Some pictures generate more interest when you emphasize or mute certain colors or groups of colors. What started out as a task where I would do this manually, turned into a programatic way to identify and mute colors which placed more emphasis on the unmuted colors. Like highlighting them.
This was an exercise in identifying colors in an image through code, using python. If you know some of the basics of how colors are represented inside of computers, you are probably aware of red, green and blue (RGB) values. RGB uses 8 bits for each color, which can range from 0 to 255. This means there are 16,777,216 possible combinations that display one color. Rather than catalogue each of those colors to run the program, it would be more efficient and space saving to calculate the color.
This is accomplished by converting the images RGB values into hues, HSL values, and using Munsell Hue Circle to determine what the color should be interpreted as. Cataloguing 360 values, the degrees in a circle, is less resource intensive than the almost 17 million values required from RGB.