"""
Title: Creating TFRecords
Author: [Dimitre Oliveira](https://www.linkedin.com/in/dimitre-oliveira-7a1a0113a/)
Date created: 2021/02/27
Last modified: 2021/02/27
Description: Converting data to the TFRecord format.
Accelerator: GPU
"""

"""
## Introduction

The TFRecord format is a simple format for storing a sequence of binary records.
Converting your data into TFRecord has many advantages, such as:

- **More efficient storage**: the TFRecord data can take up less space than the original
data; it can also be partitioned into multiple files.
- **Fast I/O**: the TFRecord format can be read with parallel I/O operations, which is
useful for [TPUs](https://www.tensorflow.org/guide/tpu) or multiple hosts.
- **Self-contained files**: the TFRecord data can be read from a single source—for
example, the [COCO2017](https://cocodataset.org/) dataset originally stores data in
two folders ("images" and "annotations").

An important use case of the TFRecord data format  is training on TPUs. First, TPUs are
fast enough to benefit from optimized I/O operations. In addition, TPUs require
data to be stored remotely (e.g. on Google Cloud Storage) and using the TFRecord format
makes it easier to load the data without batch-downloading.

Performance using the TFRecord format can be further improved if you also use
it with the [tf.data](https://www.tensorflow.org/guide/data) API.

In this example you will learn how to convert data of different types (image, text, and
numeric) into TFRecord.

**Reference**

- [TFRecord and tf.train.Example](https://www.tensorflow.org/tutorials/load_data/tfrecord)


## Dependencies
"""

import os
import json
import pprint
import tensorflow as tf
import matplotlib.pyplot as plt

"""
## Download the COCO2017 dataset

We will be using the [COCO2017](https://cocodataset.org/) dataset, because it has many
different types of features, including images, floating point data, and lists.
It will serve as a good example of how to encode different features into the TFRecord
format.

This dataset has two sets of fields: images and annotation meta-data.

The images are a collection of JPG files and the meta-data are stored in a JSON file
which, according to the [official site](https://cocodataset.org/#format-data),
contains the following properties:

```
id: int,
image_id: int,
category_id: int,
segmentation: RLE or [polygon], object segmentation mask
bbox: [x,y,width,height], object bounding box coordinates
area: float, area of the bounding box
iscrowd: 0 or 1, is single object or a collection
```
"""

root_dir = "datasets"
tfrecords_dir = "tfrecords"
images_dir = os.path.join(root_dir, "val2017")
annotations_dir = os.path.join(root_dir, "annotations")
annotation_file = os.path.join(annotations_dir, "instances_val2017.json")
images_url = "http://images.cocodataset.org/zips/val2017.zip"
annotations_url = (
    "http://images.cocodataset.org/annotations/annotations_trainval2017.zip"
)

# Download image files
if not os.path.exists(images_dir):
    image_zip = tf.keras.utils.get_file(
        "images.zip",
        cache_dir=os.path.abspath("."),
        origin=images_url,
        extract=True,
    )
    os.remove(image_zip)

# Download caption annotation files
if not os.path.exists(annotations_dir):
    annotation_zip = tf.keras.utils.get_file(
        "captions.zip",
        cache_dir=os.path.abspath("."),
        origin=annotations_url,
        extract=True,
    )
    os.remove(annotation_zip)

print("The COCO dataset has been downloaded and extracted successfully.")

with open(annotation_file, "r") as f:
    annotations = json.load(f)["annotations"]

print(f"Number of images: {len(annotations)}")

"""
### Contents of the COCO2017 dataset
"""

pprint.pprint(annotations[60])

"""
## Parameters

`num_samples` is the number of data samples on each TFRecord file.

`num_tfrecords` is total number of TFRecords that we will create.
"""

num_samples = 4096
num_tfrecords = len(annotations) // num_samples
if len(annotations) % num_samples:
    num_tfrecords += 1  # add one record if there are any remaining samples

if not os.path.exists(tfrecords_dir):
    os.makedirs(tfrecords_dir)  # creating TFRecords output folder

