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)
if save:
base = pbase(imgf)
outfile = pjoin(save, AddSuffix('out', 'jpg').run(base))
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()
res = grid.show()
if display:
