import Image
import os
os.system(
"wget http://www.eecs.berkeley.edu/Research/Projects/CS/vision/bsds/BSDS300-images.tgz"
)
os.system("tar -xzvf BSDS300-images.tgz")
imgs = glob.glob("BSDS300/images/test/*.jpg")
for n, i in enumerate(imgs):
f = i.split('/')[-1].split('.')[0]
print n, f
k = mmreadgray(i)
size = k.shape
scale = 4000.0 / min(size)
newsize = (int(size[0] * scale), int(size[1] * scale))
img = (mmhmin(k, 50) > 128).astype(numpy.uint8) * 255
img = mmareaclose(img, 20)
img = mmareaopen(img, 20)
pil_img = Image.fromarray(img, "L")
pil_img = pil_img.resize(newsize)
pil_img.save("output/" + f + ".png")
os.system("convert -compress none %s %s" %
("output/" + f + ".png", "output/" + f + ".pgm"))
import sys
sys.exit(0)
from morph import mmlabel, mmreadgray, mmhmin, mmthreshad, mmlabelflat, mmareaclose, mmareaopen
import glob
import Image
import os
os.system("wget http://www.eecs.berkeley.edu/Research/Projects/CS/vision/bsds/BSDS300-images.tgz")
os.system("tar -xzvf BSDS300-images.tgz")
imgs = glob.glob("BSDS300/images/test/*.jpg")
for n,i in enumerate(imgs):
f = i.split('/')[-1].split('.')[0]
print n, f
k = mmreadgray(i)
size = k.shape
scale = 4000.0/min(size)
newsize = (int(size[0]*scale), int(size[1]*scale))
img = (mmhmin(k, 50) > 128).astype(numpy.uint8)*255
img = mmareaclose(img, 20)
img = mmareaopen(img, 20)
pil_img = Image.fromarray(img, "L")
pil_img = pil_img.resize(newsize)
pil_img.save( "output/"+f+".png" )
os.system("convert -compress none %s %s" %
("output/"+f+".png", "output/"+f+".pgm"))
import sys
sys.exit(0)
return [numbercc, gold_serial, uf_gpu, uf_hybrid, lequiv_gpu]
l_uf = numpy.zeros(len(imgs))
l_uf_hybrid = numpy.zeros(len(imgs))
l_lequiv = numpy.zeros(len(imgs))
l_stephano = numpy.zeros(len(imgs))
for n, i in enumerate(imgs):
RESULTS = []
f = os.path.split(OUTPUT_SIMPLE + i)[1].split(".")[0]
res = run(OUTPUT_SIMPLE + f)
k = (mmreadgray(OUTPUT_SIMPLE + f + ".png") > 0)
v1 = []
for j in range(10):
t0 = time.time()
lbl = mmlabel(k)
t1 = time.time()
v1.append(1000 * (t1 - t0))
RESULTS = res + [min(v1)]
l_uf[n] = RESULTS[2]
l_uf_hybrid[n] = RESULTS[3]
l_lequiv[n] = RESULTS[4]
l_stephano[n] = RESULTS[5]
return [numbercc, gold_serial, uf_gpu, uf_hybrid, lequiv_gpu]
l_uf = numpy.zeros(len(imgs))
l_uf_hybrid = numpy.zeros(len(imgs))
l_lequiv = numpy.zeros(len(imgs))
l_stephano = numpy.zeros(len(imgs))
for n,i in enumerate(imgs):
RESULTS = []
f = os.path.split(OUTPUT_SIMPLE+i)[1].split(".")[0]
res = run(OUTPUT_SIMPLE+f)
k = (mmreadgray(OUTPUT_SIMPLE+f+".png") > 0)
v1 = []
for j in range(10):
t0 = time.time()
lbl = mmlabel(k)
t1 = time.time()
v1.append(1000*(t1-t0))
RESULTS = res+[min(v1)]
l_uf[n] = RESULTS[2]
l_uf_hybrid[n] = RESULTS[3]
l_lequiv[n] = RESULTS[4]
l_stephano[n] = RESULTS[5]
