From d69086a1b2d7d6e6def55f35e30d0623701de011 Mon Sep 17 00:00:00 2001 From: Jules Laplace Date: Tue, 4 Dec 2018 21:12:59 +0100 Subject: embedding images --- .../research/from_1_to_100_pixels/index.html | 101 +++++++++++++++++++++ 1 file changed, 101 insertions(+) create mode 100644 site/public/research/from_1_to_100_pixels/index.html (limited to 'site/public/research') diff --git a/site/public/research/from_1_to_100_pixels/index.html b/site/public/research/from_1_to_100_pixels/index.html new file mode 100644 index 00000000..751e885b --- /dev/null +++ b/site/public/research/from_1_to_100_pixels/index.html @@ -0,0 +1,101 @@ + + + + MegaPixels + + + + + + + + + +
+ + +
MegaPixels
+ The Darkside of Datasets +
+ +
+
+ +
+

From 1 to 100 Pixels

+
+
+
Posted
+
2018-12-04
+
+
+
By
+
Adam Harvey
Berit Gilma
Matthew Stender
+
+ +
+
+ +

High resolution insights from low resolution data

+

This post will be about the meaning of "face". How do people define it? How to biometrics researchers define it? How has it changed during the last decade.

+

What can you know from a very small amount of information?

+
    +
  • 1 pixel grayscale
  • +
  • 2x2 pixels grayscale, font example
  • +
  • 4x4 pixels
  • +
  • 8x8 yotta yotta
  • +
  • 5x7 face recognition
  • +
  • 12x16 activity recognition
  • +
  • 6/5 (up to 124/106) pixels in height/width, and the average is 24/20 for QMUL SurvFace
  • +
  • 20x16 tiny faces paper
  • +
  • 20x20 MNIST handwritten images http://yann.lecun.com/exdb/mnist/
  • +
  • 24x24 haarcascade detector idealized images
  • +
  • 32x32 CIFAR image dataset
  • +
  • 40x40 can do emotion detection, face recognition at scale, 3d modeling of the face. include datasets with faces at this resolution including pedestrian.
  • +
  • need more material from 60-100
  • +
  • 60x60 show how texture emerges and pupils, eye color, higher resolution of features and compare to lower resolution faces
  • +
  • 100x100 0.5% of one Instagram photo
  • +
+

Find specific cases of facial resolution being used in legal cases, forensic investigations, or military footage

+

Research

+
    +
  • NIST report on sres states several resolutions
  • +
  • "Results show that the tested face recognition systems yielded similar performance for query sets with eye-to-eye distance from 60 pixels to 30 pixels" 1
  • +
+
+
+
  1. NIST 906932. Performance Assessment of Face Recognition Using Super-Resolution. Shuowen Hu, Robert Maschal, S. Susan Young, Tsai Hong Hong, Jonathon P. Phillips

  2. +
+
+
+ +
+

MORE RESEARCH

+
+ +
+
+ +
+ + + + \ No newline at end of file -- cgit v1.2.3-70-g09d2 From 2a1b884e841efe562e0c84885a404819433b3405 Mon Sep 17 00:00:00 2001 From: Jules Laplace Date: Wed, 5 Dec 2018 16:19:50 +0100 Subject: styling images --- builder/builder.py | 4 +- builder/parser.py | 57 +++++++++--- builder/s3.py | 4 +- site/assets/css/css.css | 88 ++++++++++++++++-- site/public/about/credits/index.html | 5 +- site/public/about/disclaimer/index.html | 2 +- site/public/about/index.html | 5 +- site/public/about/press/index.html | 5 +- site/public/about/privacy/index.html | 2 +- site/public/about/style/index.html | 23 ++--- site/public/about/terms/index.html | 2 +- site/public/datasets/lfw/index.html | 56 +++++++++--- site/public/datasets/vgg_faces2/index.html | 5 +- site/public/index.html | 63 +++++++++++++ .../research/01_from_1_to_100_pixels/index.html | 101 +++++++++++++++++++++ site/templates/research.html | 2 +- 16 files changed, 357 insertions(+), 67 deletions(-) create mode 100644 site/public/index.html create mode 100644 site/public/research/01_from_1_to_100_pixels/index.html (limited to 'site/public/research') diff --git a/builder/builder.py b/builder/builder.py index 44fbd1c6..deb9eb68 100644 --- a/builder/builder.py +++ b/builder/builder.py @@ -40,11 +40,9 @@ def build_page(fn, research_posts): s3.sync_directory(dirname, s3_datasets_path, metadata) else: s3_path = "{}/{}/{}{}".format(os.getenv('S3_ENDPOINT'), os.getenv('S3_BUCKET'), s3_site_path, metadata['path']) - if 'index.md' in fn: + if 'index.md' in fn and metadata['url'] != '/': s3.sync_directory(dirname, s3_site_path, metadata) - print(s3_path) - content = parser.parse_markdown(sections, s3_path) html = template.render( diff --git a/builder/parser.py b/builder/parser.py index ea273556..529d21fa 100644 --- a/builder/parser.py +++ b/builder/parser.py @@ -1,4 +1,5 @@ import os +import re import glob import mistune from paths import * @@ -10,19 +11,28 @@ def fix_images(lines, s3_path): real_lines = [] block = "\n\n".join(lines) for line in block.split("\n"): - if "![" in line and "](http" not in line: - line = line.replace('](', '](' + s3_path) + if "![" in line: + print(line) + line = line.replace('![', '') + alt_text, tail = line.split('](', 1) + url, tail = tail.split(')', 1) + if ':' in alt_text: + tail, alt_text = alt_text.split(':', 1) + img_tag = "{}".format(s3_path + url, alt_text.replace("'", "")) + if len(alt_text): + line = "
{}
{}
".format(img_tag, alt_text) + else: + line = "
{}
".format(img_tag, alt_text) real_lines.append(line) return "\n".join(real_lines) -def wide_section(line, s3_path): - lines = fix_images(lines, s3_path) - return "
" + markdown(lines) + "
" - -def normal_section(lines, s3_path): +def format_section(lines, s3_path, type=''): if len(lines): lines = fix_images(lines, s3_path) - return "
" + markdown(lines) + "
" + if type: + return "
{}
".format(type, markdown(lines)) + else: + return "
" + markdown(lines) + "
" return "" def parse_markdown(sections, s3_path): @@ -31,13 +41,17 @@ def parse_markdown(sections, s3_path): for section in sections: if section.startswith('# '): continue - if '![wide]' in section: - groups.append(normal_section(current_group, s3_path)) - groups.append(wide_section([section], s3_path)) + if '![wide:' in section: + groups.append(format_section(current_group, s3_path)) + groups.append(format_section([section], s3_path, type='wide')) + current_group = [] + elif '![' in section: + groups.append(format_section(current_group, s3_path)) + groups.append(format_section([section], s3_path, type='images')) current_group = [] else: current_group.append(section) - groups.append(normal_section(current_group, s3_path)) + groups.append(format_section(current_group, s3_path)) content = "".join(groups) return content @@ -88,16 +102,22 @@ def parse_metadata(fn, sections): for key in default_metadata: if key not in metadata: metadata[key] = default_metadata[key] + + basedir = os.path.dirname(fn.replace(content_path, '')) basename = os.path.basename(fn) - metadata['path'] = os.path.dirname(fn.replace(content_path, '')) + '/' - if basename == 'index.md': + if basedir == '/': + metadata['path'] = '/' + metadata['url'] = '/' + elif basename == 'index.md': + metadata['path'] = basedir + '/' metadata['url'] = metadata['path'] else: + metadata['path'] = basedir + '/' metadata['url'] = metadata['path'] + basename.replace('.md', '') + '/' if metadata['status'] == 'published|draft|private': metadata['status'] = 'published' - metadata['authors'] = '
'.join(metadata['authors'].split(',')) + metadata['author_html'] = '
'.join(metadata['authors'].split(',')) return metadata, valid_sections def read_research_post_index(): @@ -107,5 +127,12 @@ def read_research_post_index(): if metadata is None or metadata['status'] == 'private' or metadata['status'] == 'draft': continue posts.append(metadata) + if not len(posts): + posts.append({ + 'title': 'Placeholder', + 'slug': 'placeholder', + 'date': 'Placeholder', + 'url': '/', + }) return posts diff --git a/builder/s3.py b/builder/s3.py index 7d4d52a0..f3dcce48 100644 --- a/builder/s3.py +++ b/builder/s3.py @@ -32,14 +32,14 @@ def sync_directory(base_fn, s3_path, metadata): del fns[fn] if obj['LastModified'].timestamp() < os.path.getmtime(os.path.join(local_fn)): print("s3 update {}".format(s3_fn)) - client.upload_file( + s3_client.upload_file( local_fn, os.getenv('S3_BUCKET'), s3_fn, ExtraArgs={ 'ACL': 'public-read' }) else: print("s3 delete {}".format(s3_fn)) - response = client.delete_object( + response = s3_client.delete_object( Bucket=os.getenv('S3_BUCKET'), Key=s3_fn, ) diff --git a/site/assets/css/css.css b/site/assets/css/css.css index c9d9b029..1024ffcd 100644 --- a/site/assets/css/css.css +++ b/site/assets/css/css.css @@ -120,14 +120,14 @@ h1 { margin: 75px 0 10px; padding: 0; } -h3 { - margin: 0 0 10px 0; +h2, h3 { + margin: 0 0 20px 0; padding: 0; font-size: 11pt; font-weight: 500; } -th, .gray, h3 { +th, .gray, h2, h3 { font-family: 'Roboto Mono', monospace; font-weight: 400; text-transform: uppercase; @@ -165,13 +165,89 @@ p { color: #fff; } code { - display: block; font-family: 'Roboto Mono', monospace; font-size: 9pt; + padding: 2px 4px; + background: rgba(255,255,255,0.1); +} +pre code { + display: block; max-height: 400px; max-width: 640px; - padding: 2px 5px; - background: rgba(255,255,255,0.1); +} +hr { + height: 1px; + background: #888; + border: 0; + width: 80px; +} +.footnotes hr { + display: none; +} +.footnotes ol:before { + content: 'Footnotes'; + margin: 0 0 10px -40px; + padding-bottom: 0; + display: block; + font-family: 'Roboto Mono', monospace; + font-weight: 400; + text-transform: uppercase; + color: #666; + font-size: 11pt; +} + +/* images */ + +section img { + max-width: 100%; + display: block; + margin: 0 auto; +} +section .image { + margin-bottom: 40px; +} +section.images { + display: flex; + flex-direction: row; + align-items: flex-start; + justify-content: center; +} +.image:only-child { + width: 100%; +} +.image:first-child { + margin-left: 0; +} +.image:nth-child(2), +.image:nth-child(3) { + margin-left: 40px; +} +.image:first-child:nth-last-child(2), +.image:first-child:nth-last-child(2) ~ .image { + width: 300px; +} +.image:first-child:nth-last-child(3), +.image:first-child:nth-last-child(3) ~ .image { + width: 186px; +} +section.wide { + width: 100%; +} +section.wide .image { + max-width: 100%; +} +.caption { + text-align: center; + font-size: 9pt; + color: #888; + max-width: 620px; + margin: 10px auto 0 auto; +} + +blockquote { + margin-left: 28px; + padding: 0 0 0 10px; + border-left: 2px solid #555; } /* top of post metadata */ diff --git a/site/public/about/credits/index.html b/site/public/about/credits/index.html index 0b3f9db8..9fec7e64 100644 --- a/site/public/about/credits/index.html +++ b/site/public/about/credits/index.html @@ -20,15 +20,14 @@

Credits

-

alt text

-
    +
alt text
alt text
  • MegaPixels by Adam Harvey
  • Made with support from Mozilla
  • Site developed by Jules Laplace
  • diff --git a/site/public/about/disclaimer/index.html b/site/public/about/disclaimer/index.html index 1c14a97c..553bf084 100644 --- a/site/public/about/disclaimer/index.html +++ b/site/public/about/disclaimer/index.html @@ -20,7 +20,7 @@ diff --git a/site/public/about/index.html b/site/public/about/index.html index 8441e317..363e8fc0 100644 --- a/site/public/about/index.html +++ b/site/public/about/index.html @@ -20,14 +20,13 @@
    -

    alt text

    -
      +
      alt text
      alt text
      • MegaPixels by Adam Harvey
      • Made with support from Mozilla
      • Site developed by Jules Laplace
      • diff --git a/site/public/about/press/index.html b/site/public/about/press/index.html index 76ba90e4..aa6e5e13 100644 --- a/site/public/about/press/index.html +++ b/site/public/about/press/index.html @@ -20,15 +20,14 @@

        Press

        -

        alt text

        -
          +
        alt text
        alt text
        • Aug 22, 2018: "Transgender YouTubers had their videos grabbed to train facial recognition software" by James Vincent https://www.theverge.com/2017/8/22/16180080/transgender-youtubers-ai-facial-recognition-dataset
        • Aug 22, 2018: "Transgender YouTubers had their videos grabbed to train facial recognition software" by James Vincent https://www.theverge.com/2017/8/22/16180080/transgender-youtubers-ai-facial-recognition-dataset
        • Aug 22, 2018: "Transgender YouTubers had their videos grabbed to train facial recognition software" by James Vincent https://www.theverge.com/2017/8/22/16180080/transgender-youtubers-ai-facial-recognition-dataset
        • diff --git a/site/public/about/privacy/index.html b/site/public/about/privacy/index.html index 21fd2255..d1ec1c77 100644 --- a/site/public/about/privacy/index.html +++ b/site/public/about/privacy/index.html @@ -20,7 +20,7 @@ diff --git a/site/public/about/style/index.html b/site/public/about/style/index.html index 2e0c80d0..24e6f5be 100644 --- a/site/public/about/style/index.html +++ b/site/public/about/style/index.html @@ -20,21 +20,19 @@
          -

          Alt text here

          -

          Header 1

          -

          Header 2

          +
          Alt text here
          Alt text here

          Header 2

          Header 3

          Header 4

          Header 5
          Header 6

          Bold text, italic text, bold italic text

          -

          At vero eos et et iusto qui blanditiis praesentium voluptatum deleniti atque corrupti1, quos dolores et quas molestias excepturi sint, obcaecati cupiditate non-provident, similique sunt in culpa, qui officia deserunt mollitia animi, id est laborum et dolorum fuga. Et harum quidem rerum facilis est et expedita distinctio2. Nam libero tempore, cum soluta nobis est eligendi optio, cumque nihil impedit, quo minus id, quod maxime placeat, facere possimus, omnis voluptas assumenda est, omnis dolor repellendus3.

          +

          At vero eos et et iusto qui blanditiis praesentium voluptatum deleniti atque corrupti[^1], quos dolores et quas molestias excepturi sint, obcaecati cupiditate non-provident, similique sunt in culpa, qui officia deserunt mollitia animi, id est laborum et dolorum fuga. Et harum quidem rerum facilis est et expedita distinctio[^2]. Nam libero tempore, cum soluta nobis est eligendi optio, cumque nihil impedit, quo minus id, quod maxime placeat, facere possimus, omnis voluptas assumenda est, omnis dolor repellendus[^3].

          • 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
          • @@ -42,9 +40,15 @@
          • Odit aut fugit, sed quia consequuntur magni dolores eos
          • Qui ratione voluptatem sequi nesciunt, neque porro quisquam
          -

          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.

          +

          single image test

          +
          This person is alone
          This person is alone

          double image test

          +
          This person is on the left
          This person is on the left
          +
          This person is on the right
          This person is on the right

          triple image test

          +
          Person 1
          Person 1
          +
          Person 2
          Person 2
          +
          Person 3. Let me tell you about Person 3.  This person has a very long description with text which wraps like crazy
          Person 3. Let me tell you about Person 3. This person has a very long description with text which wraps like crazy

          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.

          -

          Inline code has back-ticks around it.

          +
          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
          This image is extremely wide and the text beneath it will wrap but that's fine because it can also contain hyperlinks! Yes, you read that right—hyperlinks! Lorem ipsum dolor sit amet ad volotesque sic hoc ad nauseam

          Inline code has back-ticks around it.

          var s = "JavaScript syntax highlighting";
           alert(s);
           
          @@ -59,10 +63,7 @@ But let's throw in a <b>tag</b>.

          Citations below here


          -
          1. First source

          2. -
          3. Second source

          4. -
          5. Third source

          6. -
          +
            diff --git a/site/public/about/terms/index.html b/site/public/about/terms/index.html index 73155546..4b9f4445 100644 --- a/site/public/about/terms/index.html +++ b/site/public/about/terms/index.html @@ -20,7 +20,7 @@ diff --git a/site/public/datasets/lfw/index.html b/site/public/datasets/lfw/index.html index 8455bc60..a130c24e 100644 --- a/site/public/datasets/lfw/index.html +++ b/site/public/datasets/lfw/index.html @@ -4,7 +4,7 @@ MegaPixels - + @@ -20,7 +20,7 @@ @@ -31,17 +31,15 @@
          1. Images 13,233
          2. People 5,749
          3. Created From Yahoo News images
          4. -
          5. Search available Searchable
          6. +
          7. Analyzed and searchable
          -

          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”.

          -

          Eight out of 5,749 people in the Labeled Faces in the Wild dataset. The face recognition training dataset is created entirely from photos downloaded from the Internet.

          -

          INTRO

          +

          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 as “The Wild”.

          +
          Eight out of 5,749 people in the Labeled Faces in the Wild dataset. The face recognition training dataset is created entirely from photos downloaded from the Internet.
          Eight out of 5,749 people in the Labeled Faces in the Wild dataset. The face recognition training dataset is created entirely from photos downloaded from the Internet.

          INTRO

          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).

          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.

          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.

          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.

          -

          From Aaron Eckhart to Zydrunas Ilgauskas. A small sampling of the LFW dataset

          -

          In addition to commercial use as an evaluation tool, alll of the faces in LFW dataset are prepackaged into a popular machine learning code framework called scikit-learn.

          +
          From Aaron Eckhart to Zydrunas Ilgauskas. A small sampling of the LFW dataset
          From Aaron Eckhart to Zydrunas Ilgauskas. A small sampling of the LFW dataset

          In addition to commercial use as an evaluation tool, alll of the faces in LFW dataset are prepackaged into a popular machine learning code framework called scikit-learn.

          Usage

          #!/usr/bin/python
           from matplotlib import plt
          @@ -51,11 +49,39 @@ lfw_person = lfw_people[0]
           plt.imshow(lfw_person)
           

          Commercial Use

          -

          The LFW dataset is used by numerous companies for benchmarking algorithms and in some cases training. According to the benchmarking results page 1 provided by the authors, over 2 dozen companies have contributed their benchmark results

          -

          (Jules: this load the assets/lfw_vendor_results.csv)

          -

          In benchmarking, companies use a dataset to evaluate their algorithms which are typically trained on other data. After training, researchers will use LFW as a benchmark to compare results with other algorithms.

          +

          The LFW dataset is used by numerous companies for benchmarking algorithms and in some cases training. According to the benchmarking results page [^lfw_results] provided by the authors, over 2 dozen companies have contributed their benchmark results

          +
          load file: lfw_commercial_use.csv
          +name_display,company_url,example_url,country,description
          +
          + + + + + + + + + + + + + + + + + + + + + + + + +
          CompanyCountryIndustries
          AratekChinaBiometric sensors for telecom, civil identification, finance, education, POS, and transportation
          AratekChinaBiometric sensors for telecom, civil identification, finance, education, POS, and transportation
          AratekChinaBiometric sensors for telecom, civil identification, finance, education, POS, and transportation
          +

          Add 2-4 screenshots of companies mentioning LFW here

          +
          ReadSense
          ReadSense

          In benchmarking, companies use a dataset to evaluate their algorithms which are typically trained on other data. After training, researchers will use LFW as a benchmark to compare results with other algorithms.

          For example, Baidu (est. net worth $13B) uses LFW to report results for their "Targeting Ultimate Accuracy: Face Recognition via Deep Embedding". According to the three Baidu researchers who produced the paper:

          -

          LFW has been the most popular evaluation benchmark for face recognition, and played a very important role in facilitating the face recognition society to improve algorithm. 2.

          +

          LFW has been the most popular evaluation benchmark for face recognition, and played a very important role in facilitating the face recognition society to improve algorithm. 1.

          Citations

          @@ -84,10 +110,12 @@ plt.imshow(lfw_person)

          Conclusion

          The LFW face recognition training and evaluation dataset is a historically important face dataset as it was the first popular dataset to be created entirely from Internet images, paving the way for a global trend towards downloading anyone’s face from the Internet and adding it to a dataset. As will be evident with other datasets, LFW’s approach has now become the norm.

          For all the 5,000 people in this datasets, their face is forever a part of facial recognition history. It would be impossible to remove anyone from the dataset because it is so ubiquitous. For their rest of the lives and forever after, these 5,000 people will continue to be used for training facial recognition surveillance.

          +

          Notes

          +

          According to BiometricUpdate.com2, 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."


          -
          1. "LFW Results". Accessed Dec 3, 2018. http://vis-www.cs.umass.edu/lfw/results.html

          2. -
          3. "Chinese tourist town uses face recognition as an entry pass". New Scientist. November 17, 2016. https://www.newscientist.com/article/2113176-chinese-tourist-town-uses-face-recognition-as-an-entry-pass/

          4. +
            1. "Chinese tourist town uses face recognition as an entry pass". New Scientist. November 17, 2016. https://www.newscientist.com/article/2113176-chinese-tourist-town-uses-face-recognition-as-an-entry-pass/

            2. +
            3. "PING AN Tech facial recognition receives high score in latest LFW test results". https://www.biometricupdate.com/201702/ping-an-tech-facial-recognition-receives-high-score-in-latest-lfw-test-results

          diff --git a/site/public/datasets/vgg_faces2/index.html b/site/public/datasets/vgg_faces2/index.html index 19efbbbc..ee353047 100644 --- a/site/public/datasets/vgg_faces2/index.html +++ b/site/public/datasets/vgg_faces2/index.html @@ -20,7 +20,7 @@ @@ -34,8 +34,7 @@
        • Search available Searchable
        • 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”.

          -

          Eight out of 5,749 people in the Labeled Faces in the Wild dataset. The face recognition training dataset is created entirely from photos downloaded from the Internet.

          -

          INTRO

          +
          Eight out of 5,749 people in the Labeled Faces in the Wild dataset. The face recognition training dataset is created entirely from photos downloaded from the Internet.
          Eight out of 5,749 people in the Labeled Faces in the Wild dataset. The face recognition training dataset is created entirely from photos downloaded from the Internet.

          INTRO

          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).

          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.

          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.

          diff --git a/site/public/index.html b/site/public/index.html new file mode 100644 index 00000000..ea3dc24c --- /dev/null +++ b/site/public/index.html @@ -0,0 +1,63 @@ + + + + MegaPixels + + + + + + + + + +
          + + +
          MegaPixels
          + The Darkside of Datasets +
          + +
          +
          + +

          MegaPixels is an art project that explores the dark side of face recognition training data and the future of computer vision

          +

          Made by Adam Harvey in partnership with Mozilla.
          +Read more about MegaPixels

          +

          [Explore Datasets] [Explore Algorithms]

          +

          Facial Recognition Datasets

          +

          Regular Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat.

          +

          Summary

          +
            +
          • 275 datasets found
          • +
          • Created between the years 1993-2018
          • +
          • Smallest dataset: 20 images
          • +
          • Largest dataset: 10,000,000 images
          • +
          • Highest resolution faces: 450x500 (Unconstrained College Students)
          • +
          • Lowest resolution faces: 16x20 pixels (QMUL SurvFace)
          • +
          +
          + +
          + + + + \ No newline at end of file diff --git a/site/public/research/01_from_1_to_100_pixels/index.html b/site/public/research/01_from_1_to_100_pixels/index.html new file mode 100644 index 00000000..90f142e9 --- /dev/null +++ b/site/public/research/01_from_1_to_100_pixels/index.html @@ -0,0 +1,101 @@ + + + + MegaPixels + + + + + + + + + +
          + + +
          MegaPixels
          + The Darkside of Datasets +
          + +
          +
          + +
          +

          From 1 to 100 Pixels

          +
          +
          +
          Posted
          +
          2018-12-04
          +
          +
          +
          By
          +
          Adam Harvey
          +
          + +
          +
          + +

          High resolution insights from low resolution data

          +

          This post will be about the meaning of "face". How do people define it? How to biometrics researchers define it? How has it changed during the last decade.

          +

          What can you know from a very small amount of information?

