def main():
size = 12
net = 'native_'+str(size)
with open('%s/pos_%s.txt' % (net, size), 'r') as f:
pos = f.readlines()
with open('%s/neg_%s.txt' % (net, size), 'r') as f:
neg = f.readlines()
with open('%s/part_%s.txt' % (net, size), 'r') as f:
part = f.readlines()
print('\n'+'pos')
filename_cls = 'pnet_data_for_cls.tfrecords'
print('Writing')
examples = []
writer = tf.python_io.TFRecordWriter(filename_cls)
cur_ = 0
sum_ = len(pos)
for line in pos:
view_bar(cur_, sum_)
cur_ += 1
words = line.split()
image_file_name = words[0]+'.jpg'
im = cv2.imread(image_file_name)
h, w, ch = im.shape
if h != 12 or w != 12:
im = cv2.resize(im, (12, 12))
im = im.astype('uint8')
label = np.array([0, 1], dtype='float32')
label_raw = label.tostring()
image_raw = im.tostring()
example = tf.train.Example(features=tf.train.Features(feature={
'label_raw': bytes_feature(label_raw),
'image_raw': bytes_feature(image_raw)}))
examples.append(example)
print('\n'+'neg')
cur_ = 0
neg_keep = npr.choice(len(neg), size=1000000, replace=False)
sum_ = len(neg_keep)
for i in neg_keep:
line = neg[i]
view_bar(cur_, sum_)
cur_ += 1
words = line.split()
image_file_name = words[0]+'.jpg'
im = cv2.imread(image_file_name)
h, w, ch = im.shape
if h != 12 or w != 12:
im = cv2.resize(im, (12, 12))
im = im.astype('uint8')
label = np.array([1, 0], dtype='float32')
label_raw = label.tostring()
image_raw = im.tostring()
example = tf.train.Example(features=tf.train.Features(feature={
'label_raw': bytes_feature(label_raw),
'image_raw': bytes_feature(image_raw)}))
examples.append(example)
print(len(examples))
random.shuffle(examples)
for example in examples:
writer.write(example.SerializeToString())
writer.close()
print('\n'+'pos')
cur_ = 0
filename_roi = 'pnet_data_for_bbx.tfrecords'
print('Writing')
sum_ = len(pos)
examples = []
writer = tf.python_io.TFRecordWriter(filename_roi)
for line in pos:
view_bar(cur_, sum_)
cur_ += 1
words = line.split()
image_file_name = words[0]+'.jpg'
im = cv2.imread(image_file_name)
h, w, ch = im.shape
if h != 12 or w != 12:
im = cv2.resize(im, (12, 12))
im = im.astype('uint8')
label = np.array([float(words[2]), float(words[3]),
float(words[4]), float(words[5])],
dtype='float32')
label_raw = label.tostring()
image_raw = im.tostring()
example = tf.train.Example(features=tf.train.Features(feature={
'label_raw': bytes_feature(label_raw),
'image_raw': bytes_feature(image_raw)}))
examples.append(example)
print('\n'+'part')
cur_ = 0
part_keep = npr.choice(len(part), size=300000, replace=False)
sum_ = len(part_keep)
for i in part_keep:
view_bar(cur_, sum_)
line = part[i]
cur_ += 1
words = line.split()
image_file_name = words[0]+'.jpg'
im = cv2.imread(image_file_name)
h, w, ch = im.shape
if h != 12 or w != 12:
im = cv2.resize(im, (12, 12))
im = im.astype('uint8')
label = np.array([float(words[2]), float(words[3]),
float(words[4]), float(words[5])],
dtype='float32')
label_raw = label.tostring()
image_raw = im.tostring()
example = tf.train.Example(features=tf.train.Features(feature={
'label_raw': bytes_feature(label_raw),
'image_raw': bytes_feature(image_raw)}))
examples.append(example)
print(len(examples))
random.shuffle(examples)
for example in examples:
writer.write(example.SerializeToString())
writer.close()
def main():
size = 12
net = 'native_' + str(size)
with open('%s/pos_%s.txt' % (net, size), 'r') as f:
pos = f.readlines()
with open('%s/neg_%s.txt' % (net, size), 'r') as f:
neg = f.readlines()
with open('%s/part_%s.txt' % (net, size), 'r') as f:
part = f.readlines()
print('\n' + 'pos')
filename_cls = 'pnet_data_for_cls.tfrecords'
print('Writing')
examples = []
writer = tf.python_io.TFRecordWriter(filename_cls)
cur_ = 0
sum_ = len(pos)
for line in pos:
view_bar(cur_, sum_)
cur_ += 1
words = line.split()
image_file_name = words[0] + '.jpg'
im = cv2.imread(image_file_name)
h, w, ch = im.shape
if h != 12 or w != 12:
im = cv2.resize(im, (12, 12))
im = im.astype('uint8')
label = np.array([0, 1], dtype='float32')
label_raw = label.tostring()
image_raw = im.tostring()
example = tf.train.Example(features=tf.train.Features(
feature={
'label_raw': bytes_feature(label_raw),
