#!/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 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)
