/
data.py
136 lines (117 loc) · 4.46 KB
/
data.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
from ...utils.pascal_voc_clean_xml import pascal_voc_clean_xml
from numpy.random import permutation as perm
from .predict import preprocess
# from .misc import show
from copy import deepcopy
import pickle
import numpy as np
import os
def parse(self, exclusive = False):
meta = self.meta
ext = '.parsed'
ann = self.FLAGS.annotation
if not os.path.isdir(ann):
msg = 'Annotation directory not found {} .'
exit('Error: {}'.format(msg.format(ann)))
print('\n{} parsing {}'.format(meta['model'], ann))
dumps = pascal_voc_clean_xml(ann, meta['labels'], exclusive)
return dumps
def _batch(self, chunk):
"""
Takes a chunk of parsed annotations
returns value for placeholders of net's
input & loss layer correspond to this chunk
"""
meta = self.meta
S, B = meta['side'], meta['num']
C, labels = meta['classes'], meta['labels']
# preprocess
jpg = chunk[0]; w, h, allobj_ = chunk[1]
allobj = deepcopy(allobj_)
path = os.path.join(self.FLAGS.dataset, jpg)
img = self.preprocess(path, allobj)
# Calculate regression target
cellx = 1. * w / S
celly = 1. * h / S
for obj in allobj:
centerx = .5*(obj[1]+obj[3]) #xmin, xmax
centery = .5*(obj[2]+obj[4]) #ymin, ymax
cx = centerx / cellx
cy = centery / celly
if cx >= S or cy >= S: return None, None
obj[3] = float(obj[3]-obj[1]) / w
obj[4] = float(obj[4]-obj[2]) / h
obj[3] = np.sqrt(obj[3])
obj[4] = np.sqrt(obj[4])
obj[1] = cx - np.floor(cx) # centerx
obj[2] = cy - np.floor(cy) # centery
obj += [int(np.floor(cy) * S + np.floor(cx))]
# show(im, allobj, S, w, h, cellx, celly) # unit test
# Calculate placeholders' values
probs = np.zeros([S*S,C])
confs = np.zeros([S*S,B])
coord = np.zeros([S*S,B,4])
proid = np.zeros([S*S,C])
prear = np.zeros([S*S,4])
for obj in allobj:
probs[obj[5], :] = [0.] * C
probs[obj[5], labels.index(obj[0])] = 1.
proid[obj[5], :] = [1] * C
coord[obj[5], :, :] = [obj[1:5]] * B
prear[obj[5],0] = obj[1] - obj[3]**2 * .5 * S # xleft
prear[obj[5],1] = obj[2] - obj[4]**2 * .5 * S # yup
prear[obj[5],2] = obj[1] + obj[3]**2 * .5 * S # xright
prear[obj[5],3] = obj[2] + obj[4]**2 * .5 * S # ybot
confs[obj[5], :] = [1.] * B
# Finalise the placeholders' values
upleft = np.expand_dims(prear[:,0:2], 1)
botright = np.expand_dims(prear[:,2:4], 1)
wh = botright - upleft;
area = wh[:,:,0] * wh[:,:,1]
upleft = np.concatenate([upleft] * B, 1)
botright = np.concatenate([botright] * B, 1)
areas = np.concatenate([area] * B, 1)
# value for placeholder at input layer
inp_feed_val = img
# value for placeholder at loss layer
loss_feed_val = {
'probs': probs, 'confs': confs,
'coord': coord, 'proid': proid,
'areas': areas, 'upleft': upleft,
'botright': botright
}
return inp_feed_val, loss_feed_val
def shuffle(self):
batch = self.FLAGS.batch
data = self.parse()
size = len(data)
print('Dataset of {} instance(s)'.format(size))
if batch > size: self.FLAGS.batch = batch = size
batch_per_epoch = int(size / batch)
for i in range(self.FLAGS.epoch):
shuffle_idx = perm(np.arange(size))
for b in range(batch_per_epoch):
# yield these
x_batch = list()
feed_batch = dict()
for j in range(b*batch, b*batch+batch):
train_instance = data[shuffle_idx[j]]
try:
inp, new_feed = self._batch(train_instance)
except ZeroDivisionError:
print("This image's width or height are zeros: ", train_instance[0])
print('train_instance:', train_instance)
print('Please remove or fix it then try again.')
raise
if inp is None: continue
x_batch += [np.expand_dims(inp, 0)]
for key in new_feed:
new = new_feed[key]
old_feed = feed_batch.get(key,
np.zeros((0,) + new.shape))
feed_batch[key] = np.concatenate([
old_feed, [new]
])
x_batch = np.concatenate(x_batch, 0)
yield x_batch, feed_batch
print('Finish {} epoch(es)'.format(i + 1))