I am trying to generate tfrecords for png images and csv labels for training an object detection model using tensorflow API for detecting objects. I'm working with a script from a tutorial but I get this error : UnicodeDecodeError: 'utf-8' codec can't decode byte 0xe9 in position 65: invalid continuation byte
which I dont know how to solve it. Do you guys have any idea ??
Here's the program to generate tfrecords :
"""
Usage:
# From tensorflow/models/
# Create train data:
python preprocessing/csv_to_tfrecords.py --csv_input=data/train_labels.csv --output_path=data/train.record
# Create test data:
python preprocessing/csv_to_tfrecords.py --csv_input=data/test_labels.csv --output_path=data/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.compat.v1.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 == 'capsule':
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.io.gfile.GFile(os.path.join(path, '{}'.format(group.filename)), 'rb') as fid:
encoded_png = fid.read()
encoded_png_io = io.BytesIO(encoded_png)
image = Image.open(encoded_png_io)
width, height = image.size
filename = group.filename.encode('utf8')
image_format = b'png'
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_png),
'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.compat.v1.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.compat.v1.app.run()
After trying to generate the tfrecords I've got as a traceback :
python preprocessing/csv_to_tfrecords.py --csv_input=data/train_labels.csv --output_path=data/train.record
2021-05-31 21:34:20.376813: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'cudart64_110.dll'; dlerror: cudart64_110.dll not found
2021-05-31 21:34:20.377348: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.
Traceback (most recent call last):
File "preprocessing/csv_to_tfrecords.py", line 100, in <module>
tf.compat.v1.app.run()
File "C:\Users\user\AppData\Local\Programs\Python\Python37\lib\site-packages\tensorflow\python\platform\app.py", line 40, in run
_run(main=main, argv=argv, flags_parser=_parse_flags_tolerate_undef)
File "C:\Users\user\AppData\Local\Programs\Python\Python37\lib\site-packages\absl\app.py", line 303, in run
_run_main(main, args)
File "C:\Users\user\AppData\Local\Programs\Python\Python37\lib\site-packages\absl\app.py", line 251, in _run_main
sys.exit(main(argv))
File "preprocessing/csv_to_tfrecords.py", line 91, in main
tf_example = create_tf_example(group, path)
File "preprocessing/csv_to_tfrecords.py", line 46, in create_tf_example
encoded_png = fid.read()
File "C:\Users\user\AppData\Local\Programs\Python\Python37\lib\site-packages\tensorflow\python\lib\io\file_io.py", line 117, in read
self._preread_check()
File "C:\Users\user\AppData\Local\Programs\Python\Python37\lib\site-packages\tensorflow\python\lib\io\file_io.py", line 80, in _preread_check
compat.path_to_str(self.__name), 1024 * 512)
UnicodeDecodeError: 'utf-8' codec can't decode byte 0xe9 in position 65: invalid continuation byte
