From dc6a8b4ad5e29f8aea2de73646aeca6e819db0b0 Mon Sep 17 00:00:00 2001 From: Jules Laplace Date: Mon, 4 Mar 2019 16:18:54 +0100 Subject: rebuild --- client/map/index.js | 20 ++++++++------- megapixels/app/settings/app_cfg.py | 1 + megapixels/app/site/parser.py | 2 +- site/public/about/index.html | 2 +- site/public/about/terms/index.html | 2 +- site/public/datasets/lfw/index.html | 51 +++++++++++++++---------------------- 6 files changed, 36 insertions(+), 42 deletions(-) diff --git a/client/map/index.js b/client/map/index.js index 56a5abed..4b922ec5 100644 --- a/client/map/index.js +++ b/client/map/index.js @@ -76,17 +76,19 @@ export default function append(el, payload) { if (address) { source = [address.lat, address.lng].map(n => parseFloat(n)) + } else { + console.error("No address found for root paper") + console.log(data) } - // ....i dont think the sort order does anything?? - citations.sort((a, b) => sortOrder.indexOf(a) - sortOrder.indexOf(b)) - .forEach(citation => { - const citationAddress = citation.addresses[0] - const latlng = [citationAddress.lat, citationAddress.lng].map(n => parseFloat(n)) - if (Number.isNaN(latlng[0]) || Number.isNaN(latlng[1])) return - addMarker(map, latlng, citation.title, citationAddress.name) - addArc(map, source, latlng, arcStyles[citationAddress.type]) - }) + citations.forEach(citation => { + // console.log(citation) + const citationAddress = citation.addresses[0] + const latlng = [citationAddress.lat, citationAddress.lng].map(n => parseFloat(n)) + if (Number.isNaN(latlng[0]) || Number.isNaN(latlng[1])) return + addMarker(map, latlng, citation.title, citationAddress.name) + addArc(map, source, latlng, arcStyles[citationAddress.type]) + }) console.log(paper) diff --git a/megapixels/app/settings/app_cfg.py b/megapixels/app/settings/app_cfg.py index 40625958..d213f584 100644 --- a/megapixels/app/settings/app_cfg.py +++ b/megapixels/app/settings/app_cfg.py @@ -165,6 +165,7 @@ S3_DATASETS_PATH = "v1" # datasets is already in the filename DIR_SITE_PUBLIC = "../site/public" DIR_SITE_CONTENT = "../site/content/pages" DIR_SITE_TEMPLATES = "../site/templates" +DIR_SITE_INCLUDES = "../site/includes" DIR_SITE_USER_CONTENT = "../site/public/user_content" # ----------------------------------------------------------------------------- diff --git a/megapixels/app/site/parser.py b/megapixels/app/site/parser.py index 6c5aa8af..bfcf5322 100644 --- a/megapixels/app/site/parser.py +++ b/megapixels/app/site/parser.py @@ -236,7 +236,7 @@ def format_include(section): """ Include html template """ - include_dir = '/work/megapixels_dev/site/includes/' + include_dir = cfg.DIR_SITE_INCLUDES fp_html = section.strip().strip('\n').strip().strip('{%').strip().strip('%}').strip() fp_html = fp_html.strip('include').strip().strip('"').strip().strip("'").strip() try: diff --git a/site/public/about/index.html b/site/public/about/index.html index e2ce409d..16277fce 100644 --- a/site/public/about/index.html +++ b/site/public/about/index.html @@ -36,7 +36,7 @@
  • Terms and Conditions
  • Privacy Policy
    • -

    Recent Press

    +

    Recent Press

    • TheVerge: Transgender YouTubers had their videos grabbed to train facial recognition software by James Vincent
    • TheVerge: Transgender YouTubers had their videos grabbed to train facial recognition software by James Vincent
      • diff --git a/site/public/about/terms/index.html b/site/public/about/terms/index.html index c90f1c9d..650366e0 100644 --- a/site/public/about/terms/index.html +++ b/site/public/about/terms/index.html @@ -28,7 +28,7 @@

      Terms and Conditions ("Terms")

      -
        +
      • About
      • Press
      • Credits
        • diff --git a/site/public/datasets/lfw/index.html b/site/public/datasets/lfw/index.html index 9657b866..2df43e71 100644 --- a/site/public/datasets/lfw/index.html +++ b/site/public/datasets/lfw/index.html @@ -4,7 +4,7 @@ MegaPixels - + Labeled Faces in The Wild (LFW) is a database of face photographs designed for studying the problem of unconstrained face recognition." /> @@ -27,44 +27,36 @@
        -
        Labeled Faces in The Wild (LFW) is a database of face photographs designed for studying the problem of unconstrained face recognition.
        It includes 13,456 images of 4,432 people’s images copied from the Internet during 2002-2004. -
        A few 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.

