def cvt_to_tfrecords(output_path , data_path, gt_path, records_per_file = 50000):
fetcher = SynthTextDataFetcher(root_path = data_path, mat_path = gt_path)
image_idxes = range(fetcher.num_images)
np.random.shuffle(image_idxes)
record_count = 0;
for image_idx in image_idxes:
if record_count % records_per_file == 0:
fid = record_count / records_per_file
tfrecord_writer = tf.python_io.TFRecordWriter(output_path%(fid))
print("converting image %d/%d"%(record_count, fetcher.num_images))
record = fetcher.fetch_record(image_idx);
if record is None:
print('\nimage %d does not exist'%(image_idx + 1))
continue;
record_count += 1
image_path, image, txts, rect_bboxes, oriented_bboxes = record;
labels = [];
for txt in txts:
if len(txt) < 3:
labels.append(config.ignore_label)
else:
labels.append(config.text_label)
image_data = tf.gfile.FastGFile(image_path, 'r').read()
shape = image.shape
image_name = str(util.io.get_filename(image_path).split('.')[0])
example = convert_to_example(image_data, image_name, labels, txts, rect_bboxes, oriented_bboxes, shape)
tfrecord_writer.write(example.SerializeToString())
def cvt_to_tfrecords(output_path, data_path, gt_path, records_per_file=50000):
fetcher = SynthTextDataFetcher(root_path=data_path, mat_path=gt_path)
fid = 0
image_idx = -1
while image_idx < fetcher.num_images:
with tf.python_io.TFRecordWriter(output_path %
(fid)) as tfrecord_writer:
record_count = 0
while record_count != records_per_file:
image_idx += 1
if image_idx >= fetcher.num_images:
break
print "loading image %d/%d" % (image_idx + 1,
fetcher.num_images)
record = fetcher.fetch_record(image_idx)
if record is None:
print '\nimage %d does not exist' % (image_idx + 1)
continue
image_path, image, txts, rect_bboxes, oriented_bboxes = record
labels = len(rect_bboxes) * [1]
ignored = len(rect_bboxes) * [0]
image_data = tf.gfile.FastGFile(image_path, 'r').read()
shape = image.shape
image_name = str(
util.io.get_filename(image_path).split('.')[0])
example = convert_to_example(image_data, image_name, labels,
ignored, txts, rect_bboxes,
oriented_bboxes, shape)
tfrecord_writer.write(example.SerializeToString())
record_count += 1
fid += 1
def cvt_to_tfrecords(output_path , data_path, gt_path, records_per_file = 50000):
fetcher = SynthTextDataFetcher(root_path = data_path, mat_path = gt_path)
fid = 0
image_idx = -1
while image_idx < fetcher.num_images:
with tf.python_io.TFRecordWriter(output_path%(fid)) as tfrecord_writer:
record_count = 0;
while record_count != records_per_file:
image_idx += 1;
if image_idx >= fetcher.num_images:
break;
print "loading image %d/%d"%(image_idx + 1, fetcher.num_images)
record = fetcher.fetch_record(image_idx);
if record is None:
print '\nimage %d does not exist'%(image_idx + 1)
continue;
image_path, image, txts, rect_bboxes, oriented_bboxes = record;
labels = len(rect_bboxes) * [1];
ignored = len(rect_bboxes) * [0];
image_data = tf.gfile.FastGFile(image_path, 'r').read()
shape = image.shape
image_name = str(util.io.get_filename(image_path).split('.')[0])
example = convert_to_example(image_data, image_name, labels, ignored, txts, rect_bboxes, oriented_bboxes, shape)
tfrecord_writer.write(example.SerializeToString())
record_count += 1;
fid += 1;
def cvt_to_tfrecords(output_path, data_path, gt_path):
image_names = util.io.ls(data_path, '.jpg') #[0:10];
print("%d images found in %s" % (len(image_names), data_path))
with tf.python_io.TFRecordWriter(output_path) as tfrecord_writer:
for idx, image_name in enumerate(image_names):
