Given that HSV requires a bunch of conditional code it's going to be tough to vectorize. You could give our approach from last year a try :
Code:
vector<Mat> splitImage;
Mat bluePlusRed;
split(imageIn, splitImage);
addWeighted(splitImage[0], _blue_scale / 100.0,
splitImage[2], _red_scale / 100.0, 0.0,
bluePlusRed);
subtract(splitImage[1], bluePlusRed, imageOut);
This converts an RGB image into a grayscale one where higher grayscale values indicate more pure green. Pixels with lots of green and nothing else end up with high values. Pixels with no green end up as small values. Pixels with high green but also lots of blue and red also end up as low values. That last part filters out, say, white or yellow or whatever that have high G values but also high values for the other channels.
After that we did a threshold on the newly created single-channel image. We used Otsu thresholding to handle different lighting conditions but you might get away with a fixed threshold as in your previous code.
To make this fast you'd probably want to invert the red_scale and blue_scale multipliers so you could do an integer divide rather than convert to float and back - but you'd have to see which is quicker. Should be able to vdup them into all the uint8 lanes in a q register at the start of the loop and just reuse them. And be sure to do this in saturating math because overflow/underflow would ruin the result.
Oh, and I had some luck getting the compiler to vectorize your C code if it was rewritten to match the ASM code. That is, set a mask to either 0 or 0xff then and the mask with the source. Be sure to mark the function args as __restrict__ to get this to work. The code was using d and q regs but seemed a bit sketchy otherwise, but it might be fast enough where you could avoid coding in ASM.