From 1b008e4b4d11def9b13dc0a800b0d068624d43ae Mon Sep 17 00:00:00 2001 From: Jules Laplace Date: Wed, 27 Feb 2019 23:48:35 +0100 Subject: half of a footnote implementation --- megapixels/app/site/parser.py | 35 +++++++++++++++++++++++++++++------ site/assets/css/css.css | 34 ++++++++++++++++++++++++++++++++++ site/public/datasets/lfw/index.html | 15 +++++++++------ 3 files changed, 72 insertions(+), 12 deletions(-) diff --git a/megapixels/app/site/parser.py b/megapixels/app/site/parser.py index 98d9f284..ef83b655 100644 --- a/megapixels/app/site/parser.py +++ b/megapixels/app/site/parser.py @@ -18,6 +18,7 @@ def parse_markdown(metadata, sections, s3_path, skip_h1=False): current_group = [] footnotes = [] in_stats = False + in_footnotes = False ignoring = False if 'desc' in metadata and 'subdesc' in metadata: @@ -33,6 +34,7 @@ def parse_markdown(metadata, sections, s3_path, skip_h1=False): continue elif section.strip().startswith('### Footnotes'): groups.append(format_section(current_group, s3_path)) + current_group = [] footnotes = [] in_footnotes = True elif in_footnotes: @@ -82,10 +84,18 @@ def parse_markdown(metadata, sections, s3_path, skip_h1=False): current_group.append(section) groups.append(format_section(current_group, s3_path)) + footnote_txt = '' + footnote_lookup = {} + if len(footnotes): - groups.append(format_footnotes(footnotes, s3_path)) + footnote_txt, footnote_lookup = format_footnotes(footnotes, s3_path) content = "".join(groups) + + if footnote_lookup: + for key, index in footnote_lookup.items(): + content = content.replace(key, '{}'.format(key, index, index)) + content += footnote_txt return content @@ -153,8 +163,10 @@ def format_section(lines, s3_path, type='', tag='section'): return "<{}>{}".format(tag, markdown(lines), tag) return "" - def fix_meta(lines): + """ + Format metadata sections before passing to markdown + """ new_lines = [] for line in lines: if line.startswith('+ '): @@ -162,7 +174,6 @@ def fix_meta(lines): new_lines.append(line) return new_lines - def format_metadata(section): """ format a metadata section (+ key: value pairs) @@ -173,12 +184,24 @@ def format_metadata(section): meta.append("
{}
{}
".format(key, value)) return "
{}
".format(''.join(meta)) -def format_footnotes(footnotes): +def format_footnotes(footnotes, s3_path): + """ + Format the footnotes section separately and produce a lookup we can use to update the main site + """ footnotes = '\n'.join(footnotes).split('\n') + index = 1 + footnote_index_lookup = {} + footnote_list = [] for footnote in footnotes: if not len(footnote) or '[^' not in footnote: continue - key, footnote = footnotes.split(': ') + key, note = footnote.split(': ', 1) + footnote_index_lookup[key] = index + footnote_list.append('^'.format(key) + markdown(note)) + index += 1 + + footnote_txt = '
' + return footnote_txt, footnote_index_lookup def format_applet(section, s3_path): """ @@ -189,7 +212,7 @@ def format_applet(section, s3_path): applet = {} # print(payload) if ': ' in payload[0]: - command, opt = payload[0].split(': ') + command, opt = payload[0].split(': ', 1) else: command = payload[0] opt = None diff --git a/site/assets/css/css.css b/site/assets/css/css.css index fed381a7..8b4241ea 100644 --- a/site/assets/css/css.css +++ b/site/assets/css/css.css @@ -548,4 +548,38 @@ section.intro_section { .intro_section span { box-shadow: -10px -10px #000, 10px -10px #000, 10px 10px #000, -10px 10px #000; background: #000; +} + +/* footnotes */ + +a.footnote { + font-size: 10px; + position: relative; + display: inline-block; + bottom: 10px; + text-decoration: none; + color: #ff0; + left: 2px; +} +.right-sidebar a.footnote { + bottom: 8px; +} +.desktop a.footnote:hover { + background-color: #ff0; + color: #000; +} +a.footnote_anchor { + font-weight: bold; + color: #ff0; + margin-right: 10px; + text-decoration: underline; + cursor: pointer; +} +ul.footnotes { + list-style-type: decimal; + margin-left: 30px; +} +li p { + margin: 0; padding: 0; + display: inline; } \ No newline at end of file diff --git a/site/public/datasets/lfw/index.html b/site/public/datasets/lfw/index.html index 1242df0c..54b6aa22 100644 --- a/site/public/datasets/lfw/index.html +++ b/site/public/datasets/lfw/index.html @@ -31,7 +31,7 @@
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.

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[^lfw_www]
    • +
  • There are about 3 men for every 1 woman (4,277 men and 1,472 women) in the LFW dataset1
  • 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
    • @@ -39,7 +39,7 @@
  • The word "future" appears 71 times

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[^lfw_www]. It is used to evaluate and improve the performance of facial recognition algorithms in academic, commercial, and government research. According to BiometricUpdate.com[^lfw_pingan], 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."

+

Labeled Faces in The Wild (LFW) is "a database of face photographs designed for studying the problem of unconstrained face recognition1. It is used to evaluate and improve the performance of facial recognition algorithms in academic, commercial, and government research. According to BiometricUpdate.com3, 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

@@ -51,11 +51,11 @@

Additional Information

(tweet-sized snippets go here)

Supplementary Material

Text and graphics ©Adam Harvey / megapixels.cc

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+