create_tfrecords.py

"""
Create the tfrecord files for a dataset.

A lot of this code comes from the tensorflow inception example, so here is their license:

# Copyright 2016 Google Inc. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================

"""
from __future__ import absolute_import

import argparse
from datetime import datetime
import hashlib
import json
import os
import pathlib

from queue import Queue
import random
import sys
import threading

import numpy as np
import tensorflow as tf
tf.compat.v1.disable_eager_execution()

def _int64_feature(value):
    """Wrapper for inserting int64 features into Example proto."""
    if not isinstance(value, list):
        value = [value]
    return tf.train.Feature(int64_list=tf.train.Int64List(value=value))


def _float_feature(value):
    """Wrapper for inserting float features into Example proto."""
    if not isinstance(value, list):
        value = [value]
    return tf.train.Feature(float_list=tf.train.FloatList(value=value))


def _bytes_list_feature(value):
    """Wrapper for inserting bytes list features into Example proto."""
    value = [x.encode('utf8') for x in value]
    return tf.train.Feature(bytes_list=tf.train.BytesList(value=value))

def _bytes_feature(value):
    """Wrapper for inserting bytes features into Example proto."""
    if not isinstance(value, bytes):
        value = value.encode('utf8')
    return tf.train.Feature(bytes_list=tf.train.BytesList(value=[value]))

def _validate_text(text):
    """If text is not str or unicode, then try to convert it to str."""

    if isinstance(text, str):
        return text
    else:
        return str(text)

def _convert_to_example(image_example, image_buffer, height, width, colorspace='RGB',
                        channels=3, image_format='JPEG'):
    """Build an Example proto for an example.
    Args:
      image_example: dict, an image example
      image_buffer: string, JPEG encoding of RGB image
      height: integer, image height in pixels
      width: integer, image width in pixels
    Returns:
      Example proto
    """

    # Required
    filename = str(image_example['filename'])
    image_id = str(image_example['id'])

    # Class label for the whole image
    image_class = image_example.get('class', {})
    class_label = image_class.get('label', 0)
    class_text = _validate_text(image_class.get('text', ''))
    class_conf = image_class.get('conf', 1.)

    # Objects
    image_objects = image_example.get('object', {})
    object_count = image_objects.get('count', 0)

    # Bounding Boxes
    image_bboxes = image_objects.get('bbox', {})
    xmin = image_bboxes.get('xmin', [])
    xmax = image_bboxes.get('xmax', [])
    ymin = image_bboxes.get('ymin', [])
    ymax = image_bboxes.get('ymax', [])
    bbox_scores = image_bboxes.get('score', [])
    bbox_labels = image_bboxes.get('label', [])
    bbox_text = map(_validate_text, image_bboxes.get('text', []))
    bbox_label_confs = image_bboxes.get('conf', [])

    # Parts
    image_parts = image_objects.get('parts', {})
    parts_x = image_parts.get('x', [])
    parts_y = image_parts.get('y', [])
    parts_v = image_parts.get('v', [])
    parts_s = image_parts.get('score', [])

    # Areas
    object_areas = image_objects.get('area', [])

    # Ids
    object_ids = map(str, image_objects.get('id', []))

    # Any extra data (e.g. stringified json)
    extra_info = str(image_class.get('extra', ''))

    # Additional fields for the format needed by the Object Detection repository
    key = hashlib.sha256(image_buffer).hexdigest()
    is_crowd = image_objects.get('is_crowd', [])