'image_raw': bytes_feature(image_raw)
}))
examples.append(example)
print('\n' + 'neg')
cur_ = 0
neg_keep = npr.choice(len(neg), size=10000, replace=False)
sum_ = len(neg_keep)
for i in neg_keep:
line = neg[i]
view_bar(cur_, sum_)
cur_ += 1
words = line.split()
image_file_name = words[0] + '.jpg'
im = cv2.imread(image_file_name)
h, w, ch = im.shape
if h != 12 or w != 12:
im = cv2.resize(im, (12, 12))
im = im.astype('uint8')
label = np.array([1, 0], dtype='float32')
label_raw = label.tostring()
image_raw = im.tostring()
example = tf.train.Example(features=tf.train.Features(
feature={
'label_raw': bytes_feature(label_raw),
'image_raw': bytes_feature(image_raw)
}))
examples.append(example)
print(len(examples))
random.shuffle(examples)
for example in examples:
writer.write(example.SerializeToString())
writer.close()
print('\n' + 'pos')
cur_ = 0
filename_roi = 'pnet_data_for_bbx.tfrecords'
print('Writing')
sum_ = len(pos)
examples = []
writer = tf.python_io.TFRecordWriter(filename_roi)
for line in pos:
view_bar(cur_, sum_)
cur_ += 1
words = line.split()
image_file_name = words[0] + '.jpg'
im = cv2.imread(image_file_name)
h, w, ch = im.shape
if h != 12 or w != 12:
im = cv2.resize(im, (12, 12))
im = im.astype('uint8')
label = np.array([
float(words[2]),
float(words[3]),
float(words[4]),
float(words[5])
],
dtype='float32')
label_raw = label.tostring()
image_raw = im.tostring()
example = tf.train.Example(features=tf.train.Features(
feature={
'label_raw': bytes_feature(label_raw),
'image_raw': bytes_feature(image_raw)
}))
examples.append(example)
print('\n' + 'part')
cur_ = 0
part_keep = npr.choice(len(part), size=300000, replace=False)
sum_ = len(part_keep)
for i in part_keep:
view_bar(cur_, sum_)
line = part[i]
cur_ += 1
words = line.split()
image_file_name = words[0] + '.jpg'
im = cv2.imread(image_file_name)
h, w, ch = im.shape
if h != 12 or w != 12:
im = cv2.resize(im, (12, 12))
im = im.astype('uint8')
label = np.array([
float(words[2]),
float(words[3]),
float(words[4]),
float(words[5])
],
dtype='float32')
label_raw = label.tostring()
image_raw = im.tostring()
example = tf.train.Example(features=tf.train.Features(
feature={
'label_raw': bytes_feature(label_raw),
'image_raw': bytes_feature(image_raw)
}))
examples.append(example)
print(len(examples))
random.shuffle(examples)
for example in examples:
writer.write(example.SerializeToString())
writer.close()
def main(input_size, classifier_tfrecord_fp, localizer_tfrecord_fp,
root_data_dir):
net = os.path.join(root_data_dir, str(input_size))
with open('%s/pos_%s.txt' % (net, input_size), 'r') as f:
pos_hard = f.readlines()
with open('%s/neg_%s.txt' % (net, input_size), 'r') as f:
neg = f.readlines()
with open('%s/part_%s.txt' % (net, input_size), 'r') as f:
part_hard = f.readlines()
with open(
os.path.join(
root_data_dir,
'native_' + '%s/pos_%s.txt' % (input_size, input_size)),
'r') as f:
pos = f.readlines()
with open(
os.path.join(
root_data_dir,
'native_' + '%s/part_%s.txt' % (input_size, input_size)),
'r') as f:
part = f.readlines()
print('\n' + 'positive hard')
cur_ = 0
sum_ = len(pos_hard)
print('Writing')
examples = []
writer = tf.python_io.TFRecordWriter(classifier_tfrecord_fp)
for line in pos_hard:
view_bar(cur_, sum_)
cur_ += 1
words = line.split()
image_file_name = words[0]
im = cv2.imread(image_file_name)
h, w, ch = im.shape
if h != 24 or w != 24:
im = cv2.resize(im, (24, 24))
im = im.astype('uint8')
label = np.array([0, 1], dtype='float32')
label_raw = label.tostring()
image_raw = im.tostring()
example = tf.train.Example(features=tf.train.Features(
feature={
'label_raw': bytes_feature(label_raw),
'image_raw': bytes_feature(image_raw)
}))
examples.append(example)
print(len(examples))
print('\n' + 'positive random cropped')
cur_ = 0
pos_keep = npr.choice(len(pos), size=min(len(pos), 100000), replace=False)
sum_ = len(pos_keep)
print('Writing')
for i in pos_keep:
view_bar(cur_, sum_)
cur_ += 1
line = pos[i]
words = line.split()
image_file_name = words[0]
im = cv2.imread(image_file_name)
h, w, ch = im.shape
if h != 24 or w != 24:
im = cv2.resize(im, (24, 24))
