Hello,
I'm working on an image processing project using the Python Imaging
Library along with numpy. Right now, I'm trying to build a speedy
script for converting whole images between the RGB and the HSV (a.k.a.
HSB) color spaces. Unfortunately, the code I've made so far runs
dreadfully slow with even moderate-sized images.
I'm under the impression that the crux of the problem is the fact that
PIL's point method operates on only one band at a time, and to do a
proper color space conversion, you need information about all three
bands in a particular problem. This has forced me to do an awkward
work-around where the image data is loaded into a numpy array
(1600x1200x3), and a dinky for-loop run runs through the array picking
up RGB values, converting to HSV, and dumping the results back into
another array.
How can I make this more efficient?
from PIL import Image
from PIL import TiffImagePlugin
from scipy.misc import pilutil
import numpy
import colorsys
def myrgbtohsv(r,g,b):
'''A wrapper for the colorsys function rgb_to_hsv
that takes r,g,b values between 0 and 255
(standard pixel value range in most of my single-band imaging
operations)
and returns the corresponding 255-scaled h,s,v values'''
tr = r/255.0
tg = g/255.0
tb = b/255.0
ur,ug,ub = colorsys.rgb_to_hsv(tr,tg,tb)
pr = int(round(ur,0))*255
pg = int(round(ug,0))*255
pb = int(round(ub,0))*255
return pr,pg,pb
def rgbarrtohsvarr(pilarray):
'''Takes an 3d numpy ndarray loaded with a RGB PIL image and
converts
a copy of the contents to 255-scaled HSV array.
Returns the converted copy of the array.
'''
arrt = pilarray.copy()
r,c,d = arrt.shape
hsvarray = numpy.zeros((r,c,d))
print out
for i in range(0,r):
for j in range(0,c):
localrgb = (arrt[i,j,0],arrt[i,j,1],arrt[i,j,2])
(lh,ls,lv) = rgbtohsv(localrgb)
hsvarray[i,j,0],hsvarray[i,j,1],hsvarray[i,j,2]= lh,ls,lv
return hsvarray
im = Image.open(r'C:\test.tif')
arr = pilutil.fromimage(im)
out = rgbarrtohsvarr(arr)
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