From 182e9d4f036fbf136a02ceafb664594b1ce2416d Mon Sep 17 00:00:00 2001 From: Ruben van de Ven Date: Mon, 18 Dec 2023 10:56:07 +0100 Subject: [PATCH] prototype paper --- .gitattributes | 2 + .gitignore | 1 + assets/annotate.js | 1894 +++++++++++++++++++++++++++++++ assets/annotation-3A53SHHN.json | 1 + assets/annotation-3A53SHHN.mp3 | 3 + assets/annotation-3A53SHHN.svg | 19 + assets/annotation-3I7W9H8H.json | 1 + assets/annotation-3I7W9H8H.mp3 | 3 + assets/annotation-3I7W9H8H.svg | 60 + assets/annotation-9ZBW6U2O.json | 1 + assets/annotation-9ZBW6U2O.mp3 | 3 + assets/annotation-9ZBW6U2O.svg | 106 ++ assets/annotation-AKKM4OL0.json | 1 + assets/annotation-AKKM4OL0.mp3 | 3 + assets/annotation-AKKM4OL0.svg | 94 ++ assets/annotation-BIXG4VTL.json | 1 + assets/annotation-BIXG4VTL.mp3 | 3 + assets/annotation-BIXG4VTL.svg | 66 ++ assets/annotation-CZOIX919.json | 1 + assets/annotation-CZOIX919.mp3 | 3 + assets/annotation-CZOIX919.svg | 143 +++ assets/annotation-F19O9PGE.json | 1 + assets/annotation-F19O9PGE.mp3 | 3 + assets/annotation-F19O9PGE.svg | 61 + assets/annotation-HGDCWHAK.json | 1 + assets/annotation-HGDCWHAK.mp3 | 3 + assets/annotation-HGDCWHAK.svg | 117 ++ assets/annotation-HZ1JQWXS.json | 1 + assets/annotation-HZ1JQWXS.mp3 | 3 + assets/annotation-HZ1JQWXS.svg | 61 + assets/annotation-J1UANFAO.json | 1 + assets/annotation-J1UANFAO.mp3 | 3 + assets/annotation-J1UANFAO.svg | 23 + assets/annotation-J4AOBZCJ.json | 1 + assets/annotation-J4AOBZCJ.mp3 | 3 + assets/annotation-J4AOBZCJ.svg | 214 ++++ assets/annotation-JFX1YSIW.json | 1 + assets/annotation-JFX1YSIW.mp3 | 3 + assets/annotation-JFX1YSIW.svg | 55 + assets/annotation-JV2RCZ9F.json | 1 + assets/annotation-JV2RCZ9F.mp3 | 3 + assets/annotation-JV2RCZ9F.svg | 104 ++ assets/annotation-LL9NLJS7.json | 1 + assets/annotation-LL9NLJS7.mp3 | 3 + assets/annotation-LL9NLJS7.svg | 159 +++ assets/annotation-LWEAVM5W.json | 1 + assets/annotation-LWEAVM5W.mp3 | 3 + assets/annotation-LWEAVM5W.svg | 44 + assets/annotation-NKOUGNWE.json | 1 + assets/annotation-NKOUGNWE.mp3 | 3 + assets/annotation-NKOUGNWE.svg | 161 +++ assets/annotation-OZR2J5YK.json | 1 + assets/annotation-OZR2J5YK.mp3 | 3 + assets/annotation-OZR2J5YK.svg | 146 +++ assets/annotation-RSKE7LU5.json | 1 + assets/annotation-RSKE7LU5.mp3 | 3 + assets/annotation-RSKE7LU5.svg | 63 + assets/annotation-U5J2S22A.json | 1 + assets/annotation-U5J2S22A.mp3 | 3 + assets/annotation-U5J2S22A.svg | 75 ++ assets/annotation-V2I0CDFY.json | 1 + assets/annotation-V2I0CDFY.mp3 | 3 + assets/annotation-V2I0CDFY.svg | 15 + assets/annotation-VH7ONRUV.json | 1 + assets/annotation-VH7ONRUV.mp3 | 3 + assets/annotation-VH7ONRUV.svg | 17 + assets/annotation-XYGE65SB.json | 1 + assets/annotation-XYGE65SB.mp3 | 3 + assets/annotation-XYGE65SB.svg | 54 + assets/annotation-YV7F9O7T.json | 1 + assets/annotation-YV7F9O7T.mp3 | 3 + assets/annotation-YV7F9O7T.svg | 145 +++ assets/media/image1.png | 3 + assets/media/image10.png | 3 + assets/media/image11.png | 3 + assets/media/image2.png | 3 + assets/media/image3.png | 3 + assets/media/image4.png | 3 + assets/media/image5.png | 3 + assets/media/image6.png | 3 + assets/media/image7.png | 3 + assets/media/image8.png | 3 + assets/media/image9.png | 3 + assets/wNumb-1.2.0.min.js | 1 + canvas.html | 689 +++++++++++ paper.css | 210 ++++ paper.html | 1716 ++++++++++++++++++++++++++++ 87 files changed, 6640 insertions(+) create mode 100644 .gitattributes create mode 100644 .gitignore create mode 100644 assets/annotate.js create mode 100644 assets/annotation-3A53SHHN.json create mode 100644 assets/annotation-3A53SHHN.mp3 create mode 100644 assets/annotation-3A53SHHN.svg create mode 100644 assets/annotation-3I7W9H8H.json create mode 100644 assets/annotation-3I7W9H8H.mp3 create mode 100644 assets/annotation-3I7W9H8H.svg create mode 100644 assets/annotation-9ZBW6U2O.json create mode 100644 assets/annotation-9ZBW6U2O.mp3 create mode 100644 assets/annotation-9ZBW6U2O.svg create mode 100644 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create mode 100644 assets/annotation-OZR2J5YK.mp3 create mode 100644 assets/annotation-OZR2J5YK.svg create mode 100644 assets/annotation-RSKE7LU5.json create mode 100644 assets/annotation-RSKE7LU5.mp3 create mode 100644 assets/annotation-RSKE7LU5.svg create mode 100644 assets/annotation-U5J2S22A.json create mode 100644 assets/annotation-U5J2S22A.mp3 create mode 100644 assets/annotation-U5J2S22A.svg create mode 100644 assets/annotation-V2I0CDFY.json create mode 100644 assets/annotation-V2I0CDFY.mp3 create mode 100644 assets/annotation-V2I0CDFY.svg create mode 100644 assets/annotation-VH7ONRUV.json create mode 100644 assets/annotation-VH7ONRUV.mp3 create mode 100644 assets/annotation-VH7ONRUV.svg create mode 100644 assets/annotation-XYGE65SB.json create mode 100644 assets/annotation-XYGE65SB.mp3 create mode 100644 assets/annotation-XYGE65SB.svg create mode 100644 assets/annotation-YV7F9O7T.json create mode 100644 assets/annotation-YV7F9O7T.mp3 create mode 100644 assets/annotation-YV7F9O7T.svg create mode 100644 assets/media/image1.png create mode 100644 assets/media/image10.png create mode 100644 assets/media/image11.png create mode 100644 assets/media/image2.png create mode 100644 assets/media/image3.png create mode 100644 assets/media/image4.png create mode 100644 assets/media/image5.png create mode 100644 assets/media/image6.png create mode 100644 assets/media/image7.png create mode 100644 assets/media/image8.png create mode 100644 assets/media/image9.png create mode 100644 assets/wNumb-1.2.0.min.js create mode 100644 canvas.html create mode 100644 paper.css create mode 100644 paper.html diff --git a/.gitattributes b/.gitattributes new file mode 100644 index 0000000..ab26661 --- /dev/null +++ b/.gitattributes @@ -0,0 +1,2 @@ +*.mp3 filter=lfs diff=lfs merge=lfs -text +*.png filter=lfs diff=lfs merge=lfs -text diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..c4c4ffc --- /dev/null +++ b/.gitignore @@ -0,0 +1 @@ +*.zip diff --git a/assets/annotate.js b/assets/annotate.js new file mode 100644 index 0000000..0b06e31 --- /dev/null +++ b/assets/annotate.js @@ -0,0 +1,1894 @@ +class Annotation { + constructor(tag, t_in, t_out, comment) { + this.tag = tag; + this.t_in = Number.parseFloat(t_in); + this.t_out = Number.parseFloat(t_out); + this.comment = comment; + } +} + +class StrokeGroup { + constructor(group_element, player) { + this.g = group_element; + this.player = player; + } + + setStrokes(strokes) { + const pathEls = this.g.querySelectorAll('path'); + let indexes = Object.keys(strokes); + for (let pathEl of pathEls) { + const i = pathEl.dataset.path_i; + if (!indexes.includes(pathEl.dataset.path_i)) { + pathEl.parentNode.removeChild(pathEl); + } else { + // check in and outpoint using pathEl.dataset + if (strokes[i].getSliceId() != pathEl.dataset.slice) { + const d = this.points2D(strokes[i].points); + pathEl.dataset.slice = strokes[i].getSliceId(); + pathEl.setAttribute('d', d); + } + } + + // this has now been processed + indexes.splice(indexes.indexOf(i), 1); + } + + // new strokes + indexes.forEach(index => { + const stroke = strokes[index]; + + let pathEl = document.createElementNS('http://www.w3.org/2000/svg', 'path'); + pathEl.style.stroke = stroke.color; + pathEl.classList.add('path'); + pathEl.dataset.path_i = index; + pathEl.dataset.slice = stroke.getSliceId(); + this.g.appendChild(pathEl); + + const d = this.points2D(stroke.points); + pathEl.setAttribute('d', d); + }); + } + + // convert array of points to a d-attribute + points2D(strokes) { + // strokes to a d attribute for a path + let d = ""; + let last_stroke = undefined; + let cmd = ""; + for (let stroke of strokes) { + if (!last_stroke) { + d += `M${stroke[0]},${stroke[1]} `; + cmd = 'M'; + } else { + if (last_stroke[2] == 1) { + d += " m"; + cmd = 'm'; + } else if (cmd != 'l') { + d += ' l '; + cmd = 'l'; + } + let rel_stroke = [stroke[0] - last_stroke[0], stroke[1] - last_stroke[1]]; + d += `${rel_stroke[0]},${rel_stroke[1]} `; + } + last_stroke = stroke; + + } + return d; + } + + setPrecomputedStrokes(strokeDs) { + const pathEls = this.g.querySelectorAll('path'); + for (let pathEl of pathEls) { + pathEl.parentNode.removeChild(pathEl); + } + strokeDs.forEach((strokeD, index) => { + let pathEl = document.createElementNS('http://www.w3.org/2000/svg', 'path'); + // pathEl.style.stroke = stroke.color; + // pathEl.classList.add('path'); + pathEl.setAttribute('d', strokeD); + this.g.appendChild(pathEl); + + }); + } +} + +class Stroke { + constructor(color, points) { + this.color = color; + this.points = points; // [[x1,y1,t1], [x2,y2,t2], ...] + } + + getSliceId() { + return 'all'; + } +} + +class StrokeSlice { + constructor(stroke, i_in, i_out) { + this.stroke = stroke; // Stroke + this.i_in = typeof i_in === 'undefined' ? 0 : i_in; + this.i_out = typeof i_out === 'undefined' ? this.stroke.points.length - 1 : i_out; + } + + getSliceId() { + return `${this.i_in}-${this.i_out}`; + } + + // compatible with Stroke() + get points() { + return this.stroke.points.slice(this.i_in, this.i_out + 1); + } + + // compatible with Stroke() + get color() { + return this.stroke.color; + } +} + +const CropOptions = { + Fit_Selection: 'selection', + Follow_Drawing: 'follow', + Whole_Drawing: 'whole', + }; + +const CropDescriptions = { + selection: 'Crop to annotation', + follow: 'Follow drawing canvas', + whole: 'Show whole drawing', +} + +class Annotator extends EventTarget { + constructor(wrapperEl, tagFile, fileurl, config) { + fileurl = fileurl.replace("&", "&"); // little hack: tornadoweb does this automatically for some reason + super(); + + this.config = { + is_player: config && config.hasOwnProperty('is_player') ? config.is_player : false, // in player mode annotations are not loaded, nor is the annotator shown + crop_to_fit: config && config.hasOwnProperty('crop_to_fit') ? config.crop_to_fit : false, // DEPRECATED don't animate viewport, but show the whole drawing + crop: config && config.hasOwnProperty('crop') && Object.values(CropOptions).indexOf(config.crop) !== -1 ? config.crop : CropOptions.Fit_Selection, // don't animate viewport, but show the whole drawing + autoplay: config && config.hasOwnProperty('autoplay') ? config.autoplay : false, // immediately start playback + url_prefix: config && config.hasOwnProperty('url_prefix') ? config.url_prefix : '', + } + + this.formatter = wNumb({ + decimals: 2, + edit: (time) => { + let neg = ""; + if (time < 0) { + neg = "-"; + time *= -1; + } + const s = Math.floor(time / 1000); + const minutes = String(Math.floor(s / 60)).padStart(2, '0'); + const seconds = String(s - minutes * 60).padStart(2, '0'); + // show miliseconds only in annotator + const ms = !this.config.is_player ? "." + String(Math.floor((time / 1000 - s) * 1000)).padStart(3, '0') : ""; + return `${neg}${minutes}:${seconds}${ms}`; + }, + undo: (tc) => { + let [rest, ms] = tc.split(/[\.\,]/); + ms = parseFloat(typeof ms === "undefined" ? 0 : ms); + let factor = 1000; + rest.split(':').reverse().forEach((v, i) => { + ms += v * factor; + factor *= 60; + }); + return `${ms} `; + } + }); + + this.wrapperEl = wrapperEl; + + this.svgEl = document.createElementNS('http://www.w3.org/2000/svg', 'svg'); + this.wrapperEl.appendChild(this.svgEl); + this.wrapperEl.classList.add(this.config.is_player ? "svganim_player" : "svganim_annotator"); + this.wrapperEl.classList.add("crop-" + this.config.crop); + + + this.controlsEl = document.createElement('div'); + this.controlsEl.classList.add('controls') + this.wrapperEl.appendChild(this.controlsEl); + + this.playbackControlsEl = document.createElement('div'); + this.playbackControlsEl.classList.add('controls--playback') + this.controlsEl.appendChild(this.playbackControlsEl); + + this.playheadEl = document.createElement('input'); + this.playheadEl.type = "range"; + this.playheadEl.min = 0; + this.playheadEl.step = 0.01; + this.playbackControlsEl.appendChild(this.playheadEl); + + this.playheadEl.addEventListener("input", (ev) => { + this.scrubTo(ev.target.value); + }); + this.playheadEl.addEventListener('keydown', (ev) => { + ev.preventDefault(); // we don't want to use arrow keys, as these are captured in the overall keydown event + }) + + this.timeCodeEl = document.createElement('input'); + this.timeCodeEl.type = 'numeric'; + this.timeCodeEl.classList.add('timecode'); + this.timeCodeEl.disabled = true; + this.playbackControlsEl.appendChild(this.timeCodeEl); + + this.playPauseEl = document.createElement('button'); + this.playPauseEl.classList.add('paused'); + this.playbackControlsEl.appendChild(this.playPauseEl); + + this.playPauseEl.addEventListener("click", (ev) => { + this.playPause() + }) + this.playPauseEl.addEventListener('keydown', (ev) => { + ev.preventDefault(); // we don't want to spacebar, as this is captured in the overall keydown event + }) + + if (!this.config.is_player) { + this.scrubberEl = document.createElement('div'); + this.scrubberEl.classList.add('scrubber') + this.controlsEl.appendChild(this.scrubberEl); + + + this.annotationsEl = document.createElement('div'); + this.annotationsEl.classList.add('annotations') + this.controlsEl.appendChild(this.annotationsEl); + } else { + const extraEl = document.createElement('details'); + extraEl.classList.add('controls--extra'); + + const summaryEl = document.createElement('summary'); + summaryEl.innerHTML = "…"; + extraEl.appendChild(summaryEl); + + const extraControlsEl = document.createElement('ul'); + + const toggleFutureHeaderEl = document.createElement('li'); + toggleFutureHeaderEl.classList.add('config-header', 'config-future'); + toggleFutureHeaderEl.innerText = "Preview drawing"; + extraControlsEl.appendChild(toggleFutureHeaderEl); + + // TODO: add handlers to change text + const toggleFutureEl = document.createElement('li'); + toggleFutureEl.classList.add('config-future'); + toggleFutureEl.innerText = "Show" + toggleFutureEl.addEventListener('click', () => this.wrapperEl.classList.toggle('hide-drawing-preview')); + extraControlsEl.appendChild(toggleFutureEl); + + const toggleCropHeaderEl = document.createElement('li'); + toggleCropHeaderEl.classList.add('config-header'); + toggleCropHeaderEl.innerText = "Crop"; + extraControlsEl.appendChild(toggleCropHeaderEl); + + this.toggleCropPlayerEl = document.createElement('li'); + this.toggleCropPlayerEl.innerText = CropDescriptions[this.config.crop]; + this.toggleCropPlayerEl.addEventListener('click', () => this.toggleCrop()); + extraControlsEl.appendChild(this.toggleCropPlayerEl); + + extraEl.appendChild(extraControlsEl); + + this.playbackControlsEl.appendChild(extraEl); + + + + const fullScreenEl = document.createElement('div'); + fullScreenEl.classList.add('controls-fs'); + fullScreenEl.innerHTML = "⛶"; + fullScreenEl.addEventListener('click', () => { + if(document.fullscreenElement) { + document.exitFullscreen(); + } else { + // console.log(this.wrapperEl, this.shadowRoot); + this.wrapperEl.requestFullscreen(); + } + }) + this.playbackControlsEl.appendChild(fullScreenEl); + + } + + + this.inPointPosition = [0, 0]; + this.inPointTimeMs = null; + this.outPointPosition = null; + this.outPointTimeMs = null; + this._currentTimeMs = 0; + this.videoIsPlaying = false; + + const groups = ['background', 'before', 'annotation', 'after'] + this.strokeGroups = {}; + groups.forEach(group => { + let groupEl = document.createElementNS('http://www.w3.org/2000/svg', 'g'); + groupEl.classList.add(group) + this.svgEl.appendChild(groupEl); + this.strokeGroups[group] = new StrokeGroup(groupEl, this); + }); + + this.annotations = []; + + if (this.config.is_player) { + this.load(fileurl); + } else { + + this.loadTags(tagFile).then(() => { + this.tagsEl = document.createElement('ul'); + this.tagsEl.classList.add('tags'); + const addTags = (tags, tagsEl) => { + tags.forEach((tag) => { + let tagLiEl = document.createElement('li'); + let tagEl = document.createElement('div'); + + tagEl.classList.add('tag'); + tagEl.dataset.tag = tag.id; + tagEl.innerText = tag.hasOwnProperty('name') ? tag.name : tag.id; + tagEl.addEventListener('click', (e) => { + this.addTag(tag.id, this.inPointPosition, this.outPointPosition); + }); + + tagEl.title = tag.hasOwnProperty('description') ? tag.description : ""; + + let signEl = document.createElement('span'); + signEl.classList.add('annotation-' + tag.id); + signEl.style.backgroundColor = this.getColorForTag(tag.id); + tagEl.prepend(signEl); + + tagLiEl.appendChild(tagEl); + + if (tag.hasOwnProperty('children')) { + const subEl = document.createElement('ul'); + subEl.classList.add('subtags'); + addTags(tag.children, subEl); + tagLiEl.appendChild(subEl); + } + + tagsEl.appendChild(tagLiEl); + }); + }; + addTags(this.tags, this.tagsEl); + + let tagEl = document.createElement('li'); + tagEl.classList.add('tag'); + tagEl.classList.add('annotation-rm'); + tagEl.dataset.tag = 'rm'; + tagEl.title = "Remove annotation"; + tagEl.innerHTML = "🚮"; // × + tagEl.addEventListener('click', (e) => { + if (this.selectedAnnotation) { + this.removeAnnotation(this.selectedAnnotationI); + } + }); + this.tagsEl.appendChild(tagEl); + + this.wrapperEl.appendChild(this.tagsEl); + + this.commentEl = document.createElement('input'); + this.commentEl.type = 'text'; + this.commentEl.classList.add('annotation-comment'); + this.commentEl.title = "Add comment to annotation"; + this.commentEl.placeholder = "comment"; + this.commentEl.value = ""; + this.commentEl.addEventListener('keyup', (e) => { + if (e.key == 'Escape') { + this.commentEl.blur() // deselect annotation, and deselect commentEl + } else { + e.stopPropagation(); // prevent keyup event to propagate and set i/o points + } + }); + this.commentEl.addEventListener('input', (e) => { + e.stopPropagation(); // prevent keyup event + if (this.selectedAnnotation) { + this.selectedAnnotation.comment = this.commentEl.value; + this.updateAnnotations(true) + } + }); + this.controlsEl.appendChild(this.commentEl); + + this.load(fileurl); + }); + } + } + + getColorForTag(tag_id) { + const tag = this.tagMap[tag_id]; + // console.log(tag_id, tag); + if (tag && tag.hasOwnProperty('color') && tag.color) { + return tag.color; + } + if (tag && tag.hasOwnProperty('parent') && tag.parent) { + return this.getColorForTag(tag['parent'].id); + } + return 'black'; + } + + updateAnnotations(save) { + + if (this.config.is_player) { + return false; + } + + this.annotationsEl.innerHTML = ""; + for (let annotation_i in this.annotations) { + const annotation = this.annotations[annotation_i]; + this.annotationEl = document.createElement('div'); + const prerollDiff = Number.parseFloat(this.audioOffset < 0 ? this.audioOffset * -1000 : 0); + // console.log('diff', prerollDiff, annotation.t_in, typeof annotation.t_in, this.duration,annotation.t_in + prerollDiff, (annotation.t_in + prerollDiff) / this.duration); + const left = ((annotation.t_in + prerollDiff) / (this.duration * 1000)) * 100; + const right = 100 - ((annotation.t_out + prerollDiff) / (this.duration * 1000)) * 100; + this.annotationEl.style.left = left + '%'; + this.annotationEl.style.right = right + '%'; + + this.annotationEl.style.backgroundColor = this.getColorForTag(annotation.tag); + + this.annotationEl.classList.add('annotation-' + annotation.tag); + if (this.selectedAnnotationI == annotation_i) { + this.annotationEl.classList.add('selected'); + } + this.annotationEl.title = `[${annotation.tag}] ${annotation.comment} `; + + this.annotationEl.addEventListener('mouseover', (e) => { + + }); + this.annotationEl.addEventListener('mouseout', (e) => { + + }); + this.annotationEl.addEventListener('click', (e) => { + if (this.selectedAnnotationI == annotation_i) { + this.deselectAnnotation(false); + } else { + this.selectAnnotation(annotation_i); + } + }); + + this.annotationsEl.appendChild(this.annotationEl); + } + + this.tagsEl.querySelectorAll('.tag').forEach(tagEl => { + if (this.selectedAnnotation && this.selectedAnnotation.tag == tagEl.dataset.tag) { + tagEl.classList.add('selected') + } else { + tagEl.classList.remove('selected') + } + }); + + if (save) { + this.updateState(); + } + } + + selectAnnotation(annotation_i) { + this.selectedAnnotationI = annotation_i; + this.selectedAnnotation = this.annotations[annotation_i]; + + this.slider.set([this.selectedAnnotation.t_in, this.selectedAnnotation.t_out]); + + this.inPointPosition = this.findPositionForTime(this.selectedAnnotation.t_in); + this.outPointPosition = this.findPositionForTime(this.selectedAnnotation.t_out); + this.inPointTimeMs = this.selectedAnnotation.t_in; + this.outPointTimeMs = this.selectedAnnotation.t_out; + this._seekByTimeMs(this.selectedAnnotation.t_in); + // draw full stroke of annotation: + this.drawStrokePosition(this.inPointPosition, this.outPointPosition); + + this.updateAnnotations(false); //selects the right tag & highlights the annotation + + this.wrapperEl.classList.add('selected-annotation'); + this.commentEl.value = this.selectedAnnotation.comment; + } + + deselectAnnotation(keep_position) { + if (this.selectedAnnotation) { + this._seekByTimeMs(this.selectedAnnotation.t_out); + } + + this.wrapperEl.classList.remove('selected-annotation'); + this.commentEl.value = ""; + this.commentEl.blur(); // make sure we're not typing anymore + + this.selectedAnnotationI = null; + this.selectedAnnotation = null; + + if (!keep_position) { + this.setUpAnnotator(); + } + this.updateAnnotations(false); // selects the right tag & highlights the annotation + } + + setInPoint(time_ms) { + this.setInOutPoint(time_ms, this.outPointTimeMs); + } + setOutPoint(time_ms) { + this.setInOutPoint(this.inPointTimeMs, time_ms); + } + + setInOutPoint(in_ms, out_ms) { + this.inPointPosition = this.findPositionForTime(in_ms); + this.inPointTimeMs = in_ms; + this.outPointPosition = this.findPositionForTime(out_ms); + this.outPointTimeMs = out_ms; + // this._seekByTimeMs(this.audioOffset < 0 ? this.audioOffset * 1000 : 0); + // draw full stroke of annotation + console.debug('setInOut'); + this.drawStrokePosition(this.inPointPosition, this.outPointPosition); + console.debug([`${this.inPointTimeMs} `, `${this.outPointTimeMs} `]) + this.slider.set([this.inPointTimeMs, this.outPointTimeMs]); + + // console.debug(this.selectedAnnotation); + if (this.selectedAnnotation) { + this.selectedAnnotation.t_in = in_ms; + this.selectedAnnotation.t_out = out_ms; + this.updateAnnotations(false); + } + } + + resetInOutPoint() { + this.inPointPosition = [0, 0]; + this.inPointTimeMs = null; + this.outPointPosition = null; + this.outPointTimeMs = null; + this._seekByTimeMs(this.audioOffset < 0 ? this.audioOffset * 1000 : 0); + // draw full stroke of annotation + console.debug('reset!'); + this.drawStrokePosition(this.inPointPosition, [Infinity, Infinity]); + this.setUpAnnotator(); + } + + load(file) { + const request = new Request(file, { + method: 'GET', + }); + + this.wrapperEl.classList.add('loading'); + + fetch(request) + .then(response => response.json()) + .then(data => { + if (!this.config.is_player) { + + const metadata_req = new Request(`/annotations/${data.file}`, { + method: 'GET', + }); + return fetch(metadata_req) + .then(response => response.ok ? response.json() : null) + .then(metadata => { + if (metadata !== null) { + metadata.annotations = metadata.annotations.map((a) => new Annotation(a.tag, a.t_in, a.t_out, a.hasOwnProperty('comment') ? a.comment : "")) + } + return this.loadStrokes(data, metadata) + }) + .catch(e => console.error(e)); + } else { + return this.loadStrokes(data, null); + } + }) + .then(() => { + // play on click for player + if (this.config.is_player) { + this.svgEl.addEventListener('click', (ev) => { + console.debug('clicked for play/pause'); + this.playPause(); + }); + } + + // autoplay if necessary + if (this.config.autoplay) { + this.play(); // play should remove loading + } else { + this.wrapperEl.classList.remove('loading'); + } + }) + .catch(e => console.debug(e)); + } + + updateState() { + const state = { + 'title': this.title, + 'file': this.filename, + 'annotations': this.annotations, + 'audio': { + 'file': this.audioFile, + 'offset': this.audioOffset, + } + } + const newState = JSON.stringify(state); + if (newState == this.state) { + return; + } + + this.wrapperEl.classList.remove('saved'); + this.wrapperEl.classList.add('unsaved'); + this.state = newState; + // autosave on state change: + this.save(newState); + } + + setSaved(state) { + if (this.state != state) { + console.debug('already outdated'); + } + else { + this.wrapperEl.classList.add('saved'); + this.wrapperEl.classList.remove('unsaved'); + } + } + + save(state) { + const request = new Request("/annotations/" + this.filename, { + method: 'POST', + headers: { + 'Content-Type': 'application/json' + }, + body: state + }); + fetch(request) + .then((response) => { + if (response.ok) { + this.setSaved(state); + } + else { + throw Error('Something went wrong'); + } + }) + .catch((error) => { + console.error(error); + }); + + } + + removeAnnotation(annotation_i) { + this.deselectAnnotation(true); + this.annotations.splice(annotation_i, 1); + this.updateAnnotations(true); + } + + addTag(tag) { + if (this.selectedAnnotation) { + this.selectedAnnotation.tag = tag; + this.updateAnnotations(true); + } else { + + // TODO this.slider values for in and out + const [t_in, t_out] = this.slider.get(); + if (this.slider) { + this.slider.destroy(); + } + + this.annotations.push(new Annotation(tag, t_in, t_out, "")); + this.updateAnnotations(true); + + this._currentTimeMs = t_out; + this._updatePlayhead(); + this.setUpAnnotator(); + } + } + + + setUpAnnotator() { + + this.playheadEl.min = this.audioOffset < 0 ? this.audioOffset * 1000 : 0; + this.playheadEl.max = this.getEndTimeMs(); + this._updatePlayhead(); + + + this.inPointPosition = this.findPositionForTime(this.currentTime); + this.inPointTimeMs = this._currentTimeMs; + this.outPointPosition = this.findPositionForTime(this.lastFrameTime); // TODO: simplify to get the last frame indexes directly + this.outPointTimeMs = this.getEndTimeMs(); + + + if (!this.config.is_player) { + this.buildAnnotator(); + } + + this.drawStrokePosition(this.inPointPosition, this.outPointPosition); + } + + buildAnnotator() { + + if (this.scrubberEl.noUiSlider) { + this.slider.destroy(); + } + + // console.log(this._currentTimeMs, ) + const sliderMin = this.audioOffset < 0 ? this.audioOffset * 1000 : 0; + const sliderMax = this.getEndTimeMs(); + this.slider = noUiSlider.create(this.scrubberEl, { + start: [this._currentTimeMs, this.getEndTimeMs()], + connect: true, + range: { + 'min': sliderMin, + 'max': sliderMax, + }, + keyboardDefaultStep: (sliderMax - sliderMin) / 1000, + keyboardPageMultiplier: 10, // page up/down 10s + tooltips: [ + this.formatter, + this.formatter + ], + // pips: { + // mode: 'range', + // density: 3, + // format: this.formatter + // } + }); + + this.slider.on("slide", (values, handle) => { + this.videoIsPlaying = false; + this.inPointPosition = this.findPositionForTime(values[0]); + this.inPointTimeMs = Number.parseFloat(values[0]); + this.outPointPosition = this.findPositionForTime(values[1]); + this.outPointTimeMs = Number.parseFloat(values[1]); + this.drawStrokePosition(this.inPointPosition, this.outPointPosition); + + // console.log(this.selectedAnnotation); + if (this.selectedAnnotation) { + this.selectedAnnotation.t_in = Number.parseFloat(values[0]); + this.selectedAnnotation.t_out = Number.parseFloat(values[1]); + this.updateAnnotations(false); + } + }); + this.slider.on("end", (values, handle) => { + if (this.selectedAnnotation) { + this.updateAnnotations(true); + } + this._seekByTimeMs(values[0]); + this.play(); + // this.playAudioSegment(values[0], values[1]); + }); + + this.slider.getTooltips().forEach((ttEl, i) => { + // console.log(ttEl, i); + ttEl.addEventListener('click', (e) => { + let ttInputEl = document.createElement('input'); + ttInputEl.value = ttEl.innerHTML + ttEl.innerHTML = ""; + ttEl.appendChild(ttInputEl); + ttInputEl.focus(); + + const submit = () => { + console.debug(ttInputEl.value); + const tcMs = this.formatter.from(ttInputEl.value); + let points = this.slider.get(); + points[i] = tcMs; + console.debug(points); + this.slider.set(points); + }; + ttInputEl.addEventListener('keydown', (keyE) => { + keyE.stopPropagation(); //prevent movement of tooltip + if (keyE.key == "Enter") { + submit(); + } + }) + ttInputEl.addEventListener('click', (clickE) => { + clickE.stopPropagation(); //prevent retrigger on selectino + }) + ttInputEl.addEventListener('blur', submit); + }); + }) + } + + loadStrokes(drawing, metadata) { + this.audioOffset = 0; + + if (metadata) { + this.annotations = metadata.annotations; + } + + if ((metadata && metadata.hasOwnProperty('audio')) || (drawing.hasOwnProperty('audio') && drawing.audio)) { + if (metadata && metadata.hasOwnProperty('audio')) { + this.audioFile = this.config.url_prefix + metadata.audio.file + this.audioOffset = Number.parseFloat(metadata.audio.offset); + } else { + this.audioFile = this.config.url_prefix + drawing.audio.file + this.audioOffset = Number.parseFloat(drawing.audio.offset); + } + this._currentTimeMs = this.audioOffset < 0 ? this.audioOffset * 1000 : 0; + this._updatePlayhead(); + } + + this.title = null; + if (metadata && metadata.hasOwnProperty('title')) { + this.title = metadata.title; + } + else if (drawing.hasOwnProperty('title')) { + this.title = drawing.title; + } + + this.filename = drawing.file; + this.strokes = drawing.shape.map(s => new Stroke(s['color'], s['points'])); + this.backgroundStrokes = drawing.hasOwnProperty('background') ? drawing.background : []; + this.backgroundBoundingBox = drawing.hasOwnProperty('background_bounding_box') ? drawing.background_bounding_box : null; + this.viewboxes = drawing.viewboxes; + this.currentPathI = null; + this.currentPointI = null; + this.currentViewboxI = null; + this.dimensions = drawing.dimensions; + this.bounding_box = drawing.bounding_box; + this.updateViewbox(); + + // let bgEl = document.createElementNS('http://www.w3.org/2000/svg', 'rect'); + // bgEl.setAttribute("x", 0); + // bgEl.setAttribute("y", 0); + // bgEl.setAttribute("width", this.dimensions[0]); + // bgEl.setAttribute("height", this.dimensions[1]); + // bgEl.classList.add('background'); + // this.svgEl.prepend(bgEl); + + this.firstFrameTime = this.strokes.length == 0 ? 0 : this.strokes[0].points[0][3]; + this.lastFrameTime = this.getFinalFrameTime(); + this.playheadEl.max = this.lastFrameTime; + this.nextFrameTimeout = null; + this.nextViewboxTimeout = null; + this._setPausedFlag(true); + + if(this.backgroundStrokes && this.backgroundStrokes.length){ + this.strokeGroups['background'].setPrecomputedStrokes(this.backgroundStrokes) + } + + return this.setupAudioConfig().then(() => { + // this.setUpAnnotator() + let keyEl; + if (this.config.is_player) { + keyEl = this.wrapperEl; + } else { + keyEl = document.body; // always capture + this.updateAnnotations(false); + } + + keyEl.addEventListener('keyup', (ev) => { + if (ev.key == ' ') { + this.playPause(); + } + + // shift+arrow keys, jump playhead (search position) + // FIXME doesn't keep playback after initial load. Only after unfocussing the window, and refocussing it, do the keys capture. + // Probably a wrong order + if (ev.key == 'ArrowLeft' && ev.shiftKey) { + const p = this._paused; + const diff = ev.ctrlKey ? 10000 : 1000; + this.scrubTo(this._currentTimeMs - diff); + if (!p) { this.play(); } // scrubTo() causes a pause(); + } + if (ev.key == 'ArrowRight' && ev.shiftKey) { + const p = this._paused; + const diff = ev.ctrlKey ? 10000 : 1000; + this.scrubTo(this._currentTimeMs + diff); + if (!p) { this.play(); } // scrubTo() causes a pause(); + } + + // additional keys only for annotation mode + if (!this.config.is_player) { + if (ev.key == 'i') { + this.setInPoint(this.currentTime * 1000); + } + if (ev.key == 'o') { + this.setOutPoint(this.currentTime * 1000); + } + if (ev.key == 'I') { + // shift+i == jump to in point + this.scrubTo(this.inPointTimeMs); + } + if (ev.key == 'O') { + // shift+o == jump to end point + this.scrubTo(this.outPointTimeMs); + } + if (ev.key == 'Escape') { + if (this.selectedAnnotation) { + this.deselectAnnotation(); + } else { + this.resetInOutPoint(); + } + } + } + }); + }); + + // this.playStrokePosition(0, 1); + } + + + loadTags(tagFile) { + // tags config + const request = new Request(tagFile); + return fetch(request) + .then(response => response.json()) + .then(rootTag => { + this.tags = rootTag.children; + this.tagMap = {}; + const addTagsToMap = (tags, parent) => { + tags.forEach((tag) => { + tag['parent'] = typeof parent != "undefined" ? parent : null; + this.tagMap[tag.id] = tag; + if (tag.hasOwnProperty("children")) { + addTagsToMap(tag.children, tag); + } + }); + }; + addTagsToMap(this.tags); + }); + } + + setupAudioConfig() { + // audio config + return new Promise((resolve, reject) => { + this.audioEl = document.createElement('audio'); + if (!this.config.is_player) + this.audioEl.setAttribute('controls', true); + + this.audioEl.addEventListener('canplaythrough', (ev) => { + console.debug('loaded audio'); + // this.audioEl.play(); + }); + + if (this.config.is_player) { + this.wrapperEl.prepend(this.audioEl); + } + else { + + let mdConfigEl = document.createElement('div'); + mdConfigEl.classList.add('metadataconfig') + this.wrapperEl.appendChild(mdConfigEl); + + + let titleEl = document.createElement('div'); + titleEl.classList.add('drawing-title'); + titleEl.innerText = this.title ?? "[add title]" + titleEl.title = this.title ?? "[click to add title for this diagram]" + titleEl.addEventListener('click', (ev) => { + const title = prompt("Change the title for the drawing", this.title ?? ""); + if (title === null) return; //cancel + titleEl.innerText = title.length ? title : "[add title]"; + this.title = title.length ? title : null; + this.updateState(); + }) + mdConfigEl.appendChild(titleEl); + + let audioConfigEl = document.createElement('div'); + audioConfigEl.classList.add('audioconfig') + mdConfigEl.appendChild(audioConfigEl); + + + audioConfigEl.appendChild(this.audioEl); + + let audioSelectEl = document.createElement('select'); + audioSelectEl.classList.add('audioselect'); + audioConfigEl.appendChild(audioSelectEl); + + fetch('/audio') + .then(response => response.json()) + .then(data => { + data.unshift(''); // add empty, to deselect any file + data.forEach(audioFile => { + let optionEl = document.createElement('option'); + optionEl.selected = this.audioFile == audioFile; + optionEl.innerText = audioFile; + audioSelectEl.appendChild(optionEl); + }); + }) + + audioSelectEl.addEventListener('change', (ev) => { + this.setAudioFile(ev.target.value); + }); + + + let audioOffsetTextEl = document.createElement('label'); + audioOffsetTextEl.innerText = "Offset (s)"; + audioConfigEl.appendChild(audioOffsetTextEl); + + let audioOffsetEl = document.createElement('input'); + audioOffsetEl.setAttribute('type', 'number'); + audioOffsetEl.setAttribute('step', '.01'); + audioOffsetEl.value = this.audioOffset ?? 0; + audioOffsetEl.addEventListener('change', (ev) => { + this.setAudioOffset(ev.target.value); + }); + audioOffsetTextEl.appendChild(audioOffsetEl); + + } + + + + + this.audioEl.addEventListener('loadedmetadata', (ev) => { + // resolve the 'set up audio' when metadata has loaded + this.setUpAnnotator(); // if offset is negative, annotator starts at negative time + resolve(); + }) + if (this.audioFile) { + this.audioEl.setAttribute('src', this.audioFile); + } else { + this.setUpAnnotator(); + resolve(); + } + + }); + } + + setAudioFile(audioFile) { + this.audioFile = audioFile; + this.audioEl.setAttribute('src', this.audioFile); + // this.audioEl.play(); + // TODO update playhead + // TODO update this.duration after load + this.updateState(); + } + + setAudioOffset(audioOffset) { + this.audioOffset = Number.parseFloat(audioOffset); + // TODO update playhead + // TODO update this.duration + this.setUpAnnotator(); // if offset is negative, annotator starts at negative time + this.updateState(); + } + + /** + * @param float time time is ms + * @returns float + */ + getAudioTime(time) { + return Number.parseFloat(time) - (this.audioOffset * 1000 ?? 0); + } + + /** + * + * @param float t_in in point time, in ms + * @param float t_out out point time, in ms + */ + playAudioSegment(t_in, t_out) { + if (this.audioStartTimeout) clearTimeout(this.audioStartTimeout); + if (this.audioEndTimeout) clearTimeout(this.audioEndTimeout); + + // TODO, handle playback delay + const t_start = this.getAudioTime(t_in); // in ms + const t_diff = (t_out ?? this.audioEl.duration * 1000) - t_in; // in ms + + this.audioEl.pause(); + + if (t_start < 0) { + if (t_diff <= t_start * -1) { + console.debug('no audio playback in segment', t_start, t_diff); + } else { + console.debug('delay audio playback', t_start, t_diff); + // a negative audiooffset delays playback from the start + // this.audioStartTimeout = setTimeout((e) => this.audioEl.play(), t*-1000); + this.audioStartTimeout = setTimeout((e) => { this.audioEl.currentTime = 0; this.audioEl.play(); }, t_start * -1); // triggers play with "seeked" event + // this.audioEl.currentTime = 0; + } + } else { + if (this.audioEl.currentTime !== t_start / 1000) { + console.debug(this.audioEl.currentTime, t_start / 1000); + this.audioEl.currentTime = t_start / 1000; + } + this.audioEl.play(); + // this.audioEl.play(); // play is done in "seeked" evenlistener + console.debug(this.audioEl.currentTime, t_start, t_in, t_out); + } + + this.audioIsPlaying = true; // also state as playing in preroll + this.audioEndTimeout = setTimeout((e) => { + this.audioEl.pause(); + this.audioIsPlaying = false; + console.debug('done playing audio'); + }, t_diff); + } + + _scrubAudio(time_ms) { + this.audioEl.currentTime = Math.max(0, this.getAudioTime(time_ms)) / 1000; + } + + getFinalFrameTime() { + if (this.strokes.length == 0) return null; // when no strokes are loaded (eg. for annotation) + const points = this.strokes[this.strokes.length - 1].points; + return points[points.length - 1][3]; + } + + getStrokesSliceForPathRange(in_point, out_point) { + // get paths for given range. Also, split path at in & out if necessary. + let slices = {}; + for (let i = in_point[0]; i <= out_point[0]; i++) { + const stroke = this.strokes[i]; + if (typeof stroke === 'undefined') { + // out point can be Infinity. So interrupt whenever the end is reached + break; + } + const in_i = (in_point[0] === i) ? in_point[1] : 0; + const out_i = (out_point[0] === i) ? out_point[1] : Infinity; + + slices[i] = new StrokeSlice(stroke, in_i, out_i); + } + return slices; + } + + + // TODO: when drawing, have a group active & inactive. + // active is getPathRange(currentIn, currentOut) + // inactive is what comes before and after. + // then, playing the video is just running pathRanghe(0, playhead) + drawStrokePosition(in_point, out_point, show_all) { + if (typeof show_all === 'undefined') + show_all = true; + + this.strokeGroups['before'].setStrokes(this.getStrokesSliceForPathRange([0, 0], in_point)); + this.strokeGroups['annotation'].setStrokes(this.getStrokesSliceForPathRange(in_point, out_point)); + this.strokeGroups['after'].setStrokes(this.getStrokesSliceForPathRange(out_point, [Infinity, Infinity])); + + + // // an inpoint is set, so we're annotating + // // make everything coming before translucent + // if (this.inPointPosition !== null) { + // const [inPath_i, inPoint_i] = this.inPointPosition; + // // returns a static NodeList + // const currentBeforeEls = this.svgEl.querySelectorAll(`.before_in`); + // for (let currentBeforeEl of currentBeforeEls) { + // currentBeforeEl.classList.remove('before_in'); + // } + + // for (let index = 0; index < inPath_i; index++) { + // const pathEl = this.svgEl.querySelector(`.path${ index }`); + // if (pathEl) { + // pathEl.classList.add('before_in'); + // } + // } + // } + + // this.currentPathI = path_i; + // this.currentPointI = point_i; + + // const path = this.strokes[path_i]; + // // console.log(path); + // let pathEl = this.svgEl.querySelector(`.path${ path_i }`); + // if (!pathEl) { + // pathEl = document.createElementNS('http://www.w3.org/2000/svg', 'path'); + // pathEl.style.stroke = path.color; + // pathEl.classList.add('path' + path_i) + // this.svgEl.appendChild(pathEl) + // } + + // const stroke = path.points.slice(0, point_i); + // const d = this.strokes2D(stroke); + // pathEl.setAttribute('d', d); + + // this.scrubberElOld.value = path.points[point_i][3]; + // this.currentTime = path.points[point_i][3]; + } + + setViewboxPosition(box_i) { + if (this.currentViewboxI == box_i) { + return; + } + this.currentViewboxI = box_i + if (!this.config.crop_to_fit) { + this.updateViewbox(); + } + } + + updateViewbox() { + if (this.config.crop == CropOptions.Fit_Selection) { + this.svgEl.setAttribute('viewBox', `${this.bounding_box.x} ${this.bounding_box.y} ${this.bounding_box.width} ${this.bounding_box.height}`); + } else if (this.config.crop == CropOptions.Whole_Drawing && this.backgroundBoundingBox) { + this.svgEl.setAttribute('viewBox', `${this.backgroundBoundingBox.x} ${this.backgroundBoundingBox.y} ${this.backgroundBoundingBox.width} ${this.backgroundBoundingBox.height}`); + } else { + let x, y, w, h; + if (this.currentViewboxI !== null) { + x = this.viewboxes[this.currentViewboxI].x, + y = this.viewboxes[this.currentViewboxI].y, + w = this.dimensions[0], + h = this.dimensions[1]; + } else { + x = 0, + y = 0, + w = this.dimensions[0], + h = this.dimensions[1]; + } + this.svgEl.setAttribute('viewBox', `${x} ${y} ${w} ${h}`); + } + } + + setCrop(crop_option) { + if(Object.values(CropOptions).indexOf(crop_option) === -1) { + console.error('invalid crop option', crop_option); + crop_option = CropOptions.Fit_Selection; + } + + this.config.crop = crop_option; + for(let option of Object.values(CropOptions)) { + if (this.config.crop == option) { + this.wrapperEl.classList.add('crop-' + option); + } else { + this.wrapperEl.classList.remove('crop-' + option); + } + } + + this.toggleCropPlayerEl.innerText = CropDescriptions[this.config.crop]; + this.updateViewbox(); + } + + toggleCrop() { + console.log(this.config.crop, Object.values(CropOptions), Object.values(CropOptions).indexOf(this.config.crop)) + const i = (Object.values(CropOptions).indexOf(this.config.crop) + 1) % Object.keys(CropOptions).length; + const newCrop = Object.values(CropOptions)[i]; + console.log(i, newCrop); + this.setCrop(newCrop); + } + + getNextPosition(path_i, point_i) { + const path = this.strokes[path_i]; + let next_path, next_point; + if (path.points.length > point_i + 1) { + next_path = path_i; + next_point = point_i + 1; + // setTimeout(() => this.playStroke(next_path, next_point), dt); + } else if (this.strokes.length > path_i + 1) { + next_path = path_i + 1; + next_point = 0; + // use starttime instead of diff, to prevent floating + } else { + return [null, null]; + } + + // when an outpoint is set, stop playing there + if (this.outPointPosition && (next_path > this.outPointPosition[0] || + (next_path == this.outPointPosition[0] && next_point > this.outPointPosition[1]))) { + console.debug('> out point', this.outPointPosition) + return [null, null]; + } + + return [next_path, next_point]; + } + + playStrokePosition(path_i, point_i, allow_interrupt) { + if (this.strokes.length === 0) { + console.debug('No video to play back'); + this.videoIsPlaying = false; + return; + } + + if (allow_interrupt) { + if (!this.videoIsPlaying) { + console.debug('not playing because of interrupt'); + return; + } + } else { + this.videoIsPlaying = true; + } + this.drawStrokePosition(this.inPointPosition, [path_i, point_i]); + + const [next_path, next_point] = this.getNextPosition(path_i, point_i); + if (next_path === null) { + console.debug('done playing video'); + this.videoIsPlaying = false; + return; + } + + const t = this.strokes[next_path].points[next_point][3];// - path.points[point_i][3]; + + // calculate interval based on playback start to avoid drifting of time + const dt = t - (window.performance.now() - this.startTimeMs); + this.nextFrameTimeout = setTimeout(() => this.playStrokePosition(next_path, next_point, true), dt); + } + + + playViewboxPosition(box_i, allow_interrupt) { + if (allow_interrupt) { + if (!this.videoIsPlaying) { + console.debug('not playing because of interrupt'); + return; + } + } + // else { + // this.videoIsPlaying = true; + // } + this.setViewboxPosition(box_i); + + const next_box_i = box_i + 1; + if (this.viewboxes.length <= next_box_i) { + console.debug('done playing viewbox'); + return; + } + + const t = this.viewboxes[next_box_i].t; + + // calculate