# Copyright 2020 The TensorFlow Authors. 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
#
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# distributed under the License is distributed on an "AS IS" BASIS,
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# ==============================================================================
"""Keras image dataset loading utilities."""

import numpy as np
import tensorflow.compat.v2 as tf

from keras.utils import dataset_utils
from keras.utils import image_utils

# isort: off
from tensorflow.python.util.tf_export import keras_export


ALLOWLIST_FORMATS = (".bmp", ".gif", ".jpeg", ".jpg", ".png")


@keras_export(
    "keras.utils.image_dataset_from_directory",
    "keras.preprocessing.image_dataset_from_directory",
    v1=[],
)
def image_dataset_from_directory(
    directory,
    labels="inferred",
    label_mode="int",
    class_names=None,
    color_mode="rgb",
    batch_size=32,
    image_size=(256, 256),
    shuffle=True,
    seed=None,
    validation_split=None,
    subset=None,
    interpolation="bilinear",
    follow_links=False,
    crop_to_aspect_ratio=False,
    **kwargs,
):
    """Generates a `tf.data.Dataset` from image files in a directory.

    If your directory structure is:

    ```
    main_directory/
    ...class_a/
    ......a_image_1.jpg
    ......a_image_2.jpg
    ...class_b/
    ......b_image_1.jpg
    ......b_image_2.jpg
    ```

    Then calling `image_dataset_from_directory(main_directory,
    labels='inferred')` will return a `tf.data.Dataset` that yields batches of
    images from the subdirectories `class_a` and `class_b`, together with labels
    0 and 1 (0 corresponding to `class_a` and 1 corresponding to `class_b`).

    Supported image formats: jpeg, png, bmp, gif.
    Animated gifs are truncated to the first frame.

    Args:
      directory: Directory where the data is located.
          If `labels` is "inferred", it should contain
          subdirectories, each containing images for a class.
          Otherwise, the directory structure is ignored.
      labels: Either "inferred"
          (labels are generated from the directory structure),
          None (no labels),
          or a list/tuple of integer labels of the same size as the number of
          image files found in the directory. Labels should be sorted according
          to the alphanumeric order of the image file paths
          (obtained via `os.walk(directory)` in Python).
      label_mode: String describing the encoding of `labels`. Options are:
          - 'int': means that the labels are encoded as integers
              (e.g. for `sparse_categorical_crossentropy` loss).
          - 'categorical' means that the labels are
              encoded as a categorical vector
              (e.g. for `categorical_crossentropy` loss).
          - 'binary' means that the labels (there can be only 2)
              are encoded as `float32` scalars with values 0 or 1
              (e.g. for `binary_crossentropy`).
          - None (no labels).
      class_names: Only valid if "labels" is "inferred". This is the explicit
          list of class names (must match names of subdirectories). Used
          to control the order of the classes
          (otherwise alphanumerical order is used).
      color_mode: One of "grayscale", "rgb", "rgba". Default: "rgb".
          Whether the images will be converted to
          have 1, 3, or 4 channels.
      batch_size: Size of the batches of data. Default: 32.
        If `None`, the data will not be batched
        (the dataset will yield individual samples).
      image_size: Size to resize images to after they are read from disk,
          specified as `(height, width)`. Defaults to `(256, 256)`.
          Since the pipeline processes batches of images that must all have
          the same size, this must be provided.
      shuffle: Whether to shuffle the data. Default: True.
          If set to False, sorts the data in alphanumeric order.
      seed: Optional random seed for shuffling and transformations.
      validation_split: Optional float between 0 and 1,
          fraction of data to reserve for validation.
      subset: Subset of the data to return.
          One of "training", "validation" or "both".
          Only used if `validation_split` is set.
          When `subset="both"`, the utility returns a tuple of two datasets
          (the training and validation datasets respectively).
      interpolation: String, the interpolation method used when resizing images.
        Defaults to `bilinear`. Supports `bilinear`, `nearest`, `bicubic`,
        `area`, `lanczos3`, `lanczos5`, `gaussian`, `mitchellcubic`.
      follow_links: Whether to visit subdirectories pointed to by symlinks.
          Defaults to False.
      crop_to_aspect_ratio: If True, resize the images without aspect
        ratio distortion. When the original aspect ratio differs from the target
        aspect ratio, the output image will be cropped so as to return the
        largest possible window in the image (of size `image_size`) that matches
        the target aspect ratio. By default (`crop_to_aspect_ratio=False`),
        aspect ratio may not be preserved.
      **kwargs: Legacy keyword arguments.

