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raccoon_dataset/generate_tfrecord.py
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""" | |
Usage: | |
# From tensorflow/models/ | |
# Create train data: | |
python generate_tfrecord.py --csv_input=data/train_labels.csv --output_path=train.record | |
# Create test data: | |
python generate_tfrecord.py --csv_input=data/test_labels.csv --output_path=test.record | |
""" | |
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 | |
from PIL import Image | |
from object_detection.utils import dataset_util | |
from collections import namedtuple, OrderedDict | |
flags = tf.app.flags | |
flags.DEFINE_string('csv_input', '', 'Path to the CSV input') | |
flags.DEFINE_string('output_path', '', 'Path to output TFRecord') | |
flags.DEFINE_string('image_dir', '', 'Path to images') | |
FLAGS = flags.FLAGS | |
# TO-DO replace this with label map | |
def class_text_to_int(row_label): | |
if row_label == 'raccoon': | |
return 1 | |
else: | |
None | |
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): | |
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(): | |
xmins.append(row['xmin'] / width) | |
xmaxs.append(row['xmax'] / width) | |
ymins.append(row['ymin'] / height) | |
ymaxs.append(row['ymax'] / height) | |
classes_text.append(row['class'].encode('utf8')) | |
classes.append(class_text_to_int(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 main(_): | |
writer = tf.python_io.TFRecordWriter(FLAGS.output_path) | |
path = os.path.join(FLAGS.image_dir) | |
examples = pd.read_csv(FLAGS.csv_input) | |
grouped = split(examples, 'filename') | |
for group in grouped: | |
tf_example = create_tf_example(group, path) | |
writer.write(tf_example.SerializeToString()) | |
writer.close() | |
output_path = os.path.join(os.getcwd(), FLAGS.output_path) | |
print('Successfully created the TFRecords: {}'.format(output_path)) | |
if __name__ == '__main__': | |
tf.app.run() |