From 898e6cdf8df0993f853b748d4e8a9c269fad0294 Mon Sep 17 00:00:00 2001
From: Jules Laplace <julescarbon@gmail.com>
Date: Sat, 15 Dec 2018 22:14:17 +0100
Subject: inject applet payload

---
 site/public/datasets/lfw/index.html       | 51 ++++++++++++-------------------
 site/public/datasets/vgg_face2/index.html |  9 ++----
 2 files changed, 21 insertions(+), 39 deletions(-)

(limited to 'site/public/datasets')

diff --git a/site/public/datasets/lfw/index.html b/site/public/datasets/lfw/index.html
index e080229f..3c83acd3 100644
--- a/site/public/datasets/lfw/index.html
+++ b/site/public/datasets/lfw/index.html
@@ -28,12 +28,7 @@
     
   <section><h1>Labeled Faces in the Wild</h1>
 </section><section><div class='meta'><div><div class='gray'>Created</div><div>2007</div></div><div><div class='gray'>Images</div><div>13,233</div></div><div><div class='gray'>People</div><div>5,749</div></div><div><div class='gray'>Created From</div><div>Yahoo News images</div></div><div><div class='gray'>Search available</div><div>Searchable</div></div></div></section><section><p>Labeled Faces in The Wild (LFW) is amongst the most widely used facial recognition training datasets in the world and is the first of its kind to be created entirely from images posted online. The LFW dataset includes 13,233 images of 5,749 people that were collected between 2002-2004. Use the tools below to check if you were included in this dataset or scroll down to read the analysis.</p>
-<p>{INSERT IMAGE SEARCH MODULE}</p>
-<p>{INSERT TEXT SEARCH MODULE}</p>
-<pre><code>load file: lfw_names_gender_kg_min.csv
-Name, Images, Gender, Description
-</code></pre>
-</section><section class='images'><div class='image'><img src='https://nyc3.digitaloceanspaces.com/megapixels/v1/datasets/lfw/assets/lfw_feature.jpg' alt='Eighteen of the 5,749 people in the Labeled Faces in the Wild Dataset. The most widely used face dataset for benchmarking commercial face recognition algorithms.'><div class='caption'>Eighteen of the 5,749 people in the Labeled Faces in the Wild Dataset. The most widely used face dataset for benchmarking commercial face recognition algorithms.</div></div></section><section><h2>Intro</h2>
+</section><section><div class='applet' data-payload='{"command": "face_search"}'></div></section><section><div class='applet' data-payload='{"command": "name_search"}'></div></section><section><div class='applet' data-payload='{"command": "load file", "opt": "lfw_names_gender_kg_min.csv", "fields": "Name, Images, Gender, Description"}'></div></section><section class='images'><div class='image'><img src='https://nyc3.digitaloceanspaces.com/megapixels/v1/datasets/lfw/assets/lfw_feature.jpg' alt='Eighteen of the 5,749 people in the Labeled Faces in the Wild Dataset. The most widely used face dataset for benchmarking commercial face recognition algorithms.'><div class='caption'>Eighteen of the 5,749 people in the Labeled Faces in the Wild Dataset. The most widely used face dataset for benchmarking commercial face recognition algorithms.</div></div></section><section><h2>Intro</h2>
 <p>Three paragraphs describing the LFW dataset in a format that can be easily replicated for the other datasets. Nothing too custom. An analysis of the initial research papers with context relative to all the other dataset papers.</p>
 </section><section class='images'><div class='image'><img src='https://nyc3.digitaloceanspaces.com/megapixels/v1/datasets/lfw/assets/lfw_montage_everyone_1920.jpg' alt=' all 5,749 people in the LFW Dataset sorted from most to least images collected.'><div class='caption'> all 5,749 people in the LFW Dataset sorted from most to least images collected.</div></div></section><section><h2>LFW by the Numbers</h2>
 <ul>
@@ -224,36 +219,28 @@ name_display,company_url,example_url,country,description
 </tbody>
 </table>
 <h2>Code</h2>
-<pre><code class="lang-python">#!/usr/bin/python
-
-import numpy as np
+</section><section><div class='applet' data-payload='{"command": "python"}'></div></section><section><p>import numpy as np
 from sklearn.datasets import fetch_lfw_people
 import imageio
-import imutils
-
-# download LFW dataset (first run takes a while)
-lfw_people = fetch_lfw_people(min_faces_per_person=1, resize=1, color=True, funneled=False)
-
-# introspect dataset
-n_samples, h, w, c = lfw_people.images.shape
-print(&#39;{:,} images at {}x{}&#39;.format(n_samples, w, h))
+import imutils</p>
+<h1>download LFW dataset (first run takes a while)</h1>
+<p>lfw_people = fetch_lfw_people(min_faces_per_person=1, resize=1, color=True, funneled=False)</p>
+<h1>introspect dataset</h1>
+<p>n_samples, h, w, c = lfw_people.images.shape
+print('{:,} images at {}x{}'.format(n_samples, w, h))
 cols, rows = (176, 76)
-n_ims = cols * rows
-
-# build montages
-im_scale = 0.5
+n_ims = cols * rows</p>
+<h1>build montages</h1>
+<p>im_scale = 0.5
 ims = lfw_people.images[:n_ims
-montages = imutils.build_montages(ims, (int(w*im_scale, int(h*im_scale)), (cols, rows))
-montage = montages[0]
-
-# save full montage image
-imageio.imwrite(&#39;lfw_montage_full.png&#39;, montage)
-
-# make a smaller version
-montage_960 = imutils.resize(montage, width=960)
-imageio.imwrite(&#39;lfw_montage_960.jpg&#39;, montage_960)
-</code></pre>
-<h2>Disclaimer</h2>
+montages = imutils.build_montages(ims, (int(w<em>im_scale, int(h</em>im_scale)), (cols, rows))
+montage = montages[0]</p>
+<h1>save full montage image</h1>
+<p>imageio.imwrite('lfw_montage_full.png', montage)</p>
+<h1>make a smaller version</h1>
+<p>montage_960 = imutils.resize(montage, width=960)
+imageio.imwrite('lfw_montage_960.jpg', montage_960)</p>
+</section><section><div class='applet' data-payload='{"command": ""}'></div></section><section><h2>Disclaimer</h2>
 <p>MegaPixels is an educational art project designed to encourage discourse about facial recognition datasets. Any ethical or legal issues should be directed to the researcher's parent organizations. Except where necessary for contact or clarity, the names of researchers have been subsituted by their parent organization. In no way does this project aim to villify researchers who produced the datasets.</p>
 <p>Read more about <a href="about/code-of-conduct">MegaPixels Code of Conduct</a></p>
 <div class="footnotes">
diff --git a/site/public/datasets/vgg_face2/index.html b/site/public/datasets/vgg_face2/index.html
index 24a1059b..817fc9a0 100644
--- a/site/public/datasets/vgg_face2/index.html
+++ b/site/public/datasets/vgg_face2/index.html
@@ -28,12 +28,7 @@
     
