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Finding dominant colors of an image using k-means clustering

License: MIT License

Ruby 2.07% Swift 95.58% Objective-C 1.19% C 1.16%

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jordansinger wpsteak jamalk odeke-em cyndibaby905 shineit james919 alexguoq bapuqln mviamari giapdangle objcio zhaoguohui ywxstar carabina michaelnl grizzly marinehero lydonchandra tranthanhvu alexanderjarvis mailaldrin29 guosongning endash collinatherton kltsyn zhouzhun arunsirrpi pwilkniss metasmile openstack-mobile 1amageek twocentstudios mozii rich-lichkus dimohamdy nobiio zongyan86 bytearchive orlandoamorim thomgru solertis pv-g kiarsy mikezucc brushedtype colemancda marcvanolmen manaaura jsproull kushsolitary joaoamaral utabi jernejstrasner wayneeld lilieming generalscripting eminarcissus inhyek shion0111 johanrydenstam kunnareekr jixuqianxing hubert-lin 0x1306a94 zerocc2017 sarah-hs kzhiquan dagronf taroyuyu rushairer ousinkou 16songs blackcatian revanks menglifei nenosinc ebrarnld rabiaseymayilmaz bdaz adelaidesky abcincs macmark mcomisso

dominantcolor's Issues

Figure out why memoization of RGB -> LAB is slow

For the same image, using -Os optimization level:

Unmemoized

n = 100 averaged 4 ms
n = 1000 averaged 39 ms
n = 2000 averaged 81 ms
n = 5000 averaged 202 ms
n = 10000 averaged 416 ms

Memoized

n = 100 averaged 8 ms
n = 1000 averaged 68 ms
n = 2000 averaged 133 ms
n = 5000 averaged 318 ms
n = 10000 averaged 639 ms

Roughly twice as slow with memoization.

How to get the coordinates of dominant colors from UIImage?

please update to swift2.0 for Xcode 7.0

please update to swift2.0 for Xcode 7.0,thanks

Better way of choosing initial centroids

k-means++ might be better than choosing all of the centroids randomly.

Sorting more and grouping each results by hue representativeness or similarity is needed.

First of all, Thank you for your nice work! Awesome.
But in general case, library users finally needs only 1~2 color groups like primary, secondary or background(outbound) colors. Currently the result seems that in each 2 items are pretty similar. Also in this example https://github.com/indragiek/DominantColor/raw/master/mac.png, I think first color is not dominant color, it may be secondary or background with the last color.

Currently you did perform it by "size" like following.

// Sort the clusters by size in descending order so that the
// most dominant colors come first.
 clusters.sortInPlace { $0.size > $1.size }

Are you have some plans to improve? Or, please let me know some hints or important parts of your lib.

Thanks.

Compiles fine in Simulator but doesn't compile on iPhone 7 Plus device?

I'm using your code in my Swift 3 app and it works fine in the simulator but when I try to run it on my 7 Plus device, it doesn't compile, complaining that value of UIImage has no member dominantColors.

The weird thing is running your project as a stand-alone works fine on my device.

Xcode does see the DominantColor framework when I try to import it using import DominantColor, so I'm really at a lost.

Any clues?

iOS App

iOS app for testing the algorithm.

error message when build: Module compiled with Swift 4.0 cannot be imported in Swift 4.1.50:

Any fix for this issue?

Add support for CIE76 and CIE94 algorithms as well

Faster performance with the tradeoff of accuracy.

Better way to choose k

There are a number of different methods listed here

Framework targets

Framework targets for iOS and Mac for the reusable parts of the project.

CocoaPods support?

Could you add a Podspec? I can help if you want and submit a PR.

Unit tests

K-means
RGB <-> LAB
CIE 2000
Complete tests with reference images

Memoize color difference and color conversions

Might be able to make it perform a little better by memoizing the results of RGB <-> LAB conversions and the CIE 2000 color diff algorithm.

Does CIE2000 have high cost?

I have own implementation for CIE76 and CIE2000.

For 5,000 samples, it take 10times than CIE76 in my computer.