oriented_bboxes = []
bboxes = []
labels = []
labels_text = []
path = util.io.join_path(data_path, image_name)
print("\tconverting image: %d/%d %s" %
(idx, len(image_names), image_name))
image_data = tf.gfile.FastGFile(path, 'r').read()
image = util.img.imread(path, rgb=True)
shape = image.shape
h, w = shape[0:2]
h *= 1.0
w *= 1.0
image_name = util.str.split(image_name, '.')[0]
gt_name = 'gt_' + image_name + '.txt'
gt_filepath = util.io.join_path(gt_path, gt_name)
lines = util.io.read_lines(gt_filepath)
for line in lines:
line = util.str.remove_all(line, '\xef\xbb\xbf')
gt = util.str.split(line, ',')
oriented_box = [int(gt[i]) for i in range(8)]
oriented_box = np.asarray(oriented_box) / ([w, h] * 4)
oriented_bboxes.append(oriented_box)
xs = oriented_box.reshape(4, 2)[:, 0]
ys = oriented_box.reshape(4, 2)[:, 1]
def to_valid_range(v):
if v < 0:
return 0
if v > 1:
return 1
return v
xmin = to_valid_range(xs.min())
xmax = to_valid_range(xs.max())
ymin = to_valid_range(ys.min())
ymax = to_valid_range(ys.max())
bboxes.append([xmin, ymin, xmax, ymax])
# might be wrong here, but it doesn't matter because the label is not going to be used in detection
labels_text.append(gt[-1])
ignored = util.str.contains(gt[-1], '###')
if ignored:
labels.append(config.ignore_label)
else:
labels.append(config.text_label)
example = convert_to_example(image_data, image_name, labels,
labels_text, bboxes, oriented_bboxes,
shape)
tfrecord_writer.write(example.SerializeToString())
def cvt_to_tfrecords(output_path , data_path, gt_path):
image_names = util.io.ls(data_path, '.jpg')
print "{0} images found in {1}".format(len(image_names), data_path)
with tf.python_io.TFRecordWriter(output_path) as tfrecord_writer:
for idx, image_name in enumerate(image_names):
oriented_bboxes = []
bboxes = []
labels = []
labels_text = []
ignored = []
path = util.io.join_path(data_path, image_name)
print "\tconverting image: {0}/{1} {2}".format(idx, len(image_names), image_name)
image_data = tf.gfile.FastGFile(path, 'r').read()
image = util.img.imread(path, rgb=True)
shape = image.shape
h, w = shape[0:2]
h *= 1.0
w *= 1.0
image_name = util.str.split(image_name, '.')[0] # without .jpg
print image_name
# read ground-truth
gt_name = 'res_' + image_name + '.txt'
gt_filepath = util.io.join_path(gt_path, gt_name)
lines = util.io.read_lines(gt_filepath)
for line in lines:
line = util.str.remove_all(line, '\xef\xbb\xbf')
gt = util.str.split(line, ',')
oriented_box = [int(gt[i]) for i in range(8)]
# oriented_box = np.asarray(oriented_box) / ([w, h] * 4)
oriented_box = np.asarray(oriented_box) / ([w, h, w, h, w, h, w, h])
oriented_bboxes.append(oriented_box)
xs = oriented_box.reshape(4, 2)[:, 0]
ys = oriented_box.reshape(4, 2)[:, 1]
xmin = xs.min()
xmax = xs.max()
ymin = ys.min()
ymax = ys.max()
bboxes.append([xmin, ymin, xmax, ymax])
ignored.append(util.str.contains(gt[-1], '###'))
# might be wrong here, but it doesn't matter because the label is not going to be used in detection
labels_text.append(gt[-1])
labels.append(1)
example = convert_to_example(image_data, image_name, labels, ignored, labels_text, bboxes, oriented_bboxes,
shape)
tfrecord_writer.write(example.SerializeToString())
"""
def _add_to_tfrecord(dataset_dir, name, tfrecord_writer):
"""Loads data from image and annotations files and add them to a TFRecord.
Args:
dataset_dir: Dataset directory;
name: Image name to add to the TFRecord;
tfrecord_writer: The TFRecord writer to use for writing.