          +
            +
          • 1 pixel grayscale
          • +
          • 2x2 pixels grayscale, font example
          • +
          • 4x4 pixels
          • +
          • 8x8 yotta yotta
          • +
          • 5x7 face recognition
          • +
          • 12x16 activity recognition
          • +
          • 6/5 (up to 124/106) pixels in height/width, and the average is 24/20 for QMUL SurvFace
          • +
          • 20x16 tiny faces paper
          • +
          • 20x20 MNIST handwritten images http://yann.lecun.com/exdb/mnist/
          • +
          • 24x24 haarcascade detector idealized images
          • +
          • 32x32 CIFAR image dataset
          • +
          • 40x40 can do emotion detection, face recognition at scale, 3d modeling of the face. include datasets with faces at this resolution including pedestrian.
          • +
          • need more material from 60-100
          • +
          • 60x60 show how texture emerges and pupils, eye color, higher resolution of features and compare to lower resolution faces
          • +
          • 100x100 0.5% of one Instagram photo
          • +
          +

          Find specific cases of facial resolution being used in legal cases, forensic investigations, or military footage

          +

          Research

          +
            +
          • NIST report on sres states several resolutions
          • +
          • "Results show that the tested face recognition systems yielded similar performance for query sets with eye-to-eye distance from 60 pixels to 30 pixels" 1
          • +
          +
          +
          +
          1. NIST 906932. Performance Assessment of Face Recognition Using Super-Resolution. Shuowen Hu, Robert Maschal, S. Susan Young, Tsai Hong Hong, Jonathon P. Phillips

          2. +
          +
          +
          + +
          +

          MORE RESEARCH

          +
          + +
          +
          + +
          + + + + \ No newline at end of file diff --git a/site/templates/research.html b/site/templates/research.html index 4263f204..22e494c2 100644 --- a/site/templates/research.html +++ b/site/templates/research.html @@ -10,7 +10,7 @@
          By
          -
          {{ metadata.authors }}
          +
          {{ metadata.author_html }}
          {% if metadata.datasets %}
          Datasets
          -- cgit v1.2.3-70-g09d2 From 03ed12b471c1e50ae531c46fcbf5afd06ca5432b Mon Sep 17 00:00:00 2001 From: Jules Laplace Date: Wed, 5 Dec 2018 18:23:32 +0100 Subject: build --- builder/builder.py | 37 ++++-- builder/parser.py | 46 ++++++- builder/s3.py | 6 + site/assets/css/css.css | 65 +++++++--- site/assets/css/fonts.css | 18 ++- site/assets/js/app/site.js | 7 + site/public/about/credits/index.html | 8 +- site/public/about/disclaimer/index.html | 8 +- site/public/about/index.html | 11 +- site/public/about/press/index.html | 8 +- site/public/about/privacy/index.html | 9 +- site/public/about/style/index.html | 12 +- site/public/about/terms/index.html | 8 +- site/public/datasets/lfw/index.html | 20 +-- site/public/datasets/lfw/what/index.html | 141 +++++++++++++++++++++ site/public/datasets/vgg_faces2/index.html | 20 +-- site/public/index.html | 12 +- .../research/01_from_1_to_100_pixels/index.html | 15 +-- site/public/research/index.html | 50 ++++++++ site/templates/layout.html | 8 +- site/templates/research.html | 12 -- 21 files changed, 391 insertions(+), 130 deletions(-) create mode 100644 site/public/datasets/lfw/what/index.html create mode 100644 site/public/research/index.html (limited to 'site/public/research') diff --git a/builder/builder.py b/builder/builder.py index deb9eb68..0e404b88 100644 --- a/builder/builder.py +++ b/builder/builder.py @@ -29,21 +29,25 @@ def build_page(fn, research_posts): output_path = public_path + metadata['url'] output_fn = os.path.join(output_path, "index.html") + is_research = False + if 'research/' in fn: + is_research = True template = env.get_template("research.html") else: template = env.get_template("page.html") - if 'datasets' in fn: - s3_path = "{}/{}/{}{}".format(os.getenv('S3_ENDPOINT'), os.getenv('S3_BUCKET'), s3_datasets_path, metadata['path']) - if 'index.md' in fn: - s3.sync_directory(dirname, s3_datasets_path, metadata) + if 'datasets/' in fn: + s3_dir = s3_datasets_path else: - s3_path = "{}/{}/{}{}".format(os.getenv('S3_ENDPOINT'), os.getenv('S3_BUCKET'), s3_site_path, metadata['path']) - if 'index.md' in fn and metadata['url'] != '/': - s3.sync_directory(dirname, s3_site_path, metadata) + s3_dir = s3_site_path + + s3_path = s3.make_s3_path(s3_dir, metadata['path']) + + if 'index.md' in fn: + s3.sync_directory(dirname, s3_dir, metadata) - content = parser.parse_markdown(sections, s3_path) + content = parser.parse_markdown(sections, s3_path, skip_h1=is_research) html = template.render( metadata=metadata, @@ -58,10 +62,27 @@ def build_page(fn, research_posts): print("______") +def build_research_index(research_posts): + metadata, sections = parser.read_metadata('../site/content/research/index.md') + template = env.get_template("page.html") + s3_path = s3.make_s3_path(s3_site_path, metadata['path']) + content = parser.parse_markdown(sections, s3_path, skip_h1=False) + content += parser.parse_research_index(research_posts) + html = template.render( + metadata=metadata, + content=content, + research_posts=research_posts, + latest_research_post=research_posts[-1], + ) + output_fn = public_path + '/research/index.html' + with open(output_fn, "w") as file: + file.write(html) + def build_site(): research_posts = parser.read_research_post_index() for fn in glob.iglob(os.path.join(content_path, "**/*.md"), recursive=True): build_page(fn, research_posts) + build_research_index(research_posts) if __name__ == '__main__': build_site() diff --git a/builder/parser.py b/builder/parser.py index 529d21fa..da3044a0 100644 --- a/builder/parser.py +++ b/builder/parser.py @@ -2,6 +2,8 @@ import os import re import glob import mistune + +import s3 from paths import * renderer = mistune.Renderer(escape=False) @@ -12,7 +14,6 @@ def fix_images(lines, s3_path): block = "\n\n".join(lines) for line in block.split("\n"): if "![" in line: - print(line) line = line.replace('![', '') alt_text, tail = line.split('](', 1) url, tail = tail.split(')', 1) @@ -35,13 +36,26 @@ def format_section(lines, s3_path, type=''): return "
          " + markdown(lines) + "
          " return "" -def parse_markdown(sections, s3_path): +def format_metadata(section): + meta = [] + for line in section.split('\n'): + key, value = line[2:].split(': ', 1) + meta.append("
          {}
          {}
          ".format(key, value)) + return "
          {}
          ".format(''.join(meta)) + +def parse_markdown(sections, s3_path, skip_h1=False): groups = [] current_group = [] + seen_metadata = False for section in sections: - if section.startswith('# '): + if skip_h1 and section.startswith('# '): continue - if '![wide:' in section: + elif section.startswith('+ ') and not seen_metadata: + groups.append(format_section(current_group, s3_path)) + groups.append(format_metadata(section)) + current_group = [] + seen_metadata = True + elif '![wide:' in section: groups.append(format_section(current_group, s3_path)) groups.append(format_section([section], s3_path, type='wide')) current_group = [] @@ -55,6 +69,23 @@ def parse_markdown(sections, s3_path): content = "".join(groups) return content +def parse_research_index(research_posts): + content = "
          " + for post in research_posts: + s3_path = s3.make_s3_path(s3_site_path, post['path']) + if 'image' in post: + post_image = s3_path + post['image'] + else: + post_image = 'data:image/gif;base64,R0lGODlhAQABAAAAACH5BAEKAAEALAAAAAABAAEAAAICTAEAOw==' + row = "
          Research post

          {}

          {}

          ".format( + post['path'], + post_image, + post['title'], + post['tagline']) + content += row + content += '
          ' + return content + def read_metadata(fn): with open(fn, "r") as file: data = file.read() @@ -74,6 +105,8 @@ default_metadata = { 'published': '2018-12-31', 'updated': '2018-12-31', 'authors': 'Adam Harvey', + 'sync': 'true', + 'tagline': '', } def parse_metadata_section(metadata, section): @@ -117,12 +150,15 @@ def parse_metadata(fn, sections): if metadata['status'] == 'published|draft|private': metadata['status'] = 'published' + + metadata['sync'] = metadata['sync'] != 'false' + metadata['author_html'] = '
          '.join(metadata['authors'].split(',')) return metadata, valid_sections def read_research_post_index(): posts = [] - for fn in sorted(glob.glob(os.path.join(content_path, 'research/**/index.md'), recursive=True)): + for fn in sorted(glob.glob('../site/content/research/*/index.md')): metadata, valid_sections = read_metadata(fn) if metadata is None or metadata['status'] == 'private' or metadata['status'] == 'draft': continue diff --git a/builder/s3.py b/builder/s3.py index f3dcce48..41ecdf61 100644 --- a/builder/s3.py +++ b/builder/s3.py @@ -18,6 +18,9 @@ def sync_directory(base_fn, s3_path, metadata): for fn in glob.glob(os.path.join(base_fn, 'assets/*')): fns[os.path.basename(fn)] = True + if not metadata['sync']: + return + remote_path = s3_path + metadata['url'] directory = s3_client.list_objects(Bucket=os.getenv('S3_BUCKET'), Prefix=remote_path) @@ -53,3 +56,6 @@ def sync_directory(base_fn, s3_path, metadata): os.getenv('S3_BUCKET'), s3_fn, ExtraArgs={ 'ACL': 'public-read' }) + +def make_s3_path(s3_dir, metadata_path): + return "{}/{}/{}{}".format(os.getenv('S3_ENDPOINT'), os.getenv('S3_BUCKET'), s3_dir, metadata_path) diff --git a/site/assets/css/css.css b/site/assets/css/css.css index 1024ffcd..843809a8 100644 --- a/site/assets/css/css.css +++ b/site/assets/css/css.css @@ -164,16 +164,46 @@ p { .content a:hover { color: #fff; } + +/* top of post metadata */ + +.meta { + display: flex; + flex-direction: row; + justify-content: flex-start; + align-items: flex-start; + font-size: 10pt; + margin-bottom: 20px; +} +.meta > div { + margin-right: 30px; +} +.meta .gray { + font-size: 9pt; + padding-bottom: 4px; +} + +/* misc formatting */ + code { font-family: 'Roboto Mono', monospace; font-size: 9pt; padding: 2px 4px; background: rgba(255,255,255,0.1); } +pre { + margin: 0 0 40px 0; + border: 1px solid #666; + border-radius: 2px; +} pre code { display: block; max-height: 400px; max-width: 640px; + padding: 4px 10px; +} +table { + margin-bottom: 40px; } hr { height: 1px; @@ -181,6 +211,14 @@ hr { border: 0; width: 80px; } +blockquote { + margin-left: 28px; + padding: 0 0 0 10px; + border-left: 2px solid #555; +} + +/* footnotes */ + .footnotes hr { display: none; } @@ -243,29 +281,14 @@ section.wide .image { max-width: 620px; margin: 10px auto 0 auto; } - -blockquote { - margin-left: 28px; - padding: 0 0 0 10px; - border-left: 2px solid #555; -} - -/* top of post metadata */ - -.meta { - display: flex; - flex-direction: row; - justify-content: flex-start; - align-items: flex-start; - font-size: 10pt; - margin-bottom: 20px; +.research_index { + margin-top: 40px; } -.meta > div { - margin-right: 30px; +.research_index a { + text-decoration: none; } -.meta .gray { - font-size: 9pt; - padding-bottom: 4px; +.research_index h1 { + margin-top: 20px; } /* blogpost index */ diff --git a/site/assets/css/fonts.css b/site/assets/css/fonts.css index 2195c70b..8db01fbd 100644 --- a/site/assets/css/fonts.css +++ b/site/assets/css/fonts.css @@ -2,34 +2,40 @@ font-family: 'Roboto'; font-style: normal; font-weight: 300; - src: url("../fonts/Roboto_300.eot?#iefix") format("embedded-opentype"), url("../fonts/Roboto_300.woff") format("woff"), url("../fonts/Roboto_300.woff2") format("woff2"), url("../fonts/Roboto_300.svg#Roboto") format("svg"), url("../fonts/Roboto_300.ttf") format("truetype"); } + src: url("../fonts/Roboto_300.eot?#iefix") format("embedded-opentype"), url("../fonts/Roboto_300.woff") format("woff"), url("../fonts/Roboto_300.woff2") format("woff2"), url("../fonts/Roboto_300.svg#Roboto") format("svg"), url("../fonts/Roboto_300.ttf") format("truetype"); +} @font-face { font-family: 'Roboto'; font-style: normal; font-weight: 400; - src: url("../fonts/Roboto_400.eot?#iefix") format("embedded-opentype"), url("../fonts/Roboto_400.woff") format("woff"), url("../fonts/Roboto_400.woff2") format("woff2"), url("../fonts/Roboto_400.svg#Roboto") format("svg"), url("../fonts/Roboto_400.ttf") format("truetype"); } + src: url("../fonts/Roboto_400.eot?#iefix") format("embedded-opentype"), url("../fonts/Roboto_400.woff") format("woff"), url("../fonts/Roboto_400.woff2") format("woff2"), url("../fonts/Roboto_400.svg#Roboto") format("svg"), url("../fonts/Roboto_400.ttf") format("truetype"); +} @font-face { font-family: 'Roboto'; font-style: normal; font-weight: 500; - src: url("../fonts/Roboto_500.eot?#iefix") format("embedded-opentype"), url("../fonts/Roboto_500.woff") format("woff"), url("../fonts/Roboto_500.woff2") format("woff2"), url("../fonts/Roboto_500.svg#Roboto") format("svg"), url("../fonts/Roboto_500.ttf") format("truetype"); } + src: url("../fonts/Roboto_500.eot?#iefix") format("embedded-opentype"), url("../fonts/Roboto_500.woff") format("woff"), url("../fonts/Roboto_500.woff2") format("woff2"), url("../fonts/Roboto_500.svg#Roboto") format("svg"), url("../fonts/Roboto_500.ttf") format("truetype"); +} @font-face { font-family: 'Roboto Mono'; font-style: normal; font-weight: 300; - src: url("../fonts/Roboto_Mono_300.eot?#iefix") format("embedded-opentype"), url("../fonts/Roboto_Mono_300.woff") format("woff"), url("../fonts/Roboto_Mono_300.woff2") format("woff2"), url("../fonts/Roboto_Mono_300.svg#RobotoMono") format("svg"), url("../fonts/Roboto_Mono_300.ttf") format("truetype"); } + src: url("../fonts/Roboto_Mono_300.eot?#iefix") format("embedded-opentype"), url("../fonts/Roboto_Mono_300.woff") format("woff"), url("../fonts/Roboto_Mono_300.woff2") format("woff2"), url("../fonts/Roboto_Mono_300.svg#RobotoMono") format("svg"), url("../fonts/Roboto_Mono_300.ttf") format("truetype"); +} @font-face { font-family: 'Roboto Mono'; font-style: normal; font-weight: 400; - src: url("../fonts/Roboto_Mono_400.eot?#iefix") format("embedded-opentype"), url("../fonts/Roboto_Mono_400.woff") format("woff"), url("../fonts/Roboto_Mono_400.woff2") format("woff2"), url("../fonts/Roboto_Mono_400.svg#RobotoMono") format("svg"), url("../fonts/Roboto_Mono_400.ttf") format("truetype"); } + src: url("../fonts/Roboto_Mono_400.eot?#iefix") format("embedded-opentype"), url("../fonts/Roboto_Mono_400.woff") format("woff"), url("../fonts/Roboto_Mono_400.woff2") format("woff2"), url("../fonts/Roboto_Mono_400.svg#RobotoMono") format("svg"), url("../fonts/Roboto_Mono_400.ttf") format("truetype"); +} @font-face { font-family: 'Roboto Mono'; font-style: normal; font-weight: 500; - src: local("Roboto-Mono Medium"), local("RobotoMono-Medium"), url("../fonts/Roboto_Mono_500.eot?#iefix") format("embedded-opentype"), url("../fonts/Roboto_Mono_500.woff") format("woff"), url("../fonts/Roboto_Mono_500.woff2") format("woff2"), url("../fonts/Roboto_Mono_500.svg#RobotoMono") format("svg"), url("../fonts/Roboto_Mono_500.ttf") format("truetype"); } + src: local("Roboto-Mono Medium"), local("RobotoMono-Medium"), url("../fonts/Roboto_Mono_500.eot?#iefix") format("embedded-opentype"), url("../fonts/Roboto_Mono_500.woff") format("woff"), url("../fonts/Roboto_Mono_500.woff2") format("woff2"), url("../fonts/Roboto_Mono_500.svg#RobotoMono") format("svg"), url("../fonts/Roboto_Mono_500.ttf") format("truetype"); +} diff --git a/site/assets/js/app/site.js b/site/assets/js/app/site.js index 04c0c495..12bee3ec 100644 --- a/site/assets/js/app/site.js +++ b/site/assets/js/app/site.js @@ -7,6 +7,8 @@ const isDesktop = !isMobile const htmlClassList = document.body.parentNode.classList htmlClassList.add(isDesktop ? 'desktop' : 'mobile') +function toArray(A) { return Array.prototype.slice.apply(A) } + var site = (function(){ var site = {} site.init = function(){ @@ -17,6 +19,11 @@ var site = (function(){ if (paras.length) { paras[0].classList.add('first_paragraph') } + toArray(document.querySelectorAll('header .links a')).forEach(tag => { + if (window.location.href.match(tag.href)) { + tag.classList.add('active') + } + }) } site.init() })() \ No newline at end of file diff --git a/site/public/about/credits/index.html b/site/public/about/credits/index.html index 9fec7e64..f1a28b0e 100644 --- a/site/public/about/credits/index.html +++ b/site/public/about/credits/index.html @@ -18,10 +18,10 @@ The Darkside of Datasets
          diff --git a/site/public/about/disclaimer/index.html b/site/public/about/disclaimer/index.html index 553bf084..5df5d656 100644 --- a/site/public/about/disclaimer/index.html +++ b/site/public/about/disclaimer/index.html @@ -18,10 +18,10 @@ The Darkside of Datasets
          diff --git a/site/public/about/index.html b/site/public/about/index.html index 363e8fc0..f1a28b0e 100644 --- a/site/public/about/index.html +++ b/site/public/about/index.html @@ -18,15 +18,16 @@ The Darkside of Datasets
          -
          alt text
          alt text
            +

            Credits

            +
            alt text
            alt text
            • MegaPixels by Adam Harvey
            • Made with support from Mozilla
            • Site developed by Jules Laplace
            • diff --git a/site/public/about/press/index.html b/site/public/about/press/index.html index aa6e5e13..e5763036 100644 --- a/site/public/about/press/index.html +++ b/site/public/about/press/index.html @@ -18,10 +18,10 @@ The Darkside of Datasets
              diff --git a/site/public/about/privacy/index.html b/site/public/about/privacy/index.html index d1ec1c77..7ad9564f 100644 --- a/site/public/about/privacy/index.html +++ b/site/public/about/privacy/index.html @@ -18,10 +18,10 @@ The Darkside of Datasets
              @@ -84,7 +84,6 @@ megapixels.cc will take all steps reasonably necessary to ensure that your data

              Disclosure Of Data

              Legal Requirements

              megapixels.cc may disclose your Personal Data in the good faith belief that such action is necessary to:

              -

                • To comply with a legal obligation
                • To protect and defend the rights or property of megapixels.cc
                • diff --git a/site/public/about/style/index.html b/site/public/about/style/index.html index 24e6f5be..eea861ac 100644 --- a/site/public/about/style/index.html +++ b/site/public/about/style/index.html @@ -18,15 +18,17 @@ The Darkside of Datasets
                  -
                  Alt text here
                  Alt text here

                  Header 2

                  +

                  Style Examples

                  +
                  Alt text here
                  Alt text here

                  Header 1

                  +

                  Header 2

                  Header 3

                  Header 4

                  Header 5
                  diff --git a/site/public/about/terms/index.html b/site/public/about/terms/index.html index 4b9f4445..db8b9e57 100644 --- a/site/public/about/terms/index.html +++ b/site/public/about/terms/index.html @@ -18,10 +18,10 @@ The Darkside of Datasets
                  diff --git a/site/public/datasets/lfw/index.html b/site/public/datasets/lfw/index.html index a130c24e..76549d25 100644 --- a/site/public/datasets/lfw/index.html +++ b/site/public/datasets/lfw/index.html @@ -18,28 +18,22 @@ The Darkside of Datasets
                  -
                    -
                  • Created 2007
                  • -
                  • Images 13,233
                  • -
                  • People 5,749
                  • -
                  • Created From Yahoo News images
                  • -
                  • Analyzed and searchable
                  • -
                  -

                  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 as “The Wild”.

                  +

                  Labeled Faces in The Wild

                  +
                  Created
                  2007
                  Images
                  13,233
                  People
                  5,749
                  Created From
                  Yahoo News images
                  Search available
                  Searchable

                  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 as “The Wild”.

                  Eight out of 5,749 people in the Labeled Faces in the Wild dataset. The face recognition training dataset is created entirely from photos downloaded from the Internet.
                  Eight out of 5,749 people in the Labeled Faces in the Wild dataset. The face recognition training dataset is created entirely from photos downloaded from the Internet.

                  INTRO

                  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).

                  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.

                  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.

                  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.

                  -
                  From Aaron Eckhart to Zydrunas Ilgauskas. A small sampling of the LFW dataset
                  From Aaron Eckhart to Zydrunas Ilgauskas. A small sampling of the LFW dataset

                  In addition to commercial use as an evaluation tool, alll of the faces in LFW dataset are prepackaged into a popular machine learning code framework called scikit-learn.

                  +
                  From Aaron Eckhart to Zydrunas Ilgauskas. A small sampling of the LFW dataset
                  From Aaron Eckhart to Zydrunas Ilgauskas. A small sampling of the LFW dataset

                  In addition to commercial use as an evaluation tool, alll of the faces in LFW dataset are prepackaged into a popular machine learning code framework called scikit-learn.

                  Usage

                  #!/usr/bin/python
                   from matplotlib import plt
                  diff --git a/site/public/datasets/lfw/what/index.html b/site/public/datasets/lfw/what/index.html
                  new file mode 100644
                  index 00000000..52993a79
                  --- /dev/null
                  +++ b/site/public/datasets/lfw/what/index.html
                  @@ -0,0 +1,141 @@
                  +
                  +
                  +
                  +  MegaPixels
                  +  
                  +  
                  +  
                  +  
                  +  
                  +  
                  +  
                  +
                  +
                  +  
                  + + +
                  MegaPixels
                  + The Darkside of Datasets +
                  + +
                  +
                  + +

                  Labeled Faces in The Wild

                  +
                    +
                  • Created 2007 (auto)
                  • +
                  • Images 13,233 (auto)
                  • +
                  • People 5,749 (auto)
                  • +
                  • Created From Yahoo News images (auto)
                  • +
                  • Analyzed and searchable (auto)
                  • +
                  +

                  Labeled Faces in The Wild is amongst the most widely used facial recognition training datasets in the world and is the first facial recognition dataset [^lfw_names_faces] of its kind to be created entirely from Internet photos. It includes 13,233 images of 5,749 people that appeared on Yahoo News between 2002 - 2004.

                  +
                  Eight out of 5,749 people in the Labeled Faces in the Wild dataset. The face recognition training dataset is created entirely from photos downloaded from the Internet.
                  Eight out of 5,749 people in the Labeled Faces in the Wild dataset. The face recognition training dataset is created entirely from photos downloaded from the Internet.

                  INTRO

                  +

                  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).

                  +

                  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.

                  +

                  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.

                  +

                  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.

                  +
                  From Aaron Eckhart to Zydrunas Ilgauskas. A small sampling of the LFW dataset
                  From Aaron Eckhart to Zydrunas Ilgauskas. A small sampling of the LFW dataset

                  In addition to commercial use as an evaluation tool, all of the faces in LFW dataset are prepackaged into a popular machine learning code framework called scikit-learn.

                  +

                  Usage

                  +
                  #!/usr/bin/python
                  +from matplotlib import plt
                  +from sklearn.datasets import fetch_lfw_people
                  +lfw_people = fetch_lfw_people()
                  +lfw_person = lfw_people[0]
                  +plt.imshow(lfw_person)
                  +
                  +

                  Commercial Use

                  +

                  The LFW dataset is used by numerous companies for benchmarking algorithms and in some cases training. According to the benchmarking results page [^lfw_results] provided by the authors, over 2 dozen companies have contributed their benchmark results

                  +
                  load file: lfw_commercial_use.csv
                  +name_display,company_url,example_url,country,description
                  +
                  +
          + + + + + + + + + + + + + + + + + + + + + + + +
          CompanyCountryIndustries
          AratekChinaBiometric sensors for telecom, civil identification, finance, education, POS, and transportation
          AratekChinaBiometric sensors for telecom, civil identification, finance, education, POS, and transportation
          AratekChinaBiometric sensors for telecom, civil identification, finance, education, POS, and transportation
          +

          Add 2-4 screenshots of companies mentioning LFW here

          +
          ReadSense
          ReadSense

          In benchmarking, companies use a dataset to evaluate their algorithms which are typically trained on other data. After training, researchers will use LFW as a benchmark to compare results with other algorithms.

          +

          For example, Baidu (est. net worth $13B) uses LFW to report results for their "Targeting Ultimate Accuracy: Face Recognition via Deep Embedding". According to the three Baidu researchers who produced the paper:

          +

          LFW has been the most popular evaluation benchmark for face recognition, and played a very important role in facilitating the face recognition society to improve algorithm. 1.

          +
          +

          Citations

          + + + + + + + + + + + + + + + + + + + + + + +
          TitleOrganizationCountryType
          3D-aided face recognition from videosUniversity of LyonFranceedu
          A Community Detection Approach to Cleaning Extremely Large Face DatabaseNational University of Defense Technology, ChinaChinaedu
          +

          Conclusion

          +

          The LFW face recognition training and evaluation dataset is a historically important face dataset as it was the first popular dataset to be created entirely from Internet images, paving the way for a global trend towards downloading anyone’s face from the Internet and adding it to a dataset. As will be evident with other datasets, LFW’s approach has now become the norm.

          +

          For all the 5,000 people in this datasets, their face is forever a part of facial recognition history. It would be impossible to remove anyone from the dataset because it is so ubiquitous. For their rest of the lives and forever after, these 5,000 people will continue to be used for training facial recognition surveillance.

          +

          Notes

          +

          According to BiometricUpdate.com2, 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."

          +
          +
          +
          1. "Chinese tourist town uses face recognition as an entry pass". New Scientist. November 17, 2016. https://www.newscientist.com/article/2113176-chinese-tourist-town-uses-face-recognition-as-an-entry-pass/

          2. +
          3. "PING AN Tech facial recognition receives high score in latest LFW test results". https://www.biometricupdate.com/201702/ping-an-tech-facial-recognition-receives-high-score-in-latest-lfw-test-results

          4. +
          +
          +
          + +
          + + + + \ No newline at end of file diff --git a/site/public/datasets/vgg_faces2/index.html b/site/public/datasets/vgg_faces2/index.html index ee353047..95b5f7d7 100644 --- a/site/public/datasets/vgg_faces2/index.html +++ b/site/public/datasets/vgg_faces2/index.html @@ -18,23 +18,17 @@ The Darkside of Datasets
          -
            -
          • Created 2007
          • -
          • Images 13,233
          • -
          • People 5,749
          • -
          • Created From Yahoo News images
          • -
          • Search available Searchable
          • -
          -

          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”.

          -
          Eight out of 5,749 people in the Labeled Faces in the Wild dataset. The face recognition training dataset is created entirely from photos downloaded from the Internet.
          Eight out of 5,749 people in the Labeled Faces in the Wild dataset. The face recognition training dataset is created entirely from photos downloaded from the Internet.

          INTRO

          +

          Labeled Faces in The Wild

          +
          Created
          2007
          Images
          13,233
          People
          5,749
          Created From
          Yahoo News images
          Search available
          [Searchable](#)

          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”.

          +

          INTRO

          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).

          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.

          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.

          diff --git a/site/public/index.html b/site/public/index.html index ea3dc24c..3ce22936 100644 --- a/site/public/index.html +++ b/site/public/index.html @@ -18,23 +18,23 @@ The Darkside of Datasets

          MegaPixels is an art project that explores the dark side of face recognition training data and the future of computer vision

          Made by Adam Harvey in partnership with Mozilla.
          -Read more about MegaPixels

          +Read more [about MegaPixels]

          [Explore Datasets] [Explore Algorithms]

          Facial Recognition Datasets

          Regular Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat.