    Returns:
      A `tf.data.Dataset` object.
        - If `label_mode` is None, it yields `float32` tensors of shape
          `(batch_size, image_size[0], image_size[1], num_channels)`,
          encoding images (see below for rules regarding `num_channels`).
        - Otherwise, it yields a tuple `(images, labels)`, where `images`
          has shape `(batch_size, image_size[0], image_size[1], num_channels)`,
          and `labels` follows the format described below.

    Rules regarding labels format:
      - if `label_mode` is `int`, the labels are an `int32` tensor of shape
        `(batch_size,)`.
      - if `label_mode` is `binary`, the labels are a `float32` tensor of
        1s and 0s of shape `(batch_size, 1)`.
      - if `label_mode` is `categorical`, the labels are a `float32` tensor
        of shape `(batch_size, num_classes)`, representing a one-hot
        encoding of the class index.

    Rules regarding number of channels in the yielded images:
      - if `color_mode` is `grayscale`,
        there's 1 channel in the image tensors.
      - if `color_mode` is `rgb`,
        there are 3 channels in the image tensors.
      - if `color_mode` is `rgba`,
        there are 4 channels in the image tensors.
    """
    if "smart_resize" in kwargs:
        crop_to_aspect_ratio = kwargs.pop("smart_resize")
    if kwargs:
        raise TypeError(f"Unknown keywords argument(s): {tuple(kwargs.keys())}")
    if labels not in ("inferred", None):
        if not isinstance(labels, (list, tuple)):
            raise ValueError(
                "`labels` argument should be a list/tuple of integer labels, "
                "of the same size as the number of image files in the target "
                "directory. If you wish to infer the labels from the "
                "subdirectory "
                'names in the target directory, pass `labels="inferred"`. '
                "If you wish to get a dataset that only contains images "
                f"(no labels), pass `labels=None`. Received: labels={labels}"
            )
        if class_names:
            raise ValueError(
                "You can only pass `class_names` if "
                f'`labels="inferred"`. Received: labels={labels}, and '
                f"class_names={class_names}"
            )
    if label_mode not in {"int", "categorical", "binary", None}:
        raise ValueError(
            '`label_mode` argument must be one of "int", '
            '"categorical", "binary", '
            f"or None. Received: label_mode={label_mode}"
        )
    if labels is None or label_mode is None:
        labels = None
        label_mode = None
    if color_mode == "rgb":
        num_channels = 3
    elif color_mode == "rgba":
        num_channels = 4
    elif color_mode == "grayscale":
        num_channels = 1
    else:
        raise ValueError(
            '`color_mode` must be one of {"rgb", "rgba", "grayscale"}. '
            f"Received: color_mode={color_mode}"
        )
    interpolation = image_utils.get_interpolation(interpolation)
    dataset_utils.check_validation_split_arg(
        validation_split, subset, shuffle, seed
    )

    if seed is None:
        seed = np.random.randint(1e6)
    image_paths, labels, class_names = dataset_utils.index_directory(
        directory,
        labels,
        formats=ALLOWLIST_FORMATS,
        class_names=class_names,
        shuffle=shuffle,
        seed=seed,
        follow_links=follow_links,
    )

    if label_mode == "binary" and len(class_names) != 2:
        raise ValueError(
            'When passing `label_mode="binary"`, there must be exactly 2 '
            f"class_names. Received: class_names={class_names}"
        )

