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| -rw-r--r-- | site/public/about/style/index.html | 11 | ||||
| -rw-r--r-- | site/public/datasets/lfw/index.html | 51 | ||||
| -rw-r--r-- | site/public/datasets/vgg_face2/index.html | 9 |
3 files changed, 22 insertions, 49 deletions
diff --git a/site/public/about/style/index.html b/site/public/about/style/index.html index f2c0d4b8..39a44380 100644 --- a/site/public/about/style/index.html +++ b/site/public/about/style/index.html @@ -51,16 +51,7 @@ <div class='image'><img src='https://nyc3.digitaloceanspaces.com/megapixels/v1/site/about/assets/man.jpg' alt='Person 3. Let me tell you about Person 3. This person has a very long description with text which wraps like crazy'><div class='caption'>Person 3. Let me tell you about Person 3. This person has a very long description with text which wraps like crazy</div></div></section><section><blockquote><p>est, qui dolorem ipsum, quia dolor sit amet consectetur adipisci[ng] velit, sed quia non-numquam [do] eius modi tempora inci[di]dunt, ut labore et dolore magnam aliquam quaerat voluptatem.</p> </blockquote> </section><section class='wide'><div class='image'><img src='https://nyc3.digitaloceanspaces.com/megapixels/v1/site/about/assets/wide-test.jpg' alt='This image is extremely wide and the text beneath it will wrap but thats fine because it can also contain <a href="https://example.com/">hyperlinks</a>! Yes, you read that right—hyperlinks! Lorem ipsum dolor sit amet ad volotesque sic hoc ad nauseam'><div class='caption'>This image is extremely wide and the text beneath it will wrap but that's fine because it can also contain <a href="https://example.com/">hyperlinks</a>! Yes, you read that right—hyperlinks! Lorem ipsum dolor sit amet ad volotesque sic hoc ad nauseam</div></div></section><section><p>Inline <code>code</code> has <code>back-ticks around</code> it.</p> -<pre><code class="lang-javascript">var s = "JavaScript syntax highlighting"; -alert(s); -</code></pre> -<pre><code class="lang-python">s = "Python syntax highlighting" -print(s) -</code></pre> -<pre><code>No language indicated, so no syntax highlighting. -But let's throw in a <b>tag</b>. -</code></pre> -<p>Horizontal rule</p> +</section><section><div class='applet' data-payload='{"command": "javascript"}'></div></section><section><div class='applet' data-payload='{"command": "python"}'></div></section><section><div class='applet' data-payload='{"command": "No language indicated, so no syntax highlighting. "}'></div></section><section><p>Horizontal rule</p> <hr> <p>Citations below here</p> <div class="footnotes"> 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('{:,} images at {}x{}'.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('lfw_montage_full.png', montage) - -# make a smaller version -montage_960 = imutils.resize(montage, width=960) -imageio.imwrite('lfw_montage_960.jpg', 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> |