import numpy as np
import os
import sys
import argparse
import numpy as np
import h5py
sys.path.append(os.path.join(os.path.dirname(os.path.realpath(__file__)), '../../../utils/python/'))
import locker
sys.path.append(os.path.join(os.path.dirname(os.path.realpath(__file__)), '../Utils/'))
from FeatStor import load_feat
from PrintUtil import TicTocPrint
sys.path.append(os.path.dirname(os.path.realpath(__file__)))
import ITQ

tic_toc_print = TicTocPrint()

parser = argparse.ArgumentParser(description='Extract Features')
parser.add_argument('-i', '--list', type=str, required=True,
    help='File with list of images')
parser.add_argument('-d', '--dir', type=str, default='',
    help='Features directory')
parser.add_argument('-p', '--paramfile', type=str, required=True,
    help='Hdf5 file path to store the learnt parameters')
parser.add_argument('-o', '--outpath', type=str, required=True,
    help='Hdf5 file to store output hashes')
parser.add_argument('-f', '--fracfeat', type=float, default=1,
    help='Fraction of the feat to resize to')

args = vars(parser.parse_args())

outfpath = args['outpath']
if not outfpath.endswith('.h5'):
  outfpath += '.h5'

with open(args['list']) as f:
  imgslist = f.read().splitlines()

with h5py.File(args['paramfile'], 'r') as f:
  R = f['R'].value
  pc = f['pc'].value
  mean = f['mean'].value

if os.path.exists(args['outpath']):
  print('Reading the existing features')
  with h5py.File(args['outpath'], 'r') as f:
    allhashes = f['hashes'].value.tolist()
else:
  allhashes = []

nDone = len(allhashes)
for i in range(nDone, len(imgslist)):
  impath = imgslist[i]
  tic_toc_print('Done %d / %d features' % (i, len(imgslist)))
  try:
    featpath = os.path.join(args['dir'], impath + '.h5')
    feat = load_feat(featpath, args['fracfeat']).transpose()
    # Normalize this feature (that's how its used in training)
    feat = feat / np.linalg.norm(feat)
    hash = ITQ.hash(feat, pc, mean, R)
    allhashes.append(hash)
  except:
    continue

allhashes = np.squeeze(np.array(allhashes)).astype('bool')
with h5py.File(args['outpath'], 'w') as f:
  f.create_dataset('hashes', data=allhashes, compression="gzip", compression_opts=9)


c2af4cdacc267a05e7f679a0605b4c3d4f2d5dd3
<mask token>

<mask token>
sys.path.append(os.path.join(os.path.dirname(os.path.realpath(__file__)),
    '../../../utils/python/'))
<mask token>
sys.path.append(os.path.join(os.path.dirname(os.path.realpath(__file__)),
    '../Utils/'))
<mask token>
sys.path.append(os.path.dirname(os.path.realpath(__file__)))
<mask token>
parser.add_argument('-i', '--list', type=str, required=True, help=
    'File with list of images')
parser.add_argument('-d', '--dir', type=str, default='', help=
    'Features directory')
parser.add_argument('-p', '--paramfile', type=str, required=True, help=
    'Hdf5 file path to store the learnt parameters')
parser.add_argument('-o', '--outpath', type=str, required=True, help=
    'Hdf5 file to store output hashes')
parser.add_argument('-f', '--fracfeat', type=float, default=1, help=
    'Fraction of the feat to resize to')
<mask token>
if not outfpath.endswith('.h5'):
    outfpath += '.h5'
with open(args['list']) as f:
    imgslist = f.read().splitlines()
with h5py.File(args['paramfile'], 'r') as f:
    R = f['R'].value
    pc = f['pc'].value
    mean = f['mean'].value
if os.path.exists(args['outpath']):
    print('Reading the existing features')
    with h5py.File(args['outpath'], 'r') as f:
        allhashes = f['hashes'].value.tolist()
else:
    allhashes = []
<mask token>
for i in range(nDone, len(imgslist)):
    impath = imgslist[i]
    tic_toc_print('Done %d / %d features' % (i, len(imgslist)))
    try:
        featpath = os.path.join(args['dir'], impath + '.h5')
        feat = load_feat(featpath, args['fracfeat']).transpose()
        feat = feat / np.linalg.norm(feat)
        hash = ITQ.hash(feat, pc, mean, R)
        allhashes.append(hash)
    except:
        continue
<mask token>
with h5py.File(args['outpath'], 'w') as f:
    f.create_dataset('hashes', data=allhashes, compression='gzip',
        compression_opts=9)

