31:T82e,sample = (background, input, groundTruth, targetId) => { const bg_image = new Image(); bg_image.src = URL.createObjectURL(background); const image = new Image(); image.src = URL.createObjectURL(input); const imgGroundTruth = new Image(); imgGroundTruth.src = URL.createObjectURL(groundTruth); image.onload = function() { const { cv } = window; // Source and destination images const contour_src = new cv.imread(image); const ct_scan = new cv.imread(bg_image); const cols = ct_scan.cols; const rows = ct_scan.rows; cv.imshow(targetId, ct_scan); // Convert to binary cv.cvtColor(contour_src, contour_src, cv.COLOR_RGBA2GRAY, 0); cv.threshold(contour_src, contour_src, 120, 200, cv.THRESH_BINARY); // Find contours let contours = new cv.MatVector(); let hierarchy = new cv.Mat(); cv.findContours(contour_src, contours, hierarchy, cv.RETR_CCOMP, cv.CHAIN_APPROX_SIMPLE); // Draw contours on destination image const matContour = cv.Mat.zeros(contour_src.cols, contour_src.rows, cv.CV_8UC1); let color = new cv.Scalar(255); cv.drawContours(matContour, contours, -1, color, 1, cv.LINE_8, hierarchy, 32); // Ring overlap let matMask = getGroundTruthMask(imgGroundTruth); let green = new cv.Mat(matMask.cols, matMask.rows, cv.CV_8UC4, new cv.Scalar(0,255,0,255)); let greenMask = cv.Mat.zeros(cols, rows, cv.CV_8UC1); cv.bitwise_and(matMask, matContour, greenMask) green.copyTo(ct_scan, greenMask); // Not ring overlap let matInvMask = new cv.Mat(); cv.bitwise_not(matMask, matInvMask); let red = new cv.Mat(matMask.cols, matMask.rows, cv.CV_8UC4, new cv.Scalar(255,0,0,255)); let redMask = cv.Mat.zeros(cols, rows, cv.CV_8UC1); cv.bitwise_and(matInvMask, matContour, redMask); red.copyTo(ct_scan, redMask); // Show result and clean up cv.imshow(targetId, ct_scan); //getGroundTruthMask(imgGroundTruth)); contour_src.delete(); ct_scan.delete(); contours.delete(); hierarchy.delete(); }; }32:T482,getGroundTruthMask = (imgGroundTruth) => { const { cv } = window; const matGroundTruth = new cv.imread(imgGroundTruth); // Convert to binary cv.cvtColor(matGroundTruth, matGroundTruth, cv.COLOR_RGBA2GRAY, 0); cv.threshold(matGroundTruth, matGroundTruth, 120, 200, cv.THRESH_BINARY); // Find contours let contours = new cv.MatVector(); let hierarchy = new cv.Mat(); cv.findContours(matGroundTruth, contours, hierarchy, cv.RETR_CCOMP, cv.CHAIN_APPROX_SIMPLE); // Draw contours on destination image let matContours = cv.Mat.ones(matGroundTruth.cols, matGroundTruth.rows, cv.CV_8U); let color2 = new cv.Scalar(0); cv.drawContours(matContours, contours, -1, color2, 1, cv.LINE_8, hierarchy, 160); // Calculate distance transform let matDist = new cv.Mat(); cv.distanceTransform(matContours, matDist, cv.DIST_L2, 3); // Threshold (range 0 .. 1) let matThreshold = new cv.Mat(); cv.threshold(matDist, matThreshold, distance_threshold, 1, cv.THRESH_BINARY_INV); // Convert to 8U, range 0 .. 255 let matConvert = new cv.Mat(); matThreshold.convertTo(matConvert, cv.CV_8U, 255, 0); return matConvert; }33:[] 2f:{"title":"Loading local image files examples","cells":[{"id":46,"value":"md`# Loading local image files examples`","pinned":true,"hidden":false,"mode":"ojs"},{"id":33,"value":"md`## Load all files in folder`","pinned":true,"hidden":false,"mode":"ojs"},{"id":298,"value":"viewof multiFiles = html`` //load segmentation slices for this case","pinned":false,"hidden":false,"mode":"ojs"},{"id":300,"value":"multiFileList = Array.from(multiFiles);","pinned":false,"hidden":false,"mode":"ojs"},{"id":303,"value":"multiFileDict = {\n var result = {};\n \n multiFileList.forEach(function(f) { \n var name = f.name;\n var path = f.webkitRelativePath;\n var subdir = f.webkitRelativePath.match(/variations_decathlon_dataset\\/(.