    example = tf.train.Example(features=tf.train.Features(feature={
        'image/height': _int64_feature(height),
        'image/width': _int64_feature(width),
        'image/colorspace': _bytes_feature(colorspace),
        'image/channels': _int64_feature(channels),
        'image/format': _bytes_feature(image_format),
        'image/filename': _bytes_feature(filename),
        'image/id': _bytes_feature(image_id),
        'image/encoded': _bytes_feature(image_buffer),
        'image/extra': _bytes_feature(extra_info),
        'image/class/label': _int64_feature(class_label),
        'image/class/text': _bytes_list_feature(class_text),
        'image/class/conf': _float_feature(class_conf),
        'image/object/bbox/xmin': _float_feature(xmin),
        'image/object/bbox/xmax': _float_feature(xmax),
        'image/object/bbox/ymin': _float_feature(ymin),
        'image/object/bbox/ymax': _float_feature(ymax),
        'image/object/bbox/label': _int64_feature(bbox_labels),
        'image/object/bbox/text': _bytes_list_feature(bbox_text),
        'image/object/bbox/conf': _float_feature(bbox_label_confs),
        'image/object/bbox/score' : _float_feature(bbox_scores),
        'image/object/parts/x' : _float_feature(parts_x),
        'image/object/parts/y' : _float_feature(parts_y),
        'image/object/parts/v' : _int64_feature(parts_v),
        'image/object/parts/score' : _float_feature(parts_s),
        'image/object/count' : _int64_feature(object_count),
        'image/object/area' : _float_feature(object_areas),
        'image/object/id' : _bytes_list_feature(object_ids),

        # Additional fields for the format needed by the Object Detection repository
        'image/source_id': _bytes_list_feature(image_id),
        'image/key/sha256': _bytes_feature(key),
        'image/object/class/label': _int64_feature(bbox_labels),
        'image/object/class/text': _bytes_list_feature(bbox_text),
        'image/object/is_crowd': _int64_feature(is_crowd)

    }))
    return example


class ImageCoder(object):
    """Helper class that provides TensorFlow image coding utilities."""

    def __init__(self):
        # Create a single Session to run all image coding calls.
        self._sess = tf.compat.v1.Session()

        # Initializes function that converts PNG to JPEG data.
        self._png_data = tf.compat.v1.placeholder(dtype=tf.string)
        image = tf.image.decode_png(self._png_data, channels=3)
        self._png_to_jpeg = tf.image.encode_jpeg(image, format='rgb', quality=100)

        # Initializes function that decodes RGB JPEG data.
        self._decode_jpeg_data = tf.compat.v1.placeholder(dtype=tf.string)
        self._decode_jpeg = tf.image.decode_jpeg(self._decode_jpeg_data, channels=3)

    def png_to_jpeg(self, image_data):
        # Convert the image data from png to jpg
        return self._sess.run(self._png_to_jpeg,
                              feed_dict={self._png_data: image_data})

    def decode_jpeg(self, image_data):
        # Decode the image data as a jpeg image
        image = self._sess.run(self._decode_jpeg,
                               feed_dict={self._decode_jpeg_data: image_data})
        assert len(image.shape) == 3, "JPEG needs to have height x width x channels"
        assert image.shape[2] == 3, "JPEG needs to have 3 channels (RGB)"
        return image

def _is_png(filename):
    """Determine if a file contains a PNG format image.
    Args:
      filename: string, path of the image file.
    Returns:
      boolean indicating if the image is a PNG.
    """
    _, file_extension = os.path.splitext(filename)
    return file_extension.lower() == '.png'

def _process_image(filename, coder):
    """Process a single image file.
    Args:
      filename: string, path to an image file e.g., '/path/to/example.JPG'.
      coder: instance of ImageCoder to provide TensorFlow image coding utils.
    Returns:
      image_buffer: string, JPEG encoding of RGB image.
      height: integer, image height in pixels.
      width: integer, image width in pixels.
    """
    # Read the image file.
    image_data = tf.io.gfile.GFile(filename, 'rb').read()  # tf.gfile.FastGFile

