SlideShare a Scribd company logo
G’day!
Dennis Hotson
Lars Yencken
99designs
Graphic design marketplace
Graphic design
 colour trends
Design is our business
How do you track the
elements of design?
What is the
DNA of design?
Colour
How do we extract
colours from a design?
Colorific
White!
Colour theory
RGB
Lab
Lab is designed to approximate human vision.
L is for Lightness
a and b are for
    color-opponent
 dimensions, based on
nonlinearly compressed
    CIE XYZ colour
                 — Wikipedia
Colorific
http://github.com/99designs/colorific
Examples of use
pip install colorific
$ echo some_image.png | colorific
some_image.png #0047f1,#e61b31,#3e7bf4,#00b018,#f9a600
$ find . -name '*.jpg' | colorific >myphotos.colors
>>> from colorific import extract_colors, rgb_to_hex
>>> palette = extract_colors('some_image.png')

>>> hex_colors = []
>>> for color in palette.colors:
...     hex_colors.append(rgb_to_hex(color.value))

>>> hex_colors
['#0047f1', '#e61b31', '#3e7bf4', '#00b018', '#f9a600']
Search by colour
Search by colour
Search by colour
Indexing
100,000 designs
   efficiently
Rtree
Rtrees are for indexing spatial data
?
?
>>> index = Rtree()
>>> index.insert(“design1”, “#ff0000”)


>>> index.nearest(“#ff0001”)
“design1”
Colour trends
Time
2007   2008   2009   2010   2011
2007   2008   2009   2010   2011
Location
Industry
accounting      construction        medical
    agriculture       cosmetics     photography
       animals            dating        physical
   architectural      education          politics
            art    entertainment      realestate
       attorney     environment        religious
    automotive           fashion      restaurant
            bar            floral          retail
       business            food         security
       children          games              spa
       cleaning           home           sports
communications         industrial    technology
    community           internet          travel
     computer        landscaping       wedding
environment              environment  
 landscaping                  children  
             0%    7%                 0%


      floral                   floral  
     dating                 wedding  
   wedding                 cosmetics  
  cosmetics                      bar  
    fashion                      spa  
                                    
architectural             automotive  
 landscaping             landscaping  
  accounting                industrial  
    industrial                 politics  
   computer                   security  
             0%    10%                0%


    politics                attorney  
   attorney               accounting  
 accounting                  medical  
ding                   floral             automotive  
oral              restaurant                  games  
    0%    8%                 0%    20%                 0%


ment             landscaping                agriculture  
ping               agriculture            landscaping  
 ture           environment                       floral  
dren                 cleaning            environment  
 oral             accounting                        spa  
                                                       
rney           entertainment              automotive  
ment            photography                 attorney  
hion                  fashion               business  
ding                   dating                children  
 ting                wedding                      bar  
    0%    9%                 0%    12%                 0%


dren                  games                    games  
ment                     bar                 children  
 oral                   food               restaurant  
tics          environment  
spa                   spa  
   0%    5%               0%    7%


ing               politics  
art              attorney  
bar            accounting  
ing              business  
ren                sports  
                         
rity                  food  
ogy              cosmetics  
 ive          photography  
rial           landscaping  
ion                  floral  
   0%    2%               0%    7%


ing            landscaping  
 cal          environment  
spa  
Thank You
Colorific
http://github.com/99designs/colorific/


            ColorDB
 http://github.com/dhotson/colordb


    99designs tech blog
  http://99designs.com/tech-blog/
Graphic design colour trends

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Graphic design colour trends

