Last active
August 10, 2022 02:28
-
-
Save iKhushPatel/5614a36f26cf6459cc49c8248e8b5b48 to your computer and use it in GitHub Desktop.
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| """ | |
| 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 | |
| import sys | |
| sys.path.append('../') | |
| from object_detection.utils import dataset_util | |
| from collections import namedtuple, OrderedDict | |
| flags = tf.app.flags | |
| flags.DEFINE_string('csv_input', '', 'data/train_labels.csv') | |
| flags.DEFINE_string('output_path', '', 'data/train.record') | |
| flags.DEFINE_string('image_dir', '', 'images/train') | |
| FLAGS = flags.FLAGS | |
| # TO-DO replace this with label map | |
| def class_text_to_int(row_label): | |
| if row_label == 'Label1': | |
| return 1 | |
| if row_label == 'Label2': | |
| return 2 | |
| else: | |
| return 0 | |
| 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() |
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment
according your instruction from https://towardsdatascience.com/custom-object-detection-using-tensorflow-from-scratch-e61da2e10087 , that code above can generate 2 file, train.record and val.record, why i just get the train.record?