Detecting thresholds in HSV color space (from RGB) using Python / PIL

Question

I want to take an RGB image and convert it to a black and white RGB image, where a pixel is black if its HSV value is between a certain range and white otherwise.

Cu

Answer 1

EDIT 2: This now returns the same results as Paul's code, as it should...

import numpy, scipy

image = scipy.misc.imread("test.png") / 255.0

r, g, b = image[:,:,0], image[:,:,1], image[:,:,2]
m, M = numpy.min(image[:,:,:3], 2), numpy.max(image[:,:,:3], 2)
d = M - m

# Chroma and Value
c = d
v = M

# Hue
h = numpy.select([c ==0, r == M, g == M, b == M], [0, ((g - b) / c) % 6, (2 + ((b - r) / c)), (4 + ((r - g) / c))], default=0) * 60

# Saturation
s = numpy.select([c == 0, c != 0], [0, c/v])

scipy.misc.imsave("h.png", h)
scipy.misc.imsave("s.png", s)
scipy.misc.imsave("v.png", v)

which gives hue from 0 to 360, saturation from 0 to 1 and value from 0 to 1. I looked at the results in image format, and they seem good.

I wasn't sure by reading your question whether it was only the "value" as in V from HSV that you were interested in. If it is, then you can bypass most of this code.

You can then select pixels based on those values and set them to 1 (or white/black) using something like:

newimage = (v > 0.3) * 1