interval based on playback start to avoid drifting of time + const dt = t - (window.performance.now() - this.startTimeMs); + this.nextViewboxTimeout = setTimeout(() => this.playViewboxPosition(next_box_i, true), dt); + } + + scrubTo(ms) { + // const [path_i, point_i] = this.findPositionForTime(ms); + // console.log(path_i, point_i); + this.pause(); + this._seekByTime(ms / 1000); + // this.playHead = ms; + } + + playPause() { + if (this.paused) { + this.play(); + } else { + this.pause() + } + } + + /** + * Compatibility with HTMLMediaElement API + * @returns None + */ + pause() { + this._interruptPlayback(); + } + + _interruptPlayback() { + clearTimeout(this.nextFrameTimeout); + clearTimeout(this.nextViewboxTimeout); + clearTimeout(this.audioEndTimeout); + clearTimeout(this.audioStartTimeout); + clearTimeout(this.startVideoTimeout); + this.audioEl.pause(); + this.videoIsPlaying = false; + this.audioIsPlaying = false; + this._setPausedFlag(true); + } + + /** + * Compatibility with HTMLMediaElement API + * @returns Promise + */ + play() { + return new Promise((resolve, reject) => { + this._interruptPlayback(); + + if (this._currentTimeMs > this.outPointTimeMs) { + this._seekByTimeMs(this.inPointTimeMs); + } else { + this._seekByTimeMs(this._currentTimeMs); // prevent playback issue for initial load + } + + this._setPausedFlag(false); + + const startPlayback = () => { + console.debug('start playback'); + this.wrapperEl.classList.remove('loading'); // no loading anymore + + this.startTimeMs = window.performance.now() - this._currentTimeMs; + // strokes + if (this._currentTimeMs < 0) { + this.startVideoTimeout = setTimeout((e) => this.playStrokePosition(this.currentPathI, this.currentPointI), this._currentTimeMs * -1); + } else { + this.playStrokePosition(this.currentPathI, this.currentPointI); + } + // viewboxes + // const nextViewboxI = Math.max(this.currentViewboxI++, this.viewboxes.length-1); + this.playViewboxPosition(this.currentViewboxI); + + // audio + // TODO: use this.audioEl.readyState == 4 : play immediately, otherwise after event + this.playAudioSegment(this._currentTimeMs, this.outPointTimeMs); + // this.playStrokePosition(this.currentPathI, this.currentPointI); + + this.dispatchEvent(new CustomEvent('play', {})); + this._animationFrame(); + resolve(); + } + + if (this.audioEl.src.length && this.audioEl.readyState !== 4) { // not ready to play after seeking audio. + console.debug('wait for audio before playback'); + this.wrapperEl.classList.add('loading'); + this.audioEl.addEventListener('canplaythrough', () => { + startPlayback() + }, { once: true }); // only once + } else { + startPlayback(); + } + + }); + } + + _setPausedFlag(paused) { + this._paused = !!paused; //convert to boolean + if (paused) { + this.playPauseEl.classList.remove('playing'); + this.playPauseEl.classList.add('paused'); + } else { + this.playPauseEl.classList.remove('paused'); + this.playPauseEl.classList.add('playing'); + } + } + + get paused() { + return this._paused; + } + + _updatePlayhead() { + this.playheadEl.value = this._currentTimeMs; + this.timeCodeEl.value = this.formatter.to(this._currentTimeMs); + } + + + // on playback, run every windowAnimationFrame + _animationFrame(timestamp) { + // TODO, move time at end of playStrokePosition to here + const nextTime = window.performance.now() - this.startTimeMs; + const endTime = this.outPointTimeMs ?? this.duration * 1000; + let interrupt = false; + if (nextTime > endTime) { + this._currentTimeMs = endTime; + interrupt = true; + } else { + this._currentTimeMs = nextTime; + } + this._updatePlayhead(); + if (!interrupt && (this.videoIsPlaying || this.audioIsPlaying)) { + window.requestAnimationFrame((timestamp) => this._animationFrame(timestamp)); + } else { + console.debug('finished playback'); + this._interruptPlayback(true); + // this.resetPlayhead(); // Disable to not jump to start on pause. TODO: check if this causes issues e.g. on end + } + } + + resetPlayhead() { + this._seekByTimeMs(this.inPointTimeMs); + if (this.selectedAnnotation) { + // show the hole selected annotation + this.drawStrokePosition(this.inPointPosition, this.outPointPosition); + } + } + + /** + * Note that both t_in and t_out can be negative + * @param float|Array t_in in point time, in ms or array with path/frame points + * @param float|Array t_out out point time, in ms or array with path/frame points + */ + playSegment(in_point, out_point) { + if (!Array.isArray(in_point)) in_point = this.findPositionForTime(in_point); + if (!Array.isArray(out_point)) out_point = this.findPositionForTime(out_point); + + this.inPointPosition = in_point; + this.outPointPosition = out_point; + this._seekByPoint(in_point); + + this.play(); + } + + _seekByPoint(point) { + this.dispatchEvent(new CustomEvent('seeking', {})); + this._currentTimeMs = this.strokes[point[0]].points[point[1]][2]; + this.audioEl.currentTime = this.getAudioTime(this._currentTimeMs) / 1000; + [this.currentPathI, this.currentPointI] = point; + this._updatePlayhead(); + this._updateFrame(); + // TODO set audio, wait for promise to finish + this.dispatchEvent(new CustomEvent('seeked', {})); + + } + _seekByTimeMs(time) { + this._seekByTime(Number.parseFloat(time) / 1000); + } + _seekByTime(time) { + this.dispatchEvent(new CustomEvent('seeking', { detail: time })); + this._currentTimeMs = Number.parseFloat(time) * 1000; + this.audioEl.currentTime = this.getAudioTime(this._currentTimeMs) / 1000; + [this.currentPathI, this.currentPointI] = this.findPositionForTime(this._currentTimeMs); + + this._updatePlayhead(); + this._updateFrame(); + this.dispatchEvent(new CustomEvent('seeked', { detail: this.currentTime })); + } + + _updateFrame() { + this.drawStrokePosition(this.inPointPosition, [this.currentPathI, this.currentPointI]); + this.setViewboxPosition(this.findViewboxForTime(this._currentTimeMs)); + } + + /** + * For compatibility with HTMLMediaElement API convert seconds to ms of internal timer + */ + set currentTime(time) { + this._seekByTime(time); + } + + get currentTime() { + return this._currentTimeMs / 1000; + } + + getEndTimeMs() { + const videoDuration = this.getFinalFrameTime(); + const audioDuration = (this.audioEl && this.audioEl.src) ? this.audioEl.duration + this.audioOffset : 0; + + return Math.max(videoDuration, audioDuration * 1000); + } + + get duration() { + + const prerollDuration = this.audioOffset < 0 ? this.audioOffset * -1 : 0; + + return prerollDuration + this.getEndTimeMs() / 1000; + } + + findPositionForTime(ms) { + ms = Math.min(Math.max(ms, 0), this.lastFrameTime); + // console.log('scrub to', ms) + let path_i = 0; + let point_i = 0; + this.strokes.every((stroke, index) => { + const startAt = stroke.points[0][3]; + const endAt = stroke.points[stroke.points.length - 1][3]; + + if (startAt > ms) { + return false; // too far + } + if (endAt > ms) { + // we're getting close. Find the right point_i + path_i = index; + stroke.points.every((point, pi) => { + if (point[3] > ms) { + // too far + return false; + } + point_i = pi; + return true; + }); + return false; + } else { + // in case nothings comes after, we store the last best option thus far + path_i = index; + point_i = stroke.points.length - 1; + return true; + } + + }); + return [path_i, point_i]; + } + + findViewboxForTime(ms) { + ms = Math.min(Math.max(ms, 0), this.lastFrameTime); + // console.log('scrub to', ms) + let box_i = 0; + this.viewboxes.every((viewbox, index) => { + const startAt = viewbox.t; + + if (startAt > ms) { + return false; // too far + } else { + // in case nothings comes after, we store the last best option thus far + box_i = index; + return true; + } + + }); + return box_i; + } + + +} + + +class AnnotationPlayer extends HTMLElement { + constructor() { + super(); + // We don't use constructor() because an element's attributes + // are unavailable until connected to the DOM. + + // attributes: + // - data-crop (any of CropOptions) + // - autoplay + // - preload + // - data-poster-src + // - data-annotation-url + } + + connectedCallback() { + // Create a shadow root + this.attachShadow({ mode: "open" }); + + const imgWrapEl = document.createElement('div'); + imgWrapEl.classList.add('imgWrap'); + + const imgEl = document.createElement('img'); + imgWrapEl.appendChild(imgEl); + + const playerEl = document.createElement('div'); + const url_prefix = this.hasAttribute('data-url-prefix') ? this.getAttribute('data-url-prefix') + '/' : ''; + + const config = { + is_player: true, + crop: this.hasAttribute('data-crop') ? this.getAttribute('data-crop') : null, + autoplay: true, + url_prefix: url_prefix, + stroke_color: this.hasAttribute('stroke') ? this.getAttribute('stroke') : null + } + + const start = () => { + imgWrapEl.style.display = 'none'; + this.annotator = new Annotator( + playerEl, + null, //"tags.json", + url_prefix + this.getAttribute('data-annotation-url'), + config + ); + } + + if(this.hasAttribute('data-poster-url')) { + imgEl.src = url_prefix + this.getAttribute('data-poster-url'); + imgEl.addEventListener('click', start) + } else { + config.autoplay = false; + start(); + } + + playerEl.classList.add('play'); + + const styleEl = document.createElement('style'); + styleEl.textContent = ` + :host{ + overflow: hidden; + } + + :host(.active){ + --override-color: orange + } + + svg{ + --disactive-path: rgba(220,220,220,.7) + } + + div.imgWrap{ + cursor:pointer; + } + div.imgWrap::before { + color:white; + content: '\u25B6'; + background: rgba(0,0,0,0.5); + height: 30px; + border-radius: 50%; + width: 50px; + display: block; + position: absolute; + left: calc(50% - 25px); + top: calc(50% - 25px); + text-align: center; + line-height: 47px; + height: 50px; + font-size: 20px; + pointer-events: none; + cursor:pointer; + } + div.imgWrap:hover::before{ + background: rgba(0,0,0,0.2); + } + + div.play, div.imgWrap { + padding: 10px; + background: white; + } + + svg, img { + width: 100%; + height: 100%; + } + + .play:not(.loading) .controls { + visibility: hidden; + } + + :host(:hover) .controls { + visibility: visible !important; + } + + .controls--playback { + display:flex; + background: rgba(0,0,0,.5); + border-radius: 3px; + } + + .timecode { + width: 30px; + font-size: 8px; + background: none; + border: none; + color: white; + } + + + .controls--playback input[type='range'] { + flex-grow: 1; + -webkit-appearance: none; + background: none; + + } + + input[type="range"]::-webkit-slider-runnable-track, + input[type="range"]::-moz-range-track { + background: lightgray; + height: 5px; + border-radius: 3px; + } + + input[type="range"]::-moz-range-progress { + background-color: white; + height: 5px; + border-radius: 3px 0 0 3px; + } + + + input[type="range"]::-webkit-slider-thumb, + input[type="range"]::-moz-range-thumb { + -webkit-appearance: none; + height: 15px; + width: 15px; + background: white; + margin-top: -5px; + border-radius: 50%; + border:none; + } + + + + .controls button.paused, + .controls button.playing { + order: -1; + width: 30px; + height: 30px; + border: none; + background: none; + color: white; + line-height: 1; + } + + .controls .controls-fs { + width: 30px; + text-align: center; + cursor: pointer; + } + .controls .controls-fs:hover { + font-weight:bold; + } + + .controls button.paused::before { + content: '⏵'; + } + + .controls button.playing::before { + content: '⏸'; + } + + + .loading .controls button:is(.playing, .paused)::before { + content: '↺'; + display: inline-block; + animation: rotate 1s infinite; + } + + @keyframes rotate { + 0% { + transform: rotate(359deg) + } + + 100% { + transform: rotate(0deg) + } + } + + .controls { + position: absolute !important; + z-index: 100; + bottom: 10px; + left: 5%; + right: 0; + width: 90%; + color:white; + } + svg .background { + fill: white + } + + path { + fill: none; + stroke: var(--disactive-path); + stroke-width: 1mm; + stroke-linecap: round; + } + + g.before path { + opacity: 0.5; + stroke: var(--disactive-path) !important; + } + + g.after path, + path.before_in { + opacity: .1; + stroke: var(--disactive-path) !important; + } + + .hide-drawing-preview g.after path, .hide-drawing-preview path.before_in{ + opacity:0; + } + + .background{ + visibility: hidden + } + .play:not(.crop-selection) .background{ + visibility: visible; + } + + .gray { + position: absolute; + background: rgba(255, 255, 255, 0.7); + } + + details{ + color: white; + } + + summary{ + list-style: none; + cursor: pointer; + padding: 0 5px; + } + + details > ul{ + position: absolute; + bottom: 35px; + background: rgba(0,0,0, .5); + border-radius: 3px; + right: 0; + padding: 5px; + margin: 0; + list-style: none; + font-size: 10pt; + width: 150px; + } + + details > ul li:not(.config-header):hover{ + cursor: pointer; + text-decoration: underline; + } + + details .config-header { + font-weight: bold; + } + + .play:not(.hide-drawing-preview) details > ul li:first-child{ + /*text-decoration: line-through;*/ + font-weight:bold; + } + .play.crop-selection details > ul li:nth-child(2){ + /*text-decoration: line-through;*/ + /*font-weight:bold;*/ + } + .play:not(.crop-selection) details .config-future{ + display:none; + } + .annotation path.path{stroke: var(--override-color) !important; transition: stroke 1s;} + `; + + if(config.stroke_color) { + // styleEl.textContent += `.annotation path.path{stroke: ${config.stroke_color} !important;}` + styleEl.textContent += `:host{--override-color: ${config.stroke_color}; }` + } + + this.shadowRoot.appendChild(styleEl); + this.shadowRoot.appendChild(imgWrapEl); + this.shadowRoot.appendChild(playerEl); + + } + + setAnnotation(annotation) { + // this.annotation = annotation; + this.setAttribute('data-annotation-url', annotation.url) + this.setAttribute('data-poster-url', `/annotation/${annotation.id}.svg`) + } + + attributeChangedCallback(name, oldValue, newValue) { + // console.log(name, oldValue, newValue); + if (name == 'data-crop') { + if (!this.annotator) { + return; + } + this.annotator.setCrop(this.hasAttribute('data-crop')); + } + } + + // required for attributeChangedCallback() + static get observedAttributes() { return ['data-crop']; } + +} + +window.customElements.define('annotation-player', AnnotationPlayer); diff --git a/assets/annotation-3A53SHHN.json b/assets/annotation-3A53SHHN.json new file mode 100644 index 0000000..f99ac85 --- /dev/null +++ b/assets/annotation-3A53SHHN.json @@ -0,0 +1 @@ +{"file": "2022-11-30-2-50b018?t_in=803795.07&t_out=828504.07", "time": "-", "dimensions": [2048, 1152], "shape": [{"color": "black", "points": [[4404.999969482422, -174.50003051757812, 0, 7251.029999904684], [4401.800018310547, -163.30001831054688, 0, 7267.129999809316], [4399.399993896484, -140.9000244140625, 0, 7284.029999904684], [4396.999969482422, -118.50003051757812, 0, 7300.930000000051], [4396.199920654297, 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+

