## Imports

import numpy

np=numpy

import inspect

import resource

import ast

import yaml

import sys

skimage_io_import_bug_workaround=sys.argv

try:

sys.argv=sys.argv[:1]

import skimage.io

finally:

sys.argv=skimage_io_import_bug_workaround

del skimage_io_import_bug_workaround

import skimage.restoration

import sklearn.decomposition

import pickle

import time

import glob

import os

import os.path

import subprocess

import pipes

import h5py

import itertools

import scipy

import seaborn

import pandas

import matplotlib.pyplot

import totalvariation

import threadparallel

import measure

import deepart

import matchmmd

## Read PCA matrices

#data=np.load('pca.npz') # matlab

data=np.load('data_pca.npz') # python

U=data['U']

T=data['T']

mu=data['mu']

shape=data['shape'].item()

del data

print 'U',U.shape,U.dtype,U.min(),U.max()

print 'T',T.shape,T.dtype,T.min(),T.max()

print 'mu',mu.shape,mu.dtype,mu.min(),mu.max()

## Read attributes

from gen_deepart import read_lfw_attributes

from gen_deepart import attr_pairs

_,_,lfwattr=read_lfw_attributes()

# 8 is Youth

# 10 is Senior

target_indices,source_indices=attr_pairs(lfwattr,10,2000,2000)

print 'source',source_indices[:5]

print 'target',target_indices[:5]

weights=[5e-6]

test_indices=[0,1]

print 'feat_img[:5]',T[0,:5]

#P=T[source_indices].astype(np.float64)

#Q=T[target_indices].astype(np.float64)

P=T[source_indices]

Q=T[target_indices]

print 'P',P.shape,P.dtype

print 'Q',Q.shape,Q.dtype

## Match distributions

allF=[]

for i in test_indices:

#reload(matchmmd)

t0=time.time()

#x_0,x,r=matchmmd.match_distribution(T[i].astype(np.float64),P,Q,weights)

x_0,x,r=matchmmd.match_distribution(T[i],P,Q,weights)

print('Finished in {} minutes.'.format((time.time()-t0)/60.0))

print('x_0',x_0.shape,x_0.dtype,x_0.min(),x_0.max())

print('x',x.shape,x.dtype,x.min(),x.max())

print('r',r.shape,r.dtype,r.min(),r.max())

F=x.dot(U)+mu

print('F',F.shape,F.dtype,F.min(),F.max())

print(F[:,:10])

allF.append(F)

# debug stuff

x_0=x_0.dot(U)+mu

print('X',x_0.shape,x_0.dtype,x_0.min(),x_0.max())

R=r.dot(U)

print('R',R.shape,R.dtype,R.min(),R.max())

prefix='out'

blob_names=['conv3_1','conv4_1','conv5_1']

F=np.asarray(allF,dtype=np.float32)

F=F.reshape(F.shape[0]*F.shape[1],-1)

print('F',F.shape,F.dtype,F.min(),F.max())

index=0

for k in blob_names:

size=numpy.prod(shape[k][1:])

h5f=h5py.File('{}_{}.h5'.format(prefix,k),'w')

h5f.create_dataset('DS',data=F[:,index:index+size].reshape(*shape[k]))

h5f.close()

index=index+size

## Reconstruct

from gen_deepart import deepart_reconstruct

deepart_reconstruct(blob_names=blob_names,blob_weights=[1]*len(blob_names),prefix=prefix,max_iter=3000,test_indices=test_indices,data_indices=None,image_dims=(125,125),hybrid_names=['conv1_1','conv2_1'],hybrid_weights=[0.02,0.02],dataset='lfw',desc='testpy')