Sunday, March 04, 2018

Object Detection Labelling image and generating tfRecord

I made use of the tutorial from jackyle  to label my images . Note that pythonprogramming has also the exact same tutorial :) !

Mind you the hardest part is really finding the images , the rest goes more or less pretty fast.

Basically you use the tool labelImage to help in the labelling , which basically creates an XML file for each of the image that you label .

I used the windows binary which can be found here and did all the labelling from windows itself.

Your directory structure should be like this under ROOT_DIR/models/research/object_detection:

   |- train
   |- test

Once you have labelled all your images you need to do the following :

1. Place 70 % of your images + xml in a folder images/train
2. Place 30% of your images + xml in a folder images/test
3. Create a file that looks like below:
import os
import glob
import pandas as pd
import xml.etree.ElementTree as ET

def xml_to_csv(path):
    xml_list = []
    for xml_file in glob.glob(path + '/*.xml'):
        tree = ET.parse(xml_file)
        root = tree.getroot()
        for member in root.findall('object'):
            value = (root.find('filename').text,
    column_name = ['filename', 'width', 'height', 'class', 'xmin', 'ymin', 'xmax', 'ymax']
    xml_df = pd.DataFrame(xml_list, columns=column_name)
    return xml_df

def main():
    for directory in ['train','test']:
        image_path = os.path.join(os.getcwd(), 'images/{}'.format(directory))
        xml_df = xml_to_csv(image_path)
        xml_df.to_csv('data/{}_labels.csv'.format(directory), index=None)
        print('Successfully converted xml to csv.')



4. Excecute  python , this will read all the xml files and create 2 csv files in the data directory train_labels.csv and test_labels.csv

Docker Container

If you installed tensorflow using docker container  ( check my tutorial ) and cloned the following repository ( install git if you dont already have it ):

git clone 

You can copy a zip of the images folder , , and the python into the container, tensorflow,  using :

docker cp tensorflow:/notebooks/models/research/object_detection/

docker cp tensorflow:/notebooks/models/research/object_detection/

Now all you need to do is to unzip the images ( install unzip if you dont already have it) :


Then you connect to the running instance of the container using :

docker exec -it tensorflow /bin/bash

and execute :


Generating TfRecord

Now the next step is based on the generated test_labels.csv and train_labels.csv we are going to create tensorflow record files for each .

1. Copy the following file into your /notebooks/models/research/object_detection/   directory:

  # From tensorflow/models/
  # Create train data:
  python --csv_input=data/train_labels.csv  --output_path=data/train.record --images_path=images/train

  # Create test data:
  python --csv_input=data/test_labels.csv  --output_path=data/test.record --images_path=images/test
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 =
flags.DEFINE_string('csv_input', '', 'Path to the CSV input')
flags.DEFINE_string('output_path', '', 'Path to output TFRecord')
flags.DEFINE_string('images_path', '', 'Path to Images')

# TO-DO replace this with label map
def class_text_to_int(row_label):
    if row_label == 'cocacola':
        return 1

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 =
    encoded_jpg_io = io.BytesIO(encoded_jpg)
    image =
    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)

    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(os.getcwd(), FLAGS.images_path)
    examples = pd.read_csv(FLAGS.csv_input)
    grouped = split(examples, 'filename')
    for group in grouped:
        tf_example = create_tf_example(group, path)

    output_path = os.path.join(os.getcwd(), FLAGS.output_path)
    print('Successfully created the TFRecords: {}'.format(output_path))

if __name__ == '__main__':


Note that its the same file that is mentioned in the jackyle  tutorial however I kept getting file not found exceptions as it was trying to get the image from the images directory directly instead of images/test or images/train. So I made some modifications such as the images directory for train and test could be passed as a flag.

2. Execute for following command to make sure Python is on your path:

cd /notebooks/models/research/
export PYTHONPATH=$PYTHONPATH:`pwd`:`pwd`/slim
cd object_detection

3. Then create the train record:

python --csv_input=data/train_labels.csv  --output_path=data/train.record --images_path=images/train

4. Create the test record :

python --csv_input=data/test_labels.csv  --output_path=data/test.record --images_path=images/test

You should now have 2 files train.record and test.record under the /notebooks/models/research/object_detection/data   directory.

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