          Summary

            -
          • 275 datasets found
          • +
          • 275 datsets found
          • Created between the years 1993-2018
          • Smallest dataset: 20 images
          • Largest dataset: 10,000,000 images
          • diff --git a/site/public/research/01_from_1_to_100_pixels/index.html b/site/public/research/01_from_1_to_100_pixels/index.html index 90f142e9..55e02c6c 100644 --- a/site/public/research/01_from_1_to_100_pixels/index.html +++ b/site/public/research/01_from_1_to_100_pixels/index.html @@ -18,10 +18,10 @@ The Darkside of Datasets
            @@ -74,13 +74,6 @@
          -
          -

          MORE RESEARCH

          -
          - -
          -
          -
          {{ content }} - -
          -

          MORE RESEARCH

          -
          - {% for blogpost in blogposts %} -
          - {{ blogpost.title }} - {{ blogpost.date }} -
          - {% endfor %} -
          -
          {% endblock %} -- cgit v1.2.3-70-g09d2 From 2d950c3fa3b8107f941a80f88127ab45e371d128 Mon Sep 17 00:00:00 2001 From: Jules Laplace Date: Thu, 6 Dec 2018 19:39:29 +0100 Subject: homepage css --- builder/README.md | 3 + builder/builder.py | 10 +- builder/parser.py | 4 +- site/assets/css/css.css | 93 ++++++++- site/assets/js/app/face.js | 213 +++++++++++++++++++++ site/assets/js/app/site.js | 3 +- site/public/about/credits/index.html | 1 + site/public/about/disclaimer/index.html | 1 + site/public/about/index.html | 1 + site/public/about/press/index.html | 1 + site/public/about/privacy/index.html | 1 + site/public/about/style/index.html | 3 +- site/public/about/terms/index.html | 1 + site/public/datasets/lfw/index.html | 160 +++++++++++++--- site/public/datasets/lfw/what/index.html | 1 + site/public/datasets/vgg_faces2/index.html | 1 + site/public/index.html | 42 ++-- .../research/01_from_1_to_100_pixels/index.html | 1 + site/public/research/index.html | 1 + site/templates/home.html | 32 ++++ site/templates/layout.html | 1 + 21 files changed, 524 insertions(+), 50 deletions(-) create mode 100644 site/assets/js/app/face.js create mode 100644 site/templates/home.html (limited to 'site/public/research') diff --git a/builder/README.md b/builder/README.md index 1a6d3a1e..57c024cb 100644 --- a/builder/README.md +++ b/builder/README.md @@ -19,3 +19,6 @@ authors: Adam Harvey, Berit Gilma, Matthew Stender Static assets: `v1/site/about/assets/picture.jpg` Dataset assets: `v1/datasets/lfw/assets/picture.jpg` + +## Markup + diff --git a/builder/builder.py b/builder/builder.py index 0e404b88..620fc710 100644 --- a/builder/builder.py +++ b/builder/builder.py @@ -29,10 +29,12 @@ def build_page(fn, research_posts): output_path = public_path + metadata['url'] output_fn = os.path.join(output_path, "index.html") - is_research = False + skip_h1 = False - if 'research/' in fn: - is_research = True + if metadata['url'] == '/': + template = env.get_template("home.html") + elif 'research/' in fn: + skip_h1 = True template = env.get_template("research.html") else: template = env.get_template("page.html") @@ -47,7 +49,7 @@ def build_page(fn, research_posts): if 'index.md' in fn: s3.sync_directory(dirname, s3_dir, metadata) - content = parser.parse_markdown(sections, s3_path, skip_h1=is_research) + content = parser.parse_markdown(sections, s3_path, skip_h1=skip_h1) html = template.render( metadata=metadata, diff --git a/builder/parser.py b/builder/parser.py index da3044a0..dd3643bf 100644 --- a/builder/parser.py +++ b/builder/parser.py @@ -46,15 +46,13 @@ def format_metadata(section): def parse_markdown(sections, s3_path, skip_h1=False): groups = [] current_group = [] - seen_metadata = False for section in sections: if skip_h1 and section.startswith('# '): continue - elif section.startswith('+ ') and not seen_metadata: + elif section.startswith('+ '): groups.append(format_section(current_group, s3_path)) groups.append(format_metadata(section)) current_group = [] - seen_metadata = True elif '![wide:' in section: groups.append(format_section(current_group, s3_path)) groups.append(format_section([section], s3_path, type='wide')) diff --git a/site/assets/css/css.css b/site/assets/css/css.css index 843809a8..9ac35699 100644 --- a/site/assets/css/css.css +++ b/site/assets/css/css.css @@ -5,9 +5,11 @@ html, body { width: 100%; min-height: 100%; font-family: 'Roboto', sans-serif; - background: #191919; color: #b8b8b8; } +html { + background: #191919; +} /* header */ @@ -119,12 +121,14 @@ h1 { font-size: 24pt; margin: 75px 0 10px; padding: 0; + transition: color 0.2s cubic-bezier(0,0,1,1); } h2, h3 { margin: 0 0 20px 0; padding: 0; font-size: 11pt; font-weight: 500; + transition: color 0.2s cubic-bezier(0,0,1,1); } th, .gray, h2, h3 { @@ -281,6 +285,9 @@ section.wide .image { max-width: 620px; margin: 10px auto 0 auto; } + +/* blog index */ + .research_index { margin-top: 40px; } @@ -289,10 +296,88 @@ section.wide .image { } .research_index h1 { margin-top: 20px; + text-decoration: underline; +} +.desktop .research_index section:hover h1 { + color: #fff; +} +.research_index section:hover h2 { + color: #ddd; } -/* blogpost index */ +/* home page */ -.blogposts div { - margin-bottom: 5px; +.hero { + position: relative; + width: 100%; + max-width: 1200px; + height: 50vw; + max-height: 70vh; + display: flex; + align-items: center; + margin: 0 auto; +} +#face_container { + pointer-events: none; + position: absolute; + width: 50vw; + height: 50vw; + max-height: 70vh; + top: 0; + right: 0; + z-index: -1; + text-align: center; +} +.currentFace { + position: absolute; + bottom: 50px; + width: 100%; + left: 0; + text-align: center; +} +.intro { + max-width: 640px; + padding: 75px 0 75px 10px; + z-index: 1; +} +.intro .headline { + font-family: 'Roboto Mono', monospace; + font-size: 16pt; +} +.intro .buttons { + margin: 40px 0; +} +.intro button { + font-family: 'Roboto', sans-serif; + padding: 8px 12px; + border-radius: 6px; + border: 1px solid transparent; + cursor: pointer; + font-size: 11pt; + margin-right: 10px; + transition: color 0.1s cubic-bezier(0,0,1,1), background-color 0.1s cubic-bezier(0,0,1,1); +} +.intro button.normal { + background: #191919; + border-color: #444; + color: #ddd; +} +.intro button.important { + background: #444; + border-color: #444; + color: #ddd; +} +.desktop .intro button:hover { + background: #666; + border-color: #666; + color: #fff; +} +.intro .under { + color: #888; +} +.intro .under a { + color: #bbb; +} +.desktop .intro .under a:hover { + color: #fff; } \ No newline at end of file diff --git a/site/assets/js/app/face.js b/site/assets/js/app/face.js new file mode 100644 index 00000000..e8bcd313 --- /dev/null +++ b/site/assets/js/app/face.js @@ -0,0 +1,213 @@ +var face = (function(){ + var container = document.querySelector("#face_container") + var camera, controls, scene, renderer + var mouse = new THREE.Vector2(0.5, 0.5) + var mouseTarget = new THREE.Vector2(0.5, 0.5) + var POINT_SCALE = 1.8 + var FACE_POINT_COUNT = 68 + var SWAP_TIME = 500 + var cubes = [], meshes = [] + var currentFace = document.querySelector('.currentFace') + var faceBuffer = (function () { + var a = new Array(FACE_POINT_COUNT) + for (let i = 0; i < FACE_POINT_COUNT; i++) { + a[i] = new THREE.Vector3() + } + return a + })() + var last_t = 0, start_t = 0 + var colors = [ + 0xff3333, + 0xff8833, + 0xffff33, + 0x338833, + 0x3388ff, + 0x3333ff, + 0x8833ff, + 0xff3388, + 0xffffff, + ] + var swapping = false, swap_count = 0, swapFrom, swapTo, face_names, faces + init() + + function init() { + fetch("/assets/data/3dlm_0_10.json") + .then(req => req.json()) + .then(data => { + face_names = Object.keys(data) + faces = face_names.map(name => recenter(data[name])) + setup() + build(faces[0]) + updateFace(faces[0]) + setCurrentFace(face_names[0]) + swapTo = faces[0] + animate() + }) + } + function setup() { + var w = window.innerWidth / 2 + var h = Math.min(window.innerWidth / 2, window.innerHeight * 0.7) + camera = new THREE.PerspectiveCamera(70, w/h, 1, 10000) + camera.position.x = 0 + camera.position.y = 0 + camera.position.z = 250 + + scene = new THREE.Scene() + scene.background = new THREE.Color(0x191919) + + renderer = new THREE.WebGLRenderer({ antialias: true }) + renderer.setPixelRatio(window.devicePixelRatio) + renderer.setSize(w, h) + container.appendChild(renderer.domElement) + document.body.addEventListener('mousemove', onMouseMove) + // renderer.domElement.addEventListener('mousedown', swap) + setInterval(swap, 5000) + } + function build(points) { + var matrix = new THREE.Matrix4() + var quaternion = new THREE.Quaternion() + + for (var i = 0; i < FACE_POINT_COUNT; i++) { + var p = points[i] + var geometry = new THREE.BoxBufferGeometry() + var position = new THREE.Vector3(p[0], p[1], p[2]) + var rotation = new THREE.Euler() + var scale = new THREE.Vector3() + var color = new THREE.Color() + scale.x = scale.y = scale.z = POINT_SCALE + quaternion.setFromEuler(rotation, false) + matrix.compose(position, quaternion, scale) + geometry.applyMatrix(matrix) + material = new THREE.MeshBasicMaterial({ color: color.setHex(0xffffff) }) + cube = new THREE.Mesh(geometry, material) + scene.add(cube) + cubes.push(cube) + } + + meshes = getLineGeometry(points).map((geometry, i) => { + var color = new THREE.Color() + var material = new MeshLineMaterial({ + color: color.setHex(colors[i % colors.length]), + }) + var line = new MeshLine() + line.setGeometry(geometry, _ => 1.5) + var mesh = new THREE.Mesh(line.geometry, material) + mesh.geometry.dynamic = true + scene.add(mesh) + return [line, mesh] + }) + } + function lerpPoints(n, A, B, C) { + for (let i = 0, len = A.length; i < len; i++) { + lerpPoint(n, A[i], B[i], C[i]) + } + } + function lerpPoint(n, A, B, C) { + C.x = lerp(n, A.x, B.x) + C.y = lerp(n, A.y, B.y) + C.z = lerp(n, A.z, B.z) + } + function lerp(n, a, b) { + return (b-a) * n + a + } + function swap(){ + if (swapping) return + start_t = last_t + swapping = true + swap_count = (swap_count + 1) % faces.length + swapFrom = swapTo + swapTo = faces[swap_count] + setCurrentFace(face_names[swap_count]) + } + function setCurrentFace(name) { + name = name.replace('.png', '').split('_').filter(s => !s.match(/\d+/)).join(' ') + currentFace.innerHTML = name + } + function update_swap(t){ + var n = (t - start_t) / SWAP_TIME + if (n > 1) { + swapping = false + n = 1 + } + lerpPoints(n, swapFrom, swapTo, faceBuffer) + updateFace(faceBuffer) + } + function updateFace(points) { + updateCubeGeometry(points) + updateLineGeometry(points) + } + function updateCubeGeometry(points) { + cubes.forEach((cube, i) => { + const p = points[i] + cube.position.set(p.x, p.y, p.z) + }) + } + function updateLineGeometry(points) { + getLineGeometry(points).map((geometry, i) => { + var [line, mesh] = meshes[i] + line.setGeometry(geometry, _ => 1.5) + mesh.geometry.vertices = line.geometry.vertices + mesh.geometry.verticesNeedUpdate = true + }) + } + function getLineGeometry(points) { + return [ + points.slice(0, 17), + points.slice(17, 22), + points.slice(22, 27), + points.slice(27, 31), + points.slice(31, 36), + points.slice(36, 42), + points.slice(42, 48), + points.slice(48) + ].map((a, i) => { + var geometry = new THREE.Geometry() + a.forEach(p => geometry.vertices.push(p)) + if (i > 4) { + geometry.vertices.push(a[0]) + } + return geometry + }) + } + function getBounds(obj) { + return obj.reduce((a, p) => { + return [ + Math.min(a[0], p[0]), + Math.max(a[1], p[0]), + Math.min(a[2], p[1]), + Math.max(a[3], p[1]), + Math.min(a[4], p[2]), + Math.max(a[5], p[2]), + ] + }, [Infinity, -Infinity, Infinity, -Infinity, Infinity, -Infinity]) + } + function recenter(obj) { + const bounds = getBounds(obj) + const x_width = (bounds[1] - bounds[0]) / 2 + const y_width = (bounds[3] - bounds[2]) / -3 + const z_width = (bounds[5] - bounds[4]) / 2 + return obj.map(p => { + p[0] = p[0] - bounds[0] - x_width + p[1] = -p[1] + bounds[1] + y_width + p[2] = p[2] - bounds[2] + z_width + return new THREE.Vector3(p[0], p[1], p[2]) + }) + } + // + function onMouseMove(e) { + mouse.x = e.clientX / window.innerWidth + mouse.y = e.clientY / window.innerHeight + } + function animate(t) { + requestAnimationFrame(animate) + if (swapping) update_swap(t) + renderer.render(scene, camera) + scene.rotation.y += 0.01 * Math.PI + mouseTarget.x += (mouse.x - mouseTarget.x) * 0.1 + mouseTarget.y += (mouse.y - mouseTarget.y) * 0.1 + scene.rotation.x = (mouseTarget.y - 0.5) * Math.PI / 2 + // scene.rotation.y = (mouseTarget.x - 0.5) * Math.PI + scene.rotation.y += 0.01 + last_t = t + } +})() diff --git a/site/assets/js/app/site.js b/site/assets/js/app/site.js index 12bee3ec..eb6886c2 100644 --- a/site/assets/js/app/site.js +++ b/site/assets/js/app/site.js @@ -7,7 +7,8 @@ const isDesktop = !isMobile const htmlClassList = document.body.parentNode.classList htmlClassList.add(isDesktop ? 'desktop' : 'mobile') -function toArray(A) { return Array.prototype.slice.apply(A) } +function toArray(a) { return Array.prototype.slice.apply(a) } +function choice(a) { return a[Math.floor(Math.random()*a.length)]} var site = (function(){ var site = {} diff --git a/site/public/about/credits/index.html b/site/public/about/credits/index.html index f1a28b0e..65bc7ac4 100644 --- a/site/public/about/credits/index.html +++ b/site/public/about/credits/index.html @@ -52,5 +52,6 @@
          + \ No newline at end of file diff --git a/site/public/about/disclaimer/index.html b/site/public/about/disclaimer/index.html index 5df5d656..b0215bde 100644 --- a/site/public/about/disclaimer/index.html +++ b/site/public/about/disclaimer/index.html @@ -52,5 +52,6 @@
      + \ No newline at end of file diff --git a/site/public/about/index.html b/site/public/about/index.html index f1a28b0e..65bc7ac4 100644 --- a/site/public/about/index.html +++ b/site/public/about/index.html @@ -52,5 +52,6 @@
    + \ No newline at end of file diff --git a/site/public/about/press/index.html b/site/public/about/press/index.html index e5763036..09c89165 100644 --- a/site/public/about/press/index.html +++ b/site/public/about/press/index.html @@ -50,5 +50,6 @@ + \ No newline at end of file diff --git a/site/public/about/privacy/index.html b/site/public/about/privacy/index.html index 7ad9564f..5675f072 100644 --- a/site/public/about/privacy/index.html +++ b/site/public/about/privacy/index.html @@ -129,5 +129,6 @@ You are advised to review this Privacy Policy periodically for any changes. Chan + \ No newline at end of file diff --git a/site/public/about/style/index.html b/site/public/about/style/index.html index eea861ac..f2c0d4b8 100644 --- a/site/public/about/style/index.html +++ b/site/public/about/style/index.html @@ -27,7 +27,7 @@

    Style Examples

    -
    Alt text here
    Alt text here

    Header 1

    +
    Alt text here
    Alt text here
    Date
    17-Jan-2019
    Numbers
    17
    Identities
    12,139
    But also
    This is a test of the stylesheet

    Header 1

    Header 2

    Header 3

    Header 4

    @@ -85,5 +85,6 @@ But let's throw in a <b>tag</b>.
    + \ No newline at end of file diff --git a/site/public/about/terms/index.html b/site/public/about/terms/index.html index db8b9e57..078c339f 100644 --- a/site/public/about/terms/index.html +++ b/site/public/about/terms/index.html @@ -64,5 +64,6 @@ + \ No newline at end of file diff --git a/site/public/datasets/lfw/index.html b/site/public/datasets/lfw/index.html index 76549d25..39052b44 100644 --- a/site/public/datasets/lfw/index.html +++ b/site/public/datasets/lfw/index.html @@ -27,23 +27,22 @@

    Labeled Faces in The Wild

    -
    Created
    2007
    Images
    13,233
    People
    5,749
    Created From
    Yahoo News images
    Search available
    Searchable

    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 as “The Wild”.

    -
    Eight out of 5,749 people in the Labeled Faces in the Wild dataset. The face recognition training dataset is created entirely from photos downloaded from the Internet.
    Eight out of 5,749 people in the Labeled Faces in the Wild dataset. The face recognition training dataset is created entirely from photos downloaded from the Internet.

    INTRO

    +
    Created
    2007
    Images
    13,233
    People
    5,749
    Created From
    Yahoo News images
    Search available
    Searchable

    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 that were 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.

    +

    {INSERT IMAGE SEARCH MODULE}

    +

    {INSERT TEXT SEARCH MODULE}

    +
    Eight out of 5,749 people in the Labeled Faces in the Wild dataset. The face recognition training dataset is created entirely from photos downloaded from the Internet.
    Eight out of 5,749 people in the Labeled Faces in the Wild dataset. The face recognition training dataset is created entirely from photos downloaded from the Internet.

    INTRO

    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).

    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.

    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.

    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.

    -
    From Aaron Eckhart to Zydrunas Ilgauskas. A small sampling of the LFW dataset
    From Aaron Eckhart to Zydrunas Ilgauskas. A small sampling of the LFW dataset

    In addition to commercial use as an evaluation tool, alll of the faces in LFW dataset are prepackaged into a popular machine learning code framework called scikit-learn.

    -

    Usage

    -
    #!/usr/bin/python
    -from matplotlib import plt
    -from sklearn.datasets import fetch_lfw_people
    -lfw_people = fetch_lfw_people()
    -lfw_person = lfw_people[0]
    -plt.imshow(lfw_person)
    -
    +
    The entire LFW dataset cropped to facial regions
    The entire LFW dataset cropped to facial regions

    In addition to commercial use as an evaluation tool, alll of the faces in LFW dataset are prepackaged into a popular machine learning code framework called scikit-learn.

    +

    Facts

    +

    The person with the most images is: +The person with the least images is:

    Commercial Use

    -

    The LFW dataset is used by numerous companies for benchmarking algorithms and in some cases training. According to the benchmarking results page [^lfw_results] provided by the authors, over 2 dozen companies have contributed their benchmark results

    +

    The LFW dataset is used by numerous companies for benchmarking algorithms and in some cases training. According to the benchmarking results page [^lfw_results] provided by the authors, over 2 dozen companies have contributed their benchmark results.

    +

    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."

    +

    According to researchers at the Baidu Research – Institute of Deep Learning "LFW has been the most popular evaluation benchmark for face recognition, and played a very important role in facilitating the face recognition society to improve algorithm. [^lfw_baidu]."

    load file: lfw_commercial_use.csv
     name_display,company_url,example_url,country,description
     
    @@ -73,11 +72,24 @@ name_display,company_url,example_url,country,description

    Add 2-4 screenshots of companies mentioning LFW here

    -
    ReadSense
    ReadSense

    In benchmarking, companies use a dataset to evaluate their algorithms which are typically trained on other data. After training, researchers will use LFW as a benchmark to compare results with other algorithms.

    +
     "PING AN Tech facial recognition receives high score in latest LFW test results"
    "PING AN Tech facial recognition receives high score in latest LFW test results"
    +
     "Face Recognition Performance in LFW benchmark"
    "Face Recognition Performance in LFW benchmark"
    +
     "The 1st place in face verification challenge, LFW"
    "The 1st place in face verification challenge, LFW"

    In benchmarking, companies use a dataset to evaluate their algorithms which are typically trained on other data. After training, researchers will use LFW as a benchmark to compare results with other algorithms.

    For example, Baidu (est. net worth $13B) uses LFW to report results for their "Targeting Ultimate Accuracy: Face Recognition via Deep Embedding". According to the three Baidu researchers who produced the paper:

    -

    LFW has been the most popular evaluation benchmark for face recognition, and played a very important role in facilitating the face recognition society to improve algorithm. 1.

    -

    Citations

    +

    Overall, LFW has at least 456 citations from 123 countries. 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 consequuntur magni dolores eos.

    +

    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 consequuntur magni dolores eos.

    +
    Distribution of citations per year per country for the top 5 countries with citations for the LFW Dataset
    Distribution of citations per year per country for the top 5 countries with citations for the LFW Dataset
    Geographic distributions of citations for the LFW Dataset
    Geographic distributions of citations for the LFW Dataset

    Conclusion

    +

    The LFW face recognition training and evaluation dataset is a historically important face dataset as it was the first popular dataset to be created entirely from Internet images, paving the way for a global trend towards downloading anyone’s face from the Internet and adding it to a dataset. As will be evident with other datasets, LFW’s approach has now become the norm.

    +

    For all the 5,000 people in this datasets, their face is forever a part of facial recognition history. It would be impossible to remove anyone from the dataset because it is so ubiquitous. For their rest of the lives and forever after, these 5,000 people will continue to be used for training facial recognition surveillance.

    +

    Right to Removal

    +

    If you are affected by disclosure of your identity in this dataset please do contact the authors, many state that they are willing to remove images upon request. The authors of the LFW can be reached from the emails posted in their paper:

    +

    You can use the following message to request removal from the dataset:

    +

    Dear [researcher name],

    +

    I am writing to you about the "LFW Dataset". Recently I have discovered that your dataset includes my identity and no longer wish to be included in your dataset

    +

    MegaPixels is an educational art project developed for academic purposes. In no way does this project aim to villify the researchers who produced the datasets. The aim of this project is to encourage discourse around ethics and consent in artificial intelligence by providing information about these datasets that is otherwise difficult to obtain or inaccessible to other researchers.

    +

    Supplementary Data

    +

    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 consequuntur magni dolores eos.

    @@ -99,18 +111,119 @@ name_display,company_url,example_url,country,description + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    TitleChina edu
    3D-aided face recognition from videosUniversity of LyonFranceedu
    3D-aided face recognition from videosUniversity of LyonFranceedu
    3D-aided face recognition from videosUniversity of LyonFranceedu
    3D-aided face recognition from videosUniversity of LyonFranceedu
    3D-aided face recognition from videosUniversity of LyonFranceedu
    3D-aided face recognition from videosUniversity of LyonFranceedu
    3D-aided face recognition from videosUniversity of LyonFranceedu
    3D-aided face recognition from videosUniversity of LyonFranceedu
    3D-aided face recognition from videosUniversity of LyonFranceedu
    3D-aided face recognition from videosUniversity of LyonFranceedu
    3D-aided face recognition from videosUniversity of LyonFranceedu
    3D-aided face recognition from videosUniversity of LyonFranceedu
    3D-aided face recognition from videosUniversity of LyonFranceedu
    -

    Conclusion

    -

    The LFW face recognition training and evaluation dataset is a historically important face dataset as it was the first popular dataset to be created entirely from Internet images, paving the way for a global trend towards downloading anyone’s face from the Internet and adding it to a dataset. As will be evident with other datasets, LFW’s approach has now become the norm.

    -

    For all the 5,000 people in this datasets, their face is forever a part of facial recognition history. It would be impossible to remove anyone from the dataset because it is so ubiquitous. For their rest of the lives and forever after, these 5,000 people will continue to be used for training facial recognition surveillance.

    -

    Notes

    -

    According to BiometricUpdate.com2, 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."

    +

    Code

    +
    #!/usr/bin/python
    +
    +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))
    +cols, rows = (176, 76)
    +n_ims = cols * rows
    +
    +# build montages
    +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)
    +

    -
    1. "Chinese tourist town uses face recognition as an entry pass". New Scientist. November 17, 2016. https://www.newscientist.com/article/2113176-chinese-tourist-town-uses-face-recognition-as-an-entry-pass/

    2. -
    3. "PING AN Tech facial recognition receives high score in latest LFW test results". https://www.biometricupdate.com/201702/ping-an-tech-facial-recognition-receives-high-score-in-latest-lfw-test-results

    4. -
    +
      @@ -130,5 +243,6 @@ name_display,company_url,example_url,country,description
      + \ No newline at end of file diff --git a/site/public/datasets/lfw/what/index.html b/site/public/datasets/lfw/what/index.html index 52993a79..ceafb35a 100644 --- a/site/public/datasets/lfw/what/index.html +++ b/site/public/datasets/lfw/what/index.html @@ -137,5 +137,6 @@ name_display,company_url,example_url,country,description + \ No newline at end of file diff --git a/site/public/datasets/vgg_faces2/index.html b/site/public/datasets/vgg_faces2/index.html index 95b5f7d7..3f778f71 100644 --- a/site/public/datasets/vgg_faces2/index.html +++ b/site/public/datasets/vgg_faces2/index.html @@ -58,5 +58,6 @@ + \ No newline at end of file diff --git a/site/public/index.html b/site/public/index.html index 3ce22936..51006b59 100644 --- a/site/public/index.html +++ b/site/public/index.html @@ -26,22 +26,31 @@
      -

      MegaPixels is an art project that explores the dark side of face recognition training data and the future of computer vision

      -

      Made by Adam Harvey in partnership with Mozilla.
      -Read more [about MegaPixels]

      -

      [Explore Datasets] [Explore Algorithms]

      -

      Facial Recognition Datasets

      +
      +
      +
      +
      +
      +
      + MegaPixels is an art project that explores the dark side of face recognition and the future of computer vision. +
      + + + +
      + Made by Adam Harvey in partnership with Mozilla.
      + Read more about MegaPixels +
      +
      +
      + +

      Facial Recognition Datasets

      Regular Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat.