Does CIE2000 is expensive calculation? I am not sure that i make correctly or not.
Plz someone comment this issue!

n = 100 averaged 4 ms
n = 1000 averaged 39 ms
n = 2000 averaged 74 ms
n = 5000 averaged 187 ms
n = 10000 averaged 384 ms

(2.3GHz i7, 2012 rMBP)

            let c1 = image.dominantColors()
            let c2 = image.dominantColors()
            let c3 = image.dominantColors()

            print(c1)
            print(c2)
            print(c3)

Results in quite different colors:

[NSCustomColorSpace Device RGB colorspace 0.601163 0.451928 0.29805 1, NSCustomColorSpace Device RGB colorspace 0.167657 0.346547 0.366693 1, NSCustomColorSpace Device RGB colorspace 0.932639 0.899493 0.844565 1, NSCustomColorSpace Device RGB colorspace 0.300963 0.190313 0.13295 1, NSCustomColorSpace Device RGB colorspace 0.209064 0.370543 0.368402 1, NSCustomColorSpace Device RGB colorspace 0.959817 0.783332 0.505265 1, NSCustomColorSpace Device RGB colorspace 0.866478 0.734177 0.559615 1, NSCustomColorSpace Device RGB colorspace 0.80283 0.586079 0.331463 1, NSCustomColorSpace Device RGB colorspace 0.145703 0.320739 0.346772 1, NSCustomColorSpace Device RGB colorspace 0.221972 0.415689 0.443135 1, NSCustomColorSpace Device RGB colorspace 0.737809 0.513082 0.260469 1, NSCustomColorSpace Device RGB colorspace 0.281155 0.455335 0.465255 1, NSCustomColorSpace Device RGB colorspace 0.221931 0.405273 0.408174 1, NSCustomColorSpace Device RGB colorspace 0.21869 0.409611 0.429996 1, NSCustomColorSpace Device RGB colorspace 0.234584 0.400131 0.392035 1, NSCustomColorSpace Device RGB colorspace 0.21333 0.391863 0.38884 1]
[NSCustomColorSpace Device RGB colorspace 0.179195 0.35601 0.368129 1, NSCustomColorSpace Device RGB colorspace 0.221092 0.394226 0.392182 1, NSCustomColorSpace Device RGB colorspace 0.731796 0.527369 0.303106 1, NSCustomColorSpace Device RGB colorspace 0.900029 0.709735 0.447639 1, NSCustomColorSpace Device RGB colorspace 0.899187 0.828078 0.731685 1, NSCustomColorSpace Device RGB colorspace 0.981269 0.937817 0.851738 1, NSCustomColorSpace Device RGB colorspace 0.563752 0.426373 0.29075 1, NSCustomColorSpace Device RGB colorspace 0.380421 0.251262 0.160972 1, NSCustomColorSpace Device RGB colorspace 0.144352 0.32968 0.356929 1, NSCustomColorSpace Device RGB colorspace 0.243707 0.430158 0.451196 1, NSCustomColorSpace Device RGB colorspace 0.587503 0.488635 0.360218 1, NSCustomColorSpace Device RGB colorspace 0.159995 0.325696 0.34995 1, NSCustomColorSpace Device RGB colorspace 0.2791 0.162244 0.11213 1, NSCustomColorSpace Device RGB colorspace 0.215359 0.141883 0.117462 1, NSCustomColorSpace Device RGB colorspace 0.21733 0.40994 0.436859 1, NSCustomColorSpace Device RGB colorspace 0.141187 0.325496 0.352944 1]
[NSCustomColorSpace Device RGB colorspace 0.220951 0.405691 0.415336 1, NSCustomColorSpace Device RGB colorspace 0.169029 0.349393 0.370444 1, NSCustomColorSpace Device RGB colorspace 0.927746 0.764487 0.524068 1, NSCustomColorSpace Device RGB colorspace 0.938916 0.901397 0.843266 1, NSCustomColorSpace Device RGB colorspace 0.162621 0.338714 0.355336 1, NSCustomColorSpace Device RGB colorspace 0.289514 0.169255 0.111449 1, NSCustomColorSpace Device RGB colorspace 0.530895 0.42339 0.305094 1, NSCustomColorSpace Device RGB colorspace 0.645131 0.485872 0.323021 1, NSCustomColorSpace Device RGB colorspace 0.725126 0.511703 0.278788 1, NSCustomColorSpace Device RGB colorspace 0.812128 0.594165 0.335176 1, NSCustomColorSpace Device RGB colorspace 0.198869 0.375221 0.376283 1, NSCustomColorSpace Device RGB colorspace 0.260764 0.208588 0.180555 1, NSCustomColorSpace Device RGB colorspace 0.412618 0.265703 0.16161 1, NSCustomColorSpace Device RGB colorspace 0.51031 0.32944 0.17197 1, NSCustomColorSpace Device RGB colorspace 0.583589 0.607618 0.549271 1, NSCustomColorSpace Device RGB colorspace 0.289885 0.460979 0.465333 1]