-
Notifications
You must be signed in to change notification settings - Fork 9
/
Copy pathsvhn.py
187 lines (153 loc) · 6.84 KB
/
svhn.py
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
184
185
186
187
import os
import sys
import numpy
import scipy.io
import gzip
import tarfile
import h5py
from PIL import Image
import six.moves.cPickle as pickle
from six.moves import urllib
import shutil
def load_data(data_dir):
def check_dataset(dataset):
data_path = os.path.join(data_dir, dataset)
if (not os.path.isfile(data_path)):
origin = (
'http://ufldl.stanford.edu/housenumbers/' + dataset
)
print('Downloading data from %s' % origin)
urllib.request.urlretrieve(origin, data_path)
return data_path
train_dataset = check_dataset('train.tar.gz')
test_dataset = check_dataset('test.tar.gz')
def format_data(dataset):
data_path = os.path.join(data_dir, dataset)
tar = tarfile.open(data_path, 'r:gz')
data_file_split = os.path.splitext(dataset)[0]
data_type = os.path.splitext(data_file_split)[0]
def check_file(folder_name):
new_path = os.path.join(data_dir, folder_name)
if (not os.path.exists(new_path)):
tar.extractall(data_dir)
process_data()
def process_data():
print '... processing data (should only occur when downloading for the first time)'
# Access label information in digitStruct.mat
new_path = os.path.join(data_dir, data_type, 'digitStruct.mat')
f = h5py.File(new_path, 'r')
digitStructName = f['digitStruct']['name']
digitStructBbox = f['digitStruct']['bbox']
def getName(n):
return ''.join([chr(c[0]) for c in f[digitStructName[n][0]].value])
def bboxHelper(attr):
if (len(attr) > 1):
attr = [f[attr.value[j].item()].value[0][0] for j in range(len(attr))]
else:
attr = [attr.value[0][0]]
return attr
def getBbox(n):
bbox = {}
bb = digitStructBbox[n].item()
# bbox = bboxHelper(f[bb]["label"])
bbox['height'] = bboxHelper(f[bb]["height"])
bbox['label'] = bboxHelper(f[bb]["label"])
bbox['left'] = bboxHelper(f[bb]["left"])
bbox['top'] = bboxHelper(f[bb]["top"])
bbox['width'] = bboxHelper(f[bb]["width"])
return bbox
def getDigitStructure(n):
s = getBbox(n)
s['name'] = getName(n)
return s
# Process labels
print '... creating image box bound dict for %s data' % data_type
image_dict = {}
for i in range(len(digitStructName)):
image_dict[getName(i)] = getBbox(i)
if (i%1000 == 0):
print ' image dict processing: %i/%i complete' %(i,len(digitStructName))
print '... dict processing complete'
# Process the data
print('... processing image data and labels')
names = []
for item in os.listdir(os.path.join(data_dir, data_type)):
if item.endswith('.png'):
names.append(item)
y = []
x = []
for i in range(len(names)):
path = os.path.join(data_dir, data_type)
y.append(image_dict[names[i]]['label'])
image = Image.open(path + '/' + names[i]).convert('L')
left = int(min(image_dict[names[i]]['left']))
upper = int(min(image_dict[names[i]]['top']))
right = int(max(image_dict[names[i]]['left'])) + int(max(image_dict[names[i]]['width']))
lower = int(max(image_dict[names[i]]['top'])) + int(max(image_dict[names[i]]['height']))
image = image.crop(box = (left, upper, right, lower))
image = image.resize([32,32])
image_array = numpy.array(image)
x.append(image_array)
if (i%1000 == 0):
print ' image processing: %i/%i complete' %(i,len(names))
print '... image processing complete'
# Save data
print '... pickling data'
out = {}
out['names'] = names
out['labels'] = y
out['images'] = x
output_file = data_type + 'pkl.gz'
out_path = os.path.join(data_dir, output_file)
p = gzip.open(out_path, 'wb')
pickle.dump(out, p)
p.close()
tar.close()
# clean up (delete test/train folders that were used to create the pickled data)
shutil.rmtree(os.path.join(data_dir, data_type))
check_file(data_type)
# This check will run everytime load_data() is called
if (not os.path.isfile(os.path.join(data_dir, 'trainpkl.gz'))):
format_data('train.tar.gz')
f_train = gzip.open(os.path.join(data_dir, 'trainpkl.gz'), 'rb')
train_set = pickle.load(f_train)
f_train.close()
if (not os.path.isfile(os.path.join(data_dir, 'testpkl.gz'))):
format_data('test.tar.gz')
f_test = gzip.open(os.path.join(data_dir, 'testpkl.gz'), 'rb')
test_set = pickle.load(f_test)
f_test.close()
# Convert data format
def convert_data_format(data):
data['X'] = data.pop('images')
data['X'] = numpy.array(data['X'])
data['X'] = numpy.rollaxis(data['X'],0, data['X'].ndim)
data['y'] = data.pop('labels')
X = numpy.reshape(data['X'],
(numpy.prod(data['X'].shape[:-1]), data['X'].shape[-1]),
order='C').T / 255.
def process_sequence(labels):
for i in range(len(labels)):
l = len(labels[i])-1
labels[i].insert(0,l)
zeros = numpy.zeros(6-l-1).tolist()
labels[i].extend(zeros)
return numpy.array(labels)
y = process_sequence(data['y'])
return (X,y)
train_set = convert_data_format(train_set)
test_set = convert_data_format(test_set)
train_set_len = len(train_set[1])
# Extract validation dataset from train dataset (10% of the train_set)
valid_set = [x[-(train_set_len//10):] for x in train_set]
train_set = [x[:-(train_set_len//10)] for x in train_set]
# train_set, valid_set, test_set each contain a list [flattened image, sequence].
# The 'flattened image' part of the list is a 2D numpy array where each row
# corresponds to a 32x32x3 image. The sequence is a 2D numpy array of the
# number represented in the image. The first element in the sequence is the
# length of the number (where 0 = a 1 digit number), the second element in
# the sequence is the first digit of the number (where 0 means no digit
# present and 10 = 0), and so on.
return [train_set, valid_set, test_set]
if __name__ == '__main__':
load_data(sys.argv[1])