      Summary

      -
        -
      • 275 datsets found
      • -
      • Created between the years 1993-2018
      • -
      • Smallest dataset: 20 images
      • -
      • Largest dataset: 10,000,000 images
      • -
      • Highest resolution faces: 450x500 (Unconstrained College Students)
      • -
      • Lowest resolution faces: 16x20 pixels (QMUL SurvFace)
      • -
      -
      +
      Found
      275 datasets
      Created between
      1993-2018
      Smallest dataset
      20 images
      Largest dataset
      10,000,000 images
      Highest resolution faces
      450x500 (Unconstrained College Students)
      Lowest resolution faces
      16x20 pixels (QMUL SurvFace)
      +
      @@ -59,5 +68,10 @@ Read more [about MegaPixels]

      + + + + + \ No newline at end of file diff --git a/site/public/research/01_from_1_to_100_pixels/index.html b/site/public/research/01_from_1_to_100_pixels/index.html index 55e02c6c..b4c85d00 100644 --- a/site/public/research/01_from_1_to_100_pixels/index.html +++ b/site/public/research/01_from_1_to_100_pixels/index.html @@ -90,5 +90,6 @@ + \ No newline at end of file diff --git a/site/public/research/index.html b/site/public/research/index.html index 1f61dadf..cf9546e1 100644 --- a/site/public/research/index.html +++ b/site/public/research/index.html @@ -46,5 +46,6 @@ + \ No newline at end of file diff --git a/site/templates/home.html b/site/templates/home.html new file mode 100644 index 00000000..436c1ddf --- /dev/null +++ b/site/templates/home.html @@ -0,0 +1,32 @@ +{% extends 'layout.html' %} + +{% block content %} +
      +
      +
      +
      +
      +
      + MegaPixels is an art project that explores the dark side of face recognition and the future of computer vision. +
      + + + +
      + Made by Adam Harvey in partnership with Mozilla.
      + Read more about MegaPixels +
      +
      +
      + + {{ content }} + +{% endblock %} + +{% block scripts %} + + + +{% endblock %} diff --git a/site/templates/layout.html b/site/templates/layout.html index 7558163e..605f9788 100644 --- a/site/templates/layout.html +++ b/site/templates/layout.html @@ -42,5 +42,6 @@ +{% block scripts %}{% endblock %} \ No newline at end of file -- cgit v1.2.3-70-g09d2 From 485cf0e4665c660d4e5e1fba00a95bc8036809c6 Mon Sep 17 00:00:00 2001 From: Jules Laplace Date: Sat, 15 Dec 2018 16:40:34 +0100 Subject: db stuff --- faiss/__init__.py | 0 faiss/server.py | 68 ----------------- megapixels/app/server/api.py | 97 +++++++++---------------- site/public/datasets/lfw/index.html | 67 +++++++++++++---- site/public/datasets/vgg_face2/index.html | 84 +++++++++++++++++++++ site/public/index.html | 1 + site/public/research/00_introduction/index.html | 86 ++++++++++++++++++++++ site/public/research/index.html | 2 +- 8 files changed, 259 insertions(+), 146 deletions(-) delete mode 100644 faiss/__init__.py delete mode 100644 faiss/server.py create mode 100644 site/public/datasets/vgg_face2/index.html create mode 100644 site/public/research/00_introduction/index.html (limited to 'site/public/research') diff --git a/faiss/__init__.py b/faiss/__init__.py deleted file mode 100644 index e69de29b..00000000 diff --git a/faiss/server.py b/faiss/server.py deleted file mode 100644 index a8c660fa..00000000 --- a/faiss/server.py +++ /dev/null @@ -1,68 +0,0 @@ -#!python - -import os -import sys -import json -import time -import argparse -import cv2 as cv -import numpy as np -from datetime import datetime -from flask import Flask, request, render_template, jsonify -from PIL import Image # todo: try to remove PIL dependency -import re - -sanitize_re = re.compile('[\W]+') -valid_exts = ['.gif', '.jpg', '.jpeg', '.png'] - -from dotenv import load_dotenv -load_dotenv() - -from feature_extractor import FeatureExtractor - -DEFAULT_LIMIT = 50 - -app = Flask(__name__, static_url_path="/search/static", static_folder="static") - -# static api routes - this routing is actually handled in the JS -@app.route('/', methods=['GET']) -def index(): - return app.send_static_file('metadata.html') - -# search using an uploaded file -@app.route('/search/api/upload', methods=['POST']) -def upload(): - file = request.files['query_img'] - fn = file.filename - if fn.endswith('blob'): - fn = 'filename.jpg' - - basename, ext = os.path.splitext(fn) - print("got {}, type {}".format(basename, ext)) - if ext.lower() not in valid_exts: - return jsonify({ 'error': 'not an image' }) - - uploaded_fn = datetime.now().isoformat() + "_" + basename - uploaded_fn = sanitize_re.sub('', uploaded_fn) - uploaded_img_path = "static/uploaded/" + uploaded_fn + ext - uploaded_img_path = uploaded_img_path.lower() - print('query: {}'.format(uploaded_img_path)) - - img = Image.open(file.stream).convert('RGB') - # img.save(uploaded_img_path) - # vec = db.load_feature_vector_from_file(uploaded_img_path) - vec = fe.extract(img) - # print(vec.shape) - - results = db.search(vec, limit=limit) - query = { - 'timing': time.time() - start, - } - print(results) - return jsonify({ - 'results': results, - }) - -if __name__=="__main__": - app.run("0.0.0.0", debug=False) - diff --git a/megapixels/app/server/api.py b/megapixels/app/server/api.py index 428c53b1..c5e27dd2 100644 --- a/megapixels/app/server/api.py +++ b/megapixels/app/server/api.py @@ -1,30 +1,13 @@ -from flask import Blueprint, jsonify +import os +import re +import time +from flask import Blueprint, request, jsonify +from PIL import Image # todo: try to remove PIL dependency from app.models.sql_factory import list_datasets, get_dataset, get_table -# from jinja2 import TemplateNotFound - -# import os -# import sys -# import json -# import time -# import argparse -# import cv2 as cv -# import numpy as np -# from datetime import datetime -# from flask import Flask, request, render_template, jsonify -# from PIL import Image # todo: try to remove PIL dependency -# import re - -# sanitize_re = re.compile('[\W]+') -# valid_exts = ['.gif', '.jpg', '.jpeg', '.png'] - -# from dotenv import load_dotenv -# load_dotenv() - -# from feature_extractor import FeatureExtractor - -# DEFAULT_LIMIT = 50 +sanitize_re = re.compile('[\W]+') +valid_exts = ['.gif', '.jpg', '.jpeg', '.png'] api = Blueprint('api', __name__) @@ -40,40 +23,32 @@ def show(name): else: return jsonify({ 'status': 404 }) -@api.route('/dataset//test', methods=['POST']) -def test(name): - print('hiiiiii') - return jsonify({ 'test': 'OK', 'dataset': name }) - -# @router.route('//face', methods=['POST']) -# def upload(name): -# file = request.files['query_img'] -# fn = file.filename -# if fn.endswith('blob'): -# fn = 'filename.jpg' - -# basename, ext = os.path.splitext(fn) -# print("got {}, type {}".format(basename, ext)) -# if ext.lower() not in valid_exts: -# return jsonify({ 'error': 'not an image' }) - -# uploaded_fn = datetime.now().isoformat() + "_" + basename -# uploaded_fn = sanitize_re.sub('', uploaded_fn) -# uploaded_img_path = "static/uploaded/" + uploaded_fn + ext -# uploaded_img_path = uploaded_img_path.lower() -# print('query: {}'.format(uploaded_img_path)) - -# img = Image.open(file.stream).convert('RGB') -# # img.save(uploaded_img_path) -# # vec = db.load_feature_vector_from_file(uploaded_img_path) -# vec = fe.extract(img) -# # print(vec.shape) - -# results = db.search(vec, limit=limit) -# query = { -# 'timing': time.time() - start, -# } -# print(results) -# return jsonify({ -# 'results': results, -# }) +@api.route('/dataset//face', methods=['POST']) +def upload(name): + file = request.files['query_img'] + fn = file.filename + if fn.endswith('blob'): + fn = 'filename.jpg' + + basename, ext = os.path.splitext(fn) + print("got {}, type {}".format(basename, ext)) + if ext.lower() not in valid_exts: + return jsonify({ 'error': 'not an image' }) + + img = Image.open(file.stream).convert('RGB') + + # vec = db.load_feature_vector_from_file(uploaded_img_path) + # vec = fe.extract(img) + # print(vec.shape) + # results = db.search(vec, limit=limit) + + query = { + 'timing': time.time() - start, + } + results = [] + + print(results) + return jsonify({ + 'query': query, + 'results': results, + }) diff --git a/site/public/datasets/lfw/index.html b/site/public/datasets/lfw/index.html index 39052b44..e080229f 100644 --- a/site/public/datasets/lfw/index.html +++ b/site/public/datasets/lfw/index.html @@ -26,23 +26,50 @@
      -

      Labeled Faces in The Wild

      -
      Created
      2007
      Images
      13,233
      People
      5,749
      Created From
      Yahoo News images
      Search available
      Searchable

      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 that were 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.

      +

      Labeled Faces in the Wild

      +
      Created
      2007
      Images
      13,233
      People
      5,749
      Created From
      Yahoo News images
      Search available
      Searchable

      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.

      {INSERT IMAGE SEARCH MODULE}

      {INSERT TEXT SEARCH MODULE}

      -
      Eight out of 5,749 people in the Labeled Faces in the Wild dataset. The face recognition training dataset is created entirely from photos downloaded from the Internet.
      Eight out of 5,749 people in the Labeled Faces in the Wild dataset. The face recognition training dataset is created entirely from photos downloaded from the Internet.

      INTRO

      -

      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).

      -

      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.

      -

      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.

      -

      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.

      -
      The entire LFW dataset cropped to facial regions
      The entire LFW dataset cropped to facial regions

      In addition to commercial use as an evaluation tool, alll of the faces in LFW dataset are prepackaged into a popular machine learning code framework called scikit-learn.

      -

      Facts

      -

      The person with the most images is: -The person with the least images is:

      -

      Commercial Use

      +
      load file: lfw_names_gender_kg_min.csv
      +Name, Images, Gender, Description
      +
      +
      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.
      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.

      Intro

      +

      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.

      +
       all 5,749 people in the LFW Dataset sorted from most to least images collected.
      all 5,749 people in the LFW Dataset sorted from most to least images collected.

      LFW by the Numbers

      +
        +
      • Was first published in 2007
      • +
      • Developed out of a prior dataset from Berkely called "Faces in the Wild" or "Names and Faces" [^lfw_original_paper]
      • +
      • Includes 13,233 images and 5,749 different people [^lfw_website]
      • +
      • There are about 3 men for every 1 woman (4,277 men and 1,472 women)[^lfw_website]
      • +
      • The person with the most images is George W. Bush with 530
      • +
      • Most people (70%) in the dataset have only 1 image
      • +
      • Thre are 1,680 people in the dataset with 2 or more images [^lfw_website]
      • +
      • Two out of 4 of the original authors received funding from the Office of Director of National Intelligence and IARPA for their 2016 LFW survey follow up report
      • +
      • The LFW dataset includes over 500 actors, 30 models, 10 presidents, 24 football players, 124 basketball players, 11 kings, and 2 queens
      • +
      • In all the LFW publications provided by the authors the words "ethics", "consent", and "privacy" appear 0 times [^lfw_original_paper], [^lfw_survey], [^lfw_tech_report] , [^lfw_website]
      • +
      • The word "future" appears 71 times
      • +
      +

      Facts

      +
        +
      • Was created for the purpose of improving "unconstrained face recognition" [^lfw_original_paper]
      • +
      • All images in LFW were obtained "in the wild" meaning without any consent from the subject or from the photographer
      • +
      • The faces were detected using the Viola-Jones haarcascade face detector [^lfw_website] [^lfw_survey]
      • +
      • Is considered the "most popular benchmark for face recognition" [^lfw_baidu]
      • +
      • Is "the most widely used evaluation set in the field of facial recognition" [^lfw_pingan]
      • +
      • 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." [^lfw_pingan]
      • +
      +

      need citations

      +
        +
      • All images were copied from Yahoo News between 2002 - 2004 [^lfw_original_paper]
      • +
      • SenseTime, who has relied on LFW for benchmarking their facial recognition performance, is the leading provider of surveillance to the Chinese Government (need citation)
      • +
      +
       former President George W. Bush
      former President George W. Bush
      +
       Colin Powel (236), Tony Blair (144), and Donald Rumsfeld (121)
      Colin Powel (236), Tony Blair (144), and Donald Rumsfeld (121)

      People and Companies using the LFW Dataset

      +

      This section describes who is using the dataset and for what purposes. It should include specific examples of people or companies with citations and screenshots. This section is followed up by the graph, the map, and then the supplementary material.

      The LFW dataset is used by numerous companies for benchmarking algorithms and in some cases training. According to the benchmarking results page [^lfw_results] provided by the authors, over 2 dozen companies have contributed their benchmark results.

      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."

      According to researchers at the Baidu Research – Institute of Deep Learning "LFW has been the most popular evaluation benchmark for face recognition, and played a very important role in facilitating the face recognition society to improve algorithm. [^lfw_baidu]."

      +

      In addition to commercial use as an evaluation tool, alll of the faces in LFW dataset are prepackaged into a popular machine learning code framework called scikit-learn.

      load file: lfw_commercial_use.csv
       name_display,company_url,example_url,country,description
       
      @@ -83,13 +110,18 @@ name_display,company_url,example_url,country,description

      The LFW face recognition training and evaluation dataset is a historically important face dataset as it was the first popular dataset to be created entirely from Internet images, paving the way for a global trend towards downloading anyone’s face from the Internet and adding it to a dataset. As will be evident with other datasets, LFW’s approach has now become the norm.

      For all the 5,000 people in this datasets, their face is forever a part of facial recognition history. It would be impossible to remove anyone from the dataset because it is so ubiquitous. For their rest of the lives and forever after, these 5,000 people will continue to be used for training facial recognition surveillance.

      Right to Removal

      -

      If you are affected by disclosure of your identity in this dataset please do contact the authors, many state that they are willing to remove images upon request. The authors of the LFW can be reached from the emails posted in their paper:

      +

      If you are affected by disclosure of your identity in this dataset please do contact the authors. Many have stated that they are willing to remove images upon request. The authors of the LFW dataset provide the following email for inquiries:

      You can use the following message to request removal from the dataset:

      +

      To: Gary Huang mailto:gbhuang@cs.umass.edu

      +

      Subject: Request for Removal from LFW Face Dataset

      Dear [researcher name],

      -

      I am writing to you about the "LFW Dataset". Recently I have discovered that your dataset includes my identity and no longer wish to be included in your dataset

      -

      MegaPixels is an educational art project developed for academic purposes. In no way does this project aim to villify the researchers who produced the datasets. The aim of this project is to encourage discourse around ethics and consent in artificial intelligence by providing information about these datasets that is otherwise difficult to obtain or inaccessible to other researchers.

      +

      I am writing to you about the "Labeled Faces in The Wild Dataset". Recently I discovered that your dataset includes my identity and I no longer wish to be included in your dataset.

      +

      The dataset is being used thousands of companies around the world to improve facial recognition software including usage by governments for the purpose of law enforcement, national security, tracking consumers in retail environments, and tracking individuals through public spaces.

      +

      My name as it appears in your dataset is [your name]. Please remove all images from your dataset and inform your newsletter subscribers to likewise update their copies.

      +

      - [your name]

      +

      Supplementary Data

      -

      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 consequuntur magni dolores eos.

      +

      Researchers, journ

      @@ -221,6 +253,9 @@ imageio.imwrite('lfw_montage_full.png', montage) montage_960 = imutils.resize(montage, width=960) imageio.imwrite('lfw_montage_960.jpg', montage_960) +

      Disclaimer

      +

      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.

      +

      Read more about MegaPixels Code of Conduct


        diff --git a/site/public/datasets/vgg_face2/index.html b/site/public/datasets/vgg_face2/index.html new file mode 100644 index 00000000..24a1059b --- /dev/null +++ b/site/public/datasets/vgg_face2/index.html @@ -0,0 +1,84 @@ + + + + MegaPixels + + + + + + + + + +
        + + +
        MegaPixels
        + The Darkside of Datasets +
        + +
        +
        + +

        VGG Faces2

        +
        Created
        2018
        Images
        3.3M
        People
        9,000
        Created From
        Scraping search engines
        Search available
        [Searchable](#)

        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.

        +

        {INSERT IMAGE SEARCH MODULE}

        +

        {INSERT TEXT SEARCH MODULE}

        +
        load file: lfw_names_gender_kg_min.csv
        +Name, Images, Gender, Description
        +
        +

        VGG Face2 by the Numbers

        +
          +
        • 1,331 actresses, 139 presidents
        • +
        • 3 husbands and 16 wives
        • +
        • 2 snooker player
        • +
        • 1 guru
        • +
        • 1 pornographic actress
        • +
        • 3 computer programmer
        • +
        +

        Names and descriptions

        +
          +
        • The original VGGF2 name list has been updated with the results returned from Google Knowledge
        • +
        • Names with a similarity score greater than 0.75 where automatically updated. Scores computed using import difflib; seq = difflib.SequenceMatcher(a=a.lower(), b=b.lower()); score = seq.ratio()
        • +
        • 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"
        • +
        • The 'Description` text was automatically added when the Knowledge Graph score was greater than 250
        • +
        +

        TODO

        +
          +
        • create name list, and populate with Knowledge graph information like LFW
        • +
        • make list of interesting number stats, by the numbers
        • +
        • make list of interesting important facts
        • +
        • write intro abstract
        • +
        • write analysis of usage
        • +
        • find examples, citations, and screenshots of useage
        • +
        • find list of companies using it for table
        • +
        • create montages of the dataset, like LFW
        • +
        • create right to removal information
        • +
        +
        + +
        + + + + + \ No newline at end of file diff --git a/site/public/index.html b/site/public/index.html index 51006b59..91ff467a 100644 --- a/site/public/index.html +++ b/site/public/index.html @@ -71,6 +71,7 @@ + diff --git a/site/public/research/00_introduction/index.html b/site/public/research/00_introduction/index.html new file mode 100644 index 00000000..8f598f5b --- /dev/null +++ b/site/public/research/00_introduction/index.html @@ -0,0 +1,86 @@ + + + + MegaPixels + + + + + + + + + +
        + + +
        MegaPixels
        + The Darkside of Datasets +
        + +
        +
        + +
        +

        Untitled Page

        +
        +
        +
        Posted
        +
        2018-12-31
        +
        +
        +
        By
        +
        Adam Harvey
        +
        + +
        +
        + +

        It was the early 2000s. Face recognition was new and no one seemed sure exactly how well it was going to perform in practice. In theory, face recognition was poised to be a game changer, a force multiplier, a strategic military advantage, a way to make cities safer and to secure borders. This was the future John Ashcroft demanded with the Total Information Awareness act of the 2003 and that spooks had dreamed of for decades. It was a future that academics at Carnegie Mellon Universtiy and Colorado State University would help build. It was also a future that celebrities would play a significant role in building. And to the surprise of ordinary Internet users like myself and perhaps you, it was a future that millions of Internet users would unwittingly play role in creating.

        +

        Now the future has arrived and it doesn't make sense. Facial recognition works yet it doesn't actually work. Facial recognition is cheap and accessible but also expensive and out of control. Facial recognition research has achieved headline grabbing superhuman accuracies over 99.9% yet facial recognition is also dangerously inaccurate. During a trial installation at Sudkreuz station in Berlin in 2018, 20% of the matches were wrong, a number so low that it should not have any connection to law enforcement or justice. And in London, the Metropolitan police had been using facial recognition software that mistakenly identified an alarming 98% of people as criminals 1, which perhaps is a crime itself.

        +

        MegaPixels is an online art project that explores the history of facial recognition from the perspective of datasets. To paraphrase the artist Trevor Paglen, whoever controls the dataset controls the meaning. MegaPixels aims to unravel the meanings behind the data and expose the darker corners of the biometric industry that have contributed to its growth. MegaPixels does not start with a conclusion, a moralistic slant, or a

        +

        Whether or not to build facial recognition was a question that can no longer be asked. As an outspoken critic of face recognition I've developed, and hopefully furthered, my understanding during the last 10 years I've spent working with computer vision. Though I initially disagreed, I've come to see technocratic perspective as a non-negotiable reality. As Oren (nytimes article) wrote in NYT Op-Ed "the horse is out of the barn" and the only thing we can do collectively or individually is to steer towards the least worse outcome. Computational communication has entered a new era and it's both exciting and frightening to explore the potentials and opportunities. In 1997 getting access to 1 teraFLOPS of computational power would have cost you $55 million and required a strategic partnership with the Department of Defense. At the time of writing, anyone can rent 1 teraFLOPS on a cloud GPU marketplace for less than $1/day. 2.

        +

        I hope that this project will illuminate the darker areas of strange world of facial recognition that have not yet received attention and encourage discourse in academic, industry, and . By no means do I believe discourse can save the day. Nor do I think creating artwork can. In fact, I'm not exactly sure what the outcome of this project will be. The project is not so much what I publish here but what happens after. This entire project is only a prologue.

        +

        As McLuhan wrote, "You can't have a static, fixed position in the electric age". And in our hyper-connected age of mass surveillance, artificial intelligece, and unevenly distributed virtual futures the most irrational thing to be is rational. Increasingly the world is becoming a contradiction where people use surveillance to protest surveillance, use

        +

        Like many projects, MegaPixels had spent years meandering between formats, unfeasible budgets, and was generally too niche of a subject. The basic idea for this project, as proposed to the original Glass Room installation in 2016 in NYC, was to build an interactive mirror that showed people if they had been included in the LFW facial recognition dataset. The idea was based on my reaction to all the datasets I'd come across during research for the CV Dazzle project. I'd noticed strange datasets created for training and testing face detection algorithms. Most were created in labratory settings and their interpretation of face data was very strict.

        +

        About the name

        +

        About the funding

        +

        About me

        +

        About the team

        +

        Conclusion

        +

        for other post

        +

        It was the early 2000s. Face recognition was new and no one seemed sure how well it was going to perform in practice. In theory, face recognition was poised to be a game changer, a force multiplier, a strategic military advantage, a way to make cities safer and to secure the borders. It was the future that John Ashcroft demanded with the Total Information Awareness act of the 2003. It was a future that academics helped build. It was a future that celebrities helped build. And it was a future that

        +

        A decade earlier the Department of Homeland Security and the Counterdrug Technology Development Program Office initated a feasibilty study called FERET (FacE REcognition Technology) to "develop automatic face recognition capabilities that could be employed to assist security, intelligence, and law enforcement personnel in the performance of their duties [^feret_website]."

        +

        One problem with FERET dataset was that the photos were in controlled settings. For face recognition to work it would have to be used in uncontrolled settings. Even newer datasets such as the Multi-PIE (Pose, Illumination, and Expression) from Carnegie Mellon University included only indoor photos of cooperative subjects. Not only were the photos completely unrealistic, CMU's Multi-Pie included only 18 individuals and cost $500 for academic use [^cmu_multipie_cost], took years to create, and required consent from every participant.

        +
        +
        +
        1. Sharman, Jon. "Metropolitan Police's facial recognition technology 98% inaccurate, figures show". 2018. https://www.independent.co.uk/news/uk/home-news/met-police-facial-recognition-success-south-wales-trial-home-office-false-positive-a8345036.html

        2. +
        3. Calle, Dan. "Supercomptuers". 1997. http://ei.cs.vt.edu/~history/SUPERCOM.Calle.HTML

        4. +
        +
        +
        + +
        + + + + + \ No newline at end of file diff --git a/site/public/research/index.html b/site/public/research/index.html index cf9546e1..59a5fee9 100644 --- a/site/public/research/index.html +++ b/site/public/research/index.html @@ -28,7 +28,7 @@

        Research Blog

        The darkside of datasets and the future of computer vision

        -
        +
        -- cgit v1.2.3-70-g09d2 From 716f2f82a69a5e554d3e3edd70bcb1d72bff90aa Mon Sep 17 00:00:00 2001 From: Jules Laplace Date: Sat, 15 Dec 2018 21:45:45 +0100 Subject: build --- megapixels/app/builder/builder.py | 4 ++-- megapixels/app/builder/parser.py | 8 ++++---- site/public/research/index.html | 20 +++++++++++++++++--- 3 files changed, 23 insertions(+), 9 deletions(-) (limited to 'site/public/research') diff --git a/megapixels/app/builder/builder.py b/megapixels/app/builder/builder.py index d1f882d6..cb5d8fe9 100644 --- a/megapixels/app/builder/builder.py +++ b/megapixels/app/builder/builder.py @@ -6,8 +6,8 @@ from jinja2 import Environment, FileSystemLoader, select_autoescape import app.settings.app_cfg as cfg -import app.builder.s3 -import app.builder.parser +import app.builder.s3 as s3 +import app.builder.parser as parser env = Environment( loader=FileSystemLoader(cfg.DIR_SITE_TEMPLATES), diff --git a/megapixels/app/builder/parser.py b/megapixels/app/builder/parser.py index ea84f658..985111ac 100644 --- a/megapixels/app/builder/parser.py +++ b/megapixels/app/builder/parser.py @@ -3,8 +3,8 @@ import re import glob import mistune -import app.settings.app_cfg -import app.builder.s3 +import app.settings.app_cfg as cfg +import app.builder.s3 as s3 renderer = mistune.Renderer(escape=False) markdown = mistune.Markdown(renderer=renderer) @@ -70,7 +70,7 @@ def parse_markdown(sections, s3_path, skip_h1=False): def parse_research_index(research_posts): content = "
        " for post in research_posts: - s3_path = s3.make_s3_path(app_cfg.S3_SITE_PATH, post['path']) + s3_path = s3.make_s3_path(cfg.S3_SITE_PATH, post['path']) if 'image' in post: post_image = s3_path + post['image'] else: @@ -134,7 +134,7 @@ def parse_metadata(fn, sections): if key not in metadata: metadata[key] = default_metadata[key] - basedir = os.path.dirname(fn.replace(content_path, '')) + basedir = os.path.dirname(fn.replace(cfg.DIR_SITE_CONTENT, '')) basename = os.path.basename(fn) if basedir == '/': metadata['path'] = '/' diff --git a/site/public/research/index.html b/site/public/research/index.html index 59a5fee9..8b43ac17 100644 --- a/site/public/research/index.html +++ b/site/public/research/index.html @@ -26,9 +26,23 @@
        -

        Research Blog

        -

        The darkside of datasets and the future of computer vision

        -
        +
        +

        Research

        +
        +
        +
        Posted
        +
        2018-12-15
        +
        +
        +
        By
        +
        Adam Harvey
        +
        + +
        +
        + +

        The darkside of datasets and the future of computer vision

        +
        -- cgit v1.2.3-70-g09d2 From 67cd70621b7dca679cd930de66d883e0610a1427 Mon Sep 17 00:00:00 2001 From: Jules Laplace Date: Sat, 15 Dec 2018 21:47:26 +0100 Subject: builds :) --- megapixels/app/builder/builder.py | 2 +- megapixels/app/settings/app_cfg.py | 4 ++-- site/public/research/index.html | 20 +++----------------- 3 files changed, 6 insertions(+), 20 deletions(-) (limited to 'site/public/research') diff --git a/megapixels/app/builder/builder.py b/megapixels/app/builder/builder.py index cb5d8fe9..df609f60 100644 --- a/megapixels/app/builder/builder.py +++ b/megapixels/app/builder/builder.py @@ -74,7 +74,7 @@ def build_research_index(research_posts): research_posts=research_posts, latest_research_post=research_posts[-1], ) - output_fn = public_path + '/research/index.html' + output_fn = cfg.DIR_SITE_PUBLIC + '/research/index.html' with open(output_fn, "w") as file: file.write(html) diff --git a/megapixels/app/settings/app_cfg.py b/megapixels/app/settings/app_cfg.py index 9e03357b..f861700c 100644 --- a/megapixels/app/settings/app_cfg.py +++ b/megapixels/app/settings/app_cfg.py @@ -61,8 +61,8 @@ DIR_TEST_IMAGES = join(DIR_APP, 'test', 'images') # .env config for keys # ----------------------------------------------------------------------------- -DIR_DOTENV = join(DIR_APP, '.env') -load_dotenv(dotenv_path=DIR_DOTENV) +# DIR_DOTENV = join(DIR_APP, '.env') +load_dotenv() # dotenv_path=DIR_DOTENV) # ----------------------------------------------------------------------------- # Drawing, GUI settings diff --git a/site/public/research/index.html b/site/public/research/index.html index 8b43ac17..59a5fee9 100644 --- a/site/public/research/index.html +++ b/site/public/research/index.html @@ -26,23 +26,9 @@
        -
        -

