def main():
# python libs
from getopt import getopt
import sys
# 3rd party libs
# from PIL import Image
import Image
# command-line options
nPt = 8 # default
expo = 2 # default distance exponent (Euclidean)
nGrid = 512 # image size (in each dimension)
seed = 0 # default seed
useGain = False
help = False
imgOutput = True
rawOutput = False
img_name = "distmap.png"
raw_name = "distmap.dat"
usage = """
distmap.py [options]
-h help (prints this message)
-g use gain instead of numpy
-e expo set exponent (default: 2 (Euclidean))
-o out output filename (default: distmap.png)
-r raw raw output filename (default: distmap.dat)
-p points number of points (default: 8)
-s seed for random number generation
-w width image size e.g. Width X Width square
-d whether to enable profiling
"""
(optsvals, args) = getopt(sys.argv[1:], "hge:o:r:p:s:w:")
for (opt, val) in optsvals:
if opt == "-h":
help = True
elif opt == "-g":
useGain = True
elif opt == "-e":
expo = float(val)
elif opt == "-o":
img_name = str(val)
imgOutput = True
elif opt == "-r":
raw_name = str(val)
rawOutput = True
elif opt == "-p":
nPt = int(val)
elif opt == "-s":
seed = int(val)
elif opt == "-w":
nGrid = int(val)
if useGain:
import ga.gain as np
from ga.gain import me
set_np(np)
if help:
if not me():
print usage
sys.exit()
else:
import numpy as np
set_np(np)
if help:
print usage
sys.exit()
shape = (nGrid, nGrid)
# seed the random number generator
np.random.seed(seed)
# generate the distmap
pxls = distmap(shape, nPt, expo)
# write pxls to file
if (imgOutput or rawOutput) and useGain:
if me() == 0:
pxls = pxls.get()
else:
return
if imgOutput:
im_r = Image.frombuffer("F", pxls.shape, pxls, "raw", "F", 0, 1)
im_r = im_r.convert("L")
im_r.save(img_name)
if rawOutput:
f = open(raw_name, "wb")
f.write(buffer(pxls.astype(np.float32)))
f.close()
def main():
# python libs
from getopt import getopt
import sys
# 3rd party libs
#from PIL import Image
import Image
# command-line options
nPt = 8 # default
expo = 2 # default distance exponent (Euclidean)
nGrid = 512 # image size (in each dimension)
seed = 0 # default seed
useGain = False
help = False
imgOutput = True
rawOutput = False
img_name = "distmap.png"
raw_name = "distmap.dat"
usage = """
distmap.py [options]
-h help (prints this message)
-g use gain instead of numpy
-e expo set exponent (default: 2 (Euclidean))
-o out output filename (default: distmap.png)
-r raw raw output filename (default: distmap.dat)
-p points number of points (default: 8)
-s seed for random number generation
-w width image size e.g. Width X Width square
-d whether to enable profiling
"""
(optsvals, args) = getopt(sys.argv[1:], 'hge:o:r:p:s:w:')
for (opt, val) in optsvals:
if opt == '-h':
help = True
elif opt == '-g':
useGain = True
elif opt == '-e':
expo = float(val)
elif opt == '-o':
img_name = str(val)
imgOutput = True
elif opt == '-r':
raw_name = str(val)
rawOutput = True
elif opt == '-p':
nPt = int(val)
elif opt == '-s':
seed = int(val)
elif opt == '-w':
nGrid = int(val)
if useGain:
import ga.gain as np
from ga.gain import me
set_np(np)
if help:
if not me():
print usage
sys.exit()
else:
import numpy as np
set_np(np)
if help:
print usage
sys.exit()
shape = (nGrid, nGrid)
# seed the random number generator
np.random.seed(seed)
# generate the distmap
pxls = distmap(shape, nPt, expo)
# write pxls to file
if (imgOutput or rawOutput) and useGain:
if me() == 0:
pxls = pxls.get()
else:
return
if imgOutput:
im_r = Image.frombuffer("F", pxls.shape, pxls, "raw", "F", 0, 1)
im_r = im_r.convert("L")
im_r.save(img_name)
if rawOutput:
f = open(raw_name, "wb")
f.write(buffer(pxls.astype(np.float32)))
f.close()
np.random.seed(seed)
# generate the distmap
pxls = distmap(shape, nPt, expo)
# write pxls to file
if (imgOutput or rawOutput) and useGain:
if me() == 0:
pxls = pxls.get()
else:
return
if imgOutput:
im_r = Image.frombuffer("F", pxls.shape, pxls, "raw", "F", 0, 1)
im_r = im_r.convert("L")
im_r.save(img_name)
if rawOutput:
f = open(raw_name, "wb")
f.write(buffer(pxls.astype(np.float32)))
f.close()
if __name__ == "__main__":
profile = False
if profile:
import cProfile
if useGain:
cProfile.run("main()", "distmap.prof" + str(me()))
else:
cProfile.run("main()", "distmap.prof")
else:
main()
# seed the random number generator
np.random.seed(seed)
# generate the distmap
pxls = distmap(shape, nPt, expo)
# write pxls to file
if (imgOutput or rawOutput) and useGain:
if me() == 0:
pxls = pxls.get()
else:
return
if imgOutput:
im_r = Image.frombuffer("F", pxls.shape, pxls, "raw", "F", 0, 1)
im_r = im_r.convert("L")
im_r.save(img_name)
if rawOutput:
f = open(raw_name, "wb")
f.write(buffer(pxls.astype(np.float32)))
f.close()
if __name__ == '__main__':
profile = False
if profile:
import cProfile
if useGain:
cProfile.run("main()", "distmap.prof" + str(me()))
else:
cProfile.run("main()", "distmap.prof")
else:
main()
