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What does your Instagram say about you? Exploring Google Cloud Vision

August 15, 2019

What does your Instagram say about you? Exploring Google Cloud Vision

Explore application development with Google Cloud Vision API which can categorize photos, detect objects, identify landmarks, and extract corporate logos from images. From there, the sky's the limit with exploring the detected labels. We'll check out different photo sharing accounts, and see what we can know about them all through their photos. This will use Google Cloud Platform, Ruby, with JSON formatted labels that are translated into a tag cloud with what activities are being displayed through the images and shows a cross-section of different technologies.


August 15, 2019

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  1. @tech_christine @ryanhos What does your Christine Seeman and Ryan Hochstetler

    say about you?
  2. @tech_christine @ryanhos What is in an image?

  3. @tech_christine @ryanhos What is in an image?

  4. @tech_christine @ryanhos What do you focus on?

  5. @tech_christine @ryanhos What do you focus on?

  6. @tech_christine @ryanhos What is in it that you see?

  7. @tech_christine @ryanhos What is in it that you see?

  8. @tech_christine @ryanhos But what does an application see in them?

  9. @tech_christine @ryanhos Meet Google Cloud Vision

  10. @tech_christine @ryanhos

  11. @tech_christine @ryanhos

  12. @tech_christine @ryanhos Little bit different vision

  13. @tech_christine @ryanhos Cloud Vision API Landmarks Face detection Image labeling

    Optical character recognition Explicit content detection Logo detection Object localization Crop hint detection
  14. @tech_christine @ryanhos But how can we use it?

  15. @tech_christine @ryanhos Let's look at the client library

  16. @tech_christine @ryanhos Let's combine accounts with

  17. @tech_christine @ryanhos

  18. @tech_christine @ryanhos Onto the code

  19. @tech_christine @ryanhos image_annotator_client.rb

  20. @tech_christine @ryanhos image_annotator_client.rb

  21. @tech_christine @ryanhos image_annotator_client.rb

  22. @tech_christine @ryanhos image_annotator_client.rb

  23. @tech_christine @ryanhos So that got us from this

  24. @tech_christine @ryanhos To this

  25. @tech_christine @ryanhos analysis_reader.rb

  26. @tech_christine @ryanhos analysis_reader.rb

  27. @tech_christine @ryanhos After histogram generation Labels Landmarks Logos

  28. @tech_christine @ryanhos Then finally the tag cloud

  29. @tech_christine @ryanhos Let's explore the different detections

  30. @tech_christine @ryanhos Face detection

  31. @tech_christine @ryanhos What’s a “gnathion”?

  32. @tech_christine @ryanhos raster_annotator.rb

  33. @tech_christine @ryanhos raster_annotator.rb

  34. @tech_christine @ryanhos Sentiment Detection Probably should be called “Sentiment Best

  35. @tech_christine @ryanhos Joy

  36. @tech_christine @ryanhos Surprise

  37. @tech_christine @ryanhos Anger?

  38. @tech_christine @ryanhos Sorrow?

  39. @tech_christine @ryanhos Image Composition

  40. @tech_christine @ryanhos Label detection

  41. @tech_christine @ryanhos @expertvagabond

  42. @tech_christine @ryanhos @expertvagabond

  43. @tech_christine @ryanhos @ripleyandrue

  44. @tech_christine @ryanhos @ripleyandrue

  45. @tech_christine @ryanhos Safe Search detection

  46. @tech_christine @ryanhos Yes it knows if it is a hotdog

  47. @tech_christine @ryanhos ...and when it's not

  48. @tech_christine @ryanhos Seems accurate…

  49. @tech_christine @ryanhos Oh, there might be a bit of bias…

  50. @tech_christine @ryanhos Violence is difficult too…

  51. @tech_christine @ryanhos Racy but not violent?!?

