6 ms·

Using imagemagick, awk and kmeans to find dominant colors in images

93 points by rubikscube 11y ago

madsravn 11y ago
I read the same article as the guy and was also discouraged by using PIL. Python 2 vs Python 3 just makes life hard for people not used to Python. Maybe someone should do something about that. I just went ahead and implemented the code in C++ instead.
- creshal 11y ago
  It's mostly a solved problem nowadays – Pillow exists as much better and Python3-compatible PIL fork.
  - madsravn 11y ago
    I wans't talking about just PIL - I was talking about Python 2 vs Python 3 in general. EDIT: And I'm not talking about development only. I'm talking about download small scripts and being able to run them seamlessly as well.
    - JupiterMoon 11y ago
      Just pick one and use it. If it is a long project pick python 3. If you actually use python you'll barely notice the difference. EDIT If the script uses a well formed #! this isn't too much of a problem either.
      - creshal 11y ago
        > EDIT If the script uses a well formed #! this isn't too much of a problem either. He does have a point, though: Dependency management in Python is a pain in the ass. Virtualenvs aren't really a solution, distribution packages are usually horribly outdated, …
andrewgleave 11y ago
I wrote a small Go tool[1] that extracts a color palette from an image using either median or mean quantizer. The quantizer code[2] is well documented and worth reading if you're looking at doing this in Go. [1] https://github.com/andrewgleave/color-extract https://github.com/andrewgleave/color-extract [2] https://github.com/soniakeys/quant https://github.com/soniakeys/quant
bootload 11y ago
There is an improvement that can be made here. There is no attempt to find the brightest colour. If you look at the dark image, the brightest colours (red) stand out over and above the dark colours. Using this knowledge is an illustration technique. Notice how the red stands out on one image, a small blue line on the other? This particular algorythm and others that I've seen don't take this into account. Depending on the image, you could also take into account the lightness and darkness to find the dominant colour however small amounts in the image.
- minikomi 11y ago
  Hmm. I wonder if converting to HSV first would change the results.
  - bootload 11y ago
    does HSV capture colour brightness contrast? I imagine finding the brightest and darkest by looking at the grey scales.
    - morpher 11y ago
      Yes. The V in HSV is a greyscale value.
    - deleted 11y ago
      [deleted]
  - jacobolus 11y ago
    HSV is a terrible color model for any human purpose. (To be honest, for any purpose whatsoever, unless it’s required for backwards-compatibility with legacy systems.) HSV, like the RGB model it is a trivial derivative of, has dimensions which are not closely correlated with any color attributes relevant to human perception. Instead, use a model such as CIECAM02, IPT, CIELAB (from the 70s but not too bad), or Munsell (basically a big lookup table, from experiments done in the 40s).
    - 11y ago
w00kie 11y ago
I was expecting a step by step decomposition of the process. I'm a bit disappointed.
jlhonora 11y ago
Couldn't this be achieved with imagemagick's builtin blur + histogram?
- deleted 11y ago
  [deleted]
- e12e 11y ago
  Don't know if this is the best way to do it, but reading a bit[1] as well as looking at comments above about color spaces, I came up with: #shell: for space in sRGB RGB HSV LAB do # I think it should be possible to do this without writing # tiff-images to disk in-between -- but having a look at the # resulting images next to the original is actually quite nice # gives some idea of the differences colorspace makes: convert akira_800x800.jpg -quantize $space +dither \ -colors 4 akira_lab_$space.tiff echo "Histogram in $space colorspace:" convert akira_lab_$space.tiff -format %c histogram:info:- echo done Output: Histogram in sRGB colorspace: 123498: ( 5811, 4253, 6632) #16B3109D19E8 srgb(8.86702%,6.48966%,10.1198%) 110248: (17780,16101,19392) #45743EE54BC0 srgb(27.1305%,24.5686%,29.5903%) 47520: (34608,35255,34146) #873089B78562 srgb(52.8084%,53.7957%,52.1035%) 47534: (47890,19471,10567) #BB124C0F2947 srgb(73.0755%,29.7108%,16.1242%) Histogram in RGB colorspace: 182611: ( 45, 40, 51) #2D2833 srgb(45,40,51) 76221: ( 93, 86,100) #5D5664 srgb(93,86,100) 36583: (139,119,124) #8B777C srgb(139,119,124) 33385: (218,125, 74) #DA7D4A srgb(218,125,74) Histogram in HSV colorspace: 94957: ( 30, 13, 51) #1E0D33 srgb(30,13,51) 128709: ( 56, 52, 80) #383450 srgb(56,52,80) 37291: ( 60, 31, 10) #3C1F0A srgb(60,31,10) 67843: ( 78,155, 87) #4E9B57 srgb(78,155,87) Histogram in LAB colorspace: 133802: ( 42, 41, 60) #2A293C srgb(42,41,60) 74332: ( 51, 72, 87) #334857 srgb(51,72,87) 42436: ( 88, 33, 27) #58211B srgb(88,33,27) 78230: (151,117, 89) #977559 srgb(151,117,89) In fact, it became a bit of an obsession, and the hack evolved a bit: https://gist.github.com/e12e/7990e56f48ceff5506d7 https://gist.github.com/e12e/7990e56f48ceff5506d7 I'm not sure if it actually does the same thing as the script in the post - but here's a sample output: http://htmlpreview.github.io/?https://gist.githubusercontent.com/e12e/7990e56f48ceff5506d7/raw/d5d65b3ea49d09a9234b94c2e43d6263b6524cbf/index.html http://htmlpreview.github.io/?https://gist.githubusercontent... Maybe someone with more knowledge of imagemagick can improve on the pipeline etc. [ed: Just noticed that the sRGB output for "RGB" values is different enough that my script doesn't consider them to be RGB values (well, they're not) - so the line for sRGB is blank in the html. Still think it's interesting to see the difference between RGB/HSV/LAB.] [ed2: Changing the number of colors to 3, to better compare with op, op's algorithm clearly chooses different colors. Not sure which is "best", but just FYI] [1] http://www.imagemagick.org/Usage/quantize/#extract http://www.imagemagick.org/Usage/quantize/#extract
neya 11y ago
This is much easier using the LAB color space. I wrote an algorithm using the LAB color space several years ago to achieve something similar: http://i.imgur.com/M6Oo6dp.jpg http://i.imgur.com/M6Oo6dp.jpg Will open source it soon, perhaps.
brute 11y ago
Does the while (1) {...} loop always terminate? I am not entirely sure about it and would like to hear some opinions. What if the first guess is already the best possible solution? Could there be any pictures that cause trouble? I.e. ones that consist of 4 (equally spaced) colors only.
dxbydt 11y ago
To find k dominant colors using kmeans and then replace all the colors with their closest dominant color, try this - http://github.com/krishnanraman/colorquantization http://github.com/krishnanraman/colorquantization 50 lines of Scala with lots of comments. I threw in some example images as well.

