# Path: dlearn/utils/serialize.py
# def load_data(fpath):
#     with open(fpath, 'rb') as f:
#         data = cPickle.load(f)
#     return data
# 
# Path: dlearn/utils/imgproc.py
# def translate(image, translation):
# def resize(image, shape, keep_ratio=False):
# def subtract_luminance(rgbimg, mean_luminance=None):
# def scale_per_channel(img, scale_range):
# def binarize(img, threshold):
# def images2mat(images):


import os
import sys
import argparse
import numpy as np
import theano
from scipy.io import loadmat, savemat
from dlearn.utils.serialize import load_data
from dlearn.utils import imgproc
"""

parser = argparse.ArgumentParser(description=desctxt)
parser.add_argument('-d', '--dataset', nargs=1, required=True,
                    choices=['Mix'])
parser.add_argument('-s', '--segmentation', nargs=1, required=True,
                    metavar='name', help=seg_txt)
parser.add_argument('-o', '--output', nargs=1, required=True,
                    metavar='name', help=output_txt)

args = parser.parse_args()


def load_rawdata(dnames):
    rawdata = []
    for dname in dnames:
        fpath = os.path.join(homepath, 'data', 'human_attribute', dname)
        matdata = loadmat(fpath)
        m, n = matdata['images'].shape
        rawdata.extend([matdata['images'][i, 0] for i in xrange(m)])

    return rawdata


def proc_rawdata(rawdata):
    def imgresize(img):
        img = imgproc.resize(img, [160, 80], keep_ratio='height')
        return img

    def imgprep(img):

# Path: dlearn/utils/serialize.py
# def load_data(fpath):
#     with open(fpath, 'rb') as f:
#         data = cPickle.load(f)
#     return data
# 
# Path: dlearn/utils/imgproc.py
# def translate(image, translation):
# def resize(image, shape, keep_ratio=False):
# def subtract_luminance(rgbimg, mean_luminance=None):
# def scale_per_channel(img, scale_range):
# def binarize(img, threshold):
# def images2mat(images):


# Path: examples/human_sar/produce_seg.py
import os
import sys
import argparse
import numpy as np
import theano
from scipy.io import loadmat, savemat
from dlearn.utils.serialize import load_data
from dlearn.utils import imgproc

"""

parser = argparse.ArgumentParser(description=desctxt)
parser.add_argument('-d', '--dataset', nargs=1, required=True,
                    choices=['Mix'])
parser.add_argument('-s', '--segmentation', nargs=1, required=True,
                    metavar='name', help=seg_txt)
parser.add_argument('-o', '--output', nargs=1, required=True,
                    metavar='name', help=output_txt)

args = parser.parse_args()


def load_rawdata(dnames):
    rawdata = []
    for dname in dnames:
        fpath = os.path.join(homepath, 'data', 'human_attribute', dname)
        matdata = loadmat(fpath)
        m, n = matdata['images'].shape
        rawdata.extend([matdata['images'][i, 0] for i in xrange(m)])

    return rawdata


def proc_rawdata(rawdata):
    def imgresize(img):
        img = imgproc.resize(img, [160, 80], keep_ratio='height')
        return img

    def imgprep(img):

        img = imgproc.subtract_luminance(img)