Algorithmic Security Vision: Diagrams of Computer + Vision Politics

+

Ruben van de Ven, Ildikó Zonga Plájás, Cyan Bae, + Francesco Ragazzi

+

December 2023

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3. Managing error: from the sublime to the risky algorithm

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Our third emerging figuration concerns the place of the error. A + large body of literature examines actual and speculative cases of + algorithmic prediction based on self-learning systems (Azar et al., + 2021). Central to these analyses is the boundary-drawing performed by + such algorithmic devices, enacting (in)security by rendering their + subjects as more- or less-risky others (Amicelle et al., 2015: 300; + Amoore and De Goede, 2005; Aradau et al., 2008; Aradau and Blanke, 2018) + based on a spectrum of individual and environmental features (Calhoun, + 2023). In other words, these predictive devices conceptualize risk as + something produced by, and thus external to, security technologies.

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In this critical literature on algorithmic practices, practitioners + working with algorithmic technologies are often critiqued for + understanding software as “sublime” (e.g. Wilcox, 2017: 3). However, in + our diagrams, algorithmic vision appears as a practice of managing + error. The practitioners we interviewed are aware of the error-prone + nature of their systems but know it will never be perfect, and see it as + a key metric that needs to be acted upon.

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The most prominent way in which error figures in the diagrams is in + its quantified form of the true positive and false positive rates, TPR + and FPR. The significance and definition of these metrics is stressed by + CTO Gerwin van der Lugt (Diagram 6). In camera surveillance, the false + positive rate could be described as the number of fales positive + classifications relative to the number of video frames being analyzed. + Upon writing down these definitions, van der Lugt corrected his initial + definitions, as these definitions determine the work of his development + team, the ways in which his clients — security operators — engage with + the technology, and whether they perceive the output of the system as + trustworthy.

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Diagram 6. Gerwin van der Lugt corrects his initial definitions of + the true positive and false positive rates, and stresses the importance + of their precise definition.

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The figuration of algorithmic security vision as inherently imprecise + affects the operationalization of security practices. Van der Lugt’s + example concerns whether the violence detection algorithm developed by + Oddity.ai should be trained to categorize friendly fighting + (stoeien) between friends as “violence” or not. In this + context, van der Lugt finds it important to differentiate what counts as + false positive in the algorithm’s evaluation metric from an error in the + algorithm’s operationalization of a security question.

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He gives two reasons to do so. First, he anticipates that the + exclusion of stoeien from the category of violence would + negatively impact TPR. In the iterative development of self-learning + systems, the TPR and FPR, together with the true and false + negative rates must perform a balancing act. Van der Lugt + outlines that with their technology they aim for fewer than 100 false + positives per 100 million frames per week. The FPR becomes indicative of + the algorithm’s quality, as too many faulty predictions will desensitize + the human operator to system alerts. +

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This leads to van der Lugt’s second point: He fears that the + exclusion of stoeien from the violence category might cause + unexpected biases in the system. For example, instead of distinguishing + violence from stoeien based on people’s body movements, the + algorithm might make the distinction based on their age. For van der + Lugt, this would be an undesirable and hard to notice form of + discrimination. In developing algorithmic (in)security, error is figured + not merely as a mathematical concept but (as shown in Diagram 6) as a + notion that invites pre-emption — a mitigation of probable failure — for + which the developer is responsible. The algorithmic condition of + security vision is figured as the pre-emption of error.

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Diagram 7. By drawing errors on a timeline, van Rest calls attention + to the pre-emptive nature of error in the development process of + computer vision technologies.

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According to critical AI scholar Matteo Pasquinelli, “machine + learning is technically based on formulas for error correction” (2019: + 2). Therefore, any critical engagement with such algorithmic processes + needs to go beyond citing errors, “for it is precisely through these + variations that the algorithm learns what to do” (Amoore, 2019: 164), + pushing us to reconsider any argument based on the inaccuracy of the + systems.

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The example of stoeien suggests that it is not so much a + question if, or how much, these algorithms err, but how these errors are + anticipated and negotiated. Thus, taking error as a hallmark of machine + learning we can see how practices of (in)security become shaped by the + notion of mathematical error well beyond their development stages. Error + figures centrally in the development, acquisition and deployment of such + devices. As one respondent indicated, predictive devices are inherently + erroneous, but the quantification of their error makes them amenable to + "risk management.”

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While much has been written about security technologies as a device + for risk management, little is known about how security + technologies are conceptualized as objects of risk management. + What happens then in this double relation of risk? The figure of the + error enters the diagrams as a mathematical concept, throughout the + conversations we see its figure permeate the discourse around + algorithmic security vision. By figuring algorithmic security vision + through the notion of error, risk is placed at the heart of the security + apparatus. +

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Con-figurations of algorithmic security vision: fragmenting + accountability and expertise

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In the previous section we explored the changing figurations + of key dimensions of algorithmic security vision, in this section we + examine how these figurations configure. For Suchman, working + with configurations highlights “the histories and encounters through + which things are figured into meaningful existence, fixing them + through reiteration but also always engaged in ‘the perpetuity of coming + to be’ that characterizes the biographies of objects as well as + subjects” (Suchman, 2012: 50, emphasis ours) In other words, we are + interested in the practices and tensions that emerge as figurations + become embedded in material practices. We focus on two con-figurations + that emerged in the interviews: the delegation of accountability to + externally managed benchmarks, and the displacement of responsibility + through the reconfiguration of the human-in-the-loop.

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Delegating accountability to benchmarks

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The first configuration is related to the evaluation of the error + rate in the training of algorithmic vision systems: it involves + datasets, benchmark institutions, and the idea of fairness as equal + representation among different social groups. Literature on the ethical + and political effects of algorithmic vision has notoriously focused on + the distribution of errors, raising questions of ethnic and racial bias + (e.g. Buolamwini and Gebru, 2018). Our interviews reflect the concerns + of much of this literature as the pre-emption of error figured + repeatedly in relation to the uneven distribution of error across + minorities or groups. In Diagram 8, Ádám Remport draws how different + visual traits have often led to different error rates. While the general + error metric of an algorithmic system might seem "acceptable," it + actually privileges particular groups, which is invisible when only the + whole is considered. Jeroen van Rest distinguishes such errors from the + inherent algorithmic imprecision in deep machine learning models, as + systemic biases (Diagram 7), as they perpetuate inequalities in the + society in which the product is being developed.

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Diagram 8. Ádám Remport describes that facial recognition + technologies are often most accurate with white male adult faces, + reflecting the datasets they are trained with. The FPR is higher with + people with darker skin, children, or women, which may result in false + flagging and false arrests.

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To mitigate these concerns and manage their risk, many of our + interviewees who develop and implement these technologies, externalize + the reference against which the error is measured. They turn to a + benchmark run by the American National Institute of Standards and + Technology (NIST), which ranks facial recognition technologies by + different companies by their error metric across groups. John Riemen, + who is responsible for the use of forensic facial recognition technology + at the Center for Biometrics of the Dutch police, describes how their + choice for software is driven by a public tender that demands a "top-10" + score on the NIST benchmark. The mitigation of bias is thus outsourced + to an external, and in this case foreign, institution.

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We see in this outsourcing of error metrics a form of delegation that + brings about a specific regime of (in)visibility. While a particular + kind of algorithmic bias is rendered central to the NIST benchmark, the + mobilization of this reference obfuscates questions on how that metric + was achieved. That is to say, questions about training data are + invisibilized, even though that data is a known site of contestation. + For example, the NIST benchmark datasets are known to include faces of + wounded people (Keyes, 2019). The Clearview company is known to use + images scraped illegally from social media, and IBM uses a dataset that + is likely in violation of European GDPR legislation (Bommasani et al., + 2022: 154). Pasquinelli (2019) argued that machine learning models + ultimately act as data compressors: enfolding and operationalizing + imagery of which the terms of acquisition are invisibilized.

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Attention to this invisibilization reveals a discrepancy between the + developers and the implementers of these technologies. On the one hand, + the developers we interviewed expressed concerns about how their + training data is constituted to gain a maximum false positive rate/true + positive rate (FPR/TPR) ratio, while showing concern for the legality of + the data they use to train their algorithms. On the other hand, + questions about the constitution of the dataset have been virtually + non-existent in our conversations with those who implement software that + relies on models trained with such data. Occasionally this knowledge was + considered part of the developers' intellectual property that had to be + kept a trade secret. A high score on the benchmark is enough to pass + questions of fairness, legitimizing the use of the algorithmic model. + Thus, while indirectly relying on the source data, it is no longer + deemed relevant in the consideration of an algorithm. This illustrates + well how the invisibilization of the “compressed” dataset, in + Pasquinelli’s terms, into a model, with the formalization of guiding + metrics into a benchmark, permits a bracketing of accountability. One + does not need to know how outcomes are produced, as long as the + benchmarks are in order.

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The configuration of algorithmic vision’s bias across a complex + network of fragmented locations and actors, from the dataset, to the + algorithm, to the benchmark institution reveals the selective processes + of (in)visibilization. This opens up fruitful alleys for new empirical + research: What are the politics of the benchmark as a mechanism of + legitimization? How does the outsourcing of assessing the error + distribution impact attention to bias? How has the critique of bias been + institutionalized by the security industry, resulting in the + externalization of accountability, through dis-location and + fragmentation?

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Reconfiguring the human-in-the-loop

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A second central question linked to the delegation of accountability + is the configuration in which the security operator is located. The + effects of delegation and fragmentation in which the mitigation of + algorithmic errors is outsourced to an external party, becomes visible + in the ways in which the role of the security operator is configured in + relation to the institution they work for, the software’s assessment, + and the affected publics.

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The public critique of algorithms has often construed the + human-in-the loop as one of the last lines of defense in the + resistance to automated systems, able to filter and correct erroneous + outcomes (Markoff, 2020). The literature in critical security studies + has however problematized the representation of the security operator in + algorithmic assemblages by discussing how the algorithmic predictions + appear on their screen (Aradau and Blanke, 2018), and how the embodied + decision making of the operator is entangled with the algorithmic + assemblage (Wilcox, 2017). Moreover, the operator is often left guessing + at the working of the device that provides them with information to make + their decision (Møhl, 2021). +

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What our participants’ diagrams emphasized is how a whole spectrum of + system designs emerges in response to similar questions, for example the + issue of algorithmic bias. A primary difference can be found in the + degree of understanding of the systems that is expected of security + operators, as well as their perceived autonomy. Sometimes, the human + operator is central to the system’s operation, forming the interface + between the algorithmic systems and surveillance practices. Gerwin van + der Lugt, developer of software at Oddity.ai that detects criminal + behavior argues that “the responsibility for how to deal with the + violent incidents is always [on a] human, not the algorithm. The + algorithm just detects violence—that’s it—but the human needs to deal + with it.”

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Dirk Herzbach, chief of police at the Police Headquarters Mannheim, + adds that when alerted to an incident by the system, the operator + decides whether to deploy a police car. Both Herzbach and Van der Lugt + figure the human-in-the-loop as having full agency and responsibility in + operating the (in)security assemblage (cf. Hoijtink and Leese, + 2019).

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Some interviewees drew a diagram in which the operator is supposed to + be aware of the ways in which the technology errs, so they can address + them. Several other interviewees considered the technical expertise of + the human-in-the-loop to be unimportant, even a hindrance.

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Chief of police Herzbach prefers an operator to have patrol + experience to assess which situations require intervention. He is + concerned that knowledge about algorithmic biases might interfere with + such decisions. In the case of the Moscow metro, in which a facial + recognition system has been deployed to purchase tickets and open access + gates, the human-in-the-loop is reconfigured as an end user who needs to + be shielded from the algorithm’s operation (c.f. Lorusso, 2021). On + these occasions, expertise on the technological creation of the suspect + becomes fragmented.

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These different figurations of the security operator are held + together by the idea that the human operator is the expert of the + subject of security, and is expected to make decisions independent from + the information that the algorithmic system provides.

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Diagram 9. Riemen explains the process of information filtering that + is involved in querying the facial recognition database of the Dutch + police.

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Other drivers exist, however, to shield the operator from the + algorithm’s functioning, challenging individual expertise and + acknowledging the fallibility of human decision making. In Diagram 9, + John Riemen outlines the use of facial recognition by the Dutch police. + He describes how data from the police case and on the algorithmic + assessment is filtered out as much as possible from the information + provided to the operator. This, Riemen suggests, might reduce bias in + the final decision. He adds that there should be no fewer than three + humans-in-the-loop who operate independently to increase the accuracy of + the algorithmic security vision.

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Instead of increasing their number, there is another configuration of + the human-in-the-loop that responds to the fallibility of the operator. + For the Burglary-Free Neighborhood project in Rotterdam, project manager + Guido Delver draws surveillance as operated by neighborhood residents, + through a system that they own themselves. By involving different + stakeholders, Delver hopes to counter government hegemony over the + surveillance apparatus. However, residents are untrained in assessing + algorithmic predictions raising new challenges. Delver illustrates a + scenario in which the algorithmic signaling of a potential burglary may + have dangerous consequences: “Does it invoke the wrong behavior from the + citizen? [They could] go out with a bat and look for the guy who has + done nothing [because] it was a false positive.” In this case, the worry + is that the erroneous predictions will not be questioned. Therefore, in + Delver’s project the goal was to actualize an autonomous system, “with + as little interference as possible.” Human participation or + “interference” in the operation are potentially harmful. Thus, figuring + the operator — whether police officer or neighborhood resident — as + risky, can lead to the relegation of direct human intervention.

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By looking at the figurations of the operator that appear in the + diagrams we see multiple and heterogeneous configurations of + regulations, security companies, and professionals. In each + configuration, the human-in-the-loop appears in different forms. The + operator often holds the final responsibility in the ethical functioning + of the system. At times they are configured as an expert in + sophisticated but error-prone systems; at others they are figured as end + users who are activated by the alerts generated by the system, and who + need not understand how the software works and errs, or who can be left + out.

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These configurations remind us that there cannot be any theorization + of “algorithmic security vision,” both of its empirical workings and its + ethical and political consequences without close attention to the + empirical contexts in which the configurations are arranged. Each + organization of datasets, algorithms, benchmarks, hardware and operators + has specific problems. And each contains specific politics of + visibilization, invisibilization, responsibility and accountability.

