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.