-
Notifications
You must be signed in to change notification settings - Fork 255
/
test_pixel_link.py
176 lines (139 loc) · 6.98 KB
/
test_pixel_link.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
#encoding = utf-8
import numpy as np
import math
import tensorflow as tf
from tensorflow.python.ops import control_flow_ops
from tensorflow.contrib.training.python.training import evaluation
from datasets import dataset_factory
from preprocessing import ssd_vgg_preprocessing
from tf_extended import metrics as tfe_metrics
import util
import cv2
import pixel_link
from nets import pixel_link_symbol
slim = tf.contrib.slim
import config
# =========================================================================== #
# Checkpoint and running Flags
# =========================================================================== #
tf.app.flags.DEFINE_string('checkpoint_path', None,
'the path of pretrained model to be used. If there are checkpoints\
in train_dir, this config will be ignored.')
tf.app.flags.DEFINE_float('gpu_memory_fraction', -1,
'the gpu memory fraction to be used. If less than 0, allow_growth = True is used.')
# =========================================================================== #
# I/O and preprocessing Flags.
# =========================================================================== #
tf.app.flags.DEFINE_integer(
'num_readers', 1,
'The number of parallel readers that read data from the dataset.')
tf.app.flags.DEFINE_integer(
'num_preprocessing_threads', 4,
'The number of threads used to create the batches.')
tf.app.flags.DEFINE_bool('preprocessing_use_rotation', False,
'Whether to use rotation for data augmentation')
# =========================================================================== #
# Dataset Flags.
# =========================================================================== #
tf.app.flags.DEFINE_string(
'dataset_name', 'icdar2015', 'The name of the dataset to load.')
tf.app.flags.DEFINE_string(
'dataset_split_name', 'test', 'The name of the train/test split.')
tf.app.flags.DEFINE_string('dataset_dir',
util.io.get_absolute_path('~/dataset/ICDAR2015/Challenge4/ch4_test_images'),
'The directory where the dataset files are stored.')
tf.app.flags.DEFINE_integer('eval_image_width', 1280, 'Train image size')
tf.app.flags.DEFINE_integer('eval_image_height', 768, 'Train image size')
tf.app.flags.DEFINE_bool('using_moving_average', True,
'Whether to use ExponentionalMovingAverage')
tf.app.flags.DEFINE_float('moving_average_decay', 0.9999,
'The decay rate of ExponentionalMovingAverage')
FLAGS = tf.app.flags.FLAGS
def config_initialization():
# image shape and feature layers shape inference
image_shape = (FLAGS.eval_image_height, FLAGS.eval_image_width)
if not FLAGS.dataset_dir:
raise ValueError('You must supply the dataset directory with --dataset_dir')
tf.logging.set_verbosity(tf.logging.DEBUG)
config.load_config(FLAGS.checkpoint_path)
config.init_config(image_shape,
batch_size = 1,
pixel_conf_threshold = 0.8,
link_conf_threshold = 0.8,
num_gpus = 1,
)
util.proc.set_proc_name('test_pixel_link_on'+ '_' + FLAGS.dataset_name)
def to_txt(txt_path, image_name,
image_data, pixel_pos_scores, link_pos_scores):
# write detection result as txt files
def write_result_as_txt(image_name, bboxes, path):
filename = util.io.join_path(path, 'res_%s.txt'%(image_name))
lines = []
for b_idx, bbox in enumerate(bboxes):
values = [int(v) for v in bbox]
line = "%d, %d, %d, %d, %d, %d, %d, %d\n"%tuple(values)
lines.append(line)
util.io.write_lines(filename, lines)
print 'result has been written to:', filename
mask = pixel_link.decode_batch(pixel_pos_scores, link_pos_scores)[0, ...]
bboxes = pixel_link.mask_to_bboxes(mask, image_data.shape)
write_result_as_txt(image_name, bboxes, txt_path)
def test():
with tf.name_scope('test'):
image = tf.placeholder(dtype=tf.int32, shape = [None, None, 3])
image_shape = tf.placeholder(dtype = tf.int32, shape = [3, ])
processed_image, _, _, _, _ = ssd_vgg_preprocessing.preprocess_image(image, None, None, None, None,
out_shape = config.image_shape,
data_format = config.data_format,
is_training = False)
b_image = tf.expand_dims(processed_image, axis = 0)
net = pixel_link_symbol.PixelLinkNet(b_image, is_training = True)
global_step = slim.get_or_create_global_step()
sess_config = tf.ConfigProto(log_device_placement = False, allow_soft_placement = True)
if FLAGS.gpu_memory_fraction < 0:
sess_config.gpu_options.allow_growth = True
elif FLAGS.gpu_memory_fraction > 0:
sess_config.gpu_options.per_process_gpu_memory_fraction = FLAGS.gpu_memory_fraction;
checkpoint_dir = util.io.get_dir(FLAGS.checkpoint_path)
logdir = util.io.join_path(checkpoint_dir, 'test', FLAGS.dataset_name + '_' +FLAGS.dataset_split_name)
# Variables to restore: moving avg. or normal weights.
if FLAGS.using_moving_average:
variable_averages = tf.train.ExponentialMovingAverage(
FLAGS.moving_average_decay)
variables_to_restore = variable_averages.variables_to_restore()
variables_to_restore[global_step.op.name] = global_step
else:
variables_to_restore = slim.get_variables_to_restore()
saver = tf.train.Saver(var_list = variables_to_restore)
image_names = util.io.ls(FLAGS.dataset_dir)
image_names.sort()
checkpoint = FLAGS.checkpoint_path
checkpoint_name = util.io.get_filename(str(checkpoint));
dump_path = util.io.join_path(logdir, checkpoint_name)
txt_path = util.io.join_path(dump_path,'txt')
zip_path = util.io.join_path(dump_path, checkpoint_name + '_det.zip')
with tf.Session(config = sess_config) as sess:
saver.restore(sess, checkpoint)
for iter, image_name in enumerate(image_names):
image_data = util.img.imread(
util.io.join_path(FLAGS.dataset_dir, image_name), rgb = True)
image_name = image_name.split('.')[0]
pixel_pos_scores, link_pos_scores = sess.run(
[net.pixel_pos_scores, net.link_pos_scores],
feed_dict = {
image:image_data
})
print '%d/%d: %s'%(iter + 1, len(image_names), image_name)
to_txt(txt_path,
image_name, image_data,
pixel_pos_scores, link_pos_scores)
# create zip file for icdar2015
cmd = 'cd %s;zip -j %s %s/*'%(dump_path, zip_path, txt_path);
print cmd
util.cmd.cmd(cmd);
print "zip file created: ", util.io.join_path(dump_path, zip_path)
def main(_):
config_initialization()
test()
if __name__ == '__main__':
tf.app.run()