        Research

        -
        -
        -
        Posted
        -
        2018-12-15
        -
        -
        -
        By
        -
        Adam Harvey
        -
        - -
        -
        - -

        The darkside of datasets and the future of computer vision

        -
        +

        Research Blog

        +

        The darkside of datasets and the future of computer vision

        +
        -- cgit v1.2.3-70-g09d2 From fe303999bb3d1067783ea96cef71ea2a4a69a2df Mon Sep 17 00:00:00 2001 From: Jules Laplace Date: Sat, 15 Dec 2018 23:21:02 +0100 Subject: embedding applets --- .eslintrc.js | 48 + .gitignore | 2 + client/actions.js | 4 +- client/app.js | 46 - client/applet.js | 13 + client/index.js | 92 +- client/store.js | 2 + client/util.js | 26 +- package-lock.json | 10507 ++++++++++++++++++- package.json | 60 +- site/assets/css/applets.css | 4 + site/assets/js/app/site.js | 30 - site/public/about/credits/index.html | 3 +- site/public/about/disclaimer/index.html | 3 +- site/public/about/index.html | 3 +- site/public/about/press/index.html | 3 +- site/public/about/privacy/index.html | 3 +- site/public/about/style/index.html | 3 +- site/public/about/terms/index.html | 3 +- site/public/datasets/lfw/index.html | 3 +- site/public/datasets/vgg_face2/index.html | 3 +- site/public/index.html | 3 +- site/public/research/00_introduction/index.html | 3 +- .../research/01_from_1_to_100_pixels/index.html | 3 +- site/public/research/index.html | 3 +- site/templates/layout.html | 3 +- site/templates/research.html | 4 - webpack.config.dev.js | 74 + webpack.config.prod.js | 57 + 29 files changed, 10848 insertions(+), 163 deletions(-) create mode 100644 .eslintrc.js delete mode 100644 client/app.js create mode 100644 client/applet.js create mode 100644 site/assets/css/applets.css delete mode 100644 site/assets/js/app/site.js create mode 100644 webpack.config.dev.js create mode 100644 webpack.config.prod.js (limited to 'site/public/research') diff --git a/.eslintrc.js b/.eslintrc.js new file mode 100644 index 00000000..725c52a6 --- /dev/null +++ b/.eslintrc.js @@ -0,0 +1,48 @@ +module.exports = { + "extends": [ + "airbnb-base", + "plugin:react/recommended" + ], + "plugins": [ + "import", + "react", + "class-property" + ], + "parser": "babel-eslint", + "parserOptions": { + "ecmaFeatures": { + "jsx": true, + "classes": true, + } + }, + "rules": { + "react/prop-types": 1, + "react/jsx-uses-vars": 2, + "no-underscore-dangle": 0, + "comma-dangle": ["error", "only-multiline"], + "semi": ["error", "never"], + "prefer-const": "off", + "no-plusplus": "off", + "no-param-reassign": 0, + "no-return-assign": 0, + "global-require": 0, + "arrow-parens": 0, + "prefer-template": 0, + "no-nested-ternary": 0, + "no-mixed-operators": 0, + "no-confusing-arrow": 0, + "prefer-arrow-callback": 0, + "prefer-arrow-callback": 0, + "func-names": 0, + "object-curly-newline": 0, + "class-methods-use-this": 0, + }, + "env": { + "browser": true, + "mocha": true, + }, + "globals" : { + "assert": false, + "web3": false + }, +}; diff --git a/.gitignore b/.gitignore index 3cd21111..ef471c07 100644 --- a/.gitignore +++ b/.gitignore @@ -155,3 +155,5 @@ scraper/reports/papers/ .creds +site/assets/js/dist/ + diff --git a/client/actions.js b/client/actions.js index 3fd86e0d..779113f8 100644 --- a/client/actions.js +++ b/client/actions.js @@ -1,9 +1,9 @@ -import * as search from './search/search.actions' +// import * as search from './search/search.actions' // import * as review from './review/review.actions' // import * as metadata from './metadata/metadata.actions' export { - search, + // search, // review, // metadata, } diff --git a/client/app.js b/client/app.js deleted file mode 100644 index 6c008ec6..00000000 --- a/client/app.js +++ /dev/null @@ -1,46 +0,0 @@ -import React, { Component } from 'react' -import { ConnectedRouter } from 'connected-react-router' -import { Route, Switch } from 'react-router' - -import { Header, Sidebar, Footer } from './common' -import * as Metadata from './metadata' -import * as Search from './search' -import * as Review from './review' - -export default class App extends Component { - render() { - return ( - -
        -
        -
        - -
        - - - - - - - - - - - - -

        NOT FOUND

        } /> - - - - - - -
        -
        -
        -
        -
        -
        - ) - } -} diff --git a/client/applet.js b/client/applet.js new file mode 100644 index 00000000..f218a7f2 --- /dev/null +++ b/client/applet.js @@ -0,0 +1,13 @@ +import React, { Component } from 'react' + +// import { Header, Sidebar, Footer } from './common' + +export default class Applet extends Component { + render() { + return ( +
        +
        {'Megapixels'}
        +
        + ) + } +} diff --git a/client/index.js b/client/index.js index 03015988..bc57f548 100644 --- a/client/index.js +++ b/client/index.js @@ -2,56 +2,78 @@ import React from 'react' import ReactDOM from 'react-dom' import { AppContainer } from 'react-hot-loader' import { Provider } from 'react-redux' +import Tabulator from 'tabulator-tables' -// import App from './app' - +import { toArray } from './util' +import Applet from './applet' import { store } from './store' -// const container = document.createElement('div') -// document.body.appendChild(container) - -toArray(document.querySelectorAll('.applet')).forEach(el => { - try { - const payload = JSON.parse(el.dataSet.getItem('payload')) - } catch(e) { - return - } - switch (payload.command) { - case 'load file': - append_tabulator(el, payload) - break - default: - append_react_applet(el, payload) - break - } -}) - -function append_react_applet(el, payload) { +function appendReactApplet(el, payload) { ReactDOM.render( - + , el ) } -function append_tabulator(el, payload) { + +function appendTabulator(el, payload) { const table = new Tabulator(el, { - height:"311px", - layout:"fitColumns", - placeholder:"No Data Set", - columns:[ - // {title:"Name", field:"name", sorter:"string", width:200}, - // {title:"Progress", field:"progress", sorter:"number", formatter:"progress"}, - // {title:"Gender", field:"gender", sorter:"string"}, + height: '311px', + layout: 'fitColumns', + placeholder: 'No Data Set', + columns: [ + // {title:'Name', field:'name', sorter:'string', width:200}, + // {title:'Progress', field:'progress', sorter:'number', formatter:'progress'}, + // {title:'Gender', field:'gender', sorter:"string"}, // {title:"Rating", field:"rating", formatter:"star", align:"center", width:100}, // {title:"Favourite Color", field:"col", sorter:"string", sortable:false}, // {title:"Date Of Birth", field:"dob", sorter:"date", align:"center"}, // {title:"Driver", field:"car", align:"center", formatter:"tickCross", sorter:"boolean"}, ], }) - const columns = payload.fields.split(', ') + let path = payload.opt + let columns = payload.fields.split(',').map(s => s.trim()) console.log(columns) - if () - table.setData(path) -} \ No newline at end of file + if (path[0] !== '/') { + console.log(path) + } + // table.setData(path) +} + +function appendApplets() { + toArray(document.querySelectorAll('.applet')).forEach(el => { + console.log(el.dataset.payload) + let payload; + try { + payload = JSON.parse(el.dataset.payload) + } catch (e) { + return + } + console.log(payload) + switch (payload.command) { + case 'load file': + appendTabulator(el, payload) + break + default: + appendReactApplet(el, payload) + break + } + }) +} + +function main() { + const paras = document.querySelectorAll('section p') + if (paras.length) { + paras[0].classList.add('first_paragraph') + } + toArray(document.querySelectorAll('header .links a')).forEach(tag => { + if (window.location.href.match(tag.href)) { + tag.classList.add('active') + } + }) + appendApplets() +} + +main() diff --git a/client/store.js b/client/store.js index 9c0f78cd..3c7acefb 100644 --- a/client/store.js +++ b/client/store.js @@ -6,6 +6,7 @@ import thunk from 'redux-thunk' // import reviewReducer from './review/review.reducer' const rootReducer = combineReducers({ + auth: (state = {}) => state, // auth: (state = login()) => state, // metadata: metadataReducer, // search: searchReducer, @@ -16,6 +17,7 @@ function configureStore(initialState = {}) { const composeEnhancers = window.__REDUX_DEVTOOLS_EXTENSION_COMPOSE__ || compose const store = createStore( + rootReducer, initialState, composeEnhancers( applyMiddleware( diff --git a/client/util.js b/client/util.js index 92b4addc..50d4992a 100644 --- a/client/util.js +++ b/client/util.js @@ -7,6 +7,7 @@ export const isMobile = isiPhone || isiPad || isAndroid export const isDesktop = !isMobile export const toArray = a => Array.prototype.slice.apply(a) +export const choice = a => a[Math.floor(Math.random() * a.length)] const htmlClassList = document.body.parentNode.classList htmlClassList.add(isDesktop ? 'desktop' : 'mobile') @@ -142,28 +143,3 @@ export const post = (uri, data, credentials) => { // headers['X-CSRFToken'] = csrftoken return fetch(uri, opt).then(res => res.json()) } - -// api queries -export const login = () => { - if (cachedAuth) return cachedAuth - const isLocal = (window.location.hostname === '0.0.0.0' || window.location.hostname === '127.0.0.1') - try { - const auth = JSON.parse(JSON.parse(localStorage.getItem('persist:root')).auth) - // console.log('auth', auth) - token = auth.token - username = auth.user.username - if (token) { - console.log('logged in', username) - } - cachedAuth = auth - if (!token && !isLocal) { - window.location.href = '/' - } - return auth - } catch (e) { - if (!isLocal) { - window.location.href = '/' - } - return {} - } -} diff --git a/package-lock.json b/package-lock.json index c94fbb15..38588aa5 100644 --- a/package-lock.json +++ b/package-lock.json @@ -1,13 +1,10518 @@ { - "name": "megapixels_dev", + "name": "megapixels", "version": 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"name": "megapixels", "version": "1.0.0", - "description": "", - "main": "index.js", + "description": "The Darkside of Datasets", + "main": "client/index.js", "scripts": { - "test": "echo \"Error: no test specified\" && exit 1" + "watch": "NODE_ENV=development webpack --config ./webpack.config.dev.js --colors --watch", + "buildDev": "NODE_ENV=development webpack --config ./webpack.config.dev.js --colors", + "build": "NODE_ENV=production webpack --config ./webpack.config.prod.js", + "deploy": "NODE_ENV=production webpack --config ./webpack.config.prod.js && git commit -am 'deploy' && git push origin master && ssh vframe@vframe ./restart.sh" }, "repository": { "type": "git", "url": "git+https://github.com/adamhrv/megapixels_dev.git" }, - "author": "", + "author": "megapixels@megapixels.cc", "license": "UNLICENSED", "bugs": { "url": "https://github.com/adamhrv/megapixels_dev/issues" }, "homepage": "https://github.com/adamhrv/megapixels_dev#readme", "dependencies": { - "node-fetch": "^2.2.0" + "aws-sdk": "^2.322.0", + "clean-webpack-plugin": "^0.1.19", + "compression-webpack-plugin": "^2.0.0", + "connected-react-router": "^4.3.0", + "csv-stringify": "^4.3.1", + "data-uri-to-blob": "0.0.4", + "data-uri-to-buffer": "^2.0.0", + "date-fns": "^1.29.0", + "dotenv": "^6.0.0", + "fetch-jsonp": "^1.1.3", + "file-saver": "^2.0.0-rc.3", + "history": "^4.7.2", + "node-fetch": "^2.2.0", + "prop-types": "^15.6.1", + "react": "^16.3.0", + "react-dom": "^16.3.0", + "react-hot-loader": "^4.3.0", + "react-redux": "^5.0.7", + "redux": "^4.0.0", + "redux-thunk": "^2.3.0", + "store2": "^2.7.0", + "tabulator-tables": "^4.1.3" + }, + "devDependencies": { + "babel-cli": "^6.24.1", + "babel-core": "^6.24.1", + "babel-eslint": "^8.2.2", + "babel-loader": "^7.0.0", + "babel-plugin-transform-class-properties": "^6.24.1", + "babel-plugin-transform-es2015-arrow-functions": "^6.22.0", + "babel-plugin-transform-object-rest-spread": "^6.23.0", + "babel-plugin-transform-react-jsx": "^6.24.1", + "babel-plugin-transform-runtime": "^6.23.0", + "babel-preset-env": "^1.5.1", + "css-loader": "^1.0.0", + "eslint": "^4.19.1", + "eslint-config-airbnb-base": "^13.0.0", + "eslint-plugin-class-property": "^1.1.0", + "eslint-plugin-import": "^2.9.0", + "eslint-plugin-react": "^7.11.1", + "html-webpack-plugin": "^3.0.4", + "style-loader": "^0.21.0", + "uglifyjs-webpack-plugin": "^1.3.0", + "webpack": "3.x.x", + "webpack-cli": "^3.1.0", + "webpack-dev-server": "2.x.x" } } diff --git a/site/assets/css/applets.css b/site/assets/css/applets.css new file mode 100644 index 00000000..b437886b --- /dev/null +++ b/site/assets/css/applets.css @@ -0,0 +1,4 @@ +.applet { + border: 1px solid #0f0; + color: #0f0; +} diff --git a/site/assets/js/app/site.js b/site/assets/js/app/site.js deleted file mode 100644 index eb6886c2..00000000 --- a/site/assets/js/app/site.js +++ /dev/null @@ -1,30 +0,0 @@ -const isiPhone = !!((navigator.userAgent.match(/iPhone/i)) || (navigator.userAgent.match(/iPod/i))) -const isiPad = !!(navigator.userAgent.match(/iPad/i)) -const isAndroid = !!(navigator.userAgent.match(/Android/i)) -const isMobile = isiPhone || isiPad || isAndroid -const isDesktop = !isMobile - -const htmlClassList = document.body.parentNode.classList -htmlClassList.add(isDesktop ? 'desktop' : 'mobile') - -function toArray(a) { return Array.prototype.slice.apply(a) } -function choice(a) { return a[Math.floor(Math.random()*a.length)]} - -var site = (function(){ - var site = {} - site.init = function(){ - site.build() - } - site.build = function(){ - const paras = document.querySelectorAll("section p") - if (paras.length) { - paras[0].classList.add('first_paragraph') - } - toArray(document.querySelectorAll('header .links a')).forEach(tag => { - if (window.location.href.match(tag.href)) { - tag.classList.add('active') - } - }) - } - site.init() -})() \ No newline at end of file diff --git a/site/public/about/credits/index.html b/site/public/about/credits/index.html index 65bc7ac4..1b1f4d54 100644 --- a/site/public/about/credits/index.html +++ b/site/public/about/credits/index.html @@ -9,6 +9,7 @@ +
        @@ -53,5 +54,5 @@ - + \ No newline at end of file diff --git a/site/public/about/disclaimer/index.html b/site/public/about/disclaimer/index.html index b0215bde..dfd136b6 100644 --- a/site/public/about/disclaimer/index.html +++ b/site/public/about/disclaimer/index.html @@ -9,6 +9,7 @@ +
        @@ -53,5 +54,5 @@ - + \ No newline at end of file diff --git a/site/public/about/index.html b/site/public/about/index.html index 65bc7ac4..1b1f4d54 100644 --- a/site/public/about/index.html +++ b/site/public/about/index.html @@ -9,6 +9,7 @@ +
        @@ -53,5 +54,5 @@ - + \ No newline at end of file diff --git a/site/public/about/press/index.html b/site/public/about/press/index.html index 09c89165..dca2aa3c 100644 --- a/site/public/about/press/index.html +++ b/site/public/about/press/index.html @@ -9,6 +9,7 @@ +
        @@ -51,5 +52,5 @@ - + \ No newline at end of file diff --git a/site/public/about/privacy/index.html b/site/public/about/privacy/index.html index 5675f072..86b645d5 100644 --- a/site/public/about/privacy/index.html +++ b/site/public/about/privacy/index.html @@ -9,6 +9,7 @@ +
        @@ -130,5 +131,5 @@ You are advised to review this Privacy Policy periodically for any changes. Chan - + \ No newline at end of file diff --git a/site/public/about/style/index.html b/site/public/about/style/index.html index 39a44380..ed61c270 100644 --- a/site/public/about/style/index.html +++ b/site/public/about/style/index.html @@ -9,6 +9,7 @@ +
        @@ -77,5 +78,5 @@ - + \ No newline at end of file diff --git a/site/public/about/terms/index.html b/site/public/about/terms/index.html index 078c339f..dd095b64 100644 --- a/site/public/about/terms/index.html +++ b/site/public/about/terms/index.html @@ -9,6 +9,7 @@ +
        @@ -65,5 +66,5 @@ - + \ No newline at end of file diff --git a/site/public/datasets/lfw/index.html b/site/public/datasets/lfw/index.html index 3c83acd3..e75e79cf 100644 --- a/site/public/datasets/lfw/index.html +++ b/site/public/datasets/lfw/index.html @@ -9,6 +9,7 @@ +
        @@ -266,5 +267,5 @@ imageio.imwrite('lfw_montage_960.jpg', montage_960)