93 points by rubikscube 11y ago

I read the same article as the guy and was also discouraged by using PIL. Python 2 vs Python 3 just makes life hard for people not used to Python. Maybe someone should do something about that. I just went ahead and implemented the code in C++ instead.

creshal 11y ago
It's mostly a solved problem nowadays – Pillow exists as much better and Python3-compatible PIL fork.
- madsravn 11y ago
  I wans't talking about just PIL - I was talking about Python 2 vs Python 3 in general. EDIT: And I'm not talking about development only. I'm talking about download small scripts and being able to run them seamlessly as well.
  - JupiterMoon 11y ago
    Just pick one and use it. If it is a long project pick python 3. If you actually use python you'll barely notice the difference. EDIT If the script uses a well formed #! this isn't too much of a problem either.
    - creshal 11y ago
      > EDIT If the script uses a well formed #! this isn't too much of a problem either. He does have a point, though: Dependency management in Python is a pain in the ass. Virtualenvs aren't really a solution, distribution packages are usually horribly outdated, …

andrewgleave 11y ago

I wrote a small Go tool[1] that extracts a color palette from an image using either median or mean quantizer. The quantizer code[2] is well documented and worth reading if you're looking at doing this in Go. [1] https://github.com/andrewgleave/color-extract https://github.com/andrewgleave/color-extract [2] https://github.com/soniakeys/quant https://github.com/soniakeys/quant

bootload 11y ago

There is an improvement that can be made here. There is no attempt to find the brightest colour. If you look at the dark image, the brightest colours (red) stand out over and above the dark colours. Using this knowledge is an illustration technique. Notice how the red stands out on one image, a small blue line on the other? This particular algorythm and others that I've seen don't take this into account. Depending on the image, you could also take into account the lightness and darkness to find the dominant colour however small amounts in the image.