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+

Algorithmic Security Vision: Diagrams of Computer +Vision Politics

+

Ruben van de Ven, Ildikó Zonga Plájás, Cyan Bae, +Francesco Ragazzi

+

December 2023

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+

Abstract

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More images than ever are being processed by machine learning +algorithms for security purposes. Yet what technical and political +transformations do these sociotechnical developments create? This paper +charts the development of a novel set of practices which we term +"algorithmic security vision" using a method of diagramming-interviews. +Based on descriptions by activists, computer scientists and security +professionals, this article marks three shifts in security politics: the +emergence of synthetic data; the increased importance of movement, +creating a cinematic vision; and the centrality of error in the design +and functioning of these systems. The article then examines two tensions +resulting from these shifts: a fragmentation of accountability through +the use of institutionalized benchmarks, and a displacement of +responsibility through the reconfiguration of the human-in-the-loop. The +study of algorithmic security vision thus engenders a rhizome of +interrelated configurations. As a diagram of research, algorithmic +security vision invites security studies to go beyond a singular +understanding of algorithmic politics, and think instead in terms of +trajectories and pathways through situated algorithmic practices.

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Introduction

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In cities and at borders around the world, algorithms process streams +of images produced by surveillance cameras. For decades, computer +vision has been used to analyze security imagery using arithmetic +to, for example, send an alert when movement is detected in the frame, +or when a perimeter is breached. The increases in computing power and +advances in (deep) machine learning have reshaped the capabilities of +such security devices. These devices no longer simply quantify vast +amounts of image sensor data but qualify it to produce interpretations +in previously inconceivable ways. Pilot projects and off-the-shelf +products are intended to distinguish individuals in a crowd, extract +information from hours of video footage, gauge emotional states, +identify potential weapons, discern normal from anomalous behavior, and +predict intentions that may pose a security threat. Security practices +are substantially reconfigured through the use of machine learning-based +computer vision, or "algorithmic security vision."

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Algorithmic security vision represents a convergence of security +practices and what Rebecca Uliasz calls algorithmic vision: the +processing of images using machine learning techniques to produce a kind +of "vision" that does not make sense of the “visual” but that makes +realities actionable (Uliasz, 2020). It does not promise to eradicate +human sense making, but rather allows a reconsideration of how human and +nonhuman perception is interwoven with sociotechnical routines. +Algorithmic security vision thus draws together actors, institutions, +technologies, infrastructures, legislations, and sociotechnical +imaginaries (see Bucher, 2018: 3). Yet how does algorithmic security +vision work — how does it draw together these entities— and +what are the social and political implications of its use? In this +article we explain how “algorithmic vision” and “security” can map out +sociotechnical practices and explore how their coming together reframes +what it means to see and suspect. We are not concerned with the +technical features of the systems, but with the societal and political +projects that are embedded in technical choices made in their +construction.

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We ground this article in Lucy Suchman’s notions of +figuration and configuration as both a conceptual +frame and a method of analysis which offer insight into the interplay of +technology, imaginaries, and politics. Configuration allows +access to key assumptions about the boundaries that are negotiated in +the practices of algorithmic security vision, and how entities solidify +and stabilize as they circulate. The realities that are made possible by +algorithms are performed and perpetuated in the design and description +of these systems (Suchman, 2006: 239; see also Barad, 2007: 91).

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To grasp the specificities of algorithmic security vision, we turn to +the professionals who work with those technologies. How do people +working with algorithmic security vision make sense of, or +figure, their practices? An important dimension of this paper +is therefore methodological. Suchman, drawing on Haraway, mobilizes the +trope of the figure to examine the construction and circulation +of concepts: "to figure is to assign shape, designate what is to be made +noticeable and consequential, to be taken as identifying” (Suchman, +2012: 49). To expand on traditional textual analysis of such figurations +we introduce time-based diagramming where we combine qualitative +interviews with drawing. With these diagrams that record both voice and +the temporal unfolding of the drawing, the figurations appear in spatial +and temporal dimensions.

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We begin by situating our research in the debates on sensors, +algorithms and power, and outlining our theoretical and methodological +approach. Then, drawing on the time-based diagrams, we discuss three +figurations that challenge us to rethink our understandings of +algorithmic vision in security: algorithmic vision as synthetically +trained and cinematic, and the error as an inherent feature of +algorithmic vision. In a second step, we outline the fragmentation of +accountability through the use of benchmarks, and the reconfigurations +of the human-in-the-loop.

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Sensors, Algorithms, Power

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The critical reflection on the politics of algorithmic security +systems is not novel in Geography or in interdisciplinary debates, along +with Science and Technology Studies, Critical Security Studies or Media +Studies (Fourcade and Gordon, 2020; Graham, 1998; Mahony, 2021; Schurr +et al., 2023). Yet aside from a few exceptions (Andersen, 2018; +Bellanova et al., 2021), the politics specific to computer vision in the +security field have been overlooked.

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Computer vision is a term used to designate a multiplicity +of algorithms that can process still or moving images, producing +information upon which human or automated systems can make decisions. It +is meant to replicate certain aspects of human cognition. Algorithms can +be used to segment parts of an image, detect and recognize objects or +faces, track people or objects, estimate motion in a video, or +reconstruct 3D models based on multiple photo or video perspectives +(Dawson-Howe, 2014).

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Some scholars working on algorithmic security have addressed the role +of "operative images," which are "images that do not represent and +object, but rather are part of an operation" (Farocki, 2004). Authors +have shown how algorithms organize the regimes of visibility in +platforms such as YouTube and Facebook (Andersen, 2015), in war and +especially in military drone strikes (Bousquet, 2018; Suchman, 2020; +Wilcox, 2017). Others have focused on machine-mediated vision at the +European border by analyzing the functioning of EUROSUR (Dijstelbloem et +al., 2017; Tazzioli, 2018;) and SIVE (Fisher, 2018).

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These studies have contributed to theoretical debates around novel +practices algorithmic power (Bucher, 2018), surveillance capitalism +(Srnicek and De Sutter, 2017; Zuboff, 2019) and platform politics +(Carraro, 2021; Gillespie, 2018). Some works have described the social +and political effects of surveillance and social sorting (Gandy, 2021; +Lyon, 2003), as well as the reinforcement of control and marginalization +of post-colonial, gendered and racialized communities (Fraser, 2019; +Thatcher et al., 2016), defined by Graham as "software-sorted +Geographies" (Graham, 2005).

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These debates have highlighted the entanglement of these technologies +with risk assessment and pre-emptive security logics (Amoore, 2014; +Aradau and Blanke, 2018). Critical work has started catching up with +machine learning as an algorithmic technique (Amoore, 2021; Mackenzie, +2017), marking a shift from the management of "populations" to +"clusters," the acceleration of knowledge feedback loops (Isin and +Ruppert, 2020), foregrounding the normalization of behavior through the +regulation of the "normal" and the "anomaly" (Aradau and Blanke, 2018). +Or, in Pasquinelli’s words, how algorithms "normalize the abnormal +in a mathematical way" (Pasquinelli, 2015: 8 emphasis in +original).

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Yet what characterizes the state of the literature is a segmentation +between work on the politics of the “sensor,” and those on the political +specificities of deep learning models.

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On the one hand, using the notion of “sensor society,” Mark +Andrejevic and Mark Burdon (2015) have noted the prevalence of embedded +and distributed sensors. They have noted a shift from targeted, +purposeful, and discrete forms of information collection to always-on, +ubiquitous, opportunistic ever-expanding forms of data capture. +Andrejevic and Burdon insist that the sensors are only part of the +story; infrastructures are also critical: “It is […] the potential of +the automated processing of sensor-derived data that underwrites the +productive promise of data analytics in the sensor society: that the +machines can keep up with the huge volumes of information captured by a +distributed array of sensing devices” (Andrejevic and Burdon, 2015: 27). +Yet their focus is more on the sensors than on the underlying +algorithmic infrastructures.

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In their work on “sensory power,” Engin Isin and Evelyn Ruppert have +recently analyzed the effect of recent developments in technological +software and infrastructure. Unlike the three traditional forms of power +identified by Foucault (sovereign, disciplinary, and regulatory) they +argue that sensory power operates through apps, devices, and platforms +to collect and analyze data about individuals' bodies, behaviors, and +environments. For Isin and Ruppert, the central notion of sensory power +is the cluster. Clusters do not merely constitute "new" representations +of "old" populations, but rather “intermediary objects of government +between bodies and populations that a new form of power enacts and +governs through sensory assemblages” (Isin and Ruppert, 2020: 7). +Despite their contribution to, thinking about sorting techniques and +their relations to new forms of power Isin and Ruppert, like Andrejevic +and Burdon, bracket the specificities of the underlying deep learning +models.

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A growing body of literature has explored the politics of machine +learning techniques. In her latest work on the “deep border,” Louise +Amoore revisits her 2006 essay on the “biometric border.” Her focus is +on “deep machine learning,” and the “capacity to abstract and to +represent the relationships in high-dimensional data” such as in image +recognition (Amoore, 2021: 6). She shows that the change in border +technologies, from simple IF-THEN algorithmics with pre-determined +variables, to complex, deep, “neural networks” characterized by the +indeterminacy of variables marked a profound change in the logic, and +thus the political effects of these technologies. Like Isin and Ruppert +she is interested in the notion of the “cluster,” which, “with its +attendant logic of iterative partitioning and rebordering, loosens the +state’s application of categories and criteria in borders and +immigration” (Amoore, 2021: 6). Yet her approach overlooks the +importance of sensorial data posited by Andrejevic and Burdon, and Isin +and Ruppert.

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In sum, we still have only a rudimentary understanding of the +politics of algorithmic security vision. So, how does one think +politically about the new relations among sensors, algorithmic vision, +and politics? We propose a methodology for exploratory research that can +help outline a research agenda.

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Methodology

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Configuration as a methodological device

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Recent scholarship on technology and security has emphasized the +importance of algorithmic systems as enacted through relations between +human and nonhuman actors (Aradau and Blanke, 2015; Bellanova et al., +2021; Hoijtink and Leese, 2019; Suchman, 2006). Sociotechnical systems +act in "co-production" (Jasanoff, 2004), as "actants" in a network +(Latour, 2005), or in "intra-action" (Barad, 2007). In these +understandings, technology forms an ontological assemblage, in which +human agency is tied in with the sociomaterial arrangements of which it +is part. Humans and non-humans, technological objects and +infrastructures, all populate complex, sometimes messy networks where +the boundaries between entities are enacted in situated practices +(Haraway, 1988). This conception of technology "draws attention to the +fact that these relations are not a given but that they are constructed +— and thereby relates them back to cultural imaginaries of what +technology should look like and how it should be positioned vis-à-vis +humans and society" (Leese, 2019: 45)

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In this context, how can we understand the characteristics and +effects of security systems built on the analysis of sensor data through +“deep learning,” and the new security politics that they introduce? On +the technical level, the novelty of “algorithmic security vision” does +not lie in the sensors themselves, but in the new abilities of +“artificial intelligence software” (McCosker and Wilken, 2020). The +promise of the systems is that the multiplication of the sensors and +modalities of knowing, the ability to create information feeds that are +under the scrutiny of automated systems means that data collection and +data analysis are no longer separated; surveillance can happen in real +time, capturing life as it unfolds, so that the operators can act on +hotspots, clusters, or the moods and emotions of a crowd (Andrejevic and +Burdon, 2015; Isin and Ruppert, 2020).

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To make sense of such developments, Suchman’s concept of +configuration is a useful methodological “toolkit.” It helps +“delineating the composition and bounds of an object of analysis” +(Suchman, 2012: 48) and allows us to conceptualize algorithmic security +vision as heterogeneous assemblages of human and nonhuman elements whose +agency is "an effect of practices that are multiply distributed and +contingently enacted" (Suchman, 2006: 267). We are interested here in a +framework that underscores "how the entities that come into relation are +not given in advance, but rather emerge through the encounter with one +another" (van de Ven and Plájás, 2022: 52).

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Suchman also draws our attention to the “ways in which technologies +materialize cultural imaginaries, just as imaginaries narrate the +significance of technical artefacts” (2012: 48). For Suchman, +“configuration” is a tool for “studying technologies with particular +attention to the imaginaries and materialities that they join together” +(2012: 48). The configuration of humans and machines is constructed +through discourse and practice, which, drawing on Haraway, she +conceptualizes as “figurations.” Sociotechnical systems thus do not +exist without their intended uses and users. Such discourses are an +important part of individual experience, collective professional +practices, and narratives about technology. Technologies bring together +elements from various registers into stable material-semiotic +arrangements. Those configurations draw attention to the political +effects of everyday practices and how they institute bounded entities +and their relations. If we take Suchman’s suggestion that algorithmic +security vision is complex and multiple, how can we get to "know" it as +an object of research, while acknowledging its partiality? When taking +the coming together of algorithms, vision and (in)security as +configuring imaginaries and practices in heterogeneous and complex +networks, how can we explore their politics?

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Time-based Diagramming

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Suchman defines figuration as “action that holds the +material and the semiotic together in ways that become naturalized over +time, and in turn requires ‘unpacking’ to recover its constituent +elements” (2012: 49). The first step in her methodology therefore +requires us to “reanimate the figure at the heart of a given +configuration, in order to recover the practices through which it comes +into being and sustains its effects.”

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In her work, Suchman has used a variety of methods of inquiry to +“reanimate the figure.” Qualitative interviews and ethnography have been +instrumental in producing the raw material for the analysis. In this +paper, we expand the methodological toolkit envisaged by Suchman to +multimodal methods that go beyond text to capture the materiality of +imaginaries and practices. We explore the epistemic possibilities of +capturing figurations as both semiotic and material traces.

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The result of our theoretical and methodological quest is a tool that +allows us to produce “time-based diagramming.” We use this method for +both elicitation and multimodal data collection. We presented our +participants with a large digital tablet, and asked them to draw a +diagram while answering our questions. Ruben van de Ven programmed an +interface that could play back the recorded conversation in drawing and +audio. The participants could not delete or change their drawings, so +their hesitations and corrections remained. The ad hoc figuring +out of the participants’ descriptions thus remains part of the +recording.1 In the phase of data analysis, the +software allows the diagrams to be annotated, creating short clips. The +diagrams thus enable a practice of combination and composition +(O’Sullivan, 2016), providing for a material-semiotic support to analyze +various imaginaries of algorithmic security vision.

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Diagramming is a key method in the field of technology, most notably +in the conceptualization and design of computational practices +(Mackenzie, 2017; Soon and Cox, 2021: 221). We don’t assume that the +materiality of the drawings brings us any closer to the materiality of +the actors’ practices, which are of a different order. Our interest is +in the possibilities offered by the diagrams: they are composed of +elements that are not necessarily similar, but are connected by their +mere appearance on the same plane, thus allowing heterogeneous elements +to co-exist. Diagrams are composed of parts that can be separated and +recombined in different ways, creating new formations and expressions +(O’Sullivan, 2016). Using such a multimodal tool seemed a pertinent +methodological setup to capture figurations and +configurations (see van de Ven and Plájás, 2022).

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We interviewed twelve professionals who developed, deployed or +contested computer vision technologies in the field of (in)security.2 We asked them to describe the coming +together of computer, vision and (in)security from their professional +vantage points.3 In what follows, we focus on three +figurations and two configurations that emerged from the diagrams.

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Figurations of algorithmic security vision

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Diagram 1. Collage of excerpts from the conversations. Computer +vision is often depicted as camera based. The third drawing depicts a +"sensor hotel" on top of a light post in the Burglary-Free +Neighborhood.

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To understand the politics involved in the introduction of +algorithmic vision in security practices, the first step was to see how +the practitioners we spoke with figured their own practices +through the use of our diagramming method. Our aim was to capture +through shapes, relations, associations, and descriptions, the actors, +institutions, technical artifacts, and processes in situated practices +of algorithmic security vision.