        - + \ No newline at end of file diff --git a/site/public/datasets/vgg_face2/index.html b/site/public/datasets/vgg_face2/index.html index 817fc9a0..63bb2cc0 100644 --- a/site/public/datasets/vgg_face2/index.html +++ b/site/public/datasets/vgg_face2/index.html @@ -9,6 +9,7 @@ +
        @@ -75,5 +76,5 @@ - + \ No newline at end of file diff --git a/site/public/index.html b/site/public/index.html index 91ff467a..a745dd21 100644 --- a/site/public/index.html +++ b/site/public/index.html @@ -9,6 +9,7 @@ +
        @@ -74,5 +75,5 @@ - + \ No newline at end of file diff --git a/site/public/research/00_introduction/index.html b/site/public/research/00_introduction/index.html index 8f598f5b..52a2416f 100644 --- a/site/public/research/00_introduction/index.html +++ b/site/public/research/00_introduction/index.html @@ -9,6 +9,7 @@ +
        @@ -82,5 +83,5 @@ - + \ No newline at end of file diff --git a/site/public/research/01_from_1_to_100_pixels/index.html b/site/public/research/01_from_1_to_100_pixels/index.html index b4c85d00..22869459 100644 --- a/site/public/research/01_from_1_to_100_pixels/index.html +++ b/site/public/research/01_from_1_to_100_pixels/index.html @@ -9,6 +9,7 @@ +
        @@ -91,5 +92,5 @@ - + \ No newline at end of file diff --git a/site/public/research/index.html b/site/public/research/index.html index 59a5fee9..62e3208a 100644 --- a/site/public/research/index.html +++ b/site/public/research/index.html @@ -9,6 +9,7 @@ +
        @@ -47,5 +48,5 @@ - + \ No newline at end of file diff --git a/site/templates/layout.html b/site/templates/layout.html index 605f9788..986c7a3f 100644 --- a/site/templates/layout.html +++ b/site/templates/layout.html @@ -9,6 +9,7 @@ +
        @@ -43,5 +44,5 @@ {% block scripts %}{% endblock %} - + \ No newline at end of file diff --git a/site/templates/research.html b/site/templates/research.html index b2ea3a6b..0bb9fa30 100644 --- a/site/templates/research.html +++ b/site/templates/research.html @@ -21,7 +21,3 @@ {{ content }} {% endblock %} - -{% block scripts %} - -{% endblock %} \ No newline at end of file diff --git a/webpack.config.dev.js b/webpack.config.dev.js new file mode 100644 index 00000000..fcbd48ff --- /dev/null +++ b/webpack.config.dev.js @@ -0,0 +1,74 @@ +require('dotenv').config() + +const HtmlWebpackPlugin = require('html-webpack-plugin') +// const CleanWebpackPlugin = require('clean-webpack-plugin') +const webpack = require('webpack') +const path = require('path') + +module.exports = { + entry: { + main: './client/index.js' + }, + output: { + path: path.resolve(__dirname, 'site/assets/js/dist'), + filename: 'index.js' + }, + devServer: { + port: 9000, + headers: { + 'Access-Control-Allow-Origin': '*', + }, + publicPath: '/site/assets/js/dist/', + hot: true, + }, + devtool: 'inline-source-map', + resolve: { + alias: { + // 'vcat-header': path.resolve(__dirname, '../app/components/common/header.component.js'), + // 'vcat-auth-reducer': path.resolve(__dirname, '../app/reducers/auth.reducer.js'), + } + }, + plugins: [ + // new CleanWebpackPlugin(['dist']), + new webpack.DefinePlugin({ + 'process.env.NODE_ENV': '"development"', + 'process.env.S3_HOST': '"' + process.env.S3_HOST + '"', + // 'process.env.VCAT_HOST': '"http://127.0.0.1:8000"', + 'process.env.API_HOST': '""', + }), + // new HtmlWebpackPlugin({ + // title: 'VFrame Metadata', + // meta: { + // viewport: 'width=device-width,initial-scale=1.0' + // } + // }), + // new webpack.HotModuleReplacementPlugin() + ], + module: { + rules: [ + { + test: /\.css$/, + use: ['style-loader', 'css-loader'] + }, + { + test: /\.js$/, + // include: path.resolve(__dirname, 'client'), + exclude: /(node_modules|bower_components|build)/, + use: { + loader: 'babel-loader', + options: { + presets: ['env'], + plugins: [ + require('babel-plugin-transform-runtime'), + require('babel-plugin-transform-es2015-arrow-functions'), + require('babel-plugin-transform-object-rest-spread'), + require('babel-plugin-transform-class-properties'), + require('babel-plugin-transform-react-jsx'), + require('react-hot-loader/babel') + ] + } + } + } + ] + } +}; diff --git a/webpack.config.prod.js b/webpack.config.prod.js new file mode 100644 index 00000000..186509e2 --- /dev/null +++ b/webpack.config.prod.js @@ -0,0 +1,57 @@ +require('dotenv').config() + +const webpack = require('webpack') +const path = require('path') +const UglifyJsPlugin = require('uglifyjs-webpack-plugin') +// const CleanWebpackPlugin = require('clean-webpack-plugin') + +module.exports = { + entry: { + main: './client/index.js', + }, + output: { + path: path.resolve(__dirname, 'dist'), + filename: 'index.js', + }, + plugins: [ + new webpack.DefinePlugin({ + 'process.env.NODE_ENV': '"production"', + 'process.env.S3_HOST': '"' + process.env.S3_HOST + '"', + 'process.env.VCAT_HOST': '""', + 'process.env.API_HOST': '"https://syrianarchive.vframe.io"', + }), + new UglifyJsPlugin(), + new webpack.optimize.AggressiveMergingPlugin() + ], + resolve: { + alias: { + 'vcat-header': path.resolve(__dirname, '../app/components/common/header.component.js'), + 'vcat-auth-reducer': path.resolve(__dirname, '../app/reducers/auth.reducer.js'), + } + }, + devtool: 'inline-source-map', + module: { + rules: [ + { + test: /\.css$/, + use: ['style-loader', 'css-loader'] + }, + { + test: /\.js$/, + // include: path.resolve(__dirname, 'client'), + exclude: /(node_modules|bower_components|build)/, + loader: 'babel-loader', + options: { + presets: ['env'], + plugins: [ + require('babel-plugin-transform-runtime'), + require('babel-plugin-transform-es2015-arrow-functions'), + require('babel-plugin-transform-object-rest-spread'), + require('babel-plugin-transform-class-properties'), + require('babel-plugin-transform-react-jsx'), + ] + } + } + ] + }, +}; -- cgit v1.2.3-70-g09d2 From 3b10acc73247ec703ed47f0423e7d255a91f074e Mon Sep 17 00:00:00 2001 From: Jules Laplace Date: Sun, 16 Dec 2018 00:11:20 +0100 Subject: tabulator --- client/index.js | 29 +- megapixels/app/server/api.py | 17 +- megapixels/app/server/create.py | 7 + megapixels/app/site/builder.py | 10 + megapixels/app/site/parser.py | 36 +- megapixels/app/site/s3.py | 3 + site/assets/css/applets.css | 2 - site/assets/css/tabulator.css | 761 +++++++++++++++++++++ site/public/about/credits/index.html | 2 +- site/public/about/disclaimer/index.html | 2 +- site/public/about/index.html | 2 +- site/public/about/press/index.html | 2 +- site/public/about/privacy/index.html | 2 +- site/public/about/style/index.html | 2 +- site/public/about/terms/index.html | 2 +- site/public/datasets/lfw/index.html | 6 +- site/public/datasets/vgg_face2/index.html | 4 +- site/public/index.html | 2 +- site/public/research/00_introduction/index.html | 2 +- .../research/01_from_1_to_100_pixels/index.html | 2 +- site/public/research/index.html | 2 +- site/templates/layout.html | 1 + 22 files changed, 862 insertions(+), 36 deletions(-) create mode 100755 site/assets/css/tabulator.css (limited to 'site/public/research') diff --git a/client/index.js b/client/index.js index bc57f548..2ee12c14 100644 --- a/client/index.js +++ b/client/index.js @@ -23,23 +23,24 @@ function appendTabulator(el, payload) { height: '311px', layout: 'fitColumns', placeholder: 'No Data Set', - columns: [ - // {title:'Name', field:'name', sorter:'string', width:200}, - // {title:'Progress', field:'progress', sorter:'number', formatter:'progress'}, - // {title:'Gender', field:'gender', sorter:"string"}, - // {title:"Rating", field:"rating", formatter:"star", align:"center", width:100}, - // {title:"Favourite Color", field:"col", sorter:"string", sortable:false}, - // {title:"Date Of Birth", field:"dob", sorter:"date", align:"center"}, - // {title:"Driver", field:"car", align:"center", formatter:"tickCross", sorter:"boolean"}, - ], + columns: payload.fields.split(', ').map(field => { + switch (field) { + default: + return { title: field, field: field.toLowerCase(), sorter: 'string' } + } + }), + // {title:'Name', field:'name', sorter:'string', width:200}, + // {title:'Progress', field:'progress', sorter:'number', formatter:'progress'}, + // {title:'Gender', field:'gender', sorter:"string"}, + // {title:"Rating", field:"rating", formatter:"star", align:"center", width:100}, + // {title:"Favourite Color", field:"col", sorter:"string", sortable:false}, + // {title:"Date Of Birth", field:"dob", sorter:"date", align:"center"}, + // {title:"Driver", field:"car", align:"center", formatter:"tickCross", sorter:"boolean"}, }) let path = payload.opt let columns = payload.fields.split(',').map(s => s.trim()) - console.log(columns) - if (path[0] !== '/') { - console.log(path) - } - // table.setData(path) + console.log(path, columns) + table.setData(path) } function appendApplets() { diff --git a/megapixels/app/server/api.py b/megapixels/app/server/api.py index cd2b950b..cf8241bd 100644 --- a/megapixels/app/server/api.py +++ b/megapixels/app/server/api.py @@ -1,9 +1,12 @@ import os import re import time +import dlib from flask import Blueprint, request, jsonify from PIL import Image # todo: try to remove PIL dependency +from app.processors import face_recognition +from app.processors import face_detector from app.models.sql_factory import list_datasets, get_dataset, get_table sanitize_re = re.compile('[\W]+') @@ -39,8 +42,18 @@ def upload(name): img = Image.open(file.stream).convert('RGB') - # vec = db.load_feature_vector_from_file(uploaded_img_path) - # vec = fe.extract(img) + # Face detection + detector = face_detector.DetectorDLIBHOG() + + # get detection as BBox object + bboxes = detector.detect(im, largest=True) + bbox = bboxes[0] + dim = im.shape[:2][::-1] + bbox = bbox.to_dim(dim) # convert back to real dimensions + + # face recognition/vector + recognition = face_recognition.RecognitionDLIB(gpu=-1) + # print(vec.shape) # results = db.search(vec, limit=limit) diff --git a/megapixels/app/server/create.py b/megapixels/app/server/create.py index c1f41dc4..4b1333b9 100644 --- a/megapixels/app/server/create.py +++ b/megapixels/app/server/create.py @@ -7,6 +7,9 @@ from app.server.api import api db = SQLAlchemy() def create_app(script_info=None): + """ + functional pattern for creating the flask app + """ app = Flask(__name__, static_folder='static', static_url_path='') app.config['SQLALCHEMY_DATABASE_URI'] = connection_url app.config['SQLALCHEMY_TRACK_MODIFICATIONS'] = False @@ -37,6 +40,10 @@ def create_app(script_info=None): return app def serve_page(file_relative_path_to_root): + """ + trying to get this to serve /path/ with /path/index.html, + ...but it doesnt actually matter for production... + """ if file_relative_path_to_root[-1] == '/': file_relative_path_to_root += 'index.html' return send_from_directory("static", file_relative_path_to_root) diff --git a/megapixels/app/site/builder.py b/megapixels/app/site/builder.py index 91df54c2..895f265b 100644 --- a/megapixels/app/site/builder.py +++ b/megapixels/app/site/builder.py @@ -15,6 +15,10 @@ env = Environment( ) def build_page(fn, research_posts): + """ + build a single page from markdown into the appropriate template + - writes it to site/public/ + """ metadata, sections = parser.read_metadata(fn) if metadata is None: @@ -61,6 +65,9 @@ def build_page(fn, research_posts): file.write(html) def build_research_index(research_posts): + """ + build the index of research (blog) posts + """ metadata, sections = parser.read_metadata('../site/content/research/index.md') template = env.get_template("page.html") s3_path = s3.make_s3_path(cfg.S3_SITE_PATH, metadata['path']) @@ -77,6 +84,9 @@ def build_research_index(research_posts): file.write(html) def build_site(): + """ + build the site! =^) + """ research_posts = parser.read_research_post_index() for fn in glob.iglob(os.path.join(cfg.DIR_SITE_CONTENT, "**/*.md"), recursive=True): build_page(fn, research_posts) diff --git a/megapixels/app/site/parser.py b/megapixels/app/site/parser.py index d78cc402..40d9c7f6 100644 --- a/megapixels/app/site/parser.py +++ b/megapixels/app/site/parser.py @@ -11,6 +11,10 @@ renderer = mistune.Renderer(escape=False) markdown = mistune.Markdown(renderer=renderer) def fix_images(lines, s3_path): + """ + do our own tranformation of the markdown around images to handle wide images etc + lines: markdown lines + """ real_lines = [] block = "\n\n".join(lines) for line in block.split("\n"): @@ -29,6 +33,9 @@ def fix_images(lines, s3_path): return "\n".join(real_lines) def format_section(lines, s3_path, type=''): + """ + format a normal markdown section + """ if len(lines): lines = fix_images(lines, s3_path) if type: @@ -38,13 +45,16 @@ def format_section(lines, s3_path, type=''): return "" def format_metadata(section): + """ + format a metadata section (+ key: value pairs) + """ meta = [] for line in section.split('\n'): key, value = line[2:].split(': ', 1) meta.append("
        {}
        {}
        ".format(key, value)) return "
        {}
        ".format(''.join(meta)) -def format_applet(section): +def format_applet(section, s3_path): payload = section.replace('```', '').strip().split('\n') applet = {} if ': ' in payload[0]: @@ -56,10 +66,15 @@ def format_applet(section): if opt: applet['opt'] = opt if command == 'load file': + if opt[0] != '/': + applet['opt'] = s3_path + opt applet['fields'] = payload[1] return "
        ".format(json.dumps(applet)) def parse_markdown(sections, s3_path, skip_h1=False): + """ + parse page into sections, preprocess the markdown to handle our modifications + """ groups = [] current_group = [] for section in sections: @@ -67,7 +82,7 @@ def parse_markdown(sections, s3_path, skip_h1=False): continue elif section.startswith('```'): groups.append(format_section(current_group, s3_path)) - groups.append(format_applet(section)) + groups.append(format_applet(section, s3_path)) current_group = [] elif section.startswith('+ '): groups.append(format_section(current_group, s3_path)) @@ -88,6 +103,9 @@ def parse_markdown(sections, s3_path, skip_h1=False): return content def parse_research_index(research_posts): + """ + Generate an index file for the research pages + """ content = "
        " for post in research_posts: s3_path = s3.make_s3_path(cfg.S3_SITE_PATH, post['path']) @@ -105,6 +123,9 @@ def parse_research_index(research_posts): return content def read_metadata(fn): + """ + Read in read a markdown file and extract the metadata + """ with open(fn, "r") as file: data = file.read() data = data.replace("\n ", "\n") @@ -128,6 +149,9 @@ default_metadata = { } def parse_metadata_section(metadata, section): + """ + parse a metadata key: value pair + """ for line in section.split("\n"): if ': ' not in line: continue @@ -135,6 +159,11 @@ def parse_metadata_section(metadata, section): metadata[key.lower()] = value def parse_metadata(fn, sections): + """ + parse the metadata headers in a markdown file + (everything before the second ---------) + also generates appropriate urls for this page :) + """ found_meta = False metadata = {} valid_sections = [] @@ -175,6 +204,9 @@ def parse_metadata(fn, sections): return metadata, valid_sections def read_research_post_index(): + """ + Generate an index of the research (blog) posts + """ posts = [] for fn in sorted(glob.glob('../site/content/research/*/index.md')): metadata, valid_sections = read_metadata(fn) diff --git a/megapixels/app/site/s3.py b/megapixels/app/site/s3.py index 99726a4d..5464d464 100644 --- a/megapixels/app/site/s3.py +++ b/megapixels/app/site/s3.py @@ -3,6 +3,9 @@ import glob import boto3 def sync_directory(base_fn, s3_path, metadata): + """ + Synchronize a local assets folder with S3 + """ fns = {} for fn in glob.glob(os.path.join(base_fn, 'assets/*')): fns[os.path.basename(fn)] = True diff --git a/site/assets/css/applets.css b/site/assets/css/applets.css index b437886b..e106b3dd 100644 --- a/site/assets/css/applets.css +++ b/site/assets/css/applets.css @@ -1,4 +1,2 @@ .applet { - border: 1px solid #0f0; - color: #0f0; } diff --git a/site/assets/css/tabulator.css b/site/assets/css/tabulator.css new file mode 100755 index 00000000..8c123cf4 --- /dev/null +++ b/site/assets/css/tabulator.css @@ -0,0 +1,761 @@ +/* Tabulator v4.1.3 (c) Oliver Folkerd */ +.tabulator { + position: relative; + font-size: 14px; + text-align: left; + overflow: hidden; + -ms-transform: translatez(0); + transform: translatez(0); +} + +.tabulator[tabulator-layout="fitDataFill"] .tabulator-tableHolder .tabulator-table { + min-width: 100%; +} + +.tabulator.tabulator-block-select { + -webkit-user-select: none; + -moz-user-select: none; + -ms-user-select: none; + user-select: none; +} + +.tabulator .tabulator-header { + position: relative; + box-sizing: border-box; + width: 100%; + border-bottom: 1px solid #999; + color: #ddd; + white-space: nowrap; + overflow: hidden; + -moz-user-select: none; + -khtml-user-select: none; + -webkit-user-select: none; + -o-user-select: none; +} + +.tabulator .tabulator-header .tabulator-col { + display: inline-block; + position: relative; + box-sizing: border-box; + border-right: 1px solid #aaa; + text-align: left; + vertical-align: bottom; + overflow: hidden; +} + +.tabulator .tabulator-header .tabulator-col.tabulator-moving { + position: absolute; + border: 1px solid #999; + background: rgba(80,20,10,0.2); + pointer-events: none; +} + +.tabulator .tabulator-header .tabulator-col .tabulator-col-content { + box-sizing: border-box; + position: relative; + padding: 4px; +} + +.tabulator .tabulator-header .tabulator-col .tabulator-col-content .tabulator-col-title { + box-sizing: border-box; + width: 100%; + white-space: nowrap; + overflow: hidden; + text-overflow: ellipsis; + vertical-align: bottom; +} + +.tabulator .tabulator-header .tabulator-col .tabulator-col-content .tabulator-col-title .tabulator-title-editor { + box-sizing: border-box; + width: 100%; + border: 1px solid #999; + padding: 1px; + background: #fff; +} + +.tabulator .tabulator-header .tabulator-col .tabulator-col-content .tabulator-arrow { + display: inline-block; + position: absolute; + top: 9px; + right: 8px; + width: 0; + height: 0; + border-left: 6px solid transparent; + border-right: 6px solid transparent; + border-bottom: 6px solid #bbb; +} + +.tabulator .tabulator-header .tabulator-col.tabulator-col-group .tabulator-col-group-cols { + position: relative; + display: -ms-flexbox; + display: flex; + border-top: 1px solid #aaa; + overflow: hidden; +} + +.tabulator .tabulator-header .tabulator-col.tabulator-col-group .tabulator-col-group-cols .tabulator-col:last-child { + margin-right: -1px; +} + +.tabulator .tabulator-header .tabulator-col:first-child .tabulator-col-resize-handle.prev { + display: none; +} + +.tabulator .tabulator-header .tabulator-col.ui-sortable-helper { + position: absolute; + border: 1px solid #aaa; +} + +.tabulator .tabulator-header .tabulator-col .tabulator-header-filter { + position: relative; + box-sizing: border-box; + margin-top: 2px; + width: 100%; + text-align: center; +} + +.tabulator .tabulator-header .tabulator-col .tabulator-header-filter textarea { + height: auto !important; +} + +.tabulator .tabulator-header .tabulator-col .tabulator-header-filter svg { + margin-top: 3px; +} + +.tabulator .tabulator-header .tabulator-col .tabulator-header-filter input::-ms-clear { + width: 0; + height: 0; +} + +.tabulator .tabulator-header .tabulator-col.tabulator-sortable .tabulator-col-title { + padding-right: 25px; +} + +.tabulator .tabulator-header .tabulator-col.tabulator-sortable:hover { + cursor: pointer; + background-color: #cdcdcd; +} + +.tabulator .tabulator-header .tabulator-col.tabulator-sortable[aria-sort="none"] .tabulator-col-content .tabulator-arrow { + border-top: none; + border-bottom: 6px solid #bbb; +} + +.tabulator .tabulator-header .tabulator-col.tabulator-sortable[aria-sort="asc"] .tabulator-col-content .tabulator-arrow { + border-top: none; + border-bottom: 6px solid #666; +} + +.tabulator .tabulator-header .tabulator-col.tabulator-sortable[aria-sort="desc"] .tabulator-col-content .tabulator-arrow { + border-top: 6px solid #666; + border-bottom: none; +} + +.tabulator .tabulator-header .tabulator-col.tabulator-col-vertical .tabulator-col-content .tabulator-col-title { + -webkit-writing-mode: vertical-rl; + -ms-writing-mode: tb-rl; + writing-mode: vertical-rl; + text-orientation: mixed; + display: -ms-flexbox; + display: flex; + -ms-flex-align: center; + align-items: center; + -ms-flex-pack: center; + justify-content: center; +} + +.tabulator .tabulator-header .tabulator-col.tabulator-col-vertical.tabulator-col-vertical-flip .tabulator-col-title { + -ms-transform: rotate(180deg); + transform: rotate(180deg); +} + +.tabulator .tabulator-header .tabulator-col.tabulator-col-vertical.tabulator-sortable .tabulator-col-title { + padding-right: 0; + padding-top: 20px; +} + +.tabulator .tabulator-header .tabulator-col.tabulator-col-vertical.tabulator-sortable.tabulator-col-vertical-flip .tabulator-col-title { + padding-right: 0; + padding-bottom: 20px; +} + +.tabulator .tabulator-header .tabulator-col.tabulator-col-vertical.tabulator-sortable .tabulator-arrow { + right: calc(50% - 6px); +} + +.tabulator .tabulator-header .tabulator-frozen { + display: inline-block; + position: absolute; + z-index: 10; +} + +.tabulator .tabulator-header .tabulator-frozen.tabulator-frozen-left { + border-right: 2px solid #aaa; +} + +.tabulator .tabulator-header .tabulator-frozen.tabulator-frozen-right { + border-left: 2px solid #aaa; +} + +.tabulator .tabulator-header .tabulator-calcs-holder { + box-sizing: border-box; + min-width: 400%; + background: rgba(80,20,10,0.2); + border-top: 1px solid #aaa; + border-bottom: 1px solid #aaa; + overflow: hidden; +} + +.tabulator .tabulator-header .tabulator-calcs-holder .tabulator-row { + background: rgba(80,20,10,0.2); +} + +.tabulator .tabulator-header .tabulator-calcs-holder .tabulator-row .tabulator-col-resize-handle { + display: none; +} + +.tabulator .tabulator-header .tabulator-frozen-rows-holder { + min-width: 400%; +} + +.tabulator .tabulator-header .tabulator-frozen-rows-holder:empty { + display: none; +} + +.tabulator .tabulator-tableHolder { + position: relative; + width: 100%; + white-space: nowrap; + overflow: auto; + -webkit-overflow-scrolling: touch; +} + +.tabulator .tabulator-tableHolder:focus { + outline: none; +} + +.tabulator .tabulator-tableHolder .tabulator-placeholder { + box-sizing: border-box; + display: -ms-flexbox; + display: flex; + -ms-flex-align: center; + align-items: center; + width: 100%; +} + +.tabulator .tabulator-tableHolder .tabulator-placeholder[tabulator-render-mode="virtual"] { + position: absolute; + top: 0; + left: 0; + height: 100%; +} + +.tabulator .tabulator-tableHolder .tabulator-placeholder span { + display: inline-block; + margin: 0 auto; + padding: 10px; + color: #ccc; + font-weight: bold; + font-size: 20px; +} + +.tabulator .tabulator-tableHolder .tabulator-table { + position: relative; + display: inline-block; + background-color: #fff; + white-space: nowrap; + overflow: visible; + color: #333; +} + +.tabulator .tabulator-tableHolder .tabulator-table .tabulator-row.tabulator-calcs { + font-weight: bold; + background: rgba(80,20,10,0.2); +} + +.tabulator .tabulator-tableHolder .tabulator-table .tabulator-row.tabulator-calcs.tabulator-calcs-top { + border-bottom: 2px solid #aaa; +} + +.tabulator .tabulator-tableHolder .tabulator-table .tabulator-row.tabulator-calcs.tabulator-calcs-bottom { + border-top: 2px solid #aaa; +} + +.tabulator .tabulator-footer { + padding: 5px 10px; + border-top: 1px solid #999; + text-align: right; + color: #555; + font-weight: bold; + white-space: nowrap; + -ms-user-select: none; + user-select: none; + -moz-user-select: none; + -khtml-user-select: none; + -webkit-user-select: none; + -o-user-select: none; +} + +.tabulator .tabulator-footer .tabulator-calcs-holder { + box-sizing: border-box; + width: calc(100% + 20px); + margin: -5px -10px 5px -10px; + text-align: left; + background: rgba(80,20,10,0.2); + border-bottom: 1px solid #aaa; + border-top: 1px solid #aaa; + overflow: hidden; +} + +.tabulator .tabulator-footer .tabulator-calcs-holder .tabulator-row { + background: rgba(80,20,10,0.2); +} + +.tabulator .tabulator-footer .tabulator-calcs-holder .tabulator-row .tabulator-col-resize-handle { + display: none; +} + +.tabulator .tabulator-footer .tabulator-calcs-holder:only-child { + margin-bottom: -5px; + border-bottom: none; +} + +.tabulator .tabulator-footer .tabulator-pages { + margin: 0 7px; +} + +.tabulator .tabulator-footer .tabulator-page { + display: inline-block; + margin: 0 2px; + padding: 2px 5px; + border: 1px solid #aaa; + border-radius: 3px; + background: rgba(255, 255, 255, 0.2); + color: #555; + font-family: inherit; + font-weight: inherit; + font-size: inherit; +} + +.tabulator .tabulator-footer .tabulator-page.active { + color: #d00; +} + +.tabulator .tabulator-footer .tabulator-page:disabled { + opacity: .5; +} + +.tabulator .tabulator-footer .tabulator-page:not(.disabled):hover { + cursor: pointer; + background: rgba(0, 0, 0, 0.2); + color: #fff; +} + +.tabulator .tabulator-col-resize-handle { + position: absolute; + right: 0; + top: 0; + bottom: 0; + width: 5px; +} + +.tabulator .tabulator-col-resize-handle.prev { + left: 0; + right: auto; +} + +.tabulator .tabulator-col-resize-handle:hover { + cursor: ew-resize; +} + +.tabulator .tabulator-loader { + position: absolute; + display: -ms-flexbox; + display: flex; + -ms-flex-align: center; + align-items: center; + top: 0; + left: 0; + z-index: 100; + height: 100%; + width: 100%; + background: rgba(0, 0, 0, 0.4); + text-align: center; +} + +.tabulator .tabulator-loader .tabulator-loader-msg { + display: inline-block; + margin: 0 auto; + padding: 10px 20px; + border-radius: 4px; + background: #fff; + font-weight: bold; + font-size: 16px; +} + +.tabulator .tabulator-loader .tabulator-loader-msg.tabulator-loading { + border: 4px solid #333; + color: #000; +} + +.tabulator .tabulator-loader .tabulator-loader-msg.tabulator-error { + color: #000; +} + +.tabulator-row { + position: relative; + box-sizing: border-box; + min-height: 22px; + background-color: #fff; +} + +.tabulator-row.tabulator-row-even { + background-color: #EFEFEF; +} + +.tabulator-row.tabulator-selectable:hover { + background-color: #bbb; + cursor: pointer; +} + +.tabulator-row.tabulator-selected { + background-color: #9ABCEA; +} + +.tabulator-row.tabulator-selected:hover { + background-color: #769BCC; + cursor: pointer; +} + +.tabulator-row.tabulator-row-moving { + border: 1px solid #000; + background: #fff; +} + +.tabulator-row.tabulator-moving { + position: absolute; + border-top: 1px solid #aaa; + border-bottom: 1px solid #aaa; + pointer-events: none; + z-index: 15; +} + +.tabulator-row .tabulator-row-resize-handle { + position: absolute; + right: 0; + bottom: 0; + left: 0; + height: 5px; +} + +.tabulator-row .tabulator-row-resize-handle.prev { + top: 0; + bottom: auto; +} + +.tabulator-row .tabulator-row-resize-handle:hover { + cursor: ns-resize; +} + +.tabulator-row .tabulator-frozen { + display: inline-block; + position: absolute; + background-color: inherit; + z-index: 10; +} + +.tabulator-row .tabulator-frozen.tabulator-frozen-left { + border-right: 2px solid #aaa; +} + +.tabulator-row .tabulator-frozen.tabulator-frozen-right { + border-left: 2px solid #aaa; +} + +.tabulator-row .tabulator-responsive-collapse { + box-sizing: border-box; + padding: 5px; + border-top: 1px solid #aaa; + border-bottom: 1px solid #aaa; +} + +.tabulator-row .tabulator-responsive-collapse:empty { + display: none; +} + +.tabulator-row .tabulator-responsive-collapse table { + font-size: 14px; +} + +.tabulator-row .tabulator-responsive-collapse table tr td { + position: relative; +} + +.tabulator-row .tabulator-responsive-collapse table tr td:first-of-type { + padding-right: 10px; +} + +.tabulator-row .tabulator-cell { + display: inline-block; + position: relative; + box-sizing: border-box; + padding: 4px; + border-right: 1px solid #aaa; + vertical-align: middle; + white-space: nowrap; + overflow: hidden; + text-overflow: ellipsis; +} + +.tabulator-row .tabulator-cell.tabulator-editing { + border: 1px solid #1D68CD; + padding: 0; +} + +.tabulator-row .tabulator-cell.tabulator-editing input, .tabulator-row .tabulator-cell.tabulator-editing select { + border: 1px; + background: transparent; +} + +.tabulator-row .tabulator-cell.tabulator-validation-fail { + border: 1px solid #dd0000; +} + +.tabulator-row .tabulator-cell.tabulator-validation-fail input, .tabulator-row .tabulator-cell.tabulator-validation-fail select { + border: 1px; + background: transparent; + color: #dd0000; +} + +.tabulator-row .tabulator-cell:first-child .tabulator-col-resize-handle.prev { + display: none; +} + +.tabulator-row .tabulator-cell.tabulator-row-handle { + display: -ms-inline-flexbox; + display: inline-flex; + -ms-flex-align: center; + align-items: center; + -ms-flex-pack: center; + justify-content: center; + -moz-user-select: none; + -khtml-user-select: none; + -webkit-user-select: none; + -o-user-select: none; +} + +.tabulator-row .tabulator-cell.tabulator-row-handle .tabulator-row-handle-box { + width: 80%; +} + +.tabulator-row .tabulator-cell.tabulator-row-handle .tabulator-row-handle-box .tabulator-row-handle-bar { + width: 100%; + height: 3px; + margin-top: 2px; + background: #666; +} + +.tabulator-row .tabulator-cell .tabulator-data-tree-branch { + display: inline-block; + vertical-align: middle; + height: 9px; + width: 7px; + margin-top: -9px; + margin-right: 5px; + border-bottom-left-radius: 1px; + border-left: 2px solid #aaa; + border-bottom: 2px solid #aaa; +} + +.tabulator-row .tabulator-cell .tabulator-data-tree-control { + display: -ms-inline-flexbox; + display: inline-flex; + -ms-flex-pack: center; + justify-content: center; + -ms-flex-align: center; + align-items: center; + vertical-align: middle; + height: 11px; + width: 11px; + margin-right: 5px; + border: 1px solid #333; + border-radius: 2px; + background: rgba(0, 0, 0, 0.1); + overflow: hidden; +} + +.tabulator-row .tabulator-cell .tabulator-data-tree-control:hover { + cursor: pointer; + background: rgba(0, 0, 0, 0.2); +} + +.tabulator-row .tabulator-cell .tabulator-data-tree-control .tabulator-data-tree-control-collapse { + display: inline-block; + position: relative; + height: 7px; + width: 1px; + background: transparent; +} + +.tabulator-row .tabulator-cell .tabulator-data-tree-control .tabulator-data-tree-control-collapse:after { + position: absolute; + content: ""; + left: -3px; + top: 3px; + height: 1px; + width: 7px; + background: #333; +} + +.tabulator-row .tabulator-cell .tabulator-data-tree-control .tabulator-data-tree-control-expand { + display: inline-block; + position: relative; + height: 7px; + width: 1px; + background: #333; +} + +.tabulator-row .tabulator-cell .tabulator-data-tree-control .tabulator-data-tree-control-expand:after { + position: absolute; + content: ""; + left: -3px; + top: 3px; + height: 1px; + width: 7px; + background: #333; +} + +.tabulator-row .tabulator-cell .tabulator-responsive-collapse-toggle { + display: -ms-inline-flexbox; + display: inline-flex; + -ms-flex-align: center; + align-items: center; + -ms-flex-pack: center; + justify-content: center; + -moz-user-select: none; + -khtml-user-select: none; + -webkit-user-select: none; + -o-user-select: none; + height: 15px; + width: 15px; + border-radius: 4px; + background: #666; + color: #fff; + font-weight: bold; + font-size: 1.1em; +} + +.tabulator-row .tabulator-cell .tabulator-responsive-collapse-toggle:hover { + opacity: .7; +} + +.tabulator-row .tabulator-cell .tabulator-responsive-collapse-toggle.open .tabulator-responsive-collapse-toggle-close { + display: initial; +} + +.tabulator-row .tabulator-cell .tabulator-responsive-collapse-toggle.open .tabulator-responsive-collapse-toggle-open { + display: none; +} + +.tabulator-row .tabulator-cell .tabulator-responsive-collapse-toggle .tabulator-responsive-collapse-toggle-close { + display: none; +} + +.tabulator-row.tabulator-group { + box-sizing: border-box; + border-bottom: 1px solid #999; + border-right: 1px solid #aaa; + border-top: 1px solid #999; + padding: 5px; + padding-left: 10px; + background: #ccc; + font-weight: bold; + min-width: 100%; +} + +.tabulator-row.tabulator-group:hover { + cursor: pointer; + background-color: rgba(0, 0, 0, 0.1); +} + +.tabulator-row.tabulator-group.tabulator-group-visible .tabulator-arrow { + margin-right: 10px; + border-left: 6px solid transparent; + border-right: 6px solid transparent; + border-top: 6px solid #666; + border-bottom: 0; +} + +.tabulator-row.tabulator-group.tabulator-group-level-1 .tabulator-arrow { + margin-left: 20px; +} + +.tabulator-row.tabulator-group.tabulator-group-level-2 .tabulator-arrow { + margin-left: 40px; +} + +.tabulator-row.tabulator-group.tabulator-group-level-3 .tabulator-arrow { + margin-left: 60px; +} + +.tabulator-row.tabulator-group.tabulator-group-level-4 .tabulator-arrow { + margin-left: 80px; +} + +.tabulator-row.tabulator-group.tabulator-group-level-5 .tabulator-arrow { + margin-left: 100px; +} + +.tabulator-row.tabulator-group .tabulator-arrow { + display: inline-block; + width: 0; + height: 0; + margin-right: 16px; + border-top: 6px solid transparent; + border-bottom: 6px solid transparent; + border-right: 0; + border-left: 6px solid #666; + vertical-align: middle; +} + +.tabulator-row.tabulator-group span { + margin-left: 10px; + color: #d00; +} + +.tabulator-edit-select-list { + position: absolute; + display: inline-block; + box-sizing: border-box; + max-height: 200px; + background: #fff; + border: 1px solid #aaa; + font-size: 14px; + overflow-y: auto; + -webkit-overflow-scrolling: touch; + z-index: 10000; +} + +.tabulator-edit-select-list .tabulator-edit-select-list-item { + padding: 4px; + color: #333; +} + +.tabulator-edit-select-list .tabulator-edit-select-list-item.active { + color: #fff; + background: #1D68CD; +} + +.tabulator-edit-select-list .tabulator-edit-select-list-item:hover { + cursor: pointer; + color: #fff; + background: #1D68CD; +} + +.tabulator-edit-select-list .tabulator-edit-select-list-group { + border-bottom: 1px solid #aaa; + padding: 4px; + padding-top: 6px; + color: #333; + font-weight: bold; +} diff --git a/site/public/about/credits/index.html b/site/public/about/credits/index.html index 1b1f4d54..67e9dcb8 100644 --- a/site/public/about/credits/index.html +++ b/site/public/about/credits/index.html @@ -8,6 +8,7 @@ + @@ -19,7 +20,6 @@ The Darkside of Datasets
        Title
        diff --git a/site/public/datasets/vgg_face2/index.html b/site/public/datasets/vgg_face2/index.html index 63bb2cc0..46242505 100644 --- a/site/public/datasets/vgg_face2/index.html +++ b/site/public/datasets/vgg_face2/index.html @@ -8,6 +8,7 @@ + @@ -19,7 +20,6 @@ The Darkside of Datasets
        +
        diff --git a/site/public/datasets/vgg_face2/index.html b/site/public/datasets/vgg_face2/index.html index 46242505..90216add 100644 --- a/site/public/datasets/vgg_face2/index.html +++ b/site/public/datasets/vgg_face2/index.html @@ -10,6 +10,7 @@ + @@ -29,7 +30,7 @@

        VGG Faces2

        Created
        2018
        Images
        3.3M
        People
        9,000
        Created From
        Scraping search engines
        Search available
        [Searchable](#)

        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.

