I need a binary threshold and numpy.where() seems very slow on numpy
0.9.9.2800:
python -m timeit -n 10 -s "import numpy as n;a=n.ones((512,512),
n.uint8)*129"
"a_bin=n.where( a>128, 128,0)"
10 loops, best of 3: 37.9 msec per loop
I'm thinking the conversion of the min, max constants from python ints
to n.uint8 might be slowing it down? Is there a better way? Scipy is
also an option. Ive search up list quickly and nothing jumps out.
For comparison Ive got some ctypes wrapped OpenCv code (that I'd like to
avoid) doing the same thing in < 1 msec:
Cv images here are unsigned 8 bit as above:
python -m timeit -n 50 -s "import
cv;sz=cv.cvSize(512,512);a=cv.cvCreateImage(sz, 8, 1);
a_bin=cv.cvCreateImage(sz,8,1)" "cv.cvThreshold(a, a_bin, float(128),
float(255), cv.CV_THRESH_BINARY )"
50 loops, best of 3: 348 usec per loop
And with the Intel IPP optimizations turned on < 0.1msec:
python -m timeit -n 50 -s "import cv; sz=cv.cvSize(512,512);
a=cv.cvCreateImage(sz, 8, 1); a_bin=cv.cvCreateImage(sz,8,1)"
"cv.cvThreshold(a, a_bin, float(128), float(255), cv.CV_THRESH_BINARY )"
50 loops, best of 3: 59.5 usec per loop
Regards,
Mark
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