From a15195237de366d62131c3445143f90d46f4a788 Mon Sep 17 00:00:00 2001 From: Ruben van de Ven Date: Tue, 19 Dec 2023 08:35:00 +0100 Subject: [PATCH] drawing intermediate lines and playback icon --- canvas.html | 671 ++++++++++++++++++++++++++++++++++++++++++++++++++-- 1 file changed, 657 insertions(+), 14 deletions(-) diff --git a/canvas.html b/canvas.html index 71227ad..34a671b 100644 --- a/canvas.html +++ b/canvas.html @@ -44,7 +44,7 @@ margin: 0.5rem auto; } - .anchor{ + .anchor { cursor: pointer; } @@ -80,10 +80,45 @@ top: 0; } - div[data-custom-style='Body Text'] p{ + + div[data-custom-style='Body Text'] p { padding: 1em 0; margin: 0; - background-color: rgba(255,255,255, 0.8); + background-color: rgba(255, 255, 255, 0.8); + } + + .anchor{ + position: relative; + } + + .anchor.active:not(.playing)::before{ + content:'⏵'; + position: absolute; + width: 40px; + height: 40px; + background:gray; + left: calc(50% - 20px); + top: calc(50% - 20px); + vertical-align: middle; + line-height: 35px; + border-radius: 5px; + color:white; + } + .anchor.active:not(.playing):hover::before{ + background:black + } + .anchor.playing:hover::before{ + content:'⏸︎'; + position: absolute; + width: 40px; + height: 40px; + background:black; + left: calc(50% - 20px); + top: calc(50% - 20px); + vertical-align: middle; + line-height: 35px; + border-radius: 5px; + color:white; } @@ -116,13 +151,21 @@ } div.controls{display:none !important;}` ); - console.log(sheet, sheet.rules) function easeInOutQuart(x) { return x < 0.5 ? 8 * x * x * x * x : 1 - Math.pow(-2 * x + 2, 4) / 2; } + function easeInOutBack(x) { + const c1 = 1.70158; + const c2 = c1 * 1.525; + + return x < 0.5 + ? (Math.pow(2 * x, 2) * ((c2 + 1) * 2 * x - c2)) / 2 + : (Math.pow(2 * x - 2, 2) * ((c2 + 1) * (x * 2 - 2) + c2) + 2) / 2; + + } let scroll_offsets = [] @@ -154,10 +197,11 @@ paths.forEach((el) => el.style.strokeDasharray = Math.ceil(el.getTotalLength()) + 'px'); const lastAnchorEl = anchorEls[anchorEls.length - 1]; - console.log(lastAnchorEl) for (const anchorEl of anchorEls) { anchorEl.addEventListener('click', ev => playerEls[anchorEl.dataset.i].annotator.playPause()); + playerEls[anchorEl.dataset.i].annotator.addEventListener('play', ev => anchorEl.classList.add('playing')); + playerEls[anchorEl.dataset.i].annotator.addEventListener('pause', ev => anchorEl.classList.remove('playing')); } for (const player of playerEls) { player.shadowRoot.adoptedStyleSheets = [sheet]; @@ -182,7 +226,7 @@ let source_pos, target_pos, source_scale, target_scale, source_color, target_color, source_x_offset, target_x_offset; - const x_column_width = window.innerWidth - document.body.getBoundingClientRect().width+200; // for some reason the 200 is neccesary + const x_column_width = window.innerWidth - document.body.getBoundingClientRect().width + 200; // for some reason the 200 is neccesary const x_center_map = x_column_width / 2; const x_center_column = document.body.getBoundingClientRect().left + document.body.getBoundingClientRect().width / 2; @@ -231,6 +275,7 @@ const t = Math.min(1, Math.max(0, (center_y - prev[0]) / (next[0] - prev[0]))) t_ease = easeInOutQuart(t) + // t_ease = easeInOutBack(t) const dx = target_pos[0] - source_pos[0]; const dy = target_pos[1] - source_pos[1]; @@ -246,7 +291,6 @@ y = -1 * (source_pos[1] + dy * t_ease) * scale + window.innerHeight / 2; const color = (target_color - source_color) * t_ease + source_color - console.log(sheet) // sheet.rules[0].style.setProperty('--override-color', `rgba(${color},${color},${color},0.7)`); sheet.rules[0].style.setProperty('--disactive-path', `rgba(${color},${color},${color},0.7)`); @@ -259,11 +303,11 @@ else { // no paths paths.forEach((el, idx) => { - if (idx >= step_idx ) { + if (idx >= step_idx) { el.style.strokeDashoffset = Math.ceil(el.getTotalLength()) + 'px'; - } else if (idx == step_idx-1 ) { + } else if (idx == step_idx - 1) { // console.log('anim', el) - el.style.strokeDashoffset = Math.ceil(el.getTotalLength() - el.getTotalLength()*t_ease) + 'px'; + el.style.strokeDashoffset = Math.ceil(el.getTotalLength() - el.getTotalLength() * t_ease) + 'px'; } else { el.style.strokeDashoffset = 0; } @@ -282,7 +326,6 @@ // calculate whether we're nearing the conlusion, and color accordingly const last = Math.max(...Array.from(anchorEls).map((e) => e.getBoundingClientRect().bottom)) - console.log(last) if (last < 0) { for (const playerEl of playerEls) { playerEl.classList.add('ending') @@ -307,7 +350,7 @@ let options = { // root: document.querySelector("#scrollArea"), // viewport by default - rootMargin: `${-Math.round(innerHeight / 3)}px`, //"0px", + rootMargin: `${-Math.floor((window.innerHeight-10) / 2)}px 0px`, //"0px", threshold: 0, }; @@ -320,10 +363,12 @@ } playerEl = windowEl.children[index]; if (entry.isIntersecting) { + entry.target.classList.add('active'); playerEl.classList.add('active') } else { + entry.target.classList.remove('active'); playerEl.classList.remove('active') - if(typeof playerEl.annotator.paused !== 'undefined' && !playerEl.annotator.paused){ + if (typeof playerEl.annotator.paused !== 'undefined' && !playerEl.annotator.paused) { console.log('pause', playerEl.annotator, playerEl.annotator.paused) playerEl.annotator.pause() } @@ -335,7 +380,7 @@ for (const anchorEl of anchorEls) { observer.observe(anchorEl) } - console.log(anchorEls) + // console.log(anchorEls) // .forEach(el => observer.observe()); @@ -470,6 +515,8 @@
+

.... this is a demo to showcase how the chronodiagramming looks like in its interactive form. Please note + that this demo of the interface is not compatible with mobile devices ...

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

@@ -835,6 +882,602 @@ 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.

+
+
+
+

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    +
  1. +
    +

    The interface + software and code is available at https://git.rubenvandeven.com/security_vision/svganim + and https://gitlab.com/security-vision/chronodiagram +

    +
    + ↩︎ +
    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.

    +
    + ↩︎ +
    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.

    +
    + ↩︎ +
    5. +
+
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