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 numpy as np

from PIL import Image
from object_detection.utils import dataset_util
from collections import namedtuple, OrderedDict

def class_text_to_int(row_label):
    index = np.where(className==row_label)
    return index[0][0]+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 = 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 run(output_path):

    for f in ['train', 'test', 'validation']:
        # writer = tf.io.TFRecordWriter(output_path+'data/{}.record'.format(f))

        image_path = os.path.join(output_path+'{}/'.format(f))

        if not os.path.exists(image_path):
            continue

        writer = tf.python_io.TFRecordWriter(output_path+'data/{}.record'.format(f))
        examples = pd.read_csv(output_path+'data/{}_labels.csv'.format(f))
        grouped = split(examples, 'filename')

        for group in grouped:
            tf_example = create_tf_example(group, image_path)
            writer.write(tf_example.SerializeToString())

        writer.close()

        print('Successfully created the TFRecords: '+output_path+'data/{}.record'.format(f))


def generate_tfrecord(className_, output_path):
    global className
    className = className_
    run(output_path)
04331a32326eb50def5dd982c03d0a21b6c3ccd8
<mask token>


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 run(output_path):
    for f in ['train', 'test', 'validation']:
        image_path = os.path.join(output_path + '{}/'.format(f))
        if not os.path.exists(image_path):
            continue
        writer = tf.python_io.TFRecordWriter(output_path + 'data/{}.record'
            .format(f))
        examples = pd.read_csv(output_path + 'data/{}_labels.csv'.format(f))
        grouped = split(examples, 'filename')
        for group in grouped:
            tf_example = create_tf_example(group, image_path)
            writer.write(tf_example.SerializeToString())
        writer.close()
        print('Successfully created the TFRecords: ' + output_path +
            'data/{}.record'.format(f))


<mask token>

<mask token>


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 run(output_path):
    for f in ['train', 'test', 'validation']:
        image_path = os.path.join(output_path + '{}/'.format(f))
        if not os.path.exists(image_path):
            continue
        writer = tf.python_io.TFRecordWriter(output_path + 'data/{}.record'
            .format(f))
        examples = pd.read_csv(output_path + 'data/{}_labels.csv'.format(f))
        grouped = split(examples, 'filename')
        for group in grouped:
            tf_example = create_tf_example(group, image_path)
            writer.write(tf_example.SerializeToString())
        writer.close()
        print('Successfully created the TFRecords: ' + output_path +
            'data/{}.record'.format(f))


def generate_tfrecord(className_, output_path):
    global className
    className = className_
    run(output_path)

<mask token>


def class_text_to_int(row_label):
    index = np.where(className == row_label)
    return index[0][0] + 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 = 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 run(output_path):
    for f in ['train', 'test', 'validation']:
        image_path = os.path.join(output_path + '{}/'.format(f))
        if not os.path.exists(image_path):
            continue
        writer = tf.python_io.TFRecordWriter(output_path + 'data/{}.record'
            .format(f))
        examples = pd.read_csv(output_path + 'data/{}_labels.csv'.format(f))
        grouped = split(examples, 'filename')
        for group in grouped:
            tf_example = create_tf_example(group, image_path)
            writer.write(tf_example.SerializeToString())
        writer.close()
        print('Successfully created the TFRecords: ' + output_path +
            'data/{}.record'.format(f))


def generate_tfrecord(className_, output_path):
    global className
    className = className_
    run(output_path)

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 numpy as np
from PIL import Image
from object_detection.utils import dataset_util
from collections import namedtuple, OrderedDict


def class_text_to_int(row_label):
    index = np.where(className == row_label)
    return index[0][0] + 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 = 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 run(output_path):
    for f in ['train', 'test', 'validation']:
        image_path = os.path.join(output_path + '{}/'.format(f))
        if not os.path.exists(image_path):
            continue
        writer = tf.python_io.TFRecordWriter(output_path + 'data/{}.record'
            .format(f))
        examples = pd.read_csv(output_path + 'data/{}_labels.csv'.format(f))
        grouped = split(examples, 'filename')
        for group in grouped:
            tf_example = create_tf_example(group, image_path)
            writer.write(tf_example.SerializeToString())
        writer.close()
        print('Successfully created the TFRecords: ' + output_path +
            'data/{}.record'.format(f))


def generate_tfrecord(className_, output_path):
    global className
    className = className_
    run(output_path)

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 numpy as np

from PIL import Image
from object_detection.utils import dataset_util
from collections import namedtuple, OrderedDict

def class_text_to_int(row_label):
    index = np.where(className==row_label)
    return index[0][0]+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 = 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 run(output_path):

    for f in ['train', 'test', 'validation']:
        # writer = tf.io.TFRecordWriter(output_path+'data/{}.record'.format(f))

        image_path = os.path.join(output_path+'{}/'.format(f))

        if not os.path.exists(image_path):
            continue

        writer = tf.python_io.TFRecordWriter(output_path+'data/{}.record'.format(f))
        examples = pd.read_csv(output_path+'data/{}_labels.csv'.format(f))
        grouped = split(examples, 'filename')

        for group in grouped:
            tf_example = create_tf_example(group, image_path)
            writer.write(tf_example.SerializeToString())

        writer.close()

        print('Successfully created the TFRecords: '+output_path+'data/{}.record'.format(f))


def generate_tfrecord(className_, output_path):
    global className
    className = className_
    run(output_path)