"""
image_data, filename, labels, labels_text, bboxes, oriented_bboxes, shape = \
_process_image(dataset_dir, name)
example = convert_to_example(image_data, filename, labels, labels_text, bboxes, oriented_bboxes, shape)
tfrecord_writer.write(example.SerializeToString())
def convert_to_tfrecords(file_path, text_path, out_path):
output_dir = os.path.dirname(out_path)
if not os.path.exists(output_dir):
os.makedirs(output_dir)
img_paths = load_file(file_path)
print("the total img nums is:{}".format(len(img_paths)))
with tf.python_io.TFRecordWriter(out_path +
"train_tfrecords") as tfrecord_writer:
for idx, path in enumerate(img_paths):
with tf.gfile.FastGFile(path, 'rb') as f:
image_data = f.read()
# read img
img, h, w = read_img(path)
if img is None:
print("the current img is empty")
continue
# read txt info
oriented_bboxes = []
bboxes = []
labels = []
labels_text = []
ignored = []
img_name = str(path.split("/")[-1][:-4])
txt_path = os.path.join(text_path, img_name + ".txt")
with open(txt_path, "r", encoding="utf-8") as f:
lines = f.readlines()
for line in lines:
line_array = np.array(line.strip().split(","))[:8]
line_array = line_array.astype(float)
oriented_box = line_array / ([w, h] * 4)
oriented_bboxes.append(oriented_box)
xy_list = np.reshape(oriented_box, (4, 2))
xmin = xy_list[:, 0].min()
xmax = xy_list[:, 0].max()
ymin = xy_list[:, 1].min()
ymax = xy_list[:, 1].max()
bboxes.append([
max(0., xmin),
max(0., ymin),
min(xmax, 1.),
min(ymax, 1.)
])
ignored.append(0)
labels_text.append(b"text")
labels.append(1)
img_name = str.encode(str(img_name))
example = convert_to_example(image_data, img_name, labels, ignored,
labels_text, bboxes, oriented_bboxes,
img.shape)
tfrecord_writer.write(example.SerializeToString())
if idx % 100 == 0:
print(idx)
def cvt_to_tfrecords(image_names, output_path, data_path, gt_path, begin, end):
with tf.python_io.TFRecordWriter(output_path) as tfrecord_writer:
ii = 0
for idx, image_name in enumerate(image_names[begin:end]):
oriented_bboxes = []
bboxes = []
bboxesn = []
labels = []
labels_text = []
path = util.io.join_path(data_path, image_name)
image_data = tf.gfile.FastGFile(path, 'r').read()
try:
image = util.img.imread(path, rgb = True)
ii = ii+1
except:
continue
print "\tconverting image: %d/%d %s" % (ii, len(image_names), image_name)
shape = image.shape
h, w = shape[0:2]
h *= 1.0
w *= 1.0
image_name = image_name[0:-4]
gt_name = image_name + '.txt'
gt_filepath = util.io.join_path(gt_path, gt_name)
lines = util.io.read_lines(gt_filepath)
for line in lines:
line = util.str.remove_all(line, '\xef\xbb\xbf')
gt = util.str.split(line, ',')
oriented_box = [int(float(gt[i])) for i in range(8)]
oriented_box = np.asarray(oriented_box) / ([w, h] * 4)
oriented_bboxes.append(oriented_box)
xs = oriented_box.reshape(4, 2)[:, 0]
ys = oriented_box.reshape(4, 2)[:, 1]
xmin = to_valid(xs.min())
xmax = to_valid(xs.max())
ymin = to_valid(ys.min())
ymax = to_valid(ys.max())
bboxes.append([xmin, ymin, xmax, ymax])
labels_text.append(gt[-1])
ignored = util.str.contains(gt[-1], '###')
if ignored:
labels.append(config.ignore_label)
else:
labels.append(config.text_label)
example = convert_to_example(image_data, image_name, labels, labels_text, bboxes, oriented_bboxes, shape)
tfrecord_writer.write(example.SerializeToString())
def cvt_to_tfrecords(output_path , data_path, gt_path):
image_names = util.io.ls(data_path, '.jpg')#[0:10];
print "%d images found in %s"%(len(image_names), data_path);
with tf.python_io.TFRecordWriter(output_path) as tfrecord_writer:
for idx, image_name in enumerate(image_names):
oriented_bboxes = [];
bboxes = []
labels = [];
labels_text = [];
path = util.io.join_path(data_path, image_name);