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When we asked our interviewees what unites practices of computer +vision in (in)security, they start by foregrounding the camera and the +(algorithmically processed) visual image. However, when they began +drawing these assemblages based on examples, complexities emerged. In an +example of crowd detection developed for the securitization of the +Hague’s seaside boulevard, multiple sensors are installed on lampposts +and benches to count passersby. Based on behaviors and moving patterns +in the public space, operators can know, how many people are on the +boulevard at a certain moment, and whether these are individuals, or +small or large groups — the latter of which might be seen as a potential +security threat. The Burglary-Free Neighborhood in Rotterdam uses a +“sensor hotel” installed under the hood of street lamps (Diagram 1) +where the trajectory of pedestrians is analyzed with sounds like +breaking glass, gunshots or screams. In the security assemblages +described by our interviewees, the camera is but one element. During the +diagramming, the figure of the visual is pushed out of focus.

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In analyzing the twelve diagrams, three central figurations in +camera-based algorithmic security practices emerged that help us to +rethink some central notions of the literature on algorithmic security: +(1) a figure of “vision” as increasingly trained synthetically, not +organically; (2) a figure of vision as cinematic and moving in time, not +photographic; (3) a figure of the error as a permanent dimension of +algorithmic vision, not as something that could be solved or +eliminated.

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1. From skilled vision to synthetic vision

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Diagram 2. Sergei Miliaev distinguishes three sources of training +data for facial recognition technologies.

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In most of our conversations, algorithmic security vision is +understood to involve a particular subset of algorithms: deep neural +networks.4 Such a machine learning-based vision +brings to the fore one key dimension of security practices: the question +of training, and the ability to “see.” Training has been assumed as part +of the discussion around the socialization of security professionals +(Amicelle et al., 2015; Bigo, 2002), and algorithmic systems (Fourcade +and Johns, 2020) but scant attention has been paid to how training +elaborates upon and incorporates specific sets of skills.

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Some authors have explored the way in which the “seeing” of security +agents is trained at the border, by building on the literatures on +“skilled vision” (Maguire et al., 2014) or “vision work” (Olwig et al., +2019). Maguire mobilized work in anthropology that locates human vision +as a “embodied, skilled, trained sense” (Grasseni, 2004: 41) that +informs standardized practices of local “communities of vision” (see +also Goodwin, 1994). Skilled vision is useful in that it draws attention +to the sociomaterial circumstances under which vision becomes a trained +perception (Grasseni, 2018: 2), and how it becomes uniform in +communities through visual apprenticeship. This literature examines the +production of “common sense” by taking training, exercise, peer +monitoring and other practices of visual apprenticeship as locus of +attention. Yet these works fail to capture the specificities of the type +of machine learning we encountered in our research. How then is visual +apprenticeship reconfigured under algorithmic security vision?

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In our conversations, the “training of the algorithms,” figures as a +key stake of algorithmic security vision. The participants in our +diagram interviews explained how deep learning algorithms are trained on +a multiplicity of visual data which provides the patterns a system +should discriminate on. In Diagram 2, Sergei Miliaev, head of the facial +recognition research team at VisionLabs in Rotterdam illustrated this +point.

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Miliaev distinguishes three sources for training images: web +scraping, “operational” data collected through its partners or clients, +and “synthetic” data. The first two options, Miliaev argues, have some +limitations. Under European data protection regulation it is very +difficult to obtain or be allowed to use data “from the wild” because it +is often illegal to collect data of real people in the places where the +algorithm will be used. Additionally, partners sometimes resist sharing +their operational footage outside of their own digital infrastructures. +Finally, when engineering a dataset, one cannot control what kind of +footage is encountered in “the wild.” This has led to the emergence of a +new phenomenon: training data generated in the lab.

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Synthetic training data is often collected by acting in front of a +camera. We see this in the case of intelligent video surveillance +(Intelligente Videoüberwachung) deployed in Mannheim +since 2018. Commenting on this case, chief of police +(Polizeidirektor) Dirk Herzbach explains that self-defense +trainers imitated 120 body positions to create the annotated data used +to train the behavior recognition technology. In another example, Gerwin +van der Lugt, developer of software that detects violent behavior, +stated that given the insufficiency of data available, they “rely on +some data synth techniques,” such as simulating violent acts in front a +green screen. Sometimes even the developers, computer scientists or +engineers themselves re-enact certain movements or scenes for training +their algorithms. In Diagram 3, two developers involved in the project +at seaside boulevard in Scheveningen give a striking example of how such +enactments of suspicious events require the upfront development of a +threat model that contains visual indicators that distinguish threat (a +positive detection) from non-threat (a negative detection). The acting +of the developers embeds these desirable and undesirable traits into the +computer model.

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Diagram 3. Two developers involved in a project at the seaside +boulevard in Scheveningen describe the use of computer vision to +distinguish the legal use balloons from their illegal use for inhaling +the nitrous oxide gas.

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Sometimes the meaning of “synthetic” or “fake” data is pushed. Sergei +Miliaev explains how in the context of highly sensitive facial +recognition algorithms, software companies use faces generated entirely +through artificial neural networks to train their algorithms. Miliaev +mentions Microsoft’s DigiFace-1M (Bae et al., 2023), a training dataset +containing one million algorithmically generated faces. Such synthetic +training sets complicate the borders between sensor-originated and other +types of images. In the use of artificially generated images, one GPU +generates bytes that are interpreted by another. Algorithmic vision +occurs without direct reference to people or things that live outside +electronic circuits.

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These technical developments offer a changing figure of the skilled +vision of security that calls for new research directions. While these +technologies are still in their infancy, our interviewees see it as a +token of “better” and “fairer” technology that can circumvent racial +bias, as any minority can be generated to form an equal distribution in +the training dataset (Stevens and Keyes, 2021). But with an emerging +concern for algorithmic hallucination (Ji et al., 2023), glitches or +undesirable artifacts in the generated data, one wonders what kind of +vision is trained using such collections. Learning from synthetic data +thus produces an internalized vision, providing insights by circulating +data through a chain of artificial neural networks. While appearing in +new technological assemblages, the processing of images to form +archetypes is reminiscent of the composite photographs created by Galton +(1879). His composites were used to train police officers to identify +people as belonging to a particular group, circulating and reinforcing +the group boundaries based on appearance (Hopman and M’charek, 2020). +Which boundaries does “fake” or synthetized training data perpetuate? +Skilled vision shifts attention to the negotiations that happen before +algorithmic vision is trained, such as how algorithmic vision depends on +access to data and regulations around data protection.

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With the use of synthetic data, the question of a "community of +lookers" — the embodied social and material practices through which +apprenticeship is perpetuated — appears in a new light. Such a community +becomes more dispersed as generative models circulate freely online. For +instance, a generative model from Microsoft, trained on images shared +online, is used for training an authentication system in the Moscow +Metro system. Such models are informed by communities of looking from +which their training data is sourced, and the norms of that platform. +These norms then circulate with the model and become "plugged-in" to +other systems. Algorithmic vision, trained on synthetic data, is thus a +composable vision, in which different sources of training data mobilize +imagery from all kinds of aesthetic apprenticeships. The cascading of +generative and discriminative models thus reshapes security practices. +Furthermore, to comprehend changes in the politics of vision, attention +to the training of vision, as a moment of standardization and +operationalization, could be extended to the training of security +professionals.

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2. Figuring time: from +photographic to cinematic vision

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Conversations with practitioners revealed yet another dimension of +the figure of vision in flux: its relation to time and movement. Deep +learning-based technologies distinguish themselves from earlier +algorithmic security systems based on their status as prediction models, +which by definition raises questions on the temporal dimensions of their +processing (Sudmann, 2021). Yet, how algorithmic security vision +reconfigures temporalities has yet to receive scholarly attention in CSS +and related disciplines. While literature on border studies has located +border security in multiple places and temporalities (e.g. Bigo and +Guild, 2005), scholarship on image-based algorithmic security practices +have often focused on a photography-centric paradigm: biometric images +(Pugliese, 2010) facial, iris and fingerprint recognition (Møhl, 2021), +and body scanners (Leese, 2015). These technologies capture immutable +features of suspect identities. In the diagrams, however, vision appears +less static. Instead, two central dimensions of the figure of vision +appear: the ability to capture and make sense of the movement of the +bodies in a fixed space, and the movement of bodies across spaces.

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On the first point, we notice increasing attention to corporeality, +how physical movements render certain individuals suspicious. This +process takes place through the production and analysis of motion by +composing a sequence of frames. Gerwin van der Lugt, who helped develop +a violence detection algorithm at Oddity.ai, stresses how “temporal +information integration” is the biggest technical challenge in detecting +violence in surveillance footage: a raised hand might be either a punch +or a high-five. In Diagram 4, van der Lugt visualizes the differences +between the static and dynamic models. A first layer of pose or object +detection often analyzes a merely static image. Oddity.ai then uses +custom algorithms to integrate individual detections into one that +tracks movement. It is then the movement that can be assessed as violent +or harmless. From these outputs, Oddity.ai runs “another [...] process +that [they] call temporal information integration—it’s quite +important—to [...] find patterns that are [even] longer.” This case +illustrates how algorithmic security vision temporarily attributes risk +to bodies, in accordance with the ways violence is imagined and +choreographed in the training data.

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Diagram 4. Top: Frame 1 is processed by YOLO, an object detection +model, producing Output 1 (O1). Other frames are processed +independently. Bottom: Frames 1 to 10 are combined for processing by the +customized model (“M”), where it produces outputs (O 1-10, O 11-20). +These outputs are then processed in relation to one another by the +temporal information integration to find body patterns over longer +periods. Drawn by Gerwin van der Lugt.

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Our interviewees figured movement in a second way. Bodies are tracked +in space, leading to an accumulation of suspicious data over time. Ádám +Remport explained how facial recognition technology (FRT) works by +drawing a geographical map featuring building blocks and streets. In +this map (Diagram 5), Person A could visit a bar, a church, or an +NGO.

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Diagram 5. Ádám Remport explains how a person’s everyday routes can +be inferred when facial recognition technology is deployed in various +sites, montaging a local, photographic vision into spatio-temporal, +cinematic terms.

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If “FRT is fully deployed and constantly functioning,” explains +Remport, people can be “followed wherever [they] go.” Remport’s drawing +therefore suggests that in this setting it is not important to be able +to identify the person under surveillance; what matters is that this +person can be tracked over different surveillance camera feeds. The +trajectories of bodies and their “signature” marked through the +reconstruction of their habitual movements through space are used as a +benchmark for the construction of suspicion. Cinematic vision is thus +made possible thanks to the broader infrastructure that allows for the +collection and analysis of data over longer periods of time, and their +summarization through montage.

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The emerging centrality of movement thus opens up a new research +agenda for security, focused not on who and what features are +considered risky, but when, and through which movements +specific bodies become suspicious. While earlier studies on biometric +technologies have located the operational logic on identification, +verification, authentication, thus knowing the individual +(Ajana, 2013; Muller, 2010), figuring algorithmic security vision as +cinematic locates its operational logic in the mobility of embodied life +(see Huysmans, 2022). While many legal and political debates revolve +around the storage of images as individual frames, and the privacy +issues involved, less is known about the consequences of putting these +frames into a sequence on a timeline and the movements that emerge +through the integration of frames over time.

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3. Managing error: from the sublime to the risky algorithm

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Our third emerging figuration concerns the place of the error. A +large body of literature examines actual and speculative cases of +algorithmic prediction based on self-learning systems (Azar et al., +2021). Central to these analyses is the boundary-drawing performed by +such algorithmic devices, enacting (in)security by rendering their +subjects as more- or less-risky others (Amicelle et al., 2015: 300; +Amoore and De Goede, 2005; Aradau et al., 2008; Aradau and Blanke, 2018) +based on a spectrum of individual and environmental features (Calhoun, +2023). In other words, these predictive devices conceptualize risk as +something produced by, and thus external to, security technologies.

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In this critical literature on algorithmic practices, practitioners +working with algorithmic technologies are often critiqued for +understanding software as “sublime” (e.g. Wilcox, 2017: 3). However, in +our diagrams, algorithmic vision appears as a practice of managing +error. The practitioners we interviewed are aware of the error-prone +nature of their systems but know it will never be perfect, and see it as +a key metric that needs to be acted upon.

+
+
+

The most prominent way in which error figures in the diagrams is in +its quantified form of the true positive and false positive rates, TPR +and FPR. The significance and definition of these metrics is stressed by +CTO Gerwin van der Lugt (Diagram 6). In camera surveillance, the false +positive rate could be described as the number of fales positive +classifications relative to the number of video frames being analyzed. +Upon writing down these definitions, van der Lugt corrected his initial +definitions, as these definitions determine the work of his development +team, the ways in which his clients — security operators — engage with +the technology, and whether they perceive the output of the system as +trustworthy.

+
+
+

+
+
+

Diagram 6. Gerwin van der Lugt corrects his initial definitions of +the true positive and false positive rates, and stresses the importance +of their precise definition.

+
+
+

The figuration of algorithmic security vision as inherently imprecise +affects the operationalization of security practices. Van der Lugt’s +example concerns whether the violence detection algorithm developed by +Oddity.ai should be trained to categorize friendly fighting +(stoeien) between friends as “violence” or not. In this +context, van der Lugt finds it important to differentiate what counts as +false positive in the algorithm’s evaluation metric from an error in the +algorithm’s operationalization of a security question.

+
+
+

He gives two reasons to do so. First, he anticipates that the +exclusion of stoeien from the category of violence would +negatively impact TPR. In the iterative development of self-learning +systems, the TPR and FPR, together with the true and false +negative rates must perform a balancing act. Van der Lugt +outlines that with their technology they aim for fewer than 100 false +positives per 100 million frames per week. The FPR becomes indicative of +the algorithm’s quality, as too many faulty predictions will desensitize +the human operator to system alerts.

+
+
+

This leads to van der Lugt’s second point: He fears that the +exclusion of stoeien from the violence category might cause +unexpected biases in the system. For example, instead of distinguishing +violence from stoeien based on people’s body movements, the +algorithm might make the distinction based on their age. For van der +Lugt, this would be an undesirable and hard to notice form of +discrimination. In developing algorithmic (in)security, error is figured +not merely as a mathematical concept but (as shown in Diagram 6) as a +notion that invites pre-emption — a mitigation of probable failure — for +which the developer is responsible. The algorithmic condition of +security vision is figured as the pre-emption of error.

+
+
+

+
+
+

Diagram 7. By drawing errors on a timeline, van Rest calls attention +to the pre-emptive nature of error in the development process of +computer vision technologies.

+
+
+

According to critical AI scholar Matteo Pasquinelli, “machine +learning is technically based on formulas for error correction” (2019: +2). Therefore, any critical engagement with such algorithmic processes +needs to go beyond citing errors, “for it is precisely through these +variations that the algorithm learns what to do” (Amoore, 2019: 164), +pushing us to reconsider any argument based on the inaccuracy of the +systems.

+
+
+

The example of stoeien suggests that it is not so much a +question if, or how much, these algorithms err, but how these errors are +anticipated and negotiated. Thus, taking error as a hallmark of machine +learning we can see how practices of (in)security become shaped by the +notion of mathematical error well beyond their development stages. Error +figures centrally in the development, acquisition and deployment of such +devices. As one respondent indicated, predictive devices are inherently +erroneous, but the quantification of their error makes them amenable to +"risk management.”

+
+
+

While much has been written about security technologies as a device +for risk management, little is known about how security +technologies are conceptualized as objects of risk management. +What happens then in this double relation of risk? The figure of the +error enters the diagrams as a mathematical concept, throughout the +conversations we see its figure permeate the discourse around +algorithmic security vision. By figuring algorithmic security vision +through the notion of error, risk is placed at the heart of the security +apparatus.

