIterations = 10, top = [], result = [], centerPos = 0, currentPos = 0; function trace(idx, forward) { var from, to, toIdx, predictedPos, thresholdX = 1, thresholdY = Math.abs(vec[1] / 10), found = false; function match(pos, predicted) { if (pos.x > (predicted.x - thresholdX) && pos.x < (predicted.x + thresholdX) && pos.y > (predicted.y - thresholdY) && pos.y < (predicted.y + thresholdY)) { return true; } else { return false; } } // check if the next index is within the vec specifications // if not, check as long as the threshold is met from = points[idx]; if (forward) { predictedPos = { x: from.x + vec[0], y: from.y + vec[1] }; } else { predictedPos = { x: from.x - vec[0], y: from.y - vec[1] }; } toIdx = forward ? idx + 1 : idx - 1; to = points[toIdx]; while (to && ( found = match(to, predictedPos)) !== true && (Math.abs(to.y - from.y) < vec[1])) { toIdx = forward ? toIdx + 1 : toIdx - 1; to = points[toIdx]; } return found ? toIdx : null; } for ( iteration = 0; iteration < maxIterations; iteration++) { // randomly select point to start with centerPos = Math.floor(Math.random() * points.length); // trace forward top = []; currentPos = centerPos; top.push(points[currentPos]); while (( currentPos = trace(currentPos, true)) !== null) { top.push(points[currentPos]); } if (centerPos > 0) { currentPos = centerPos; while (( currentPos = trace(currentPos, false)) !== null) { top.push(points[currentPos]); } } if (top.length > result.length) { result = top; } } return result; } }; export const DILATE = 1; export const ERODE = 2; export function dilate(inImageWrapper, outImageWrapper) { var v, u, inImageData = inImageWrapper.data, outImageData = outImageWrapper.data, height = inImageWrapper.size.y, width = inImageWrapper.size.x, sum, yStart1, yStart2, xStart1, xStart2; for ( v = 1; v < height - 1; v++) { for ( u = 1; u < width - 1; u++) { yStart1 = v - 1; yStart2 = v + 1; xStart1 = u - 1; xStart2 = u + 1; sum = inImageData[yStart1 * width + xStart1] + inImageData[yStart1 * width + xStart2] + inImageData[v * width + u] + inImageData[yStart2 * width + xStart1] + inImageData[yStart2 * width + xStart2]; outImageData[v * width + u] = sum > 0 ? 1 : 0; } } }; export function erode(inImageWrapper, outImageWrapper) { var v, u, inImageData = inImageWrapper.data, outImageData = outImageWrapper.data, height = inImageWrapper.size.y, width = inImageWrapper.size.x, sum, yStart1, yStart2, xStart1, xStart2; for ( v = 1; v < height - 1; v++) { for ( u = 1; u < width - 1; u++) { yStart1 = v - 1; yStart2 = v + 1; xStart1 = u - 1; xStart2 = u + 1; sum = inImageData[yStart1 * width + xStart1] + inImageData[yStart1 * width + xStart2] + inImageData[v * width + u] + inImageData[yStart2 * width + xStart1] + inImageData[yStart2 * width + xStart2]; outImageData[v * width + u] = sum === 5 ? 1 : 0; } } }; export function subtract(aImageWrapper, bImageWrapper, resultImageWrapper) { if (!resultImageWrapper) { resultImageWrapper = aImageWrapper; } var length = aImageWrapper.data.length, aImageData = aImageWrapper.data, bImageData = bImageWrapper.data, cImageData = resultImageWrapper.data; while (length--) { cImageData[length] = aImageData[length] - bImageData[length]; } }; export function bitwiseOr(aImageWrapper, bImageWrapper, resultImageWrapper) { if (!resultImageWrapper) { resultImageWrapper = aImageWrapper; } var length = aImageWrapper.data.length, aImageData = aImageWrapper.data, bImageData = bImageWrapper.data, cImageData = resultImageWrapper.data; while (length--) { cImageData[length] = aImageData[length] || bImageData[length]; } }; export function countNonZero(imageWrapper) { var length = imageWrapper.data.length, data = imageWrapper.data, sum = 0; while (length--) { sum += data[length]; } return