print "\tconverting image: %d/%d %s"%(idx, len(image_names), image_name);
image_data = tf.gfile.FastGFile(path, 'r').read()
image = util.img.imread(path, rgb = True);
shape = image.shape
h, w = shape[0:2];
h *= 1.0;
w *= 1.0;
image_name = util.str.split(image_name, '.')[0];
gt_name = 'gt_' + image_name + '.txt';
gt_filepath = util.io.join_path(gt_path, gt_name);
lines = util.io.read_lines(gt_filepath);
for line in lines:
line = util.str.remove_all(line, '\xef\xbb\xbf')
gt = util.str.split(line, ',');
oriented_box = [int(gt[i]) for i in range(8)];
oriented_box = np.asarray(oriented_box) / ([w, h] * 4);
oriented_bboxes.append(oriented_box);
xs = oriented_box.reshape(4, 2)[:, 0]
ys = oriented_box.reshape(4, 2)[:, 1]
xmin = xs.min()
xmax = xs.max()
ymin = ys.min()
ymax = ys.max()
bboxes.append([xmin, ymin, xmax, ymax])
# might be wrong here, but it doesn't matter because the label is not going to be used in detection
labels_text.append(gt[-1]);
ignored = util.str.contains(gt[-1], '###')
if ignored:
labels.append(config.ignore_label);
else:
labels.append(config.text_label)
example = convert_to_example(image_data, image_name, labels, labels_text, bboxes, oriented_bboxes, shape)
tfrecord_writer.write(example.SerializeToString())
def cvt_to_tfrecords(output_path , images_dir, annotations_dir, files_txt):
with tf.python_io.TFRecordWriter(output_path) as tfrecord_writer:
lines = util.io.read_lines(files_txt)
count = len(lines)
for idx, filename in enumerate(lines):
oriented_bboxes = []
bboxes = []
labels = []
labels_text = []
filename = filename.replace('\n', '')
print('{} / {} {}'.format(idx, count, filename))
annotation_path = util.io.join_path(annotations_dir, filename + '.xml')
annotation_xml = ET.ElementTree(file=annotation_path)
image_filename = annotation_xml.find('filename').text
image_path = util.io.join_path(images_dir, image_filename)
image_data = tf.gfile.FastGFile(image_path, 'rb').read()
image = util.img.imread(image_path, rgb=True)
shape = image.shape
h, w = shape[0:2]
h *= 1.0
w *= 1.0
for obj_ele in annotation_xml.iterfind('object'):
content = obj_ele.find('content').text
content = str.encode(content)
labels_text.append(content)
labels.append(config.text_label)
bndbox = obj_ele.find('bndbox')
xmin = int(bndbox.find('xmin').text) / w
ymin = int(bndbox.find('ymin').text) / h
xmax = int(bndbox.find('xmax').text) / w
ymax = int(bndbox.find('ymax').text) / h
bboxes.append([xmin, ymin, xmax, ymax])
if xmin < 0 or ymin < 0 or xmax > 1 or ymax > 1:
print(filename)
return
oriented_box = [xmin, ymin, xmax, ymin, xmax, ymax, xmin, ymin]
oriented_bboxes.append(oriented_box)
filename = str.encode(filename)
example = convert_to_example(image_data, filename, labels, labels_text, bboxes, oriented_bboxes, shape)
tfrecord_writer.write(example.SerializeToString())
def cvt_to_tfrecords(output_path , data_path, gt_path):
image_names = util.io.ls(data_path, '.jpg')#[0:10];
print "%d images found in %s"%(len(image_names), data_path);
with tf.python_io.TFRecordWriter(output_path) as tfrecord_writer:
for idx, image_name in enumerate(image_names):
oriented_bboxes = [];
bboxes = []
labels = [];
labels_text = [];
ignored = []
path = util.io.join_path(data_path, image_name);
print "\tconverting image: %d/%d %s"%(idx, len(image_names), image_name);
image_data = tf.gfile.FastGFile(path, 'r').read()
image = util.img.imread(path, rgb = True);
shape = image.shape
h, w = shape[0:2];
h *= 1.0;
w *= 1.0;
image_name = util.str.split(image_name, '.')[0];
gt_name = 'gt_' + image_name + '.txt';
gt_filepath = util.io.join_path(gt_path, gt_name);
lines = util.io.read_lines(gt_filepath);
for line in lines:
line = util.str.remove_all(line, '\xef\xbb\xbf')
