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ae4627d | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 | #!/usr/bin/python
# Copyright (c) 2015, Yaroslav Ganin (yaroslav.ganin@gmail.com)
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without modification,
# are permitted provided that the following conditions are met:
#
# - Redistributions of source code must retain the above copyright notice,
# this list of conditions and the following disclaimer.
#
# - Redistributions in binary form must reproduce the above copyright notice,
# this list of conditions and the following disclaimer in the documentation
# and/or other materials provided with the distribution.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
# ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE
# LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
# LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
# ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING
# NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE,
# EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
from __future__ import print_function
import os
import glob
import argparse
import shutil
import lmdb
import numpy as np
import numpy.random as nr
import scipy.misc
import cv2
print_spaces = 100
# Make sure that caffe is on the python path:
caffe_root = '/home/yganin/Arbeit/Projects/NN/skaffe' # this file is expected to be in {caffe_root}/examples
import sys
sys.path.insert(0, os.path.join(caffe_root, 'python'))
from caffe.proto.caffe_pb2 import Datum
def read_data(source_path):
images = [] # images
labels = [] # corresponding labels
r = [x for x in os.listdir(source_path)
if os.path.isdir(os.path.join(source_path, x))]
r.sort()
for c, subdir in enumerate(r):
class_root = os.path.join(source_path, subdir)
files = glob.iglob(os.path.join(class_root, '*.jpg'))
for f in files:
# CAUTION: The following call reads an image in BGR order.
img = cv2.imread(f)
img = cv2.resize(img, (256, 256))
img = img.transpose((2, 0, 1))
images.append(img)
labels.append(c)
# Print status.
sys.stdout.write("\r%s\r Processed %d of %d classes" %
(print_spaces * ' ', c + 1, len(r)))
sys.stdout.flush()
print()
labels = np.array(labels, dtype=np.int32)
return images, labels
def convert_dataset(source_path, target_path, domain, examples, iters):
print('[*] Reading Office (%s) dataset...' % domain)
images, labels = read_data(os.path.join(source_path, domain, 'images'))
print(' Total samples: %d' % labels.size)
suffixes = ['train', 'test']
num_classes = labels.max() + 1
rnd = nr.RandomState(1349)
for t in xrange(iters):
if examples > 0:
all_train_indices = []
all_test_indices = []
for c in xrange(num_classes):
indices = np.where(labels == c)[0]
to_pick = min(examples, indices.size)
train_indices = rnd.choice(indices, size=to_pick, replace=False)
test_indices = np.setdiff1d(indices, train_indices)
all_train_indices.append(train_indices)
all_test_indices.append(test_indices)
all_train_indices = np.concatenate(all_train_indices, axis=0)
all_test_indices = np.concatenate(all_test_indices, axis=0)
splits = [
all_train_indices,
all_test_indices
]
else:
splits = [np.arange(labels.size)]
print('[*] Writing splits (iteration %d)...' % (t + 1))
for i, indices in enumerate(splits):
print(' Split %s: %d samples' % (suffixes[i], indices.size))
if examples > 0:
name = domain + '_' + suffixes[i] + '_' + str(examples) + '_' + str(t)
else:
name = domain + '_' + suffixes[i] + '_' + str(t)
write_dataset(images, labels, indices,
name,
target_path)
def write_dataset(images, labels, indices, suffix, target_path):
db_path = os.path.join(target_path, '{0}_lmdb'.format(suffix))
try:
shutil.rmtree(db_path)
except:
pass
os.makedirs(db_path, mode=0744)
num_images = indices.size
datum = Datum();
datum.channels = 3
datum.height = images[0].shape[1]
datum.width = images[0].shape[2]
mdb_env = lmdb.Environment(db_path, map_size=1099511627776, mode=0664)
mdb_txn = mdb_env.begin(write=True)
mdb_dbi = mdb_env.open_db(txn=mdb_txn)
for i, img_idx in enumerate(indices):
img = images[img_idx]
datum.data = img.tostring()
datum.label = np.int(labels.ravel()[img_idx])
value = datum.SerializeToString()
key = '{:08d}'.format(i)
mdb_txn.put(key, value, db=mdb_dbi)
if i % 1000 == 0:
mdb_txn.commit()
mdb_txn = mdb_env.begin(write=True)
if num_images % 1000 != 0:
mdb_txn.commit()
mdb_env.close()
if __name__ == '__main__':
parser = argparse.ArgumentParser(description='Converts Office data into lmdb/caffe format')
parser.add_argument('-s', '--source-path', dest='source_path', required=True)
parser.add_argument('-t', '--target-path', dest='target_path', required=True)
parser.add_argument('-d', '--domain', choices=['amazon', 'webcam', 'dslr'])
parser.add_argument('-x', '--training-examples', dest='examples', type=int, default=-1)
parser.add_argument('-i', '--iterations', type=int, default=5)
args = parser.parse_args()
convert_dataset(args.source_path, args.target_path, args.domain, args.examples, args.iterations)
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