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@douglasrizzo @datitran @VictorDDT
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from __future__ import division
from __future__ import print_function
from __future__ import absolute_import
import os
import io
import pandas as pd
import tensorflow as tf
import argparse
from PIL import Image
from tqdm import tqdm
from object_detection.utils import dataset_util
from collections import namedtuple, OrderedDict
def __split(df, group):
data = namedtuple('data', ['filename', 'object'])
gb = df.groupby(group)
return [
data(filename, gb.get_group(x))
for filename, x in zip(gb.groups.keys(), gb.groups)
]
def create_tf_example(group, path, class_dict):
with tf.gfile.GFile(os.path.join(path, '{}'.format(group.filename)),
'rb') as fid:
encoded_jpg = fid.read()
encoded_jpg_io = io.BytesIO(encoded_jpg)
image = Image.open(encoded_jpg_io)
width, height = image.size
filename = group.filename.encode('utf8')
image_format = b'jpg'
xmins = []
xmaxs = []
ymins = []
ymaxs = []
classes_text = []
classes = []
for index, row in group.object.iterrows():
if set(['xmin_rel', 'xmax_rel', 'ymin_rel', 'ymax_rel']).issubset(
set(row.index)):
xmin = row['xmin_rel']
xmax = row['xmax_rel']
ymin = row['ymin_rel']
ymax = row['ymax_rel']
elif set(['xmin', 'xmax', 'ymin', 'ymax']).issubset(set(row.index)):
xmin = row['xmin'] / width
xmax = row['xmax'] / width
ymin = row['ymin'] / height
ymax = row['ymax'] / height
xmins.append(xmin)
xmaxs.append(xmax)
ymins.append(ymin)
ymaxs.append(ymax)
classes_text.append(row['class'].encode('utf8'))
classes.append(class_dict[row['class']])
tf_example = tf.train.Example(
features=tf.train.Features(
feature={
'image/height':
dataset_util.int64_feature(height),
'image/width':
dataset_util.int64_feature(width),
'image/filename':
dataset_util.bytes_feature(filename),
'image/source_id':
dataset_util.bytes_feature(filename),
'image/encoded':
dataset_util.bytes_feature(encoded_jpg),
'image/format':
dataset_util.bytes_feature(image_format),
'image/object/bbox/xmin':
dataset_util.float_list_feature(xmins),
'image/object/bbox/xmax':
dataset_util.float_list_feature(xmaxs),
'image/object/bbox/ymin':
dataset_util.float_list_feature(ymins),
'image/object/bbox/ymax':
dataset_util.float_list_feature(ymaxs),
'image/object/class/text':
dataset_util.bytes_list_feature(classes_text),
'image/object/class/label':
dataset_util.int64_list_feature(classes),
}))
return tf_example
def class_dict_from_pbtxt(pbtxt_path):
# open file, strip \n, trim lines and keep only
# lines beginning with id or display_name
data = [
l.rstrip('\n').strip()
for l in open(pbtxt_path, 'r', encoding='utf-8-sig')
if 'id:' in l or 'display_name:'
]
ids = [int(l.replace('id:', '')) for l in data if l.startswith('id')]
names = [
l.replace('display_name:', '').replace('"', '').strip() for l in data
if l.startswith('display_name')
]
print(data)
# join ids and display_names into a single dictionary
class_dict = {}
for i in range(len(ids)):
class_dict[names[i]] = ids[i]
return class_dict
if __name__ == '__main__':
parser = argparse.ArgumentParser(
description=
'Create a TFRecord file for use with the TensorFlow Object Detection API.',
formatter_class=argparse.RawDescriptionHelpFormatter)
parser.add_argument(
'csv_input',
metavar='csv_input',
type=str,
help='Path to the CSV input')
parser.add_argument(
'pbtxt_input',
metavar='pbtxt_input',
type=str,
help='Path to a pbtxt file containing class ids and display names')
parser.add_argument(
'image_dir',
metavar='image_dir',
type=str,
help='Path to the directory containing all images')
parser.add_argument(
'output_path',
metavar='output_path',
type=str,
help='Path to output TFRecord')
args = parser.parse_args()
class_dict = class_dict_from_pbtxt(args.pbtxt_input)
writer = tf.python_io.TFRecordWriter(args.output_path)
path = os.path.join(args.image_dir)
examples = pd.read_csv(args.csv_input)
grouped = __split(examples, 'filename')
for group in tqdm(grouped, desc='groups'):
tf_example = create_tf_example(group, path, class_dict)
writer.write(tf_example.SerializeToString())
writer.close()
output_path = os.path.join(os.getcwd(), args.output_path)
print('Successfully created the TFRecords: {}'.format(output_path))
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