+
+
+
+
+

Con-figurations of algorithmic security vision: fragmenting +accountability and expertise

+
+

In the previous section we explored the changing figurations +of key dimensions of algorithmic security vision, in this section we +examine how these figurations configure. For Suchman, working +with configurations highlights “the histories and encounters through +which things are figured into meaningful existence, fixing them +through reiteration but also always engaged in ‘the perpetuity of coming +to be’ that characterizes the biographies of objects as well as +subjects” (Suchman, 2012: 50, emphasis ours) In other words, we are +interested in the practices and tensions that emerge as figurations +become embedded in material practices. We focus on two con-figurations +that emerged in the interviews: the delegation of accountability to +externally managed benchmarks, and the displacement of responsibility +through the reconfiguration of the human-in-the-loop.

+
+
+

Delegating accountability to benchmarks

+
+

The first configuration is related to the evaluation of the error +rate in the training of algorithmic vision systems: it involves +datasets, benchmark institutions, and the idea of fairness as equal +representation among different social groups. Literature on the ethical +and political effects of algorithmic vision has notoriously focused on +the distribution of errors, raising questions of ethnic and racial bias +(e.g. Buolamwini and Gebru, 2018). Our interviews reflect the concerns +of much of this literature as the pre-emption of error figured +repeatedly in relation to the uneven distribution of error across +minorities or groups. In Diagram 8, Ádám Remport draws how different +visual traits have often led to different error rates. While the general +error metric of an algorithmic system might seem "acceptable," it +actually privileges particular groups, which is invisible when only the +whole is considered. Jeroen van Rest distinguishes such errors from the +inherent algorithmic imprecision in deep machine learning models, as +systemic biases (Diagram 7), as they perpetuate inequalities in the +society in which the product is being developed.

+
+
+

To mitigate these concerns and manage their risk, many of our +interviewees who develop and implement these technologies, externalize +the reference against which the error is measured. They turn to a +benchmark run by the American National Institute of Standards and +Technology (NIST), which ranks facial recognition technologies by +different companies by their error metric across groups. John Riemen, +who is responsible for the use of forensic facial recognition technology +at the Center for Biometrics of the Dutch police, describes how their +choice for software is driven by a public tender that demands a "top-10" +score on the NIST benchmark. The mitigation of bias is thus outsourced +to an external, and in this case foreign, institution.

+
+
+

+
+
+

Diagram 8. Ádám Remport describes that facial recognition +technologies are often most accurate with white male adult faces, +reflecting the datasets they are trained with. The FPR is higher with +people with darker skin, children, or women, which may result in false +flagging and false arrests.

+
+
+

We see in this outsourcing of error metrics a form of delegation that +brings about a specific regime of (in)visibility. While a particular +kind of algorithmic bias is rendered central to the NIST benchmark, the +mobilization of this reference obfuscates questions on how that metric +was achieved. That is to say, questions about training data are +invisibilized, even though that data is a known site of contestation. +For example, the NIST benchmark datasets are known to include faces of +wounded people (Keyes, 2019). The Clearview company is known to use +images scraped illegally from social media, and IBM uses a dataset that +is likely in violation of European GDPR legislation (Bommasani et al., +2022: 154). Pasquinelli (2019) argued that machine learning models +ultimately act as data compressors: enfolding and operationalizing +imagery of which the terms of acquisition are invisibilized.

+
+
+

Attention to this invisibilization reveals a discrepancy between the +developers and the implementers of these technologies. On the one hand, +the developers we interviewed expressed concerns about how their +training data is constituted to gain a maximum false positive rate/true +positive rate (FPR/TPR) ratio, while showing concern for the legality of +the data they use to train their algorithms. On the other hand, +questions about the constitution of the dataset have been virtually +non-existent in our conversations with those who implement software that +relies on models trained with such data. Occasionally this knowledge was +considered part of the developers' intellectual property that had to be +kept a trade secret. A high score on the benchmark is enough to pass +questions of fairness, legitimizing the use of the algorithmic model. +Thus, while indirectly relying on the source data, it is no longer +deemed relevant in the consideration of an algorithm. This illustrates +well how the invisibilization of the “compressed” dataset, in +Pasquinelli’s terms, into a model, with the formalization of guiding +metrics into a benchmark, permits a bracketing of accountability. One +does not need to know how outcomes are produced, as long as the +benchmarks are in order.

+
+
+

The configuration of algorithmic vision’s bias across a complex +network of fragmented locations and actors, from the dataset, to the +algorithm, to the benchmark institution reveals the selective processes +of (in)visibilization. This opens up fruitful alleys for new empirical +research: What are the politics of the benchmark as a mechanism of +legitimization? How does the outsourcing of assessing the error +distribution impact attention to bias? How has the critique of bias been +institutionalized by the security industry, resulting in the +externalization of accountability, through dis-location and +fragmentation?

+
+
+
+

Reconfiguring the human-in-the-loop

+
+

A second central question linked to the delegation of accountability +is the configuration in which the security operator is located. The +effects of delegation and fragmentation in which the mitigation of +algorithmic errors is outsourced to an external party, becomes visible +in the ways in which the role of the security operator is configured in +relation to the institution they work for, the software’s assessment, +and the affected publics.

+
+
+

The public critique of algorithms has often construed the +human-in-the loop as one of the last lines of defense in the +resistance to automated systems, able to filter and correct erroneous +outcomes (Markoff, 2020). The literature in critical security studies +has however problematized the representation of the security operator in +algorithmic assemblages by discussing how the algorithmic predictions +appear on their screen (Aradau and Blanke, 2018), and how the embodied +decision making of the operator is entangled with the algorithmic +assemblage (Wilcox, 2017). Moreover, the operator is often left guessing +at the working of the device that provides them with information to make +their decision (Møhl, 2021).

+
+
+

What our participants’ diagrams emphasized is how a whole spectrum of +system designs emerges in response to similar questions, for example the +issue of algorithmic bias. A primary difference can be found in the +degree of understanding of the systems that is expected of security +operators, as well as their perceived autonomy. Sometimes, the human +operator is central to the system’s operation, forming the interface +between the algorithmic systems and surveillance practices. Gerwin van +der Lugt, developer of software at Oddity.ai that detects criminal +behavior argues that “the responsibility for how to deal with the +violent incidents is always [on a] human, not the algorithm. The +algorithm just detects violence—that’s it—but the human needs to deal +with it.”

+
+
+

Dirk Herzbach, chief of police at the Police Headquarters Mannheim, +adds that when alerted to an incident by the system, the operator +decides whether to deploy a police car. Both Herzbach and Van der Lugt +figure the human-in-the-loop as having full agency and responsibility in +operating the (in)security assemblage (cf. Hoijtink and Leese, +2019).

+
+
+

Some interviewees drew a diagram in which the operator is supposed to +be aware of the ways in which the technology errs, so they can address +them. Several other interviewees considered the technical expertise of +the human-in-the-loop to be unimportant, even a hindrance.

+
+
+

Chief of police Herzbach prefers an operator to have patrol +experience to assess which situations require intervention. He is +concerned that knowledge about algorithmic biases might interfere with +such decisions. In the case of the Moscow metro, in which a facial +recognition system has been deployed to purchase tickets and open access +gates, the human-in-the-loop is reconfigured as an end user who needs to +be shielded from the algorithm’s operation (c.f. Lorusso, 2021). On +these occasions, expertise on the technological creation of the suspect +becomes fragmented.

+
+
+

These different figurations of the security operator are held +together by the idea that the human operator is the expert of the +subject of security, and is expected to make decisions independent from +the information that the algorithmic system provides.

+
+
+

+
+
+

Diagram 9. Riemen explains the process of information filtering that +is involved in querying the facial recognition database of the Dutch +police.

+
+
+

Other drivers exist, however, to shield the operator from the +algorithm’s functioning, challenging individual expertise and +acknowledging the fallibility of human decision making. In Diagram 9, +John Riemen outlines the use of facial recognition by the Dutch police. +He describes how data from the police case and on the algorithmic +assessment is filtered out as much as possible from the information +provided to the operator. This, Riemen suggests, might reduce bias in +the final decision. He adds that there should be no fewer than three +humans-in-the-loop who operate independently to increase the accuracy of +the algorithmic security vision.

+
+
+

Instead of increasing their number, there is another configuration of +the human-in-the-loop that responds to the fallibility of the operator. +For the Burglary-Free Neighborhood project in Rotterdam, project manager +Guido Delver draws surveillance as operated by neighborhood residents, +through a system that they own themselves. By involving different +stakeholders, Delver hopes to counter government hegemony over the +surveillance apparatus. However, residents are untrained in assessing +algorithmic predictions raising new challenges. Delver illustrates a +scenario in which the algorithmic signaling of a potential burglary may +have dangerous consequences: “Does it invoke the wrong behavior from the +citizen? [They could] go out with a bat and look for the guy who has +done nothing [because] it was a false positive.” In this case, the worry +is that the erroneous predictions will not be questioned. Therefore, in +Delver’s project the goal was to actualize an autonomous system, “with +as little interference as possible.” Human participation or +“interference” in the operation are potentially harmful. Thus, figuring +the operator — whether police officer or neighborhood resident — as +risky, can lead to the relegation of direct human intervention.

+
+
+

By looking at the figurations of the operator that appear in the +diagrams we see multiple and heterogeneous configurations of +regulations, security companies, and professionals. In each +configuration, the human-in-the-loop appears in different forms. The +operator often holds the final responsibility in the ethical functioning +of the system. At times they are configured as an expert in +sophisticated but error-prone systems; at others they are figured as end +users who are activated by the alerts generated by the system, and who +need not understand how the software works and errs, or who can be left +out.

+
+
+

These configurations remind us that there cannot be any theorization +of “algorithmic security vision,” both of its empirical workings and its +ethical and political consequences without close attention to the +empirical contexts in which the configurations are arranged. Each +organization of datasets, algorithms, benchmarks, hardware and operators +has specific problems. And each contains specific politics of +visibilization, invisibilization, responsibility and accountability.

+
+
+
+
+

A diagram of research

+
+

In this conclusion, we reflect upon a final dimension of the method +of diagraming in the context of figurations and configurations: its +potential as an alternative to the conventional research program.

+
+
+

While writing this text, indeed, the search for a coherent structure +through which we could map the problems that emerged from analyzing the +diagrams in a straightforward narrative proved elusive. We considered +various organizational frameworks, but consistently encountered +resistance from one or two sections. It became evident that our +interviews yielded a rhizome of interrelated problems, creating a +multitude of possible inquiries and overlapping trajectories. Some +dimensions of these problems are related, but not to every problem.

+
+
+

If we take for example the understanding of algorithmic security +vision as practices of error management as a starting point, we see how +the actors we interviewed have incorporated the societal critique of +algorithmic bias. This serves as a catalyst for novel strategies aimed +at mitigating the repercussions of imperfect systems. The societal +critique has driven the development of synthetic datasets, which promise +equitable representation across diverse demographic groups. It has also +been the reason for the reliance on institutionalized benchmarks to +assess the impartiality of algorithms. Moreover, different +configurations of the human-in-the-loop emerge, all promised to rectify +algorithmic fallibility. We see a causal chain there.

+
+
+

But how does the question of algorithmic error relate to the shift +from photographic to cinematic vision that algorithmic security vision +brings about? Certainly, there are reverberations. The relegation of +stable identity that we outlined, could be seen as a way to mitigate the +impact of those errors. But it would be a leap to identify these +questions of error as the central driver for the increased incorporation +of moving images in algorithmic security vision.

+
+
+

However, if we take as our starting point the formidable strides in +computing power and the advancements in camera technologies, we face +similar problems. These developments make the analysis of movement +possible while helping to elucidate the advances in the real-time +analysis that are required to remove the human-in-the-loop, as trialed +in the Burglary-Free Neighborhood. These developments account for the +feasibility of the synthetic data generation, a computing-intense +process which opens a vast horizon of possibilities for developers to +detect objects or actions. Such an account, however, does not address +the need for such a synthetic dataset. A focus on the computation of +movement, however, would highlight how a lack of training data +necessitates many of the practices described. Synthetic data is +necessitated by the glaring absence of pre-existing security datasets +that contain moving bodies. While facial recognition algorithms could be +trained and operated on quickly repurposed photographic datasets of +national identity cards or drivers’ license registries, no dataset for +moving bodies has been available to be repurposed by states or +corporations. This absence of training data requires programmers to +stage scenes for the camera. Thus, while one issue contains echoes of +the other, the network of interrelated problematizations cannot be +flattened into a single narrative.

+
+
+

The constraints imposed by the linear structure of an academic +article certainly necessitate a specific ordering of sections. Yet the +different research directions we highlight form something else. The +multiple figurations analyzed here generate fresh tensions when put in +relation with security and political practices. What appears from the +diagrams is a network of figurations in various configurations. Instead +of a research program, our interviews point toward a larger +research diagram of interrelated questions, which invites us to +think in terms of pathways through this dynamic and evolving network of +relations.

+
+
+
+

Interviewees

+
    +
  • +

    Gerwin van der Lugt develops software that detects “high-impact +crimes” in camera streams.

    +
    • +
  • +

    András Lukács is a senior researcher and coordinator of the AI Lab at +the Department of Mathematics of the Eötvös Loránd University in +Budapest, and an AI adviser for the Hungarian Ministry of Technology and +Innovation.

    +
    • +
  • +

    Guido Delver is an engineer and coordinator of the Rotterdam-based +Burglary-Free Neighborhood that builds autonomous systems into street +lamps to reinforce public security.

    +
    • +
  • +

    Attila Batorfy is a journalist and data visualization expert who +teaches journalism, media studies and information graphics at the Media +Department of Eötvös Loránd University.

    +
    • +
  • +

    Peter Smith is a senior security expert working for a European +organization employing border technologies.

    +
    • +
  • +

    Adam Remport is a Hungarian legal expert and activist working on +state actors’ use of biometric technologies.

    +
    • +
  • +

    John Riemen is head of the Center for Biometricts for the Dutch +police.

    +
    • +
  • +

    Jeroen van Rest is a safety expert and senior consultant in +risk-based security at TNO, the Netherlands.

    +
    • +
  • +

    Two anonymous respondents involved in the Living Lab Scheveningen, in +the Hague.

    +
    • +
  • +

    Sergei Miliaev is principal researcher and facial recognition team +lead at VisionLabs in Amsterdam.

    +
    • +
  • +

    Dirk Herzbach is chief of police of the Polizeipräsidium +Mannheim.

    +
    • +
+
+
+

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  1. +↩︎
    2. +
  2. +

    The interviews +were conducted in several European countries: the majority in the +Netherlands, but also in Belgium, Hungary and Poland. Based on an +initial survey of algorithmic security vision practices in Europe we +identified various roles that are involved in such practices. Being a +rather small group of people, these interviewees do not serve as +“illustrative representatives” (Mol & Law 2002, 16-17) of the field +they work in. However, as the interviewees have different cultural and +institutional affiliations, and hold different positions in working with +algorithms, vision and security, they cover a wide spectrum of +engagements with our research object.

    +
    +↩︎
    3. +
  3. +

    The interviews +were conducted by the first two authors, and at a later stage by Clemens +Baier. The conversations were largely unstructured, but began with two +basic questions. First, we asked the interviewees if they use diagrams +in their daily practice. We then asked: “when we speak of ‘security +vision’ we speak of the use of computer vision in a security context. +Can you explain from your perspective what these concepts mean and how +they come together?” After the first few interviews, we identified some +recurrent themes, which we then specifically asked later interviewees to +discuss.

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    +↩︎
    4. +
  4. +

    Using +anthropomorphizing terms such as “neural networks,” “learning” and +“training” to denote algorithmic configurations and processes is +suggested to hype “artificial intelligence.” While we support the need +for an alternative terminology as proposed by Hunger (2023), here we +preserve the language of our interviewees.

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    5. +
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+ +