    if subset == "both":
        (
            image_paths_train,
            labels_train,
        ) = dataset_utils.get_training_or_validation_split(
            image_paths, labels, validation_split, "training"
        )
        (
            image_paths_val,
            labels_val,
        ) = dataset_utils.get_training_or_validation_split(
            image_paths, labels, validation_split, "validation"
        )
        if not image_paths_train:
            raise ValueError(
                f"No training images found in directory {directory}. "
                f"Allowed formats: {ALLOWLIST_FORMATS}"
            )
        if not image_paths_val:
            raise ValueError(
                f"No validation images found in directory {directory}. "
                f"Allowed formats: {ALLOWLIST_FORMATS}"
            )
        train_dataset = paths_and_labels_to_dataset(
            image_paths=image_paths_train,
            image_size=image_size,
            num_channels=num_channels,
            labels=labels_train,
            label_mode=label_mode,
            num_classes=len(class_names),
            interpolation=interpolation,
            crop_to_aspect_ratio=crop_to_aspect_ratio,
        )
        val_dataset = paths_and_labels_to_dataset(
            image_paths=image_paths_val,
            image_size=image_size,
            num_channels=num_channels,
            labels=labels_val,
            label_mode=label_mode,
            num_classes=len(class_names),
            interpolation=interpolation,
            crop_to_aspect_ratio=crop_to_aspect_ratio,
        )
        train_dataset = train_dataset.prefetch(tf.data.AUTOTUNE)
        val_dataset = val_dataset.prefetch(tf.data.AUTOTUNE)
        if batch_size is not None:
            if shuffle:
                # Shuffle locally at each iteration
                train_dataset = train_dataset.shuffle(
                    buffer_size=batch_size * 8, seed=seed
                )
            train_dataset = train_dataset.batch(batch_size)
            val_dataset = val_dataset.batch(batch_size)
        else:
            if shuffle:
                train_dataset = train_dataset.shuffle(
                    buffer_size=1024, seed=seed
                )

        # Users may need to reference `class_names`.
        train_dataset.class_names = class_names
        val_dataset.class_names = class_names
        # Include file paths for images as attribute.
        train_dataset.file_paths = image_paths_train
        val_dataset.file_paths = image_paths_val
        dataset = [train_dataset, val_dataset]
    else:
        image_paths, labels = dataset_utils.get_training_or_validation_split(
            image_paths, labels, validation_split, subset
        )
        if not image_paths:
            raise ValueError(
                f"No images found in directory {directory}. "
                f"Allowed formats: {ALLOWLIST_FORMATS}"
            )

        dataset = paths_and_labels_to_dataset(
            image_paths=image_paths,
            image_size=image_size,
            num_channels=num_channels,
            labels=labels,
            label_mode=label_mode,
            num_classes=len(class_names),
            interpolation=interpolation,
            crop_to_aspect_ratio=crop_to_aspect_ratio,
        )
        dataset = dataset.prefetch(tf.data.AUTOTUNE)
        if batch_size is not None:
            if shuffle:
                # Shuffle locally at each iteration
                dataset = dataset.shuffle(buffer_size=batch_size * 8, seed=seed)
            dataset = dataset.batch(batch_size)
        else:
            if shuffle:
                dataset = dataset.shuffle(buffer_size=1024, seed=seed)

        # Users may need to reference `class_names`.
        dataset.class_names = class_names
        # Include file paths for images as attribute.
        dataset.file_paths = image_paths
    return dataset


def paths_and_labels_to_dataset(
    image_paths,
    image_size,
    num_channels,
    labels,
    label_mode,
    num_classes,
    interpolation,
    crop_to_aspect_ratio=False,
):
    """Constructs a dataset of images and labels."""
    # TODO(fchollet): consider making num_parallel_calls settable
    path_ds = tf.data.Dataset.from_tensor_slices(image_paths)
    args = (image_size, num_channels, interpolation, crop_to_aspect_ratio)
    img_ds = path_ds.map(
        lambda x: load_image(x, *args), num_parallel_calls=tf.data.AUTOTUNE
    )
    if label_mode:
        label_ds = dataset_utils.labels_to_dataset(
            labels, label_mode, num_classes
        )
        img_ds = tf.data.Dataset.zip((img_ds, label_ds))
    return img_ds


def load_image(
    path, image_size, num_channels, interpolation, crop_to_aspect_ratio=False
):
    """Load an image from a path and resize it."""
    img = tf.io.read_file(path)
    img = tf.image.decode_image(
        img, channels=num_channels, expand_animations=False
    )
    if crop_to_aspect_ratio:
        img = image_utils.smart_resize(
            img, image_size, interpolation=interpolation
        )
    else:
        img = tf.image.resize(img, image_size, method=interpolation)
    img.set_shape((image_size[0], image_size[1], num_channels))
    return img