gt = util.str.split(line, ',');
oriented_box = [int(gt[i]) for i in range(8)];
oriented_box = np.asarray(oriented_box) / ([w, h] * 4);
oriented_bboxes.append(oriented_box);
xs = oriented_box.reshape(4, 2)[:, 0]
ys = oriented_box.reshape(4, 2)[:, 1]
xmin = xs.min()
xmax = xs.max()
ymin = ys.min()
ymax = ys.max()
bboxes.append([xmin, ymin, xmax, ymax])
ignored.append(util.str.contains(gt[-1], '###'));
# might be wrong here, but it doesn't matter because the label is not going to be used in detection
labels_text.append(gt[-1]);
labels.append(1);
example = convert_to_example(image_data, image_name, labels, ignored, labels_text, bboxes, oriented_bboxes, shape)
tfrecord_writer.write(example.SerializeToString())
def cvt_to_tfrecords(output_path, data_path, gt_path):
image_names = util.io.ls(data_path, '.jpg') #[0:10];
print "%d images found in %s" % (len(image_names), data_path)
with tf.python_io.TFRecordWriter(output_path) as tfrecord_writer:
for idx, image_name in enumerate(image_names):
bboxes = []
oriented_bboxes = []
labels = []
labels_text = []
ignored = []
path = util.io.join_path(data_path, image_name)
if not util.img.is_valid_jpg(path):
continue
image = util.img.imread(path)
print "\tconverting image:%s, %d/%d" % (image_name, idx,
len(image_names))
image_data = tf.gfile.FastGFile(path, 'r').read()
#image = util.img.imread(path, rgb = True);
shape = image.shape
h, w = shape[0:2]
h *= 1.0
w *= 1.0
image_name = util.str.split(image_name, '.')[0]
gt_name = image_name + '.txt'
gt_filepath = util.io.join_path(gt_path, gt_name)
lines = util.io.read_lines(gt_filepath)
for line in lines:
spt = line.split(',')
locs = spt[0:-1]
xmin, ymin, bw, bh = [int(v) for v in locs]
xmax = xmin + bw - 1
ymax = ymin + bh - 1
xmin, ymin, xmax, ymax = xmin / w, ymin / h, xmax / w, ymax / h
bboxes.append([xmin, ymin, xmax, ymax])
oriented_bboxes.append(
[xmin, ymin, xmax, ymin, xmax, ymax, xmin, ymax])
labels_text.append(str(spt[-1]))
labels.append(1)
ignored.append(0)
example = convert_to_example(image_data, image_name, labels,
ignored, labels_text, bboxes,
oriented_bboxes, shape)
tfrecord_writer.write(example.SerializeToString())
def cvt_to_tfrecords(output_path, data_path, gt_path):
image_names = util.io.ls(data_path, '.jpg') #[0:10];
print "%d images found in %s" % (len(image_names), data_path)
with tf.python_io.TFRecordWriter(output_path) as tfrecord_writer:
for idx, image_name in enumerate(image_names):
oriented_bboxes = []
bboxes = []
labels = []
labels_text = []
ignored = []
path = util.io.join_path(data_path, image_name)
print "\tconverting image: %d/%d %s" % (idx, len(image_names),
image_name)
image_data = tf.gfile.FastGFile(path, 'r').read()
image = util.img.imread(path, rgb=True)
shape = image.shape
h, w = shape[0:2]
h *= 1.0
w *= 1.0
image_name = util.str.split(image_name, '.')[0]
gt_name = 'gt_' + image_name + '.txt'
gt_filepath = util.io.join_path(gt_path, gt_name)
lines = util.io.read_lines(gt_filepath)
for line in lines:
gt = util.str.remove_all(line, ',')
gt = util.str.split(gt, ' ')
bbox = [int(gt[i]) for i in range(4)]
xmin, ymin, xmax, ymax = np.asarray(bbox) / [w, h, w, h]
oriented_bboxes.append(
[xmin, ymin, xmax, ymin, xmax, ymax, xmin, ymax])
bboxes.append([xmin, ymin, xmax, ymax])
ignored.append(0)
labels_text.append(line.split('"')[1])
labels.append(1)
example = convert_to_example(image_data, image_name, labels,
ignored, labels_text, bboxes,
oriented_bboxes, shape)
tfrecord_writer.write(example.SerializeToString())
def cvt_to_tfrecords(output_path , data_path, gt_path):
image_names = util.io.ls(data_path, '.jpg')#[0:10];
print "%d images found in %s"%(len(image_names), data_path);
with tf.python_io.TFRecordWriter(output_path) as tfrecord_writer:
for idx, image_name in enumerate(image_names):
bboxes = [];
oriented_bboxes = []
labels = [];
labels_text = [];
ignored = []
path = util.io.join_path(data_path, image_name);
if not util.img.is_valid_jpg(path):
continue
image = util.img.imread(path)
print "\tconverting image:%s, %d/%d"%(image_name, idx, len(image_names));
image_data = tf.gfile.FastGFile(path, 'r').read()
#image = util.img.imread(path, rgb = True);
shape = image.shape
h, w = shape[0:2];
h *= 1.0;
w *= 1.0;
image_name = util.str.split(image_name, '.')[0];
gt_name = image_name + '.txt';
gt_filepath = util.io.join_path(gt_path, gt_name);
lines = util.io.read_lines(gt_filepath);
for line in lines:
spt = line.split(',')
locs = spt[0: -1]
xmin, ymin, bw, bh = [int(v) for v in locs]
xmax = xmin + bw - 1
ymax = ymin + bh - 1
xmin, ymin, xmax, ymax = xmin / w, ymin/ h, xmax / w, ymax / h
bboxes.append([xmin, ymin, xmax, ymax]);
oriented_bboxes.append([xmin, ymin, xmax, ymin, xmax, ymax, xmin, ymax])
labels_text.append(str(spt[-1]));
labels.append(1);
ignored.append(0)
example = convert_to_example(image_data, image_name, labels, ignored, labels_text, bboxes, oriented_bboxes, shape)
tfrecord_writer.write(example.SerializeToString())
def cvt_to_tfrecords(output_path, data_path, gt_path):
image_names = util.io.ls(data_path, '.jpg') #[0:10];
print "%d images found in %s" % (len(image_names), data_path)
with tf.python_io.TFRecordWriter(output_path) as tfrecord_writer:
for idx, image_name in enumerate(image_names):
oriented_bboxes = []
bboxes = []
labels = []
labels_text = []
path = util.io.join_path(data_path, image_name)
print "\tconverting image: %d/%d %s" % (idx, len(image_names),
image_name)
image_data = tf.gfile.FastGFile(path, 'r').read()
image = util.img.imread(path, rgb=True)
shape = image.shape
h, w = shape[0:2]
h *= 1.0
w *= 1.0
image_name = util.str.split(image_name, '.')[0]
## Annotations
gt_name = image_name + '.json'
gt_filepath = util.io.join_path(gt_path, gt_name)
if not os.path.exists(gt_filepath):
continue
# print 'gt_name', gt_name, gt_filepath
### json file
with open(gt_filepath) as jsonbuffer:
jsondict = json.loads(jsonbuffer.read())
print " >>>>>> ", image_name, w, jsondict[
'imageWidth'], h, jsondict['imageHeight']
for obj in jsondict['shapes']:
gt = []
for pt in obj['points']:
ptx, pty = pt
ptx = check_out_boundary(ptx, 0, w)
pty = check_out_boundary(pty, 0, h)
gt.append(ptx)
gt.append(pty)
num_pts = len(obj['points'])
oriented_box = [int(gt[i]) for i in range(len(gt))]
oriented_box = np.asarray(oriented_box) / ([w, h] * num_pts)
oriented_bboxes.append(oriented_box)
xs = oriented_box.reshape(num_pts, 2)[:, 0]
ys = oriented_box.reshape(num_pts, 2)[:, 1]
# for pt in obj['points']:
# ptx, pty = pt
# ptx = check_out_boundary(ptx, 0, w)
# pty = check_out_boundary(pty, 0 ,h)
# gt.append(pty)
# gt.append(ptx)
# num_pts = len(obj['points'])
# oriented_box = [int(gt[i]) for i in range(len(gt))]
# oriented_box = np.asarray(oriented_box) / ([h, w] * num_pts)
# oriented_bboxes.append(oriented_box)
# ys = oriented_box.reshape(num_pts, 2)[:, 0]
# xs = oriented_box.reshape(num_pts, 2)[:, 1]
xmin = xs.min()
xmax = xs.max()
ymin = ys.min()
ymax = ys.max()
bboxes.append([ymin, xmin, ymax, xmax])
print " >>>>> ", num_pts, len(
gt), '\t', xmin, ymin, xmax, ymax
labels_text.append(
str(obj['label'].encode('utf8'))
) ## unicode to byte, should convert back to unicode in result
ignored = util.str.contains(obj['label'], '#')
if ignored:
labels.append(config.ignore_label)
else:
labels.append(config.text_label)
example = convert_to_example(image_data, image_name, labels,
labels_text, bboxes, oriented_bboxes,
shape)
tfrecord_writer.write(example.SerializeToString())