  52. @tech_christine @ryanhos Doctored images are hit and miss We chose

    not to include the image of a former US president photoshopped to look like a dictator and war criminal. Google labeled it “Possibly” a spoof; i.e. altered to be funny or offensive
  53. @tech_christine @ryanhos Text detection

  54. @tech_christine @ryanhos @letterfolk

  55. @tech_christine @ryanhos @letterfolk "MY\nBRAIN\nHAS\nTO0\nMANY\nTABS\nOPEN\nDechtlatte\nTHIS WEEK\npt de C\nCAit s pg\n" MY

    BRAIN HAS TO0 MANY TABS OPEN Dechtlatte THIS WEEK pt de C CAit s pg
  56. @tech_christine @ryanhos @letterfolk

  57. @tech_christine @ryanhos @letterfolk YOURE NOT THE\nBOSS OF ME..\nIWHISPER UNDER\nMY BREATH

  58. @tech_christine @ryanhos @letterfolk

  59. @tech_christine @ryanhos @letterfolk Seth\nElizabeth\nMat\nIT'S BEGINNING TO\namazon Prime\nLOOK A LOT LIKE...\nvmazon

    Prime\necho\nI GOT MY MONEY'S\nWORTH FROM MY\n1t\nare en\na ton Prime\nAMAZON PRIME\nMEMBERSHIP\nNany\necho\neciro\n Seth Elizabeth Mat IT'S BEGINNING TO amazon Prime LOOK A LOT LIKE... vmazon Prime echo I GOT MY MONEY'S WORTH FROM MY 1t\nare en a ton Prime AMAZON PRIME MEMBERSHIP Nany echo eciro
  60. @tech_christine @ryanhos Logo detection

  61. @tech_christine @ryanhos

  62. @tech_christine @ryanhos @nike

  63. @tech_christine @ryanhos @nike

  64. @tech_christine @ryanhos @nike 1. nike 2. nike plus 3. nike

    azul 4. blue nike 5. nike store
  65. @tech_christine @ryanhos @christine_seeman

  66. @tech_christine @ryanhos @christine_seeman

  67. @tech_christine @ryanhos @christine_seeman 1. etelä suomen sanomat 2. rogue status

    3. american horror story 4. delta skymiles 5. deník !"!"
  68. @tech_christine @ryanhos Landmark detection

  69. @tech_christine @ryanhos @visit_nebraska

  70. @tech_christine @ryanhos @iloveny

  71. @tech_christine @ryanhos @iloveny

  72. @tech_christine @ryanhos @expertvagabond

  73. @tech_christine @ryanhos Have you been to London, NE?

  74. @tech_christine @ryanhos The British Museum there is lovely

  75. @tech_christine @ryanhos British Museum, alternate photo

  76. @tech_christine @ryanhos So much alike

  77. @tech_christine @ryanhos This wasn’t even a building…

  78. @tech_christine @ryanhos But what does my instagram say about me?

  79. @tech_christine @ryanhos

  80. @tech_christine @ryanhos @christineseeman

  81. @tech_christine @ryanhos Blooper cloud

  82. @tech_christine @ryanhos Where to go from here? What to do

    next with Google Cloud Vision
  83. @tech_christine @ryanhos @visit_nebraska

  84. @tech_christine @ryanhos Vs @visitcalifornia

  85. @tech_christine @ryanhos Vs @iloveny

  86. @tech_christine @ryanhos Can we teach a machine to know if

    a pic is from NY vs CA vs NE
  87. @tech_christine @ryanhos Explore bias in Machine Learning?

  88. @tech_christine @ryanhos How much will this cost you? For us,

    about $65
  89. @tech_christine @ryanhos Technology Used •Ruby (No Rails) •Rspec •Ruby Vision

    API Client Libraries •Google Cloud Storage •Rmagick (Ruby binding to Imagemagick) •MagicCloud tag cloud gem
  90. @tech_christine @ryanhos Reference Links •https://cloud.google.com/vision/ •https://cloud.google.com/vision/docs/ •https://googleapis.github.io/google-cloud-ruby/docs/ •https://github.com/GoogleCloudPlatform/ruby-docs- samples/blob/master/vision/quickstart.rb •https://github.com/zverok/magic_cloud

  91. @tech_christine @ryanhos All the instagram accounts* @ripleyandrue @visit_nebraska @wolffolins @expertvagabond

    @iloveny @letterfolk @myraswim @visitcalifornia @nationalportraitgallery @nike * and none were harmed in the making of this talk
  92. @tech_christine @ryanhos getflywheel.com/about/careers

  93. @tech_christine @ryanhos Thank you for attending!

  94. @tech_christine @ryanhos For your evaluation consideration...