        -

        VGG Face2 by the Numbers

        +

        VGG Face2 by the Numbers

        • 1,331 actresses, 139 presidents
        • 3 husbands and 16 wives
        • diff --git a/site/public/index.html b/site/public/index.html index 78d02434..688eb700 100644 --- a/site/public/index.html +++ b/site/public/index.html @@ -10,6 +10,7 @@ + @@ -50,7 +51,7 @@

          Facial Recognition Datasets

          Regular Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat.

          Summary

          -
          Found
          275 datasets
          Created between
          1993-2018
          Smallest dataset
          20 images
          Largest dataset
          10,000,000 images
          Highest resolution faces
          450x500 (Unconstrained College Students)
          Lowest resolution faces
          16x20 pixels (QMUL SurvFace)
          +
        Found
        275 datasets
        Created between
        1993-2018
        Smallest dataset
        20 images
        Largest dataset
        10,000,000 images
        Highest resolution faces
        450x500 (Unconstrained College Students)
        Lowest resolution faces
        16x20 pixels (QMUL SurvFace)
        diff --git a/site/public/research/00_introduction/index.html b/site/public/research/00_introduction/index.html index 4349bcaf..7b132cd5 100644 --- a/site/public/research/00_introduction/index.html +++ b/site/public/research/00_introduction/index.html @@ -10,6 +10,7 @@ + diff --git a/site/public/research/01_from_1_to_100_pixels/index.html b/site/public/research/01_from_1_to_100_pixels/index.html index 31df84bb..aac4b7e1 100644 --- a/site/public/research/01_from_1_to_100_pixels/index.html +++ b/site/public/research/01_from_1_to_100_pixels/index.html @@ -10,6 +10,7 @@ + diff --git a/site/public/research/index.html b/site/public/research/index.html index 1acbe844..dc69cdc5 100644 --- a/site/public/research/index.html +++ b/site/public/research/index.html @@ -10,6 +10,7 @@ + diff --git a/site/public/test/citations/index.html b/site/public/test/citations/index.html index fec2e674..cb5aa92f 100644 --- a/site/public/test/citations/index.html +++ b/site/public/test/citations/index.html @@ -10,6 +10,7 @@ + diff --git a/site/public/test/csv/index.html b/site/public/test/csv/index.html index 0ef3592d..52c87d4d 100644 --- a/site/public/test/csv/index.html +++ b/site/public/test/csv/index.html @@ -10,6 +10,7 @@ + @@ -29,7 +30,7 @@

        CSV Test

        ← Back to test index

        -
        +
        diff --git a/site/public/test/datasets/index.html b/site/public/test/datasets/index.html index 976f8ffe..1554ae2b 100644 --- a/site/public/test/datasets/index.html +++ b/site/public/test/datasets/index.html @@ -10,6 +10,7 @@ + @@ -29,7 +30,7 @@

        Index of datasets

        ← Back to test index

        -
        +
        diff --git a/site/public/test/face_search/index.html b/site/public/test/face_search/index.html index c6c49c7f..43703ed5 100644 --- a/site/public/test/face_search/index.html +++ b/site/public/test/face_search/index.html @@ -10,6 +10,7 @@ + diff --git a/site/public/test/gallery/index.html b/site/public/test/gallery/index.html index 841c8225..8ead03eb 100644 --- a/site/public/test/gallery/index.html +++ b/site/public/test/gallery/index.html @@ -10,6 +10,7 @@ + diff --git a/site/public/test/index.html b/site/public/test/index.html index 66f2e619..62837b3b 100644 --- a/site/public/test/index.html +++ b/site/public/test/index.html @@ -10,6 +10,7 @@ + diff --git a/site/public/test/map/index.html b/site/public/test/map/index.html index ae75bcc6..bdec69f5 100644 --- a/site/public/test/map/index.html +++ b/site/public/test/map/index.html @@ -10,6 +10,7 @@ + diff --git a/site/public/test/name_search/index.html b/site/public/test/name_search/index.html index b8ce1e3a..a23aa53d 100644 --- a/site/public/test/name_search/index.html +++ b/site/public/test/name_search/index.html @@ -10,6 +10,7 @@ + diff --git a/site/public/test/style/index.html b/site/public/test/style/index.html index 34eeae53..3d14bd77 100644 --- a/site/public/test/style/index.html +++ b/site/public/test/style/index.html @@ -10,6 +10,7 @@ + diff --git a/site/templates/layout.html b/site/templates/layout.html index 75682849..b80c6363 100644 --- a/site/templates/layout.html +++ b/site/templates/layout.html @@ -10,6 +10,7 @@ + -- cgit v1.2.3-70-g09d2 From 76f36c6c5dafe754b066903b1ee8ecdd1b92dcab Mon Sep 17 00:00:00 2001 From: Jules Laplace Date: Sun, 16 Dec 2018 20:01:23 +0100 Subject: faceSearch client --- client/actions.js | 2 +- client/applet.js | 14 ++-- client/faceSearch/faceSearch.actions.js | 57 ++++++++++++++ client/faceSearch/faceSearch.container.js | 31 ++++++++ client/faceSearch/faceSearch.query.js | 73 +++++++++++++++++ client/faceSearch/faceSearch.reducer.js | 32 ++++++++ client/faceSearch/faceSearch.result.js | 30 +++++++ client/faceSearch/index.js | 5 ++ client/index.js | 6 +- client/map/leaflet.arc.js | 2 - client/map/map.js | 92 ---------------------- client/map/snap.svg-min.js | 23 ------ client/metadata/index.js | 25 ------ client/store.js | 4 +- client/tables.js | 2 + client/types.js | 11 +-- package-lock.json | 48 +++++++++++ package.json | 2 + scraper/s2-citation-report.py | 4 +- site/assets/css/applets.css | 45 +++++++++++ site/assets/css/css.css | 16 +++- site/assets/img/icon_camera.svg | 2 +- site/public/about/privacy/index.html | 8 +- site/public/about/terms/index.html | 10 +-- site/public/datasets/lfw/index.html | 12 +-- site/public/datasets/vgg_face2/index.html | 6 +- site/public/research/00_introduction/index.html | 5 +- .../research/01_from_1_to_100_pixels/index.html | 4 +- site/public/research/index.html | 2 +- site/public/test/citations/index.html | 2 +- site/public/test/csv/index.html | 2 +- site/public/test/datasets/index.html | 2 +- site/public/test/face_search/index.html | 2 +- site/public/test/gallery/index.html | 2 +- site/public/test/index.html | 10 +-- site/public/test/map/index.html | 2 +- site/public/test/name_search/index.html | 2 +- site/public/test/style/index.html | 8 +- webpack.config.dev.js | 18 +++-- webpack.config.prod.js | 10 ++- 40 files changed, 413 insertions(+), 220 deletions(-) create mode 100644 client/faceSearch/faceSearch.actions.js create mode 100644 client/faceSearch/faceSearch.container.js create mode 100644 client/faceSearch/faceSearch.query.js create mode 100644 client/faceSearch/faceSearch.reducer.js create mode 100644 client/faceSearch/faceSearch.result.js create mode 100644 client/faceSearch/index.js delete mode 100644 client/map/leaflet.arc.js delete mode 100644 client/map/map.js delete mode 100755 client/map/snap.svg-min.js delete mode 100644 client/metadata/index.js (limited to 'site/public/research') diff --git a/client/actions.js b/client/actions.js index 779113f8..37b4eb2e 100644 --- a/client/actions.js +++ b/client/actions.js @@ -1,4 +1,4 @@ -// import * as search from './search/search.actions' +import * as faceSearch from './faceSearch/faceSearch.actions' // import * as review from './review/review.actions' // import * as metadata from './metadata/metadata.actions' diff --git a/client/applet.js b/client/applet.js index f218a7f2..4d2a8e6c 100644 --- a/client/applet.js +++ b/client/applet.js @@ -1,13 +1,15 @@ import React, { Component } from 'react' -// import { Header, Sidebar, Footer } from './common' +import { Container as FaceSearchContainer } from './faceSearch' export default class Applet extends Component { render() { - return ( -
        -
        {'Megapixels'}
        -
        - ) + console.log(this.props) + switch (this.props.payload.cmd) { + case 'face_search': + return + default: + return
        {'Megapixels'}
        + } } } diff --git a/client/faceSearch/faceSearch.actions.js b/client/faceSearch/faceSearch.actions.js new file mode 100644 index 00000000..ccd51201 --- /dev/null +++ b/client/faceSearch/faceSearch.actions.js @@ -0,0 +1,57 @@ +// import fetchJsonp from 'fetch-jsonp' +import * as types from '../types' +// import { hashPath } from '../util' +import { store } from '../store' +import { post, preloadImage } from '../util' +// import querystring from 'query-string' + +// urls + +const url = { + upload: (dataset) => process.env.API_HOST + '/api/dataset/' + dataset + '/face/', +} +export const publicUrl = { +} + +// standard loading events + +const loading = (tag, offset) => ({ + type: types.faceSearch.loading, + tag, + offset +}) +const loaded = (tag, data, offset = 0) => ({ + type: types.faceSearch.loaded, + tag, + data, + offset +}) +const error = (tag, err) => ({ + type: types.faceSearch.error, + tag, + err +}) + +// search UI functions + +export const updateOptions = opt => dispatch => { + dispatch({ type: types.faceSearch.update_options, opt }) +} + +// API functions + +export const upload = (file, query) => dispatch => { + // const { options } = store.getState().faceSearch + const tag = 'result' + const fd = new FormData() + fd.append('query_img', file) + // fd.append('limit', options.perPage) + if (!query) { + dispatch(loading(tag)) + } + post(url.upload(), fd) + .then(data => { + dispatch(loaded(tag, data)) + }) + .catch(err => dispatch(error(tag, err))) +} diff --git a/client/faceSearch/faceSearch.container.js b/client/faceSearch/faceSearch.container.js new file mode 100644 index 00000000..e5fae24b --- /dev/null +++ b/client/faceSearch/faceSearch.container.js @@ -0,0 +1,31 @@ +import React, { Component } from 'react' +import { bindActionCreators } from 'redux' +import { connect } from 'react-redux' + +import * as actions from './faceSearch.actions' + +import FaceSearchQuery from './faceSearch.query' +import FaceSearchResult from './faceSearch.result' + +class FaceSearchContainer extends Component { + render() { + return ( +
        + + +
        + ) + } +} + +const mapStateToProps = state => ({ + query: state.faceSearch.query, + result: state.faceSearch.result, + options: state.faceSearch.options, +}) + +const mapDispatchToProps = dispatch => ({ + actions: bindActionCreators({ ...actions }, dispatch), +}) + +export default connect(mapStateToProps, mapDispatchToProps)(FaceSearchContainer) diff --git a/client/faceSearch/faceSearch.query.js b/client/faceSearch/faceSearch.query.js new file mode 100644 index 00000000..9313c538 --- /dev/null +++ b/client/faceSearch/faceSearch.query.js @@ -0,0 +1,73 @@ +import React, { Component } from 'react' +import { bindActionCreators } from 'redux' +import { connect } from 'react-redux' + +import * as actions from './faceSearch.actions' + +class FaceSearchQuery extends Component { + upload(e) { + const files = e.dataTransfer ? e.dataTransfer.files : e.target.files + let i + let file + for (i = 0; i < files.length; i++) { + file = files[i] + if (file && file.type.match('image.*')) break + } + if (!file) return + this.props.actions.upload(file) + } + + render() { + const { result } = this.props + return ( +
        +
        + {result.loading ? +
        + Loading... +
        + : +
        + + +
        + } +
        +
        +

        Search This Dataset

        +

        Searching {13456} images

        +

        + Use facial recognition to reverse search into the LFW dataset and see if it contains your photos. +

        +
          +
        1. Upload a photo of yourself
        2. +
        3. Use a photo similar to examples below
        4. +
        5. Only matches over 85% will be displayed
        6. +
        7. Read more tips to improve search results
        8. +
        9. Your search data is never stored and immediately cleared once you leave this page.
        10. +
        +

        + Read more about privacy. +

        +
        +
        + ) + } +} + +const mapStateToProps = state => ({ + result: state.faceSearch.result, + options: state.faceSearch.options, +}) + +const mapDispatchToProps = dispatch => ({ + actions: bindActionCreators({ ...actions }, dispatch), +}) + +export default connect(mapStateToProps, mapDispatchToProps)(FaceSearchQuery) diff --git a/client/faceSearch/faceSearch.reducer.js b/client/faceSearch/faceSearch.reducer.js new file mode 100644 index 00000000..da8bd25e --- /dev/null +++ b/client/faceSearch/faceSearch.reducer.js @@ -0,0 +1,32 @@ +import * as types from '../types' + +const initialState = () => ({ + query: {}, + result: {}, + loading: false, +}) + +export default function faceSearchReducer(state = initialState(), action) { + switch (action.type) { + case types.faceSearch.loading: + return { + ...state, + [action.tag]: { loading: true }, + } + + case types.faceSearch.loaded: + return { + ...state, + [action.tag]: action.data, + } + + case types.faceSearch.error: + return { + ...state, + [action.tag]: { error: action.err }, + } + + default: + return state + } +} diff --git a/client/faceSearch/faceSearch.result.js b/client/faceSearch/faceSearch.result.js new file mode 100644 index 00000000..844a5a70 --- /dev/null +++ b/client/faceSearch/faceSearch.result.js @@ -0,0 +1,30 @@ +import React, { Component } from 'react' +import { bindActionCreators } from 'redux' +import { connect } from 'react-redux' + +import * as actions from './faceSearch.actions' + +class FaceSearchResult extends Component { + componentDidMount() { + } + + render() { + return ( +
        + Result here +
        + ) + } +} + +const mapStateToProps = state => ({ + query: state.faceSearch.query, + result: state.faceSearch.result, + options: state.faceSearch.options, +}) + +const mapDispatchToProps = dispatch => ({ + actions: bindActionCreators({ ...actions }, dispatch), +}) + +export default connect(mapStateToProps, mapDispatchToProps)(FaceSearchResult) diff --git a/client/faceSearch/index.js b/client/faceSearch/index.js new file mode 100644 index 00000000..32f6dcc6 --- /dev/null +++ b/client/faceSearch/index.js @@ -0,0 +1,5 @@ +import Container from './faceSearch.container' + +export { + Container, +} diff --git a/client/index.js b/client/index.js index 86bc8979..90fc22e1 100644 --- a/client/index.js +++ b/client/index.js @@ -13,13 +13,12 @@ function appendReactApplet(el, payload) { ReactDOM.render( - + , el ) } - function fetchDataset(payload) { const url = "https://megapixels.nyc3.digitaloceanspaces.com/v1/citations/" + payload.dataset + ".json" return fetch(url, { mode: 'cors' }).then(r => r.json()) @@ -36,9 +35,12 @@ function appendApplets(applets) { el.parentNode.classList.add('wide') el.classList.add(payload.cmd) appendMap(el, payload) + el.classList.add('loaded') break default: + console.log('react', el, payload) appendReactApplet(el, payload) + el.classList.add('loaded') break } }) diff --git a/client/map/leaflet.arc.js b/client/map/leaflet.arc.js deleted file mode 100644 index 062b22a0..00000000 --- a/client/map/leaflet.arc.js +++ /dev/null @@ -1,2 +0,0 @@ -!function(t,e){"object"==typeof exports&&"object"==typeof module?module.exports=e():"function"==typeof define&&define.amd?define("leaflet-arc",[],e):"object"==typeof exports?exports["leaflet-arc"]=e():t["leaflet-arc"]=e()}(this,function(){return function(t){function e(o){if(r[o])return r[o].exports;var s=r[o]={exports:{},id:o,loaded:!1};return t[o].call(s.exports,s,s.exports,e),s.loaded=!0,s.exports}var r={};return e.m=t,e.c=r,e.p="",e(0)}([function(t,e,r){"use strict";function o(t){return t&&t.__esModule?t:{"default":t}}function s(t,e){if(!t.geometries[0]||!t.geometries[0].coords[0])return[];var r=function(){var r=e.lng-t.geometries[0].coords[0][0]-360;return{v:t.geometries.map(function(t){return r+=360,t.coords.map(function(t){return L.latLng([t[1],t[0]+r])})}).reduce(function(t,e){return t.concat(e)})}}();return"object"===("undefined"==typeof r?"undefined":n(r))?r.v:void 0}var i=Object.assign||function(t){for(var e=1;ed&&(v>f&&gf&&vu&&(u=M)}var m=[];if(p&&u0&&Math.abs(b-r[x-1][0])>d){var L=parseFloat(r[x-1][0]),S=parseFloat(r[x-1][1]),j=parseFloat(r[x][0]),E=parseFloat(r[x][1]);if(L>-180&&L-180&&r[x-1][0]f&&L<180&&j==-180&&x+1f&&r[x-1][0]<180){w.push([180,r[x][1]]),x++,w.push([r[x][0],r[x][1]]);continue}if(Lf){var F=L;L=j,j=F;var C=S;S=E,E=C}if(L>f&&j=180&&Lf?180:-180,I]),w=[],w.push([r[x-1][0]>f?-180:180,I]),m.push(w)}else w=[],m.push(w);w.push([b,r[x][1]])}else w.push([r[x][0],r[x][1]])}}else{var N=[];m.push(N);for(var A=0;AOpenStreetMap contributors, CC-BY-SA, Imagery © Mapbox', - maxZoom: 18, - id: 'mapbox.dark', - style: 'mapbox://styles/mapbox/dark-v9', - accessToken: 'pk.eyJ1IjoiZmFuc2FsY3kiLCJhIjoiY2pvN3I1czJwMHF5NDNrbWRoMWpteHlrdCJ9.kMpM5syQUhVjKkn1iVx9fg' -}).addTo(map); -let points = read_json('citations') -let address = read_json('address') -let source = [0,0] -if (address) { - source = address.slice(3,5).map(n => parseFloat(n)) - console.log(address, source) -} - -var redDot = L.icon({ - iconUrl: '../reddot.png', - iconSize: [17, 17], // size of the icon - iconAnchor: [8, 8], // point of the icon which will correspond to marker's location - popupAnchor: [0, -5] // point from which the popup should open relative to the iconAnchor -}); - -points.forEach(point => { - /* - [ - "Face Alignment by Local Deep Descriptor Regression", - "Rutgers University", - [ - "Rutgers University", - "40.47913175", - "-74.431688684404", - "Rutgers Cook Campus - North, Biel Road, New Brunswick, Middlesex County, New Jersey, 08901, USA" - ] - ] - */ - - const latlng = point.slice(5,7).map(n => parseFloat(n)) - // console.log(point) - if (!latlng.length || isNaN(latlng[0]) || isNaN(latlng[1])) return - var marker = L.marker(latlng, { icon: redDot }).addTo(map); - marker.bindPopup([ - "", point[0], "", - "
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c in b.mui)b.mui.hasOwnProperty(c)&&(a[c]=b.mui[c])}}),d}); - -export default Snap \ No newline at end of file diff --git a/client/metadata/index.js b/client/metadata/index.js deleted file mode 100644 index 0eef814e..00000000 --- a/client/metadata/index.js +++ /dev/null @@ -1,25 +0,0 @@ -import Heading from './heading.component' -import MediaInfo from './mediaInfo.component' -import MediaRecord from './mediaRecord.component' -import Summary from './summary.component' -import KeyframeList from './keyframeList.component' -import KeyframeSingle from './keyframeSingle.component' -import KeyframeStatus from './keyframeStatus.component' -import Coco from './coco.component' -import Places365 from './places365.component' -import Sugarcube from './sugarcube.component' - -import './metadata.css' - -export { - Heading, - MediaRecord, - MediaInfo, - Summary, - KeyframeList, - KeyframeSingle, - KeyframeStatus, - Coco, - Places365, - Sugarcube, -} diff --git a/client/store.js b/client/store.js index 3c7acefb..03f983a5 100644 --- a/client/store.js +++ b/client/store.js @@ -2,14 +2,14 @@ import { applyMiddleware, compose, combineReducers, createStore } from 'redux' import thunk from 'redux-thunk' // import metadataReducer from './metadata/metadata.reducer' -// import searchReducer from './search/search.reducer' +import faceSearchReducer from './faceSearch/faceSearch.reducer' // import reviewReducer from './review/review.reducer' const rootReducer = combineReducers({ auth: (state = {}) => state, // auth: (state = login()) => state, // metadata: metadataReducer, - // search: searchReducer, + faceSearch: faceSearchReducer, // review: reviewReducer, }) diff --git a/client/tables.js b/client/tables.js index a30abc32..b2b3d39c 100644 --- a/client/tables.js +++ b/client/tables.js @@ -57,6 +57,7 @@ export default function append(el, payload) { const citations = getCitations(data) console.log(citations) table.setData(citations) + el.classList.add('loaded') } else { fetch(payload.url, { mode: 'cors' }) .then(r => r.text()) @@ -65,6 +66,7 @@ export default function append(el, payload) { const data = csv.toJSON(text, { headers: { included: true } }) // console.log(data) table.setData(data) + el.classList.add('loaded') } catch (e) { console.error("error parsing json:", payload.url) console.error(e) diff --git a/client/types.js b/client/types.js index e3c64691..d295d0d1 100644 --- a/client/types.js +++ b/client/types.js @@ -6,16 +6,9 @@ export const tagAsType = (type, names) => ( }, {}) ) -export const metadata = tagAsType('metadata', [ - 'loading', 'loaded', 'loaded_many', 'error', 'set_hash' +export const faceSearch = tagAsType('faceSearch', [ + 'loading', 'loaded', 'error', 'update_options', ]) -export const search = tagAsType('search', [ - 'loading', 'loaded', 'error', 'panic', 'update_options', -]) - -export const review = tagAsType('review', [ - 'loading', 'loaded', 'error', 'save', 'unsave', 'refresh', 'clear', 'dedupe', 'create', 'set_count' -]) export const init = '@@INIT' diff --git a/package-lock.json b/package-lock.json index 90318e8e..a42dca34 100644 --- a/package-lock.json +++ b/package-lock.json @@ -5091,6 +5091,14 @@ "integrity": "sha512-Pgs951kaMm5GXP7MOvxERINe3gsaVjUWFm+UZPSq9xYriQAksyhg0csnS0KXSNRD5NmNdapXEpjxG49+AKh/ug==", "dev": true }, + "immutability-helper": { + "version": "2.9.0", + "resolved": "https://registry.npmjs.org/immutability-helper/-/immutability-helper-2.9.0.tgz", + "integrity": "sha512-2LYtDuGugMLyoFV0qGvblnq39E2VVQ9m4dDktlRLVBBVV1LnUMK0rlqkbtlUjfT1UJO876OobtPlNZTEbOOYVQ==", + "requires": { + "invariant": "^2.2.0" + } + }, "immutable": { "version": "3.8.2", "resolved": "https://registry.npmjs.org/immutable/-/immutable-3.8.2.tgz", @@ -6803,6 +6811,36 @@ "integrity": "sha512-pISE66AbVkp4fDQ7VHBwRNXzAAKJjw4Vw7nWI/+Q3vuly7SNfgYXvm6i5IgFylHGK5sP/xHAbB7N49OS4gWNyQ==", "dev": true }, + "preact": { + "version": "8.4.2", + "resolved": "https://registry.npmjs.org/preact/-/preact-8.4.2.tgz", + "integrity": "sha512-TsINETWiisfB6RTk0wh3/mvxbGRvx+ljeBccZ4Z6MPFKgu/KFGyf2Bmw3Z/jlXhL5JlNKY6QAbA9PVyzIy9//A==" + }, + "preact-compat": { + "version": "3.18.4", + "resolved": "https://registry.npmjs.org/preact-compat/-/preact-compat-3.18.4.tgz", + "integrity": "sha512-aR5CvCIDerE2Y201ERVkWQdTAQKhKGNYujEk4tbyfQDInFTrnCCa3KCeGtULZrwy0PNRBjdQa2/Za7qv7ALNFg==", + "requires": { + "immutability-helper": "^2.7.1", + "preact-render-to-string": "^3.8.2", + "preact-transition-group": "^1.1.1", + "prop-types": "^15.6.2", + "standalone-react-addons-pure-render-mixin": "^0.1.1" + } + }, + "preact-render-to-string": { + "version": "3.8.2", + "resolved": "https://registry.npmjs.org/preact-render-to-string/-/preact-render-to-string-3.8.2.tgz", + "integrity": "sha512-przuZPajiurStGgxMoJP0EJeC4xj5CgHv+M7GfF3YxAdhGgEWAkhOSE0xympAFN20uMayntBZpttIZqqLl77fw==", + "requires": { + "pretty-format": "^3.5.1" + } + }, + "preact-transition-group": { + "version": "1.1.1", + "resolved": "https://registry.npmjs.org/preact-transition-group/-/preact-transition-group-1.1.1.tgz", + "integrity": "sha1-8KSTJ+pRXs406ivoZMSn0p5dbhA=" + }, "prelude-ls": { "version": "1.1.2", "resolved": "https://registry.npmjs.org/prelude-ls/-/prelude-ls-1.1.2.tgz", @@ -6826,6 +6864,11 @@ "utila": "~0.4" } }, + "pretty-format": { + "version": "3.8.0", + "resolved": "http://registry.npmjs.org/pretty-format/-/pretty-format-3.8.0.tgz", + "integrity": "sha1-v77VbV6ad2ZF9LH/eqGjrE+jw4U=" + }, "private": { "version": "0.1.8", "resolved": "https://registry.npmjs.org/private/-/private-0.1.8.tgz", @@ -8346,6 +8389,11 @@ "figgy-pudding": "^3.5.1" } }, + "standalone-react-addons-pure-render-mixin": { + "version": "0.1.1", + "resolved": "https://registry.npmjs.org/standalone-react-addons-pure-render-mixin/-/standalone-react-addons-pure-render-mixin-0.1.1.tgz", + "integrity": "sha1-PHQJ9MecQN6axyxhbPZ5qZTzdVE=" + }, "static-extend": { "version": "0.1.2", "resolved": "https://registry.npmjs.org/static-extend/-/static-extend-0.1.2.tgz", diff --git a/package.json b/package.json index ca70592e..feef9b94 100644 --- a/package.json +++ b/package.json @@ -37,6 +37,8 @@ "leaflet-arc": "^1.0.2", "node-fetch": "^2.2.0", "parse-csv": "^0.2.0", + "preact": "^8.4.2", + "preact-compat": "^3.18.4", "prop-types": "^15.6.1", "react": "^16.3.0", "react-dom": "^16.3.0", diff --git a/scraper/s2-citation-report.py b/scraper/s2-citation-report.py index d70a378a..580312ff 100644 --- a/scraper/s2-citation-report.py +++ b/scraper/s2-citation-report.py @@ -274,7 +274,9 @@ def process_paper(row, addresses, success): geocoded_citations.append([ citation.title, institution, - ] + address) + ] + address + [ + citation.year, + ]) display_geocoded_citations.append([ citationId, LinkLine(citation.pdf_link, '[pdf]'), diff --git a/site/assets/css/applets.css b/site/assets/css/applets.css index fc71ecc4..2b531908 100644 --- a/site/assets/css/applets.css +++ b/site/assets/css/applets.css @@ -1,7 +1,52 @@ .applet { margin-bottom: 40px; + transition: opacity 0.2s cubic-bezier(0,0,1,1); + opacity: 0; } .applet.map { width: 100vw; height: 50vh; +} +.applet.loaded { + opacity: 1; +} + +.row { + display: flex; + flex-direction: row; + justify-content: flex-start; +} + +.query h2 { + margin-top: 0; padding-top: 0; +} +.cta { + padding-left: 20px; +} +.uploadContainer > div { + position: relative; + width: 300px; + height: 300px; + display: flex; + align-items: center; + justify-content: center; + background: #333; + border: 3px dashed #fff; + border-radius: 10px; + opacity: 0.3; + transition: opacity 0.2s cubic-bezier(0,0,1,1); +} +.uploadContainer.active, +.desktop .uploadContainer > div:hover { + opacity: 1; +} +.uploadContainer input { + position: absolute; + top: 0; left: 0; + width: 100%; height: 100%; + opacity: 0; + cursor: pointer; +} +.uploadContainer img { + max-width: 40px; } \ No newline at end of file diff --git a/site/assets/css/css.css b/site/assets/css/css.css index b6742cdc..4f2d7c6e 100644 --- a/site/assets/css/css.css +++ b/site/assets/css/css.css @@ -131,23 +131,31 @@ h1 { padding: 0; transition: color 0.2s cubic-bezier(0,0,1,1); } -h2, h3 { +h2 { + color: #ddd; + font-weight: 300; + font-size: 18pt; + margin: 20px 0 10px; + padding: 0; + transition: color 0.2s cubic-bezier(0,0,1,1); +} +h3 { margin: 0 0 20px 0; padding: 0; font-size: 11pt; font-weight: 500; transition: color 0.2s cubic-bezier(0,0,1,1); } -.content h2 a { +.content h3 a { color: #888; text-decoration: none; } -.desktop .content h2 a:hover { +.desktop .content h3 a:hover { color: #fff; text-decoration: underline; } -th, .gray, h2, h3 { +th, .gray, h3 { font-family: 'Roboto Mono', monospace; font-weight: 400; text-transform: uppercase; diff --git a/site/assets/img/icon_camera.svg b/site/assets/img/icon_camera.svg index b349072e..605fcfe1 100644 --- a/site/assets/img/icon_camera.svg +++ b/site/assets/img/icon_camera.svg @@ -1,2 +1,2 @@ - \ No newline at end of file + \ No newline at end of file diff --git a/site/public/about/privacy/index.html b/site/public/about/privacy/index.html index 6b4ac42f..ab4f12b8 100644 --- a/site/public/about/privacy/index.html +++ b/site/public/about/privacy/index.html @@ -49,15 +49,15 @@

