I'm wirtting a program that changes all the image pixels to grayscale except for the red ones. At first, i thought it would be easier, but I'm having trouble trying to find the best way to determine if a pixel is red or not.
The first method I tried was a formula: Green < Red/2 && Blue < Red/1.5
results:
Michael Jordan's image shows some not red pixels that pass the formula, like #7F3222 and #B15432. So i tried a different method, hue >= 345 || hue <= 9, trying to limit only the red part of the color wheel.
results:
Michael Jordan's image now has less not red pixels and goldhill's image has more red pixels than before but still not what I want.
My methods are incorrect or just some adjustments are missing? if they're incorrect, how can I solve this problem?
CodePudding user response：
Your question "How to identify 'real' red pixels", begs the question "what a red pixel actually is, especially if it has to be 'real'".
The RGB (red, green, blue) color model is not well suited to answer that question, therefore you should use the HSV (hue, saturation, value) model.
- Hue defines the color in degrees (0 - 360 degrees)
- Saturation defines the intensity of the color (0 - 100 %)
- Value or Brightness defines the luminosity (0 - 100 %)
Steps:
- convert RGB to HSV
- if the H value is not red ( /- 30 degrees, you'll have to define a threshold range of what you consider to be red, 'real' red would be 0 degrees)
- set S to 0 (zero), by doing so we remove the saturation of the color, which results in a gray shade
- leave the brightness (V) as it is (or play around with it and see how it effects the results)
- convert HSV to RGB
Convert from RGB to HSV and vice versa:
More info on HSV:
"All cats are gray in the dark"
Implement a dynamic color range. Adjust the 'red' range based on the brightness and/or saturation of the current pixel. Put a weight scale (on how much they affect the range in %) on the saturation and brightness values to determine your range ... play around to achieve the best results.
CodePudding user response：
You used RGB, and HSV method, which it is good, and both are ok.
The problem is about defining red. Hue (or R) is not enough: it contains many other colours (in the broader sense): browns are dark/unsaturated reds (or oranges). Pink is also a tint of red (so red white, so unsaturated).
So in your first method, I would add a condition: R > 127 (you must check yourself a good threshold). And possibly change the other conditions with a higher ratio of R to G and B and possibly adding also the ration R to (G B). The first new added condition is about reds (and not "dark reds/browns), and brightness. Your two conditions are about "hue" (hue is defined by the top two values), and the last condition I wrote is about saturation.
You can do in a similar way for HSV: filter H (as you did), but you must filter also V (you want just bright reds), and also an high saturation, so you must filter all channels.
You should test yourself the saturation levels. The problem is that eyes adapt quickly to colours, so some images with a lot of redish colours are seen normally (less redish) by humans, but more red by above calculation. Etc. (so usually for such works there is some sliders to modify, e.v. you can try to automatize, but you need to find overall hue and brightness of image, and possibly complex methods, see CIECAM).