*)\\//i)[1];\n var caseNr = parseInt(f.name.match(/Case([0-9]+)_/i)[1]);\n var slice = parseInt(f.name.match(/Case[0-9]+[a-zA-Z_]+([0-9]+)/i)[1]);\n \n if (result[subdir] === undefined) { \n result[subdir] = [];\n }\n if (result[subdir][caseNr] === undefined) {\n result[subdir][caseNr] = [];\n }\n result[subdir][caseNr][slice] = {\n name: name,\n path: subdir,\n file: f\n }; \n })\n return result;\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":111,"value":"numberOfCases = Object.keys(multiFileDict[\"images\"]).length","pinned":false,"hidden":false,"mode":"ojs"},{"id":276,"value":"numberOfSlices = Object.keys(multiFileDict[\"images\"][caseNo]).length","pinned":false,"hidden":false,"mode":"ojs"},{"id":194,"value":"viewof caseSlider = html``","pinned":false,"hidden":false,"mode":"ojs"},{"id":311,"value":"caseNo = Object.keys(multiFileDict.images)[caseSlider];","pinned":false,"hidden":false,"mode":"ojs"},{"id":106,"value":"viewof sliceNo = html``","pinned":false,"hidden":false,"mode":"ojs"},{"id":108,"value":"sliceNo\n","pinned":false,"hidden":false,"mode":"ojs"},{"id":460,"value":"canvasses = html`\n\n\n

\n\n

`","pinned":false,"hidden":false,"mode":"ojs"},{"id":374,"value":"loadImage = function(subDir, caseNo, sliceNo) {\n if (multiFileDict[subDir][caseNo] != undefined &&\n multiFileDict[subDir][caseNo][sliceNo] != undefined) {\n var file = multiFileDict[subDir][caseNo][sliceNo].file;\n if (file != undefined) {\n return file;\n }\n }\n}","pinned":true,"hidden":false,"mode":"ojs"},{"id":418,"value":"{\n const groundTruth = loadImage(\"segmentations_without_variations\", caseNo, sliceNo);\n sample(loadImage(\"images\", caseNo, sliceNo), loadImage(\"segmentations_without_variations\", caseNo, sliceNo), groundTruth, 'swv');\n sample(loadImage(\"images\", caseNo, sliceNo), loadImage(\"segmentations_with_shifts_5\", caseNo, sliceNo), groundTruth, 'sws');\n sample(loadImage(\"images\", caseNo, sliceNo), loadImage(\"segmentations_bottom_over_under_segment_10\", caseNo, sliceNo), groundTruth, 'sbo')\n sample(loadImage(\"images\", caseNo, sliceNo), loadImage(\"segmentations_top_over_under_segment_10\", caseNo, sliceNo), groundTruth, 'sto')\n sample(loadImage(\"images\", caseNo, sliceNo), loadImage(\"segmentations_with_erosion_dilation_5\", caseNo, sliceNo), groundTruth, 'swe')\n}","pinned":true,"hidden":false,"mode":"ojs"},{"id":635,"value":"distance_threshold = 2","pinned":true,"hidden":false,"mode":"ojs"},{"id":372,"value":"$31","pinned":true,"hidden":false,"mode":"ojs"},{"id":678,"value":"$32","pinned":true,"hidden":false,"mode":"ojs"},{"id":369,"value":"loadOpenCv = {\n return new Promise((resolve, reject) => {\n const script = document.createElement('script');\n script.src = 'https://docs.opencv.org/4.2.0/opencv.js';\n script.type = 'text/javascript';\n script.async = true;\n\n script.onabort = () => {\n reject('OpenCV Load failed.');\n };\n\n script.onload = () => {\n if (window.cv !== undefined) {\n resolve('OpenCV Load success.');\n }\n console.log('hallo');\n };\n \n document.body.appendChild(script);\n });\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":741,"value":"type2str = function(depth, channels) {\n const { cv } = window;\n \n let r;\n\n //let depth = type & cv.CV_MAT_DEPTH_MASK;\n // not work? let channels = 1 + (type >> cv.CV_CN_SHIFT);\n\n switch ( depth ) {\n case cv.CV_8U: r = \"8U\"; break;\n case cv.CV_8S: r = \"8S\"; break;\n case cv.CV_16U: r = \"16U\"; break;\n case cv.CV_16S: r = \"16S\"; break;\n case cv.CV_32S: r = \"32S\"; break;\n case cv.CV_32F: r = \"32F\"; break;\n case cv.CV_64F: r = \"64F\"; break;\n default: r = \"User\"; break;\n }\n\n r += \"C\";\n r += (channels);\n return r;\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":751,"value":"logImageProps = (label, src) => {\n const { cv } = window;\n \n // Mat: type = depth + channels\n console.log(label + \" \" + type2str(src.depth(), src.channels()));\n //const minMaxLoc = cv.minMaxLoc(src);\n //console.log(\"value range: \", minMaxLoc.minVal, minMaxLoc.maxVal);\n /*console.log('image width: ' + src.cols + '\\n' +\n 'image height: ' + src.rows + '\\n' +\n 'image size: ' + src.size().width + '*' + src.size().height + '\\n' +\n 'image depth: ' + src.depth() + '\\n' +\n 'image channels ' + src.channels() + '\\n' +\n 'image type: ' + src.type() + '\\n');*/\n}","pinned":false,"hidden":false,"mode":"ojs"}],"files":"$W33","stdlib":"1","titling":"auto"}