        Usage Data is data collected automatically either generated by the use of the Service or from the Service infrastructure itself

        Information Collection and Use

        We collect several different types of information for various purposes to provide and improve our Service to you.

        -

        Types of Data Collected

        -

        Personal Data

        +

        Types of Data Collected

        +

        Personal Data

        While using our Service, we may ask you to provide us with certain personally identifiable information that can be used to contact or identify you ("Personal Data"). Personally identifiable information may include, but is not limited to:

        • Cookies and Usage Data
        -

        Usage Data

        +

        Usage Data

        We may also collect information how the Service is accessed and used ("Usage Data"). This Usage Data may include information such as your computer's Internet Protocol address (e.g. IP address), browser type, browser version, the pages of our Service that you visit, the time and date of your visit, the time spent on those pages, unique device identifiers and other diagnostic data.

        -

        Tracking & Cookies Data

        +

        Tracking & Cookies Data

        We use cookies and similar tracking technologies to track the activity on our Service and we hold certain information. Cookies are files with a small amount of data which may include an anonymous unique identifier. Cookies are sent to your browser from a website and stored on your device. Other tracking technologies are also used such as beacons, tags and scripts to collect and track information and to improve and analyse our Service.

        You can instruct your browser to refuse all cookies or to indicate when a cookie is being sent. However, if you do not accept cookies, you may not be able to use some portions of our Service. diff --git a/site/public/about/terms/index.html b/site/public/about/terms/index.html index 1e317715..72c1b670 100644 --- a/site/public/about/terms/index.html +++ b/site/public/about/terms/index.html @@ -33,20 +33,20 @@

        Please read these Terms and Conditions ("Terms", "Terms and Conditions") carefully before using the MegaPixels website (the "Service") operated by megapixels.cc ("us", "we", or "our").

        Your access to and use of the Service is conditioned on your acceptance of and compliance with these Terms.

        By accessing or using the Service you agree to be bound by these Terms. If you disagree with any part of the terms then you may not access the Service.

        -

        Links To Other Web Sites

        +

        Links To Other Web Sites

        Our Service may contain links to third-party web sites or services that are not owned or controlled by megapixels.cc.

        megapixels.cc has no control over, and assumes no responsibility for, the content, privacy policies, or practices of any third party web sites or services. You further acknowledge and agree that megapixels.cc shall not be responsible or liable, directly or indirectly, for any damage or loss caused or alleged to be caused by or in connection with use of or reliance on any such content, goods or services available on or through any such web sites or services.

        We strongly advise you to read the terms and conditions and privacy policies of any third-party web sites or services that you visit.

        -

        Termination

        +

        Termination

        We may terminate or suspend access to our Service immediately, without prior notice or liability, for any reason whatsoever, including without limitation if you breach the Terms.

        All provisions of the Terms which by their nature should survive termination shall survive termination, including, without limitation, ownership provisions, warranty disclaimers, indemnity and limitations of liability.

        -

        Governing Law

        +

        Governing Law

        These Terms shall be governed and construed in accordance with the laws of Berlin, Germany, without regard to its conflict of law provisions.

        Our failure to enforce any right or provision of these Terms will not be considered a waiver of those rights. If any provision of these Terms is held to be invalid or unenforceable by a court, the remaining provisions of these Terms will remain in effect. These Terms constitute the entire agreement between us regarding our Service, and supersede and replace any prior agreements we might have between us regarding the Service.

        -

        Changes

        +

        Changes

        We reserve the right, at our sole discretion, to modify or replace these Terms at any time. If a revision is material we will try to provide at least 30 days notice prior to any new terms taking effect. What constitutes a material change will be determined at our sole discretion.

        By continuing to access or use our Service after those revisions become effective, you agree to be bound by the revised terms. If you do not agree to the new terms, please stop using the Service.

        -

        Contact Us

        +

        Contact Us

        If you have any questions about these Terms, please contact us.

        diff --git a/site/public/datasets/lfw/index.html b/site/public/datasets/lfw/index.html index 9adf29b1..ddb3f1d1 100644 --- a/site/public/datasets/lfw/index.html +++ b/site/public/datasets/lfw/index.html @@ -30,9 +30,9 @@

        Labeled Faces in the Wild

        Created
        2007
        Images
        13,233
        People
        5,749
        Created From
        Yahoo News images
        Search available
        Searchable

        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.

        -
        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.
        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.

        Intro

        +
        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.
        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.

        Intro

        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.

        -
         all 5,749 people in the LFW Dataset sorted from most to least images collected.
        all 5,749 people in the LFW Dataset sorted from most to least images collected.

        LFW by the Numbers

        +
         all 5,749 people in the LFW Dataset sorted from most to least images collected.
        all 5,749 people in the LFW Dataset sorted from most to least images collected.

        LFW by the Numbers

        • Was first published in 2007
        • Developed out of a prior dataset from Berkely called "Faces in the Wild" or "Names and Faces" [^lfw_original_paper]
        • @@ -46,7 +46,7 @@
        • In all the LFW publications provided by the authors the words "ethics", "consent", and "privacy" appear 0 times [^lfw_original_paper], [^lfw_survey], [^lfw_tech_report] , [^lfw_website]
        • The word "future" appears 71 times
        -

        Facts

        +

        Facts

        • Was created for the purpose of improving "unconstrained face recognition" [^lfw_original_paper]
        • All images in LFW were obtained "in the wild" meaning without any consent from the subject or from the photographer
        • @@ -61,7 +61,7 @@
        • SenseTime, who has relied on LFW for benchmarking their facial recognition performance, is the leading provider of surveillance to the Chinese Government (need citation)
         former President George W. Bush
        former President George W. Bush
        -
         Colin Powel (236), Tony Blair (144), and Donald Rumsfeld (121)
        Colin Powel (236), Tony Blair (144), and Donald Rumsfeld (121)

        People and Companies using the LFW Dataset

        +
         Colin Powel (236), Tony Blair (144), and Donald Rumsfeld (121)
        Colin Powel (236), Tony Blair (144), and Donald Rumsfeld (121)

        People and Companies using the LFW Dataset

        This section describes who is using the dataset and for what purposes. It should include specific examples of people or companies with citations and screenshots. This section is followed up by the graph, the map, and then the supplementary material.

        The LFW dataset is used by numerous companies for benchmarking algorithms and in some cases training. According to the benchmarking results page [^lfw_results] provided by the authors, over 2 dozen companies have contributed their benchmark results.

        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."

        @@ -97,10 +97,10 @@
         "Face Recognition Performance in LFW benchmark"
        "Face Recognition Performance in LFW benchmark"
         "The 1st place in face verification challenge, LFW"
        "The 1st place in face verification challenge, LFW"

        In benchmarking, companies use a dataset to evaluate their algorithms which are typically trained on other data. After training, researchers will use LFW as a benchmark to compare results with other algorithms.

        For example, Baidu (est. net worth $13B) uses LFW to report results for their "Targeting Ultimate Accuracy: Face Recognition via Deep Embedding". According to the three Baidu researchers who produced the paper:

        -

        Citations

        +

        Citations

        Overall, LFW has at least 456 citations from 123 countries. 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 consequuntur magni dolores eos.

        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 consequuntur magni dolores eos.

        -
        Distribution of citations per year per country for the top 5 countries with citations for the LFW Dataset
        Distribution of citations per year per country for the top 5 countries with citations for the LFW Dataset

        Conclusion

        +
        Distribution of citations per year per country for the top 5 countries with citations for the LFW Dataset
        Distribution of citations per year per country for the top 5 countries with citations for the LFW Dataset

        Conclusion

        The LFW face recognition training and evaluation dataset is a historically important face dataset as it was the first popular dataset to be created entirely from Internet images, paving the way for a global trend towards downloading anyone’s face from the Internet and adding it to a dataset. As will be evident with other datasets, LFW’s approach has now become the norm.

        For all the 5,000 people in this datasets, their face is forever a part of facial recognition history. It would be impossible to remove anyone from the dataset because it is so ubiquitous. For their rest of the lives and forever after, these 5,000 people will continue to be used for training facial recognition surveillance.

        Right to Removal

        diff --git a/site/public/datasets/vgg_face2/index.html b/site/public/datasets/vgg_face2/index.html index 63715a4f..6e6c7ac1 100644 --- a/site/public/datasets/vgg_face2/index.html +++ b/site/public/datasets/vgg_face2/index.html @@ -30,7 +30,7 @@

        VGG Faces2

        Created
        2018
        Images
        3.3M
        People
        9,000
        Created From
        Scraping search engines
        Search available
        [Searchable](#)

        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.

        -

        VGG Face2 by the Numbers

        +

        VGG Face2 by the Numbers

        • 1,331 actresses, 139 presidents
        • 3 husbands and 16 wives
        • @@ -39,14 +39,14 @@
        • 1 pornographic actress
        • 3 computer programmer
        -

        Names and descriptions

        +

        Names and descriptions

        • The original VGGF2 name list has been updated with the results returned from Google Knowledge
        • Names with a similarity score greater than 0.75 where automatically updated. Scores computed using import difflib; seq = difflib.SequenceMatcher(a=a.lower(), b=b.lower()); score = seq.ratio()
        • 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"
        • The 'Description' text was automatically added when the Knowledge Graph score was greater than 250
        -

        TODO

        +

        TODO

        • create name list, and populate with Knowledge graph information like LFW
        • make list of interesting number stats, by the numbers
        • diff --git a/site/public/research/00_introduction/index.html b/site/public/research/00_introduction/index.html index 7b132cd5..edaf8206 100644 --- a/site/public/research/00_introduction/index.html +++ b/site/public/research/00_introduction/index.html @@ -46,7 +46,7 @@
          Posted
          Dec. 15
          Author
          Adam Harvey

          It was the early 2000s. Face recognition was new and no one seemed sure exactly how well it was going to perform in practice. In theory, face recognition was poised to be a game changer, a force multiplier, a strategic military advantage, a way to make cities safer and to secure borders. This was the future John Ashcroft demanded with the Total Information Awareness act of the 2003 and that spooks had dreamed of for decades. It was a future that academics at Carnegie Mellon Universtiy and Colorado State University would help build. It was also a future that celebrities would play a significant role in building. And to the surprise of ordinary Internet users like myself and perhaps you, it was a future that millions of Internet users would unwittingly play role in creating.

          Now the future has arrived and it doesn't make sense. Facial recognition works yet it doesn't actually work. Facial recognition is cheap and accessible but also expensive and out of control. Facial recognition research has achieved headline grabbing superhuman accuracies over 99.9% yet facial recognition is also dangerously inaccurate. During a trial installation at Sudkreuz station in Berlin in 2018, 20% of the matches were wrong, a number so low that it should not have any connection to law enforcement or justice. And in London, the Metropolitan police had been using facial recognition software that mistakenly identified an alarming 98% of people as criminals 1, which perhaps is a crime itself.

          MegaPixels is an online art project that explores the history of facial recognition from the perspective of datasets. To paraphrase the artist Trevor Paglen, whoever controls the dataset controls the meaning. MegaPixels aims to unravel the meanings behind the data and expose the darker corners of the biometric industry that have contributed to its growth. MegaPixels does not start with a conclusion, a moralistic slant, or a

          -

          Whether or not to build facial recognition was a question that can no longer be asked. As an outspoken critic of face recognition I've developed, and hopefully furthered, my understanding during the last 10 years I've spent working with computer vision. Though I initially disagreed, I've come to see technocratic perspective as a non-negotiable reality. As Oren (nytimes article) wrote in NYT Op-Ed "the horse is out of the barn" and the only thing we can do collectively or individually is to steer towards the least worse outcome. Computational communication has entered a new era and it's both exciting and frightening to explore the potentials and opportunities. In 1997 getting access to 1 teraFLOPS of computational power would have cost you $55 million and required a strategic partnership with the Department of Defense. At the time of writing, anyone can rent 1 teraFLOPS on a cloud GPU marketplace for less than $1/day. 2.

          +

          Whether or not to build facial recognition was a question that can no longer be asked. As an outspoken critic of face recognition I've developed, and hopefully furthered, my understanding during the last 10 years I've spent working with computer vision. Though I initially disagreed, I've come to see technocratic perspective as a non-negotiable reality. As Oren (nytimes article) wrote in NYT Op-Ed "the horse is out of the barn" and the only thing we can do collectively or individually is to steer towards the least worse outcome. Computational communication has entered a new era and it's both exciting and frightening to explore the potentials and opportunities. In 1997 getting access to 1 teraFLOPS of computational power would have cost you $55 million and required a strategic partnership with the Department of Defense. At the time of writing, anyone can rent 1 teraFLOPS on a cloud GPU marketplace for less than $1/day. 2.

          I hope that this project will illuminate the darker areas of strange world of facial recognition that have not yet received attention and encourage discourse in academic, industry, and . By no means do I believe discourse can save the day. Nor do I think creating artwork can. In fact, I'm not exactly sure what the outcome of this project will be. The project is not so much what I publish here but what happens after. This entire project is only a prologue.

          As McLuhan wrote, "You can't have a static, fixed position in the electric age". And in our hyper-connected age of mass surveillance, artificial intelligece, and unevenly distributed virtual futures the most irrational thing to be is rational. Increasingly the world is becoming a contradiction where people use surveillance to protest surveillance, use

          Like many projects, MegaPixels had spent years meandering between formats, unfeasible budgets, and was generally too niche of a subject. The basic idea for this project, as proposed to the original Glass Room installation in 2016 in NYC, was to build an interactive mirror that showed people if they had been included in the LFW facial recognition dataset. The idea was based on my reaction to all the datasets I'd come across during research for the CV Dazzle project. I'd noticed strange datasets created for training and testing face detection algorithms. Most were created in labratory settings and their interpretation of face data was very strict.

          @@ -55,10 +55,11 @@

          About me

          About the team

          Conclusion

          -

          for other post

          +

          for other post

          It was the early 2000s. Face recognition was new and no one seemed sure how well it was going to perform in practice. In theory, face recognition was poised to be a game changer, a force multiplier, a strategic military advantage, a way to make cities safer and to secure the borders. It was the future that John Ashcroft demanded with the Total Information Awareness act of the 2003. It was a future that academics helped build. It was a future that celebrities helped build. And it was a future that

          A decade earlier the Department of Homeland Security and the Counterdrug Technology Development Program Office initated a feasibilty study called FERET (FacE REcognition Technology) to "develop automatic face recognition capabilities that could be employed to assist security, intelligence, and law enforcement personnel in the performance of their duties [^feret_website]."

          One problem with FERET dataset was that the photos were in controlled settings. For face recognition to work it would have to be used in uncontrolled settings. Even newer datasets such as the Multi-PIE (Pose, Illumination, and Expression) from Carnegie Mellon University included only indoor photos of cooperative subjects. Not only were the photos completely unrealistic, CMU's Multi-Pie included only 18 individuals and cost $500 for academic use [^cmu_multipie_cost], took years to create, and required consent from every participant.

          +

          Add progressive gan of FERET


          1. Sharman, Jon. "Metropolitan Police's facial recognition technology 98% inaccurate, figures show". 2018. https://www.independent.co.uk/news/uk/home-news/met-police-facial-recognition-success-south-wales-trial-home-office-false-positive-a8345036.html

          2. diff --git a/site/public/research/01_from_1_to_100_pixels/index.html b/site/public/research/01_from_1_to_100_pixels/index.html index aac4b7e1..e24e5d9a 100644 --- a/site/public/research/01_from_1_to_100_pixels/index.html +++ b/site/public/research/01_from_1_to_100_pixels/index.html @@ -43,7 +43,7 @@
          -

          High resolution insights from low resolution data

          +

          High resolution insights from low resolution data

          This post will be about the meaning of "face". How do people define it? How to biometrics researchers define it? How has it changed during the last decade.

          What can you know from a very small amount of information?

            @@ -64,7 +64,7 @@
          • 100x100 0.5% of one Instagram photo

          Find specific cases of facial resolution being used in legal cases, forensic investigations, or military footage

          -

          Research

          +

          Research

          • NIST report on sres states several resolutions
          • "Results show that the tested face recognition systems yielded similar performance for query sets with eye-to-eye distance from 60 pixels to 30 pixels" 1
          • diff --git a/site/public/research/index.html b/site/public/research/index.html index dc69cdc5..cfaa039b 100644 --- a/site/public/research/index.html +++ b/site/public/research/index.html @@ -29,7 +29,7 @@

            Research Blog

            -

            The darkside of datasets and the future of computer vision

            +

            The darkside of datasets and the future of computer vision

            diff --git a/site/public/test/citations/index.html b/site/public/test/citations/index.html index c2bed996..60860190 100644 --- a/site/public/test/citations/index.html +++ b/site/public/test/citations/index.html @@ -29,7 +29,7 @@ diff --git a/site/public/test/csv/index.html b/site/public/test/csv/index.html index e53c1421..b9a0ba7b 100644 --- a/site/public/test/csv/index.html +++ b/site/public/test/csv/index.html @@ -29,7 +29,7 @@ diff --git a/site/public/test/datasets/index.html b/site/public/test/datasets/index.html index 421ecb97..e310bc48 100644 --- a/site/public/test/datasets/index.html +++ b/site/public/test/datasets/index.html @@ -29,7 +29,7 @@ diff --git a/site/public/test/face_search/index.html b/site/public/test/face_search/index.html index 1823318d..569b4d41 100644 --- a/site/public/test/face_search/index.html +++ b/site/public/test/face_search/index.html @@ -29,7 +29,7 @@ diff --git a/site/public/test/gallery/index.html b/site/public/test/gallery/index.html index 8ead03eb..14f10b7c 100644 --- a/site/public/test/gallery/index.html +++ b/site/public/test/gallery/index.html @@ -29,7 +29,7 @@

            Gallery test

            -

            ← Back to test index

            +

            ← Back to test index

            Modal image 1
            Modal image 1
            Modal image 2
            Modal image 2
            Modal image 3
            Modal image 3
            diff --git a/site/public/test/index.html b/site/public/test/index.html index 62837b3b..b4d16036 100644 --- a/site/public/test/index.html +++ b/site/public/test/index.html @@ -30,15 +30,13 @@

            Megapixels UI Tests

            diff --git a/site/public/test/map/index.html b/site/public/test/map/index.html index c1f67471..206aef5a 100644 --- a/site/public/test/map/index.html +++ b/site/public/test/map/index.html @@ -29,7 +29,7 @@ diff --git a/site/public/test/name_search/index.html b/site/public/test/name_search/index.html index db38ba04..1b6769c8 100644 --- a/site/public/test/name_search/index.html +++ b/site/public/test/name_search/index.html @@ -29,7 +29,7 @@ diff --git a/site/public/test/style/index.html b/site/public/test/style/index.html index 3ef7d918..6d99a236 100644 --- a/site/public/test/style/index.html +++ b/site/public/test/style/index.html @@ -29,7 +29,7 @@

            Style Examples

            -

            ← Back to test index

            +

            ← Back to test index

            Style Guide Test
            Style Guide Test
            Date
            17-Jan-2019
            Numbers
            17
            Identities
            12,139
            But also
            This is a test of the stylesheet

            Header 1

            Header 2

            Header 3

            @@ -45,10 +45,10 @@
          • Odit aut fugit, sed quia consequuntur magni dolores eos
          • Qui ratione voluptatem sequi nesciunt, neque porro quisquam
          -

          single image test

          -
          This person is alone
          This person is alone

          double image test

          +

          single image test

          +
          This person is alone
          This person is alone

          double image test

          This person is on the left
          This person is on the left
          -
          This person is on the right
          This person is on the right

          triple image test

          +
          This person is on the right
          This person is on the right

          triple image test

          Person 1
          Person 1
          Person 2
          Person 2
          Person 3. Let me tell you about Person 3.  This person has a very long description with text which wraps like crazy
          Person 3. Let me tell you about Person 3. This person has a very long description with text which wraps like crazy

          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.

          diff --git a/webpack.config.dev.js b/webpack.config.dev.js index fcbd48ff..d6f7af46 100644 --- a/webpack.config.dev.js +++ b/webpack.config.dev.js @@ -13,19 +13,21 @@ module.exports = { path: path.resolve(__dirname, 'site/assets/js/dist'), filename: 'index.js' }, - devServer: { - port: 9000, - headers: { - 'Access-Control-Allow-Origin': '*', - }, - publicPath: '/site/assets/js/dist/', - hot: true, - }, + // devServer: { + // port: 9000, + // headers: { + // 'Access-Control-Allow-Origin': '*', + // }, + // publicPath: '/site/assets/js/dist/', + // hot: true, + // }, devtool: 'inline-source-map', resolve: { alias: { // 'vcat-header': path.resolve(__dirname, '../app/components/common/header.component.js'), // 'vcat-auth-reducer': path.resolve(__dirname, '../app/reducers/auth.reducer.js'), + "react": "preact-compat", + "react-dom": "preact-compat" } }, plugins: [ diff --git a/webpack.config.prod.js b/webpack.config.prod.js index 186509e2..69bc63ec 100644 --- a/webpack.config.prod.js +++ b/webpack.config.prod.js @@ -17,16 +17,18 @@ module.exports = { new webpack.DefinePlugin({ 'process.env.NODE_ENV': '"production"', 'process.env.S3_HOST': '"' + process.env.S3_HOST + '"', - 'process.env.VCAT_HOST': '""', - 'process.env.API_HOST': '"https://syrianarchive.vframe.io"', + // 'process.env.VCAT_HOST': '""', + // 'process.env.API_HOST': '"https://syrianarchive.vframe.io"', }), new UglifyJsPlugin(), new webpack.optimize.AggressiveMergingPlugin() ], resolve: { alias: { - 'vcat-header': path.resolve(__dirname, '../app/components/common/header.component.js'), - 'vcat-auth-reducer': path.resolve(__dirname, '../app/reducers/auth.reducer.js'), + // 'vcat-header': path.resolve(__dirname, '../app/components/common/header.component.js'), + // 'vcat-auth-reducer': path.resolve(__dirname, '../app/reducers/auth.reducer.js'), + "react": "preact-compat", + "react-dom": "preact-compat" } }, devtool: 'inline-source-map', -- cgit v1.2.3-70-g09d2
        Company Country