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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"> |