If using TF2.0 generate_tfrecord.py is not working

[aafaqin]

Gfile and and python io error is there minor 2 line changes this is thee working file `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.io.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

with open(pbtxt_path, 'r', encoding='utf-8-sig') as f: data = f.readlines()

name_key = None if any('display_name:' in s for s in data): name_key = 'display_name:' elif any('name:' in s for s in data): name_key = 'name:'

if name_key is None: raise ValueError( "label map does not have class names, provided by values with the 'display_name' or 'name' keys in the contents of the file" )

data = [l.rstrip('\n').strip() for l in data if 'id:' in l or name_key in l]

ids = [int(l.replace('id:', '')) for l in data if l.startswith('id')] names = [ l.replace(name_key, '').replace('"', '').replace("'", '').strip() for l in data if l.startswith(name_key)]

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.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))`