"""
## Define TFRecords helper functions
"""


def image_feature(value):
    """Returns a bytes_list from a string / byte."""
    return tf.train.Feature(
        bytes_list=tf.train.BytesList(value=[tf.io.encode_jpeg(value).numpy()])
    )


def bytes_feature(value):
    """Returns a bytes_list from a string / byte."""
    return tf.train.Feature(bytes_list=tf.train.BytesList(value=[value.encode()]))


def float_feature(value):
    """Returns a float_list from a float / double."""
    return tf.train.Feature(float_list=tf.train.FloatList(value=[value]))


def int64_feature(value):
    """Returns an int64_list from a bool / enum / int / uint."""
    return tf.train.Feature(int64_list=tf.train.Int64List(value=[value]))


def float_feature_list(value):
    """Returns a list of float_list from a float / double."""
    return tf.train.Feature(float_list=tf.train.FloatList(value=value))


def create_example(image, path, example):
    feature = {
        "image": image_feature(image),
        "path": bytes_feature(path),
        "area": float_feature(example["area"]),
        "bbox": float_feature_list(example["bbox"]),
        "category_id": int64_feature(example["category_id"]),
        "id": int64_feature(example["id"]),
        "image_id": int64_feature(example["image_id"]),
    }
    return tf.train.Example(features=tf.train.Features(feature=feature))


def parse_tfrecord_fn(example):
    feature_description = {
        "image": tf.io.FixedLenFeature([], tf.string),
        "path": tf.io.FixedLenFeature([], tf.string),
        "area": tf.io.FixedLenFeature([], tf.float32),
        "bbox": tf.io.VarLenFeature(tf.float32),
        "category_id": tf.io.FixedLenFeature([], tf.int64),
        "id": tf.io.FixedLenFeature([], tf.int64),
        "image_id": tf.io.FixedLenFeature([], tf.int64),
    }
    example = tf.io.parse_single_example(example, feature_description)
    example["image"] = tf.io.decode_jpeg(example["image"], channels=3)
    example["bbox"] = tf.sparse.to_dense(example["bbox"])
    return example


"""
## Generate data in the TFRecord format

Let's generate the COCO2017 data in the TFRecord format. The format will be
`file_{number}.tfrec` (this is optional, but including the number sequences in the file
names can make counting easier).
"""

for tfrec_num in range(num_tfrecords):
    samples = annotations[(tfrec_num * num_samples) : ((tfrec_num + 1) * num_samples)]

    with tf.io.TFRecordWriter(
        tfrecords_dir + "/file_%.2i-%i.tfrec" % (tfrec_num, len(samples))
    ) as writer:
        for sample in samples:
            image_path = f"{images_dir}/{sample['image_id']:012d}.jpg"
            image = tf.io.decode_jpeg(tf.io.read_file(image_path))
            example = create_example(image, image_path, sample)
            writer.write(example.SerializeToString())

"""
## Explore one sample from the generated TFRecord
"""

raw_dataset = tf.data.TFRecordDataset(f"{tfrecords_dir}/file_00-{num_samples}.tfrec")
parsed_dataset = raw_dataset.map(parse_tfrecord_fn)

for features in parsed_dataset.take(1):
    for key in features.keys():
        if key != "image":
            print(f"{key}: {features[key]}")

    print(f"Image shape: {features['image'].shape}")
    plt.figure(figsize=(7, 7))
    plt.imshow(features["image"].numpy())
    plt.show()

"""
## Train a simple model using the generated TFRecords

Another advantage of TFRecord is that you are able to add many features to it and later
use only a few of them, in this case, we are going to use only `image` and `category_id`.

"""

"""

## Define dataset helper functions
"""


def prepare_sample(features):
    image = tf.image.resize(features["image"], size=(224, 224))
    return image, features["category_id"]


def get_dataset(filenames, batch_size):
    dataset = (
        tf.data.TFRecordDataset(filenames, num_parallel_reads=AUTOTUNE)
        .map(parse_tfrecord_fn, num_parallel_calls=AUTOTUNE)
        .map(prepare_sample, num_parallel_calls=AUTOTUNE)
        .shuffle(batch_size * 10)
        .batch(batch_size)
        .prefetch(AUTOTUNE)
    )
    return dataset


train_filenames = tf.io.gfile.glob(f"{tfrecords_dir}/*.tfrec")
batch_size = 32
epochs = 1
steps_per_epoch = 50
AUTOTUNE = tf.data.AUTOTUNE

input_tensor = tf.keras.layers.Input(shape=(224, 224, 3), name="image")
model = tf.keras.applications.EfficientNetB0(
    input_tensor=input_tensor, weights=None, classes=91
)


model.compile(
    optimizer=tf.keras.optimizers.Adam(),
    loss=tf.keras.losses.SparseCategoricalCrossentropy(),
    metrics=[tf.keras.metrics.SparseCategoricalAccuracy()],
)


model.fit(
    x=get_dataset(train_filenames, batch_size),
    epochs=epochs,
    steps_per_epoch=steps_per_epoch,
    verbose=1,
)

"""
## Conclusion

This example demonstrates that instead of reading images and annotations from different
sources you can have your data coming from a single source thanks to TFRecord.
This process can make storing and reading data simpler and more efficient.
For more information, you can go to the [TFRecord and
tf.train.Example](https://www.tensorflow.org/tutorials/load_data/tfrecord) tutorial.
"""