   <section><h1>VGG Faces2</h1>
 </section><section><div class='meta'><div><div class='gray'>Created</div><div>2018</div></div><div><div class='gray'>Images</div><div>3.3M</div></div><div><div class='gray'>People</div><div>9,000</div></div><div><div class='gray'>Created From</div><div>Scraping search engines</div></div><div><div class='gray'>Search available</div><div>[Searchable](#)</div></div></div></section><section><p>VGG Face2 is the updated version of the VGG Face dataset and now includes over 3.3M face images from over 9K people. The identities were selected by taking the top 500K identities in Google's Knowledge Graph of celebrities and then selecting only the names that yielded enough training images. The dataset was created in the UK but funded by Office of Director of National Intelligence in the United States.</p>
-<p>{INSERT IMAGE SEARCH MODULE}</p>
-<p>{INSERT TEXT SEARCH MODULE}</p>
-<pre><code>load file: lfw_names_gender_kg_min.csv
-Name, Images, Gender, Description
-</code></pre>
-<h2>VGG Face2 by the Numbers</h2>
+</section><section><div class='applet' data-payload='{"command": "face_search"}'></div></section><section><div class='applet' data-payload='{"command": "name_search"}'></div></section><section><div class='applet' data-payload='{"command": "load file", "opt": "lfw_names_gender_kg_min.csv", "fields": "Name, Images, Gender, Description"}'></div></section><section><h2>VGG Face2 by the Numbers</h2>
 <ul>
 <li>1,331 actresses, 139 presidents</li>
 <li>3 husbands and 16 wives</li>
@@ -47,7 +42,7 @@ Name, Images, Gender, Description
 <li>The original VGGF2 name list has been updated with the results returned from Google Knowledge</li>
 <li>Names with a similarity score greater than 0.75 where automatically updated. Scores computed using <code>import difflib; seq = difflib.SequenceMatcher(a=a.lower(), b=b.lower()); score = seq.ratio()</code></li>
 <li>The 97 names with a score of 0.75 or lower were manually reviewed and includes name changes validating using Wikipedia.org results for names such as "Bruce Jenner" to "Caitlyn Jenner", spousal last-name changes, and discretionary changes to improve search results such as combining nicknames with full name when appropriate, for example changing "Aleksandar Petrović" to "Aleksandar 'Aco' Petrović" and minor changes such as "Mohammad Ali" to "Muhammad Ali"</li>
-<li>The 'Description` text was automatically added when the Knowledge Graph score was greater than 250</li>
+<li>The 'Description' text was automatically added when the Knowledge Graph score was greater than 250</li>
 </ul>
 <h1>TODO</h1>
 <ul>
-- 
cgit v1.2.3-70-g09d2


