<s> import os <EOL> import sys <EOL> import argparse <EOL> import numpy as np <EOL> import theano <EOL> from scipy . io import loadmat , savemat <EOL> homepath = os . path . join ( '<STR_LIT:..>' , '<STR_LIT:..>' ) <EOL> if not homepath in sys . path : <EOL> sys . path . insert ( <NUM_LIT:0> , homepath ) <EOL> from dlearn . utils . serialize import load_data <EOL> from dlearn . utils import imgproc <EOL> desctxt = """<STR_LIT>""" <EOL> seg_txt = """<STR_LIT>""" <EOL> output_txt = """<STR_LIT>""" <EOL> parser = argparse . ArgumentParser ( description = desctxt ) <EOL> parser . add_argument ( '<STR_LIT>' , '<STR_LIT>' , nargs = <NUM_LIT:1> , required = True , <EOL> choices = [ '<STR_LIT>' ] ) <EOL> parser . add_argument ( '<STR_LIT>' , '<STR_LIT>' , nargs = <NUM_LIT:1> , required = True , <EOL> metavar = '<STR_LIT:name>' , help = seg_txt ) <EOL> parser . add_argument ( '<STR_LIT>' , '<STR_LIT>' , nargs = <NUM_LIT:1> , required = True , <EOL> metavar = '<STR_LIT:name>' , help = output_txt ) <EOL> args = parser . parse_args ( ) <EOL> def load_rawdata ( dnames ) : <EOL> rawdata = [ ] <EOL> for dname in dnames : <EOL> fpath = os . path . join ( homepath , '<STR_LIT:data>' , '<STR_LIT>' , dname ) <EOL> matdata = loadmat ( fpath ) <EOL> m , n = matdata [ '<STR_LIT>' ] . shape <EOL> rawdata . extend ( [ matdata [ '<STR_LIT>' ] [ i , <NUM_LIT:0> ] for i in xrange ( m ) ] ) <EOL> return rawdata <EOL> def proc_rawdata ( rawdata ) : <EOL> def imgresize ( img ) : <EOL> img = imgproc . resize ( img , [ <NUM_LIT> , <NUM_LIT> ] , keep_ratio = '<STR_LIT>' ) <EOL> return img <EOL> def imgprep ( img ) : <EOL> img = imgproc . subtract_luminance ( img ) <EOL> img = np . rollaxis ( img , <NUM_LIT:2> ) <EOL> return ( img / <NUM_LIT> ) . astype ( np . float32 ) <EOL> I = [ imgresize ( img ) for img in rawdata ] <EOL> X = [ imgprep ( img ) for img in I ] <EOL> X = np . asarray ( X ) <EOL> X = X - X . mean ( axis = <NUM_LIT:0> ) <EOL> return I , X <EOL> def produce_segmentation ( data , model ) : <EOL> f = theano . function ( <EOL> inputs = [ model . input ] , <EOL> outputs = model . blocks [ <NUM_LIT:3> ] . output <EOL> ) <EOL> m = data . shape [ <NUM_LIT:0> ] <EOL> output = [ <NUM_LIT:0> ] * m <EOL> for i in xrange ( m ) : <EOL> S = f ( data [ i : i + <NUM_LIT:1> ] ) . reshape ( [ <NUM_LIT> , <NUM_LIT> ] ) <EOL> output [ i ] = imgproc . resize ( S , [ <NUM_LIT> , <NUM_LIT> ] ) <EOL> return np . asarray ( output ) <EOL> def save_segmentation ( images , output , fpath ) : <EOL> savemat ( fpath , { '<STR_LIT>' : images , '<STR_LIT>' : output } ) <EOL> if __name__ == '<STR_LIT:__main__>' : <EOL> seg_file = '<STR_LIT>' . format ( args . segmentation [ <NUM_LIT:0> ] ) <EOL> out_file = '<STR_LIT>' . format ( args . output [ <NUM_LIT:0> ] ) <EOL> data = load_rawdata ( args . dataset ) <EOL> images , data = proc_rawdata ( data ) <EOL> model = load_data ( seg_file ) <EOL> output = produce_segmentation ( data , model ) <EOL> save_segmentation ( images , output , out_file ) </s>