  • 4. Design is our business
  • 5. How do you track the elements of design?
  • 6. What is the DNA of design?
  • 8. How do we extract colours from a design?
  • 9.
  • 10.
  • 12.
  • 14.
  • 15.
  • 16.
  • 17.
  • 19. RGB
  • 20. Lab Lab is designed to approximate human vision.
  • 21. L is for Lightness
  • 22. a and b are for color-opponent dimensions, based on nonlinearly compressed CIE XYZ colour — Wikipedia
  • 23.
  • 24.
  • 28. $ echo some_image.png | colorific some_image.png #0047f1,#e61b31,#3e7bf4,#00b018,#f9a600
  • 29. $ find . -name '*.jpg' | colorific >myphotos.colors
  • 30. >>> from colorific import extract_colors, rgb_to_hex >>> palette = extract_colors('some_image.png') >>> hex_colors = [] >>> for color in palette.colors: ... hex_colors.append(rgb_to_hex(color.value)) >>> hex_colors ['#0047f1', '#e61b31', '#3e7bf4', '#00b018', '#f9a600']
  • 35. Rtree Rtrees are for indexing spatial data
  • 36.
  • 37.
  • 38. ?
  • 39. ?
  • 40. >>> index = Rtree() >>> index.insert(“design1”, “#ff0000”) >>> index.nearest(“#ff0001”) “design1”
  • 41.
  • 42.
  • 43.
  • 45. Time
  • 46. 2007 2008 2009 2010 2011
  • 47. 2007 2008 2009 2010 2011
  • 49.
  • 50.
  • 51.
  • 53. accounting construction medical agriculture cosmetics photography animals dating physical architectural education politics art entertainment realestate attorney environment religious automotive fashion restaurant bar floral retail business food security children games spa cleaning home sports communications industrial technology community internet travel computer landscaping wedding
  • 54. environment   environment   landscaping   children   0% 7% 0% floral   floral   dating   wedding   wedding   cosmetics   cosmetics   bar   fashion   spa         architectural   automotive   landscaping   landscaping   accounting   industrial   industrial   politics   computer   security   0% 10% 0% politics   attorney   attorney   accounting   accounting   medical  
  • 55. ding   floral   automotive   oral   restaurant   games   0% 8% 0% 20% 0% ment   landscaping   agriculture   ping   agriculture   landscaping   ture   environment   floral   dren   cleaning   environment   oral   accounting   spa            rney   entertainment   automotive   ment   photography   attorney   hion   fashion   business   ding   dating   children   ting   wedding   bar   0% 9% 0% 12% 0% dren   games   games   ment   bar   children   oral   food   restaurant  
  • 56. tics   environment   spa   spa   0% 5% 0% 7% ing   politics   art   attorney   bar   accounting   ing   business   ren   sports         rity   food   ogy   cosmetics   ive   photography   rial   landscaping   ion   floral   0% 2% 0% 7% ing   landscaping   cal   environment   spa  
  • 57.
  • 59. Colorific http://github.com/99designs/colorific/ ColorDB http://github.com/dhotson/colordb 99designs tech blog http://99designs.com/tech-blog/