im = im.astype('uint8')
label = np.array([0, 1], dtype='float32')
label_raw = label.tostring()
image_raw = im.tostring()
example = tf.train.Example(features=tf.train.Features(
feature={
'label_raw': bytes_feature(label_raw),
'image_raw': bytes_feature(image_raw)
}))
examples.append(example)
print(len(examples))
print('\n' + 'negative random cropped')
cur_ = 0
neg_keep = npr.choice(len(neg), size=min(len(neg), 1300000), replace=False)
sum_ = len(neg_keep)
for i in neg_keep:
line = neg[i]
view_bar(cur_, sum_)
cur_ += 1
words = line.split()
image_file_name = words[0]
im = cv2.imread(image_file_name)
h, w, ch = im.shape
if h != 24 or w != 24:
im = cv2.resize(im, (24, 24))
im = im.astype('uint8')
label = np.array([1, 0], dtype='float32')
label_raw = label.tostring()
image_raw = im.tostring()
example = tf.train.Example(features=tf.train.Features(
feature={
'label_raw': bytes_feature(label_raw),
'image_raw': bytes_feature(image_raw)
}))
examples.append(example)
print(len(examples))
random.shuffle(examples)
for example in examples:
writer.write(example.SerializeToString())
writer.close()
examples = []
print('\n' + 'positive random cropped')
cur_ = 0
print('Writing')
writer = tf.python_io.TFRecordWriter(localizer_tfrecord_fp)
pos_keep = npr.choice(len(pos), size=min(len(pos), 100000), replace=False)
sum_ = len(pos_keep)
print('Writing')
for i in pos_keep:
view_bar(cur_, sum_)
cur_ += 1
line = pos[i]
words = line.split()
image_file_name = words[0]
im = cv2.imread(image_file_name)
h, w, ch = im.shape
if h != 24 or w != 24:
im = cv2.resize(im, (24, 24))
im = im.astype('uint8')
label = np.array([
float(words[2]),
float(words[3]),
float(words[4]),
float(words[5])
],
dtype='float32')
label_raw = label.tostring()
image_raw = im.tostring()
example = tf.train.Example(features=tf.train.Features(
feature={
'label_raw': bytes_feature(label_raw),
'image_raw': bytes_feature(image_raw)
}))
examples.append(example)
print(len(examples))
print('\n' + 'positive hard')
cur_ = 0
print('Writing')
sum_ = len(pos_hard)
for line in pos_hard:
view_bar(cur_, sum_)
cur_ += 1
words = line.split()
image_file_name = words[0]
im = cv2.imread(image_file_name)
h, w, ch = im.shape
if h != 24 or w != 24:
im = cv2.resize(im, (24, 24))
im = im.astype('uint8')
label = np.array([
float(words[2]),
float(words[3]),
float(words[4]),
float(words[5])
],
dtype='float32')
label_raw = label.tostring()
image_raw = im.tostring()
example = tf.train.Example(features=tf.train.Features(
feature={
'label_raw': bytes_feature(label_raw),
'image_raw': bytes_feature(image_raw)
}))
examples.append(example)
print(len(examples))
print('\n' + 'part hard')
cur_ = 0
sum_ = len(part_hard)
for line in part_hard:
view_bar(cur_, sum_)
cur_ += 1
words = line.split()
image_file_name = words[0]
im = cv2.imread(image_file_name)
h, w, ch = im.shape
if h != 24 or w != 24:
im = cv2.resize(im, (24, 24))
im = im.astype('uint8')
label = np.array([
float(words[2]),
float(words[3]),
float(words[4]),
float(words[5])
],
dtype='float32')
label_raw = label.tostring()
image_raw = im.tostring()
example = tf.train.Example(features=tf.train.Features(
feature={
'label_raw': bytes_feature(label_raw),
'image_raw': bytes_feature(image_raw)
}))
examples.append(example)
print(len(examples))
print('\n' + 'part random cropped')
cur_ = 0
part_keep = npr.choice(len(part),
size=min(len(part), 100000),
replace=False)
sum_ = len(part_keep)
for i in part_keep:
view_bar(cur_, sum_)
line = part[i]
cur_ += 1
words = line.split()
image_file_name = words[0]
im = cv2.imread(image_file_name)
h, w, ch = im.shape
if h != 24 or w != 24:
im = cv2.resize(im, (24, 24))
im = im.astype('uint8')
label = np.array([
float(words[2]),
float(words[3]),
float(words[4]),
float(words[5])
],
dtype='float32')
label_raw = label.tostring()
image_raw = im.tostring()
example = tf.train.Example(features=tf.train.Features(
feature={
'label_raw': bytes_feature(label_raw),
'image_raw': bytes_feature(image_raw)
}))
examples.append(example)
print(len(examples))
random.shuffle(examples)
for example in examples:
writer.write(example.SerializeToString())
writer.close()