        Statistics

        -
        Years
        2002-2004
        Images
        13,233
        Identities
        5,749
        Origin
        Yahoo News Images
        Funding
        (Possibly, partially CIA)

        INSIGHTS

        -
          -
        • There are about 3 men for every 1 woman (4,277 men and 1,472 women) in the LFW dataset 1
          • +
          Labeled Faces in The Wild (LFW) is a database of face photographs designed for studying the problem of unconstrained face recognition.
          It includes 13,456 images of 4,432 people’s images copied from the Internet during 2002-2004. +
          A few of the 5,749 people in the Labeled Faces in the Wild Dataset, thee most widely used face dataset for benchmarking face recognition algorithms.
          Created
          2002-2004
          Images
          13,233
          Identities
          5,749
          Origin
          Yahoo! News Images
          Funding
          IARPA*, CIA*
            +
          • There are about 3 men for every 1 woman in the LFW dataset 1
          • The person with the most images is George W. Bush with 530
          • There are about 3 George W. Bush's for every 1 Tony Blair
          • The LFW dataset includes over 500 actors, 30 models, 10 presidents, 124 basketball players, 24 football players, 11 kings, 7 queens, and 1 Moby
          • In all 3 of the LFW publications [^lfw_original_paper], [^lfw_survey], [^lfw_tech_report] the words "ethics", "consent", and "privacy" appear 0 times
          • The word "future" appears 71 times
            • +
          • * denotes partial funding for related research

          Labeled Faces in the Wild

          Labeled Faces in The Wild (LFW) is "a database of face photographs designed for studying the problem of unconstrained face recognition 1. It is used to evaluate and improve the performance of facial recognition algorithms in academic, commercial, and government research. According to BiometricUpdate.com 3, LFW is "the most widely used evaluation set in the field of facial recognition, LFW attracts a few dozen teams from around the globe including Google, Facebook, Microsoft Research Asia, Baidu, Tencent, SenseTime, Face++ and Chinese University of Hong Kong."

          The LFW dataset includes 13,233 images of 5,749 people that were collected between 2002-2004. LFW is a subset of Names of Faces and is part of the first facial recognition training dataset created entirely from images appearing on the Internet. The people appearing in LFW are...

          The Names and Faces dataset was the first face recognition dataset created entire from online photos. However, Names and Faces and LFW are not the first face recognition dataset created entirely "in the wild". That title belongs to the UCD dataset. Images obtained "in the wild" means using an image without explicit consent or awareness from the subject or photographer.

          -

          Biometric Trade Routes

          -

          [convert to template] To understand how this dataset has been used, its citations have been geocoded to show an approximate geographic digital trade route of the biometric data. Lines indicate an organization (education, commercial, or governmental) that has cited the LFW dataset in their research. Data is compiled from Semantic Scholar.

          -

          Synthetic Faces

          +

          The Names and Faces dataset was the first face recognition dataset created entire from online photos. However, Names and Faces and LFW are not the first face recognition dataset created entirely "in the wild". That title belongs to the UCD dataset. Images obtained "in the wild" means using an image without explicit consent or awareness from the subject or photographer.

          +
          All 5,379 people in the Labeled Faces in The Wild Dataset. Showing one face per person
          All 5,379 people in the Labeled Faces in The Wild Dataset. Showing one face per person

          The Names and Faces dataset was the first face recognition dataset created entire from online photos. However, Names and Faces and LFW are not the first face recognition dataset created entirely "in the wild". That title belongs to the UCD dataset. Images obtained "in the wild" means using an image without explicit consent or awareness from the subject or photographer.

          +

          The Names and Faces dataset was the first face recognition dataset created entire from online photos. However, Names and Faces and LFW are not the first face recognition dataset created entirely "in the wild". That title belongs to the UCD dataset. Images obtained "in the wild" means using an image without explicit consent or awareness from the subject or photographer.

