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Colorfulness in node.js inspired from Datta R., Joshi D., Li J., Wang J.Z.: Studying aesthetics in photographic images using a computational approach. ECCV (2006)

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Colorfulness

Node.js implementation of colorfulness using node-opencv binder for OpenCV.

Related research studies

  • From Rubner 2000, EMD is a good way to compare 2 images
  • From Datta 2006 has used a "perfectly colored" image BGR distribution to compare image with EMD This is what they called the colorfulness measure.

Prerequisites

You will need to make node-opencv work on your local machine, so havind, opencv, node, npm.

Histograms calculation

Which image is the most colorfull ? This library will give you the answer in node.js !

Pre-requisites

  • opencv

Installation

npm install colorfulness

Example

var colorfulness = require('colorfulness');

colorfulness("example/image.png", function(err, res){  
  // res is a number of colorfullness between 0 (not colorfull) and 1 (colorfull)
});

// or with open cv lib

var cv = require("opencv");
cv.readImage("example/image.png", function(err, im){
  if(err){
    //handle error
  }

  colorfulness({
    image : im
  }, function(err, res){  
    // res is a number of colorfullness between 0 (not colorfull) and 1 (colorfull)
  });
})

Test

npm test

Results

Images

File Image Colorfulness
mona.png 60%
car1.jpg 69%
stuff.png 72%
neutral.png 100%
amaro.png 90%
FFFFFF.png 56%
000000.png 49%
00FFFF.png 57%

Non-symetric of measure in BGR space

Remark : FFFFFF.png (white image) is more colorful than 000000.png (black image), it is because the cost function is done in the "LUV" color space.

To understand this, let's consider BGR-centers distance cost matrix in LUV_L2 distance space. To simplify my explanation i will use 2x2x2 = 8 BGR cubes (instead of 64 as used in the code);

Cubes centers are

Cube number BGR center position LUV center position
cube 0 [64,64,64] [69,97,139]
cube 1 [64,64,192] [117,166,160]
cube 2 [64,192,64] [176,57,211]
cube 3 [64,192,192] [193,100,212]
cube 4 [192,64,64] [90,92,46]
cube 5 [192,64,192] [128,136,68]
cube 6 [192,192,64] [182,61,129]
cube 7 [192,192,192] [198,97,139]

Matrix of distance in LUV space is looks like :

cube 0 cube 1 cube 2 cube 3 cube 4 cube 5 cube 6 cube 7 SUM
cube 0 0.00 0.44 0.69 0.74 0.49 0.51 0.61 0.66 4.14
cube 1 0.44 0.00 0.69 0.58 0.71 0.50 0.65 0.55 4.12
cube 2 0.69 0.69 0.00 0.24 0.97 0.87 0.42 0.44 4.31
cube 3 0.74 0.58 0.24 0.00 1.00 0.83 0.47 0.37 4.23
cube 4 0.49 0.71 0.97 1.00 0.00 0.32 0.65 0.73 4.87
cube 5 0.51 0.50 0.87 0.83 0.32 0.00 0.57 0.55 4.14
cube 6 0.61 0.65 0.42 0.47 0.65 0.57 0.00 0.21 3.58
cube 7 0.66 0.55 0.44 0.37 0.73 0.55 0.21 0.00 3.51

So pure "cube 0"-distribution (corresponding to FFFFFF image) will not be symetric with "cube 7"-distribution (corresponding to 000000 image).

Pure "cube 4"-distribution (corresponding to 00FFFF image), is even more colorful.

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Colorfulness in node.js inspired from Datta R., Joshi D., Li J., Wang J.Z.: Studying aesthetics in photographic images using a computational approach. ECCV (2006)

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