gurkandemir/color-quantizer

Color quantization is the process of reducing number of colors used in an image while trying to maintain the visual appearance of the original image. In general, it is a form of cluster analysis, if each RGB color value is considered as a coordinate triple in the 3D colorspace.

Color-Quantizer

Color quantization is the process of reducing number of colors used in an image while trying to maintain the visual appearance of the original image. In gen- eral, it is a form of cluster analysis, if each RGB color value is considered as a coordinate triple in the 3D colorspace

K-Means Algorithm

1. Convert input image to a matrix of pixels.
2. Pick K initial colors to begin quantization by clicking on the image or randomly.
3. Implement K-Means Algoritm. Repeat followings 10 times.
   • Assign each pixel to closest clusters by comparing their R, G, B values.
   • Find clusters' new R, G, B values by getting means of assigned pixels' values.

How to Run?

In order to run color quantizer, execute the following from the command line:

python3 colorQuantizer.py [IMG] [K] [TYPE]

WHERE

1. IMG : Path of image want to be quantized.

2. K : Number of colors in an image we want to quantize.

3. TYPE : Type of selecting cluster centers.

   • 1 for choosing cluster centers by clicking on photo.
   • 2 for choosing cluster centers randomly.

   For example, if you want to quantize cat.png into 8 colors, by choosing random cluster centers. You have to execute following command: \textbf{python3 colorQuantizer.py cat.png 8 2 }

python3 colorQuantizer.py cat.png 8 2