    # Clean the dirty data.
    if _is_png(filename):
        image_data = coder.png_to_jpeg(image_data)

    # Decode the RGB JPEG.
    image = coder.decode_jpeg(image_data)

    # Check that image converted to RGB
    assert len(image.shape) == 3
    height = image.shape[0]
    width = image.shape[1]
    assert image.shape[2] == 3

    return image_data, height, width


def _process_image_files_batch(coder, thread_index, ranges, name, output_directory,
                               dataset, num_shards, store_images, error_queue):
    """Processes and saves list of images as TFRecord in 1 thread.
    Args:
      coder: instance of ImageCoder to provide TensorFlow image coding utils.
      thread_index: integer, unique batch to run index is within [0, len(ranges)).
      ranges: list of pairs of integers specifying ranges of each batches to
        analyze in parallel.
      name: string, unique identifier specifying the data set (e.g. `train` or `test`)
      output_directory: string, file path to store the tfrecord files.
      dataset: list, a list of image example dicts
      num_shards: integer number of shards for this data set.
      store_images: bool, should the image be stored in the tfrecord
      error_queue: Queue, a queue to place image examples that failed.
    """
    # Each thread produces N shards where N = int(num_shards / num_threads).
    # For instance, if num_shards = 128, and the num_threads = 2, then the first
    # thread would produce shards [0, 64).
    num_threads = len(ranges)
    assert not num_shards % num_threads
    num_shards_per_batch = int(num_shards / num_threads)

    shard_ranges = np.linspace(ranges[thread_index][0],
                               ranges[thread_index][1],
                               num_shards_per_batch + 1).astype(int)
    num_files_in_thread = ranges[thread_index][1] - ranges[thread_index][0]

    counter = 0
    error_counter = 0
    for s in range(num_shards_per_batch):
        # Generate a sharded version of the file name, e.g. 'train-00002-of-00010'
        shard = thread_index * num_shards_per_batch + s  
        # output_filename = '%s-%.5d-of-%.5d' % (name, shard, num_shards)

        # write count in tfrecord filename -- tfrecords don't store metadata info - 
        # so its better to store count in filname - 
        # it will avoid iterating whole dataset to counting records
        output_filename = '%s-%.2d-of-%.2d-cnt-.tfrec' % (name, shard, num_shards)

        output_file = os.path.join(output_directory, output_filename)
        writer = tf.io.TFRecordWriter(output_file)

        shard_counter = 0
        files_in_shard = np.arange(shard_ranges[s], shard_ranges[s + 1], dtype=int)
        for i in files_in_shard:

            image_example = dataset[i]

            filename = str(image_example['filename'])

            try:
                if store_images:
                    if 'encoded' in image_example:
                        image_buffer = image_example['encoded']
                        height = image_example['height']
                        width = image_example['width']
                        colorspace = image_example['colorspace']
                        image_format = image_example['format']
                        num_channels = image_example['channels']
                        example = _convert_to_example(image_example, image_buffer, height,
                                                    width, colorspace, num_channels,
                                                    image_format)

                    else:
                        image_buffer, height, width = _process_image(filename, coder)
                        example = _convert_to_example(image_example, image_buffer, height,
                                                    width)
                else:
                    image_buffer=''
                    height = int(image_example['height'])
                    width = int(image_example['width'])
                    example = _convert_to_example(image_example, image_buffer, height,
                                                    width)

                writer.write(example.SerializeToString())
                shard_counter += 1
                counter += 1
            except Exception as e:
                raise
                error_counter += 1
                error_msg = repr(e)
                image_example['error_msg'] = error_msg
                error_queue.put(image_example)

            if not counter % 1000:
                print('%s [thread %d]: Processed %d of %d images in thread batch, with %d errors.' %
                      (datetime.now(), thread_index, counter, num_files_in_thread, error_counter))
                sys.stdout.flush()

        # add total image count in tfrecord filename
        # it will avoid iterating dataset to get count
        path = pathlib.Path(output_file)
        old_name = path.stem
        old_extension = path.suffix
        directory = path.parent
        new_name = old_name + str(shard_counter)  + old_extension
        new_output_file = pathlib.Path(directory, new_name)
        path.rename(new_output_file) # rename file on disk

        print('%s [thread %d]: Wrote %d images to %s, with %d errors.' %
              (datetime.now(), thread_index, shard_counter, new_output_file, error_counter))
        
        sys.stdout.flush()
        shard_counter = 0

    print('%s [thread %d]: Wrote %d images to %d shards, with %d errors.' %
          (datetime.now(), thread_index, counter, num_files_in_thread, error_counter))
    sys.stdout.flush()


def create(dataset, dataset_name, output_directory, num_shards, num_threads, shuffle=True, store_images=True):
    """Create the tfrecord files to be used to train or test a model.