<mask token>
sys.path.append(os.path.join(os.path.dirname(os.path.realpath(__file__)),
    '../../../utils/python/'))
<mask token>
sys.path.append(os.path.join(os.path.dirname(os.path.realpath(__file__)),
    '../Utils/'))
<mask token>
sys.path.append(os.path.dirname(os.path.realpath(__file__)))
<mask token>
tic_toc_print = TicTocPrint()
parser = argparse.ArgumentParser(description='Extract Features')
parser.add_argument('-i', '--list', type=str, required=True, help=
    'File with list of images')
parser.add_argument('-d', '--dir', type=str, default='', help=
    'Features directory')
parser.add_argument('-p', '--paramfile', type=str, required=True, help=
    'Hdf5 file path to store the learnt parameters')
parser.add_argument('-o', '--outpath', type=str, required=True, help=
    'Hdf5 file to store output hashes')
parser.add_argument('-f', '--fracfeat', type=float, default=1, help=
    'Fraction of the feat to resize to')
args = vars(parser.parse_args())
outfpath = args['outpath']
if not outfpath.endswith('.h5'):
    outfpath += '.h5'
with open(args['list']) as f:
    imgslist = f.read().splitlines()
with h5py.File(args['paramfile'], 'r') as f:
    R = f['R'].value
    pc = f['pc'].value
    mean = f['mean'].value
if os.path.exists(args['outpath']):
    print('Reading the existing features')
    with h5py.File(args['outpath'], 'r') as f:
        allhashes = f['hashes'].value.tolist()
else:
    allhashes = []
nDone = len(allhashes)
for i in range(nDone, len(imgslist)):
    impath = imgslist[i]
    tic_toc_print('Done %d / %d features' % (i, len(imgslist)))
    try:
        featpath = os.path.join(args['dir'], impath + '.h5')
        feat = load_feat(featpath, args['fracfeat']).transpose()
        feat = feat / np.linalg.norm(feat)
        hash = ITQ.hash(feat, pc, mean, R)
        allhashes.append(hash)
    except:
        continue
allhashes = np.squeeze(np.array(allhashes)).astype('bool')
with h5py.File(args['outpath'], 'w') as f:
    f.create_dataset('hashes', data=allhashes, compression='gzip',
        compression_opts=9)

