[Numpy-discussion] Optimizing speed for large-array inter-element algorithms (specifically, color space conversion)

theodore test tteststudent@gmail....
Mon Jan 21 08:39:21 CST 2008


Hello all,

I'm scratching my head over how to make this image color space conversion
from "RGB" to "HSV" quicker.  It requires input from all three bands of a
given pixel at each pixel, and thus can't be easily flattened.  I've already
implemented psyco and removed the extra step of calling colorsys's
rgb_to_hsv function by adapting the code into my primary for loop.

Right now it takes around 9 seconds for a single 1600x1200 RGB image, a
conversion that I've seen implemented more or less instantly in, say,
ImageJ.  What can I do to make this conversion more efficient?

Thank you in advance,
Theodore Test

------------


#Necessary imports and hand-waving at psyco usage.
import numpy
from numpy import *
from scipy.misc import pilutil

import psyco
psyco.full()


# Read image file, cram it into a normalized array with one row per pixel
# with each column holding the given pixel's R,G, or B band-value.
s = pilutil.imread('A_Biggish_Image.tif')
r,c,d = s.shape

l = r*c
t = t.reshape(l,3)
t = t/255.0


# Cycle through all of the pixels, converting them to HSV space and putting
them
# back into the array.  This is the big time-waster.
#
# The conversion is adapted directly from colorsys.rgb_to_hsv to reduce call
time
for x in range(0,l):
    tr = float(t[x,0])
    tg = float(t[x,1])
    tb = float(t[x,2])
    maxc = max(tr, tg, tb)
    minc = min(tr, tg, tb)
    v = maxc
    if minc == maxc:
        t[x,0],t[x,1],t[x,2] = 0.0, 0.0, v
    else:
        s = (maxc-minc) / maxc
        rc = (maxc-tr) / (maxc-minc)
        gc = (maxc-tg) / (maxc-minc)
        bc = (maxc-tb) / (maxc-minc)
        if tr == maxc: h = bc-gc
        elif tg == maxc: h = 2.0+rc-bc
        else: h = 4.0+gc-rc
        h = (h/6.0) % 1.0
        t[x,0],t[x,1],t[x,2]  = h, s, v

# Renormalize shape and contents to image-file standards.
t = t*255.0
t = t.astype(uint8)
t = t.reshape(r,c,d)

finaloutputarray = t

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