31:T76a,canvas = { try { imgs; } catch(e) { return await FileAttachment("defaultImage.png").image(); } var image = imgs[0]; if (image.width > width) { let aux = image.width; image.width = width; image.height = image.height*width/aux; } const canvas = DOM.canvas(image.width, image.height); const context = canvas.getContext("2d"); context.drawImage(image, 0, 0, image.width, image.height); const imageData = context.getImageData( 0, 0, image.width, image.height ); // Calculate faces const shouldRedactFaces = redactFaces == 'redact'; let dets; if (shouldRedactFaces) { const picoImage = { pixels: rgba_to_grayscale(imageData.data, image.height, image.width), nrows: image.height, ncols: image.width, ldim: image.width }; dets = pico.run_cascade(picoImage, facefinder_classify_region, picoParams); dets = pico.cluster_detections(dets, 0.2); // set IoU threshold to 0.2 } brightness(imageData.data, brightness_slider); multiplication(imageData.data, multiplication_slider); contrast(imageData.data, contrast_slider); gamma(imageData.data, gamma_slider); grayscale(imageData.data, grayscale_slider); extreme(imageData.data, extreme_slider); context.putImageData(imageData, 0, 0); if (shouldRedactFaces) { context.fillStyle = 'white'; context.strokeStyle = 'black'; context.lineWidth = 2; dets.forEach(det => { const [y, x, size, q] = det; if (q < 10) return; const X_PADDING = Math.random() * size * 0.1; const Y_PADDING = Math.random() * size * 0.1; context.fillRect(x - size / 2 - X_PADDING, y - size / 2 - Y_PADDING, size + X_PADDING * 2, size + Y_PADDING * 2); context.strokeRect(x - size / 2 - X_PADDING, y - size / 2 - Y_PADDING, size + X_PADDING * 2, size + Y_PADDING * 2); }); } return canvas; }32:T18dc,pico = { // pico.js -- from https://github.com/tehnokv/picojs /* This library is released under the MIT license, see https://github.com/tehnokv/picojs */ const pico = {} pico.unpack_cascade = function(bytes) { // const dview = new DataView(new ArrayBuffer(4)); /* we skip the first 8 bytes of the cascade file (cascade version number and some data used during the learning process) */ let p = 8; /* read the depth (size) of each tree first: a 32-bit signed integer */ dview.setUint8(0, bytes[p+0]), dview.setUint8(1, bytes[p+1]), dview.setUint8(2, bytes[p+2]), dview.setUint8(3, bytes[p+3]); const tdepth = dview.getInt32(0, true); p = p + 4 /* next, read the number of trees in the cascade: another 32-bit signed integer */ dview.setUint8(0, bytes[p+0]), dview.setUint8(1, bytes[p+1]), dview.setUint8(2, bytes[p+2]), dview.setUint8(3, bytes[p+3]); const ntrees = dview.getInt32(0, true); p = p + 4 /* read the actual trees and cascade thresholds */ const tcodes_ls = []; const tpreds_ls = []; const thresh_ls = []; for(let t=0; t> 0; // '>>0' transforms this number to int for(let i=0; i> 8' here to perform an integer division: this seems important for performance idx = 2*idx + (pixels[((r+tcodes[root + 4*idx + 0]*s) >> 8)*ldim+((c+tcodes[root + 4*idx + 1]*s) >> 8)]<=pixels[((r+tcodes[root + 4*idx + 2]*s) >> 8)*ldim+((c+tcodes[root + 4*idx + 3]*s) >> 8)]); o = o + tpreds[pow2tdepth*i + idx-pow2tdepth]; if(o<=thresh[i]) return -1; root += 4*pow2tdepth; } return o - thresh[ntrees-1]; } /* we're done */ return classify_region; } pico.run_cascade = function(image, classify_region, params) { const pixels = image.pixels; const nrows = image.nrows; const ncols = image.ncols; const ldim = image.ldim; const shiftfactor = params.shiftfactor; const minsize = params.minsize; const maxsize = params.maxsize; const scalefactor = params.scalefactor; let scale = minsize; const detections = []; while(scale<=maxsize) { const step = Math.max(shiftfactor*scale, 1) >> 0; // '>>0' transforms this number to int const offset = (scale/2 + 1) >> 0; for(let r=offset; r<=nrows-offset; r+=step) for(let c=offset; c<=ncols-offset; c+=step) { const q = classify_region(r, c, scale, pixels, ldim); if (q > 0.0) detections.push([r, c, scale, q]); } scale = scale*scalefactor; } return detections; } pico.cluster_detections = function(dets, iouthreshold) { /* sort detections by their score */ dets = dets.sort(function(a, b) { return b[3] - a[3]; }); /* this helper function calculates the intersection over union for two detections */ function calculate_iou(det1, det2) { // unpack the position and size of each detection const r1=det1[0], c1=det1[1], s1=det1[2]; const r2=det2[0], c2=det2[1], s2=det2[2]; // calculate detection overlap in each dimension const overr = Math.max(0, Math.min(r1+s1/2, r2+s2/2) - Math.max(r1-s1/2, r2-s2/2)); const overc = Math.max(0, Math.min(c1+s1/2, c2+s2/2) - Math.max(c1-s1/2, c2-s2/2)); // calculate and return IoU return overr*overc/(s1*s1+s2*s2-overr*overc); } /* do clustering through non-maximum suppression */ const assignments = new Array(dets.length).fill(0); const clusters = []; for(let i=0; iiouthreshold) { assignments[j] = 1; r = r + dets[j][0]; c = c + dets[j][1]; s = s + dets[j][2]; q = q + dets[j][3]; n = n + 1; } // make a cluster representative clusters.push([r/n, c/n, s/n, q]); } } return clusters; } pico.instantiate_detection_memory = function(size) { /* initialize a circular buffer of `size` elements */ let n = 0; const memory = []; for(let i=0; i 127 ? 255 : 0;\n const desired2 = data[i + 1] > 127 ? 255 : 0;\n const desired3 = data[i + 2] > 127 ? 255 : 0;\n data[i + 0] = data[i + 0] + (desired1 - data[i + 0]) * factor;\n data[i + 1] = data[i + 1] + (desired2 - data[i + 1]) * factor;\n data[i + 2] = data[i + 2] + (desired3 - data[i + 2]) * factor;\n }\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":234,"value":"function gamma(data, power) {\n for (let i = 0; i < data.length; i += 4) {\n data[i + 0] = 255 * Math.pow(data[i + 0] / 255, power);\n data[i + 1] = 255 * Math.pow(data[i + 1] / 255, power);\n data[i + 2] = 255 * Math.pow(data[i + 2] / 255, power);\n }\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":337,"value":"imgs = {\n const file = imageButton;\n let src = await Files.url(imageButton); \n let img = await new Promise((resolve, reject) => {\n const image = new Image;\n image.crossOrigin = \"anonymous\";\n image.onload = () => resolve(image);\n image.src = src;\n image.onerror = reject;\n });\n return [img]\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":529,"value":"function rgba_to_grayscale(rgba, nrows, ncols) {\n var gray = new Uint8Array(nrows*ncols);\n for(var r=0; r