    Args:
      dataset : [{
        "filename" : <REQUIRED: path to the image file>,
        "id" : <REQUIRED: id of the image>,
        "class" : {
          "label" : <[0, num_classes)>,
          "text" : <text description of class>
        },
        "object" : {
          "bbox" : {
            "xmin" : [],
            "xmax" : [],
            "ymin" : [],
            "ymax" : [],
            "label" : []
          }
        }
      }]

      dataset_name: a name for the dataset

      output_directory: path to a directory to write the tfrecord files

      num_shards: the number of tfrecord files to create

      num_threads: the number of threads to use

      shuffle : bool, should the image examples be shuffled or not prior to creating the tfrecords.

    Returns:
      list : a list of image examples that failed to process.
    """

    # Images in the tfrecords set must be shuffled properly
    if shuffle:
        random.shuffle(dataset)

    # Break all images into batches with a [ranges[i][0], ranges[i][1]].
    spacing = np.linspace(0, len(dataset), num_threads + 1).astype(np.int)
    ranges = []
    threads = []
    for i in range(len(spacing) - 1):
        ranges.append([spacing[i], spacing[i+1]])

    # Launch a thread for each batch.
    print('Launching %d threads for spacings: %s' % (num_threads, ranges))
    sys.stdout.flush()

    # Create a mechanism for monitoring when all threads are finished.
    coord = tf.train.Coordinator()

    # Create a generic TensorFlow-based utility for converting all image codings.
    coder = ImageCoder()

    # A Queue to hold the image examples that fail to process.
    error_queue = Queue()

    threads = []
    for thread_index in range(len(ranges)):
        args = (coder, thread_index, ranges, dataset_name, output_directory, dataset,
                num_shards, store_images, error_queue)
        t = threading.Thread(target=_process_image_files_batch, args=args)
        t.start()
        threads.append(t)

    # Wait for all the threads to terminate.
    coord.join(threads)
    print('%s: Finished writing all %d images in data set.' %
          (datetime.now(), len(dataset)))

    # Collect the errors
    errors = []
    while not error_queue.empty():
        errors.append(error_queue.get())
    print ('%d examples failed.' % (len(errors),))

    return errors


def parse_args():

    parser = argparse.ArgumentParser(description='Basic statistics on tfrecord files')

    parser.add_argument('--dataset_path', dest='dataset_path',
                        help='Path to the dataset json file.', type=str,
                        required=True)

    parser.add_argument('--prefix', dest='dataset_name',
                        help='Prefix for the tfrecords (e.g. `train`, `test`, `val`).', type=str,
                        required=True)

    parser.add_argument('--output_dir', dest='output_dir',
                        help='Directory for the tfrecords.', type=str,
                        required=True)

    parser.add_argument('--shards', dest='num_shards',
                        help='Number of shards to make.', type=int,
                        required=True)

    parser.add_argument('--threads', dest='num_threads',
                        help='Number of threads to make.', type=int,
                        required=True)

    parser.add_argument('--shuffle', dest='shuffle',
                        help='Shuffle the records before saving them.',
                        required=False, action='store_true', default=False)

    parser.add_argument('--store_images', dest='store_images',
                        help='Store the images in the tfrecords.',
                        required=False, action='store_true', default=False)

    parsed_args = parser.parse_args()

    return parsed_args

def main():

    args = parse_args()

    with open(args.dataset_path) as f:
        dataset = json.load(f)

    errors = create(
        dataset=dataset,
        dataset_name=args.dataset_name,
        output_directory=args.output_dir,
        num_shards=args.num_shards,
        num_threads=args.num_threads,
        shuffle=args.shuffle,
        store_images=args.store_images
    )

    return errors

if __name__ == '__main__':
    main()