Editor's Notes

  1. \n
  2. Hi everyone, I’m Dennis Hotson and this is Lars Yencken.\n
  3. We work at 99designs, which is a marketplace for graphic design.\n
  4. In this talk, we want to show you how we were able to analyse hundreds of thousands of designs—and get a glimpse into colour usage in our design community.\n
  5. So we’ve got tens of thousands of designers submitting designs. There’s a design submitted every 3 seconds.\nWe track all kinds of stats on our customers and designers, trying to make sure the design community is thriving.\n
  6. But—none of these metrics give us any insight into the type of design that is happening. How can we know what is “hot” right now?\n
  7. We want to break down designs into their fundamental elements. We want to break each design down so we can judge it objectively. We looked at various design elements such as typography, geometry, texture. However, the design element we chose to investigate is of course— .... colour.\n
  8. Colour is a significant part of graphic design.\n
  9. How can we break down a design into its component colours?\n
  10. You might ask, how do we take a logo like the one above—and turn it into something like this?\nExcellent question!\n
  11. You might ask, how do we take a logo like the one above—and turn it into something like this?\nExcellent question!\n
  12. Lars and I started working on a project called colorific, which is designed to extract a colour palette from an image. I’ll give you a taste of how it works.\n
  13. Let’s start with something simple. What is the main colour in this image?\n
  14. \n
  15. So it turns out if you just count the pixels the background colour dominates—but that’s not really very interesting. In colorific we discard background by comparing the colours of the pixels in the corners.\n
  16. Quiz time: How many colours in this logo? 4?\n
  17. There’s actually 255 colours in this image. If you look closely you can see there are gradients with various shades of blue and artefacts around the edges.\n
  18. You can see we end up with something like this, we’ve got two blues that are similar but look about the same. We want to group these colours together somehow.\n
  19. It turns out there’s some useful colour theory that we can take advantage of.\n
  20. You may have used the RGB colour space before.\nRGB is not bad, but we can do better..\n
  21. For comparing and aggregating colours it’s better to use the Lab colour space.\nLab is designed to approximate human vision.\n
  22. The L channel represents lightness. Colours in Lab with the same Lightness will appear to be the same brightness to the eye.\nWhen using RGB, colours such as yellow appear lighter than others. Lab is able to adjust for this.\n
  23. The a and b components..... well, it’s complicated. :-)\n\nThese components describe the colour. But the magic is that when you describe colours in this way, the distance between colours corresponds to the distance for the human eye. The distance in Lab space is called the delta-E and it’s backed by research into human vision.\n
  24. The Lab model is a little hard to visualise but hopefully this will help.\n\nLab is three dimensional and it’s a little hard to visualise in 2d but here’s a few slices.\nYou can hopefully see that all the colours within each slice look to be the same lightness.\n
  25. \n
  26. \n
  27. Coming back to our example from before, we can see that by grouping colours\n
  28. \n
  29. \n
  30. \n
  31. Colorific is designed to be run in a streaming manner, you feed in filenames and it spits out filenames and their colours. It might seem a bit weird, but the reason is that it works well when running it over lots of images.\n
  32. For example, this style makes it easy to run across an entire folder of images.\n
  33. It can also be used as a Python library.\n
  34. So now that we’re able to extract colours from a design we can start to do some more interesting things.\nOne application we’ve been experimenting with is...search by colour.\n
  35. We want our users to be able to choose a colour...\n
  36. .. and find designs featuring that colour.\n
  37. In our case, we want to do this at a pretty large scale. So how can we achieve this?\n
  38. In order to index colours, we’re using an rtree to efficiently index and search by colour.\nRtree’s are a really useful data structure for indexing spatial data.\nNormally you use them to index spatial data such as the location of nearby coffee shops, but in our case we’re using it to index colours since they’re also kind of three dimensional.\n
  39. Let’s take a look at brief look at how you use an Rtree.\nSo here’s our Rtree.\n
  40. You can insert colours into it.\n
  41. And then you can search for a colour...\n
  42. And find colours that are nearby.\n
  43. Here’s an example of what it might look like in code. It’s really quite simple. One additional detail is that we index and search using the Lab colour space so that distances in the Rtree space correspond to differences in your eyes perception.\n
  44. Here’s a quick demo of what we managed to build.\nWe used a combination of colorific, Rtree, colormath and Flask.\n
  45. So isn’t that cool? By using a clever combination of computer science and Python libraries we’ve managed to achieve something quite sophisticated with not very much code.\n
  46. Now Lars is going to talk about how we can gain insight into how colour is being used on our site.\n
  47. 120,000 designs, run through colorific (top 5+1 colors for each)\n450,000 distinct colours\n\n
  48. \n
  49. \n
  50. \n
  51. seasonal trends in fashion, but not in graphic design\n\nwhilst finer-grained colours might vary over time, broadly speaking colour usage tends to be the same\n\nour take: people choose designs which will work for them for years, hence less seasonal\n
  52. \n
  53. \n
  54. \n
  55. overrepresented\n
  56. \n
  57. overrepresented\n
  58. \n
  59. \n
  60. green\n
  61. blue\n
  62. Conclusion.\n
  63. \n
  64. \n
  65. \n