import numpy as np
import os
import sys
import argparse
import numpy as np
import h5py
sys.path.append(os.path.join(os.path.dirname(os.path.realpath(__file__)),
    '../../../utils/python/'))
import locker
sys.path.append(os.path.join(os.path.dirname(os.path.realpath(__file__)),
    '../Utils/'))
from FeatStor import load_feat
from PrintUtil import TicTocPrint
sys.path.append(os.path.dirname(os.path.realpath(__file__)))
import ITQ
tic_toc_print = TicTocPrint()
parser = argparse.ArgumentParser(description='Extract Features')
parser.add_argument('-i', '--list', type=str, required=True, help=
    'File with list of images')
parser.add_argument('-d', '--dir', type=str, default='', help=
    'Features directory')
parser.add_argument('-p', '--paramfile', type=str, required=True, help=
    'Hdf5 file path to store the learnt parameters')
parser.add_argument('-o', '--outpath', type=str, required=True, help=
    'Hdf5 file to store output hashes')
parser.add_argument('-f', '--fracfeat', type=float, default=1, help=
    'Fraction of the feat to resize to')
args = vars(parser.parse_args())
outfpath = args['outpath']
if not outfpath.endswith('.h5'):
    outfpath += '.h5'
with open(args['list']) as f:
    imgslist = f.read().splitlines()
with h5py.File(args['paramfile'], 'r') as f:
    R = f['R'].value
    pc = f['pc'].value
    mean = f['mean'].value
if os.path.exists(args['outpath']):
    print('Reading the existing features')
    with h5py.File(args['outpath'], 'r') as f:
        allhashes = f['hashes'].value.tolist()
else:
    allhashes = []
nDone = len(allhashes)
for i in range(nDone, len(imgslist)):
    impath = imgslist[i]
    tic_toc_print('Done %d / %d features' % (i, len(imgslist)))
    try:
        featpath = os.path.join(args['dir'], impath + '.h5')
        feat = load_feat(featpath, args['fracfeat']).transpose()
        feat = feat / np.linalg.norm(feat)
        hash = ITQ.hash(feat, pc, mean, R)
        allhashes.append(hash)
    except:
        continue
allhashes = np.squeeze(np.array(allhashes)).astype('bool')
with h5py.File(args['outpath'], 'w') as f:
    f.create_dataset('hashes', data=allhashes, compression='gzip',
        compression_opts=9)

import numpy as np
import os
import sys
import argparse
import numpy as np
import h5py
sys.path.append(os.path.join(os.path.dirname(os.path.realpath(__file__)), '../../../utils/python/'))
import locker
sys.path.append(os.path.join(os.path.dirname(os.path.realpath(__file__)), '../Utils/'))
from FeatStor import load_feat
from PrintUtil import TicTocPrint
sys.path.append(os.path.dirname(os.path.realpath(__file__)))
import ITQ

tic_toc_print = TicTocPrint()

parser = argparse.ArgumentParser(description='Extract Features')
parser.add_argument('-i', '--list', type=str, required=True,
    help='File with list of images')
parser.add_argument('-d', '--dir', type=str, default='',
    help='Features directory')
parser.add_argument('-p', '--paramfile', type=str, required=True,
    help='Hdf5 file path to store the learnt parameters')
parser.add_argument('-o', '--outpath', type=str, required=True,
    help='Hdf5 file to store output hashes')
parser.add_argument('-f', '--fracfeat', type=float, default=1,
    help='Fraction of the feat to resize to')

args = vars(parser.parse_args())

outfpath = args['outpath']
if not outfpath.endswith('.h5'):
  outfpath += '.h5'

with open(args['list']) as f:
  imgslist = f.read().splitlines()

with h5py.File(args['paramfile'], 'r') as f:
  R = f['R'].value
  pc = f['pc'].value
  mean = f['mean'].value

if os.path.exists(args['outpath']):
  print('Reading the existing features')
  with h5py.File(args['outpath'], 'r') as f:
    allhashes = f['hashes'].value.tolist()
else:
  allhashes = []

nDone = len(allhashes)
for i in range(nDone, len(imgslist)):
  impath = imgslist[i]
  tic_toc_print('Done %d / %d features' % (i, len(imgslist)))
  try:
    featpath = os.path.join(args['dir'], impath + '.h5')
    feat = load_feat(featpath, args['fracfeat']).transpose()
    # Normalize this feature (that's how its used in training)
    feat = feat / np.linalg.norm(feat)
    hash = ITQ.hash(feat, pc, mean, R)
    allhashes.append(hash)
  except:
    continue

allhashes = np.squeeze(np.array(allhashes)).astype('bool')
with h5py.File(args['outpath'], 'w') as f:
  f.create_dataset('hashes', data=allhashes, compression="gzip", compression_opts=9)