From f91c6d8e870bd3b2c69a130e4b1ed8f6be250b92 Mon Sep 17 00:00:00 2001
From: Jules Laplace <julescarbon@gmail.com>
Date: Sat, 15 Dec 2018 22:14:29 +0100
Subject: rm

---
 site/public/datasets/vgg_faces2/index.html | 63 ------------------------------
 1 file changed, 63 deletions(-)
 delete mode 100644 site/public/datasets/vgg_faces2/index.html

(limited to 'site/public/datasets')

diff --git a/site/public/datasets/vgg_faces2/index.html b/site/public/datasets/vgg_faces2/index.html
deleted file mode 100644
index 3f778f71..00000000
--- a/site/public/datasets/vgg_faces2/index.html
+++ /dev/null
@@ -1,63 +0,0 @@
-<!doctype html>
-<html>
-<head>
-  <title>MegaPixels</title>
-  <meta charset="utf-8" />
-  <meta name="author" content="Adam Harvey" />
-  <meta name="description" content="" />
-  <meta name="referrer" content="no-referrer" />
-  <meta name="viewport" content="width=device-width, initial-scale=1.0, user-scalable=yes" />
-  <link rel='stylesheet' href='/assets/css/fonts.css' />
-  <link rel='stylesheet' href='/assets/css/css.css' />
-</head>
-<body>
-  <header>
-    <a class='slogan' href="/">
-      <div class='logo'></div>
-      <div class='site_name'>MegaPixels</div>
-      <span class='sub'>The Darkside of Datasets</span>
-    </a>
-    <div class='links'>
-      <a href="/search/">Face Search</a>
-      <a href="/datasets/">Datasets</a>
-      <a href="/research/">Research</a>
-      <a href="/about/">About</a>
-    </div>
-  </header>
-  <div class="content">
-    
-  <section><h1>Labeled Faces in The Wild</h1>
-</section><section><div class='meta'><div><div class='gray'>Created</div><div>2007</div></div><div><div class='gray'>Images</div><div>13,233</div></div><div><div class='gray'>People</div><div>5,749</div></div><div><div class='gray'>Created From</div><div>Yahoo News images</div></div><div><div class='gray'>Search available</div><div>[Searchable](#)</div></div></div></section><section><p>Labeled Faces in The Wild is amongst the most widely used facial recognition training datasets in the world and is the first dataset of its kind to be created entirely from Internet photos. It includes 13,233 images of 5,749 people downloaded from the Internet, otherwise referred to by researchers as “The Wild”.</p>
-<h2>INTRO</h2>
-<p>It began in 2002. Researchers at University of Massachusetts Amherst were developing algorithms for facial recognition and they needed more data. Between 2002-2004 they scraped Yahoo News for images of public figures. Two years later they cleaned up the dataset and repackaged it as Labeled Faces in the Wild (LFW).</p>
-<p>Since then the LFW dataset has become one of the most widely used datasets used for evaluating face recognition algorithms. The associated research paper “Labeled Faces in the Wild: A Database for Studying Face Recognition in Unconstrained Environments” has been cited 996 times reaching 45 different countries throughout the world.</p>
-<p>The faces come from news stories and are mostly celebrities from the entertainment industry, politicians, and villains. It’s a sampling of current affairs and breaking news that has come to pass. The images, detached from their original context now server a new purpose: to train, evaluate, and improve facial recognition.</p>
-<p>As the most widely used facial recognition dataset, it can be said that each individual in LFW has, in a small way, contributed to the current state of the art in facial recognition surveillance. John Cusack, Julianne Moore, Barry Bonds, Osama bin Laden, and even Moby are amongst these biometric pillars, exemplar faces provided the visual dimensions of a new computer vision future.</p>
-<h2>Commercial Use</h2>
-<p>The dataset is used by numerous companies for benchmarking algorithms. According to the benchmarking results page <sup class="footnote-ref" id="fnref-lfw_results"><a href="#fn-lfw_results">1</a></sup> provided by the authors, there over 2 dozen commercial uses of the LFW face dataset.</p>
-<div class="footnotes">
-<hr>
-<ol><li id="fn-lfw_results"><p>"LFW Results". Accessed Dec 3, 2018. <a href="http://vis-www.cs.umass.edu/lfw/results.html">http://vis-www.cs.umass.edu/lfw/results.html</a><a href="#fnref-lfw_results" class="footnote">&#8617;</a></p></li>
-</ol>
-</div>
-</section>
-
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cgit v1.2.3-70-g09d2