#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
Usage: db_prep.py -d <data> -l <label> -n <ndim> -o <outfile>
Options:
-h --help show this message
"""
def parse_args():
args = docopt(__doc__)
data = args['<data>']
label = args['<label>']
ndim = int(args['<ndim>'])
outfile = args['<outfile>']
return data, label, ndim, outfile
if __name__ == '__main__':
from docopt import docopt
from numpy import load
from mscr.bovw import BoVW
data, label, ndim, outfile = parse_args()
X, y = load(data), load(label)
bbb = BoVW()
bbb.fit(X, y, ndim)
bbb.save_prep(outfile)
from docopt import docopt
from mscr.util import load_gray, imshow, AddSuffix, MyKNN
from mscr.bovw import BoVW
from mscr.blocks import RandBlockIter
from mscr.blockVote import Vote, BlockVote, Votes2Img
from mscr.grid import Grid, GridClassifier
imfile, model, nblock, nneigh, display, save = parse_args()
img = load_gray(imfile)
print '#------------------'
print imfile
# random block voting
bvw = BoVW()
bvw.load(model)
rbv = BlockVote(
Vote(bvw), RandBlockIter(nblock, pdiv, sdiv, md, Md))
votes = rbv.run(img)
if display:
rbv.show()
# corase segmentation
coarse = Votes2Img(img.shape[:2]).run(votes)
# final segmentation
grid = GridClassifier(MyKNN(labels, nn=nneigh), Grid(microsize))
grid.run(img, coarse)
grid.finalize()
from docopt import docopt
from os.path import join as pjoin
from os.path import basename as pbase
from mscr.util import load_gray, MyKNN, imshow, AddSuffix
from mscr.bovw import BoVW
from mscr.blocks import TrivialBlockIter
from mscr.blockVote import Vote, BlockVote, Votes2Img
from mscr.grid import Grid, GridClassifier
imgf, model, w, h, nn, display, save = parse_args()
img = load_gray(imgf)
print '#-----------------------'
print imgf
bvw = BoVW()
bvw.load(model)
ubv = BlockVote(Vote(bvw), TrivialBlockIter(w, h))
votes = ubv.run(img)
coarse = Votes2Img(img.shape[:2]).run(votes)
grid = GridClassifier(MyKNN(labels, nn=nn), Grid(microsize))
grid.run(img, coarse)
grid.finalize()
res = grid.show()
if display:
imshow(res)
#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
Usage: fit_bovw.py -i <prep_data> -o <out>
Options:
-h --help
"""
def parse_args():
args = docopt(__doc__)
infile = args['<prep_data>']
outfile = args['<out>']
return infile, outfile
if __name__ == '__main__':
from os.path import exists as pex
from docopt import docopt
from mscr.bovw import BoVW
infile, outfile = parse_args()
assert pex(infile), "file %s dosen't exist" % infile
bbb = BoVW()
bbb.fit_from_prep(infile)
bbb.save(outfile)
if __name__ == '__main__':
from docopt import docopt
from os.path import basename as pbase
from os.path import join as pjoin
from mscr.util import load_gray, AddSuffix
from mscr.bovw import BoVW
from mscr.blocks import RandBlockIter
from mscr.blockVote import Vote, BlockVote, Votes2Img
imfile, nblock, model, display, save = parse_args()
print '#----------------------------'
print imfile
img = load_gray(imfile)
bbb = BoVW()
bbb.load(model)
rbv = BlockVote(Vote(bbb), RandBlockIter(nblock, pdiv, sdiv, md, Md))
asd = rbv.run(img)
if display:
rbv.show()
vimg = Votes2Img(img.shape[:2])
vimg.run(asd)
vimg.get_bounding_box()
if save:
base = pbase(imfile)
outfile = pjoin(save, AddSuffix('out', 'pck').run(base))
#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
Usage: fit_bovw.py -i <prep_data> -o <out>
Options:
-h --help
"""
def parse_args():
args = docopt(__doc__)
infile = args['<prep_data>']
outfile = args['<out>']
return infile, outfile
if __name__ == '__main__':
from os.path import exists as pex
from docopt import docopt
from mscr.bovw import BoVW
infile, outfile = parse_args()
assert pex(infile), "file %s dosen't exist" % infile
bbb = BoVW()
bbb.fit_from_prep(infile)
bbb.save(outfile)
if __name__ == '__main__':
from docopt import docopt
from os.path import basename as pbase
from os.path import join as pjoin
from mscr.util import load_gray, AddSuffix
from mscr.bovw import BoVW
from mscr.blocks import RandBlockIter
from mscr.blockVote import Vote, BlockVote, Votes2Img
imfile, nblock, model, display, save = parse_args()
print '#----------------------------'
print imfile
img = load_gray(imfile)
bbb = BoVW()
bbb.load(model)
rbv = BlockVote(
Vote(bbb), RandBlockIter(nblock, pdiv, sdiv, md, Md))
asd = rbv.run(img)
if display:
rbv.show()
vimg = Votes2Img(img.shape[:2])
vimg.run(asd)
vimg.get_bounding_box()
if save:
base = pbase(imfile)