          +

          Biometric Trade Routes

          To understand how this dataset has been used, its citations have been geocoded to show an approximate geographic digital trade route of the biometric data. Lines indicate an organization (education, commercial, or governmental) that has cited the LFW dataset in their research. Data is compiled from Semantic Scholar.

          ■ Academic
          ■ Industry
          ■ Government

          Sed ut perspiciatis, unde omnis iste natus error sit voluptatem accusantium doloremque laudantium, totam rem aperiam eaque ipsa, quae ab illo inventore veritatis et quasi architecto beatae vitae dicta sunt, explicabo. Nemo enim ipsam voluptatem, quia voluptas sit, aspernatur aut odit aut fugit, sed quia.

          +
          + +

          Supplementary Information for Labeled Faces in The Wild

          +

          Citations

          Add information about how the citations were generated

          Synthetic Faces

          To visualize the types of photos in the dataset without explicitly publishing individual's identities a generative adversarial network (GAN) was trained on the entire dataset. The images in this video show a neural network learning the visual latent space and then interpolating between archetypical identities within the LFW dataset.

          Synthetically generated face from the visual space of LFW dataset
          Synthetically generated face from the visual space of LFW dataset
          Synthetically generated face from the visual space of LFW dataset
          Synthetically generated face from the visual space of LFW dataset
          -
          Synthetically generated face from the visual space of LFW dataset
          Synthetically generated face from the visual space of LFW dataset

          Citations

          -

          Browse or download the geocoded citation data collected for the LFW dataset.

          -

          Additional Information

          -

          (tweet-sized snippets go here)

          -
            -
          • The LFW dataset is considered the "most popular benchmark for face recognition" 2
            • -
          • The LFW dataset is "the most widely used evaluation set in the field of facial recognition" 3
            • -
          • All images in LFW dataset were obtained "in the wild" meaning without any consent from the subject or from the photographer
            • -
          • The faces in the LFW dataset were detected using the Viola-Jones haarcascade face detector [^lfw_website] [^lfw-survey]
            • -
          • The LFW dataset is used by several of the largest tech companies in the world including "Google, Facebook, Microsoft Research Asia, Baidu, Tencent, SenseTime, Face++ and Chinese University of Hong Kong." 3
            • -
          • All images in the LFW dataset were copied from Yahoo News between 2002 - 2004
            • -
          • In 2014, two of the four original authors of the LFW dataset received funding from IARPA and ODNI for their followup paper Labeled Faces in the Wild: Updates and New Reporting Procedures via IARPA contract number 2014-14071600010
            • -
          • The dataset includes 2 images of George Tenet, the former Director of Central Intelligence (DCI) for the Central Intelligence Agency whose facial biometrics were eventually used to help train facial recognition software in China and Russia
            • -
          -
          former President George W. Bush
          former President George W. Bush
          -
          Colin Powell (236), Tony Blair (144), and Donald Rumsfeld (121)
          Colin Powell (236), Tony Blair (144), and Donald Rumsfeld (121)
          All 5,379 faces in the Labeled Faces in The Wild Dataset
          All 5,379 faces in the Labeled Faces in The Wild Dataset

          Code

          -

          The LFW dataset is so widely used that a popular code library called Sci-Kit Learn includes a function called fetch_lfw_people to download the faces in the LFW dataset.

          +
          Synthetically generated face from the visual space of LFW dataset
          Synthetically generated face from the visual space of LFW dataset
          +
          Synthetically generated face from the visual space of LFW dataset
          Synthetically generated face from the visual space of LFW dataset

          Commercial Use of Labeled Faces in The Wild

          +

          Add a paragraph about how usage extends far beyond academia into research centers for largest companies in the world. And even funnels into CIA funded research in the US and defense industry usage in China.

          +

          Code

          +

          The LFW dataset is so widely used that access to the facial data has built directly into a popular code library called Sci-Kit Learn. It includes a function called fetch_lfw_people to download the faces in the LFW dataset.

          #!/usr/bin/python
 
 import numpy as np
@@ -94,11 +86,10 @@ imageio.imwrite('lfw_montage_full.png', montage)
 montage = imutils.resize(montage, width=960)
 imageio.imwrite('lfw_montage_960.jpg', montage)
          -

          Supplementary Material

          -

          Text and graphics ©Adam Harvey / megapixels.cc

          +

          Research, text, and graphics ©Adam Harvey / megapixels.cc

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