generate_tfrecord.py

"""
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):
    classes = {
        'D00': 1,
        'D01': 2,
        'D10': 3,
        'D11': 4,
        'D20': 5,
        'D30': 6,
        'D40': 7,
        'D43': 8,
        'D44': 9
    }

    return classes[row_label]


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()

images_to_records.sh

export PYTHONPATH=$PYTHONPATH:/home/freefly19/projects/tf-models/research:/home/freefly19/projects/tf-models/research/slim:/home/freefly19/projects/tf-models/research/object_detection:/home/freefly19/projects/tf-models/research/object_detection/utils

python split.py
python xml_to_csv.py

cd train
python ../generate_tfrecord.py --csv_input=../data/train_labels.csv  --output_path=../data/train.record
cd ../test
python ../generate_tfrecord.py --csv_input=../data/test_labels.csv  --output_path=../data/test.record
cd ..

python /home/freefly19/projects/tf-models/research/object_detection/model_main.py --logtostderr --pipeline_config_path=training/ssd_mobilenet_v1_coco.config --model_dir=training

tensorboard --logdir=training-ssd-mobilenet_300_300

python export_inference_graph.py \
    --input_type image_tensor \
    --pipeline_config_path training-3/faster_rcnn_inception_resnet_v2_atrous_coco.config \
    --trained_checkpoint_prefix training-3/model.ckpt-200000 \
    --output_directory training-3-3output

sensors
nvidia-smi --query-gpu=temperature.gpu --format=csv,noheader

-----

split.py

import os
from random import shuffle
from math import floor
import shutil


def get_training_and_testing_sets(file_list):
    split = 0.7
    split_index = int(floor(len(file_list) * split))
    training = file_list[:split_index]
    testing = file_list[split_index:]
    return training, testing


def get_file_list_from_dir(datadir):
    all_files = os.listdir(os.path.abspath(datadir))
    data_files = list(map(lambda file: file.replace('.jpg', ''), filter(lambda file: file.endswith('.jpg'), all_files)))
    return data_files


regions = ['Chiba', 'Sumida', 'Adachi', 'Ichihara', 'Nagakute', 'Muroran', 'Numazu']

for r in regions:
    file_names = get_file_list_from_dir(r + '/JPEGImages')
    shuffle(file_names)
    training, test = get_training_and_testing_sets(file_names)

    if not os.path.exists('train'):
        os.makedirs('train')
    if not os.path.exists('test'):
        os.makedirs('test')

    for name in training:
        if os.path.exists(r + '/Annotations/' + name + '.xml'):
            shutil.copy(r + '/JPEGImages/' + name + '.jpg', 'train/' + name + '.jpg')
            shutil.copy(r + '/Annotations/' + name + '.xml', 'train/' + name + '.xml')

    for name in test:
        if os.path.exists(r + '/Annotations/' + name + '.xml'):
            shutil.copy(r + '/JPEGImages/' + name + '.jpg', 'test/' + name + '.jpg')
            shutil.copy(r + '/Annotations/' + name + '.xml', 'test/' + name + '.xml')

xml_to_csv.py

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,
                     int(float(root.find('size')[0].text)),
                     int(float(root.find('size')[1].text)),
                     member.find('name').text,
                     int(float(member.find('bndbox').find('xmin').text)),
                     int(float(member.find('bndbox').find('ymin').text)),
                     int(float(member.find('bndbox').find('xmax').text)),
                     int(float(member.find('bndbox').find('ymax').text))
                     )
            xml_list.append(value)
    column_name = ['filename', 'width', 'height', 'class', 'xmin', 'ymin', 'xmax', 'ymax']
    xml_df = pd.DataFrame(xml_list, columns=column_name)
    return xml_df


def main():
    if not os.path.exists('data'):
        os.makedirs('data')

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

main()