minikomi 11y ago
Hmm. I wonder if converting to HSV first would change the results.
- bootload 11y ago
  does HSV capture colour brightness contrast? I imagine finding the brightest and darkest by looking at the grey scales.
  - morpher 11y ago
    Yes. The V in HSV is a greyscale value.
  - deleted 11y ago
    [deleted]
- jacobolus 11y ago
  HSV is a terrible color model for any human purpose. (To be honest, for any purpose whatsoever, unless it’s required for backwards-compatibility with legacy systems.) HSV, like the RGB model it is a trivial derivative of, has dimensions which are not closely correlated with any color attributes relevant to human perception. Instead, use a model such as CIECAM02, IPT, CIELAB (from the 70s but not too bad), or Munsell (basically a big lookup table, from experiments done in the 40s).
  - 11y ago

w00kie 11y ago

I was expecting a step by step decomposition of the process. I'm a bit disappointed.

jlhonora 11y ago

Couldn't this be achieved with imagemagick's builtin blur + histogram?

deleted 11y ago
[deleted]
e12e 11y ago
Don't know if this is the best way to do it, but reading a bit[1] as well as looking at comments above about color spaces, I came up with: #shell: for space in sRGB RGB HSV LAB do # I think it should be possible to do this without writing # tiff-images to disk in-between -- but having a look at the # resulting images next to the original is actually quite nice # gives some idea of the differences colorspace makes: convert akira_800x800.jpg -quantize $space +dither \ -colors 4 akira_lab_$space.tiff echo "Histogram in $space colorspace:" convert akira_lab_$space.tiff -format %c histogram:info:- echo done Output: Histogram in sRGB colorspace: 123498: ( 5811, 4253, 6632) #16B3109D19E8 srgb(8.86702%,6.48966%,10.1198%) 110248: (17780,16101,19392) #45743EE54BC0 srgb(27.1305%,24.5686%,29.5903%) 47520: (34608,35255,34146) #873089B78562 srgb(52.8084%,53.7957%,52.1035%) 47534: (47890,19471,10567) #BB124C0F2947 srgb(73.0755%,29.7108%,16.1242%) Histogram in RGB colorspace: 182611: ( 45, 40, 51) #2D2833 srgb(45,40,51) 76221: ( 93, 86,100) #5D5664 srgb(93,86,100) 36583: (139,119,124) #8B777C srgb(139,119,124) 33385: (218,125, 74) #DA7D4A srgb(218,125,74) Histogram in HSV colorspace: 94957: ( 30, 13, 51) #1E0D33 srgb(30,13,51) 128709: ( 56, 52, 80) #383450 srgb(56,52,80) 37291: ( 60, 31, 10) #3C1F0A srgb(60,31,10) 67843: ( 78,155, 87) #4E9B57 srgb(78,155,87) Histogram in LAB colorspace: 133802: ( 42, 41, 60) #2A293C srgb(42,41,60) 74332: ( 51, 72, 87) #334857 srgb(51,72,87) 42436: ( 88, 33, 27) #58211B srgb(88,33,27) 78230: (151,117, 89) #977559 srgb(151,117,89) In fact, it became a bit of an obsession, and the hack evolved a bit: https://gist.github.com/e12e/7990e56f48ceff5506d7 https://gist.github.com/e12e/7990e56f48ceff5506d7 I'm not sure if it actually does the same thing as the script in the post - but here's a sample output: http://htmlpreview.github.io/?https://gist.githubusercontent.com/e12e/7990e56f48ceff5506d7/raw/d5d65b3ea49d09a9234b94c2e43d6263b6524cbf/index.html http://htmlpreview.github.io/?https://gist.githubusercontent... Maybe someone with more knowledge of imagemagick can improve on the pipeline etc. [ed: Just noticed that the sRGB output for "RGB" values is different enough that my script doesn't consider them to be RGB values (well, they're not) - so the line for sRGB is blank in the html. Still think it's interesting to see the difference between RGB/HSV/LAB.] [ed2: Changing the number of colors to 3, to better compare with op, op's algorithm clearly chooses different colors. Not sure which is "best", but just FYI] [1] http://www.imagemagick.org/Usage/quantize/#extract http://www.imagemagick.org/Usage/quantize/#extract

neya 11y ago

This is much easier using the LAB color space. I wrote an algorithm using the LAB color space several years ago to achieve something similar: http://i.imgur.com/M6Oo6dp.jpg http://i.imgur.com/M6Oo6dp.jpg Will open source it soon, perhaps.

brute 11y ago

Does the while (1) {...} loop always terminate? I am not entirely sure about it and would like to hear some opinions. What if the first guess is already the best possible solution? Could there be any pictures that cause trouble? I.e. ones that consist of 4 (equally spaced) colors only.

dxbydt 11y ago

To find k dominant colors using kmeans and then replace all the colors with their closest dominant color, try this - http://github.com/krishnanraman/colorquantization http://github.com/krishnanraman/colorquantization 50 lines of Scala with lots of comments. I threw in some example images as well.