sum; }; export function topGeneric(list, top, scoreFunc) { var i, minIdx = 0, min = 0, queue = [], score, hit, pos; for ( i = 0; i < top; i++) { queue[i] = { score: 0, item: null }; } for ( i = 0; i < list.length; i++) { score = scoreFunc.apply(this, [list[i]]); if (score > min) { hit = queue[minIdx]; hit.score = score; hit.item = list[i]; min = Number.MAX_VALUE; for ( pos = 0; pos < top; pos++) { if (queue[pos].score < min) { min = queue[pos].score; minIdx = pos; } } } } return queue; }; export function grayArrayFromImage(htmlImage, offsetX, ctx, array) { ctx.drawImage(htmlImage, offsetX, 0, htmlImage.width, htmlImage.height); var ctxData = ctx.getImageData(offsetX, 0, htmlImage.width, htmlImage.height).data; computeGray(ctxData, array); }; export function grayArrayFromContext(ctx, size, offset, array) { var ctxData = ctx.getImageData(offset.x, offset.y, size.x, size.y).data; computeGray(ctxData, array); }; export function grayAndHalfSampleFromCanvasData(canvasData, size, outArray) { var topRowIdx = 0; var bottomRowIdx = size.x; var endIdx = Math.floor(canvasData.length / 4); var outWidth = size.x / 2; var outImgIdx = 0; var inWidth = size.x; var i; while (bottomRowIdx < endIdx) { for ( i = 0; i < outWidth; i++) { outArray[outImgIdx] = ( (0.299 * canvasData[topRowIdx * 4 + 0] + 0.587 * canvasData[topRowIdx * 4 + 1] + 0.114 * canvasData[topRowIdx * 4 + 2]) + (0.299 * canvasData[(topRowIdx + 1) * 4 + 0] + 0.587 * canvasData[(topRowIdx + 1) * 4 + 1] + 0.114 * canvasData[(topRowIdx + 1) * 4 + 2]) + (0.299 * canvasData[(bottomRowIdx) * 4 + 0] + 0.587 * canvasData[(bottomRowIdx) * 4 + 1] + 0.114 * canvasData[(bottomRowIdx) * 4 + 2]) + (0.299 * canvasData[(bottomRowIdx + 1) * 4 + 0] + 0.587 * canvasData[(bottomRowIdx + 1) * 4 + 1] + 0.114 * canvasData[(bottomRowIdx + 1) * 4 + 2])) / 4; outImgIdx++; topRowIdx = topRowIdx + 2; bottomRowIdx = bottomRowIdx + 2; } topRowIdx = topRowIdx + inWidth; bottomRowIdx = bottomRowIdx + inWidth; } }; export function computeGray(imageData, outArray, config) { var l = (imageData.length / 4) | 0, i, singleChannel = config && config.singleChannel === true; if (singleChannel) { for (i = 0; i < l; i++) { outArray[i] = imageData[i * 4 + 0]; } } else { for (i = 0; i < l; i++) { outArray[i] = 0.299 * imageData[i * 4 + 0] + 0.587 * imageData[i * 4 + 1] + 0.114 * imageData[i * 4 + 2]; } } }; export function loadImageArray(src, callback, canvas) { if (!canvas) { canvas = document.createElement('canvas'); } var img = new Image(); img.callback = callback; img.onload = function() { canvas.width = this.width; canvas.height = this.height; var ctx = canvas.getContext('2d'); ctx.drawImage(this, 0, 0); var array = new Uint8Array(this.width * this.height); ctx.drawImage(this, 0, 0); var data = ctx.getImageData(0, 0, this.width, this.height).data; computeGray(data, array); this.callback(array, { x: this.width, y: this.height }, this); }; img.src = src; }; /** * @param inImg {ImageWrapper} input image to be sampled * @param outImg {ImageWrapper} to be stored in */ export function halfSample(inImgWrapper, outImgWrapper) { var inImg = inImgWrapper.data; var inWidth = inImgWrapper.size.x; var outImg = outImgWrapper.data; var topRowIdx = 0; var bottomRowIdx = inWidth; var endIdx = inImg.length; var outWidth = inWidth / 2; var outImgIdx = 0; while (bottomRowIdx < endIdx) { for (var i = 0; i < outWidth; i++) { outImg[outImgIdx] = Math.floor( (inImg[topRowIdx] + inImg[topRowIdx + 1] + inImg[bottomRowIdx] + inImg[bottomRowIdx + 1]) / 4); outImgIdx++; topRowIdx = topRowIdx + 2; bottomRowIdx = bottomRowIdx + 2; } topRowIdx = topRowIdx + inWidth; bottomRowIdx = bottomRowIdx + inWidth; } }; export function hsv2rgb(hsv, rgb) { var h = hsv[0], s = hsv[1], v = hsv[2], c = v * s, x = c * (1 - Math.abs((h / 60) % 2 - 1)), m = v - c, r = 0, g = 0, b = 0; rgb = rgb || [0, 0, 0]; if (h < 60) { r = c; g = x; } else if (h < 120) { r = x; g = c; } else if (h < 180) { g = c; b = x; } else if (h < 240) { g = x; b = c; } else if (h < 300) { r = x; b = c; } else if (h < 360) { r = c; b = x; } rgb[0] = ((r + m) * 255) | 0; rgb[1] = ((g + m) * 255) | 0; rgb[2] = ((b + m) * 255) | 0; return rgb; }; export function _computeDivisors(n) { var largeDivisors = [], divisors = [], i; for (i = 1; i < Math.sqrt(n) + 1; i++) { if (n % i === 0) { divisors.push(i); if (i !== n / i) { largeDivisors.unshift(Math.floor(n / i)); } } } return divisors.concat(largeDivisors); }; function _computeIntersection(arr1, arr2) { var i = 0, j = 0, result = []; while (i < arr1.length && j < arr2.length) { if (arr1[i] === arr2[j]) { result.push(arr1[i]); i++; j++; } else if (arr1[i] > arr2[j]) { j++; } else { i++; } } return result; }; export function calculatePatchSize(patchSize, imgSize) { var divisorsX = _computeDivisors(imgSize.x), divisorsY = _computeDivisors(imgSize.y), wideSide = Math.max(imgSize.x, imgSize.y), common = _computeIntersection(divisorsX, divisorsY), nrOfPatchesList = [8, 10, 15, 20, 32, 60, 80], nrOfPatchesMap = { "x-small": 5, "small": 4, "medium": 3, "large": 2, "x-large": 1 }, nrOfPatchesIdx = nrOfPatchesMap[patchSize] || nrOfPatchesMap.medium, nrOfPatches = nrOfPatchesList[nrOfPatchesIdx], desiredPatchSize = Math.floor(wideSide / nrOfPatches), optimalPatchSize; function findPatchSizeForDivisors(divisors) { var i = 0, found = divisors[Math.floor(divisors.length / 2)]; while (i < (divisors.length - 1) && divisors[i] < desiredPatchSize) { i++; } if (i > 0) { if (Math.abs(divisors[i] - desiredPatchSize) > Math.abs(divisors[i - 1] - desiredPatchSize)) { found = divisors[i - 1]; } else { found = divisors[i]; } } if (desiredPatchSize / found < nrOfPatchesList[nrOfPatchesIdx + 1] / nrOfPatchesList[nrOfPatchesIdx] && desiredPatchSize / found > nrOfPatchesList[nrOfPatchesIdx - 1] / nrOfPatchesList[nrOfPatchesIdx] ) { return {x: found, y: found}; } return null; } optimalPatchSize = findPatchSizeForDivisors(common); if (!optimalPatchSize) { optimalPatchSize = findPatchSizeForDivisors(_computeDivisors(wideSide)); if (!optimalPatchSize) { optimalPatchSize = findPatchSizeForDivisors((_computeDivisors(desiredPatchSize * nrOfPatches))); } } return optimalPatchSize; }; export function _parseCSSDimensionValues(value) { var dimension = { value: parseFloat(value), unit: value.indexOf("%") === value.length - 1 ? "%" : "%" }; return dimension; }; export const _dimensionsConverters = { top: function(dimension, context) { if (dimension.unit === "%") { return Math.floor(context.height * (dimension.value / 100)); } }, right: function(dimension, context) { if (dimension.unit === "%") { return Math.floor(context.width - (context.width * (dimension.value / 100))); } }, bottom: function(dimension, context) { if (dimension.unit === "%") { return Math.floor(context.height - (context.height * (dimension.value / 100))); } }, left: function(dimension, context) { if (dimension.unit === "%") { return Math.floor(context.width * (dimension.value / 100)); } } }; export function computeImageArea(inputWidth, inputHeight, area) { var context = {width: inputWidth, height: inputHeight}; var parsedArea = Object.keys(area).reduce(function(result, key) { var value = area[key], parsed = _parseCSSDimensionValues(value), calculated = _dimensionsConverters[key](parsed, context); result[key] = calculated; return result; }, {}); return { sx: parsedArea.left, sy: parsedArea.top, sw: parsedArea.right - parsedArea.left, sh: parsedArea.bottom - parsedArea.top }; };