# Copyright 2022 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
#
# 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.
# ==============================================================================
"""Utilities related to image handling."""


import io
import pathlib
import warnings

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

from keras.src import backend

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

try:
    from PIL import Image as pil_image

    try:
        pil_image_resampling = pil_image.Resampling
    except AttributeError:
        pil_image_resampling = pil_image
except ImportError:
    pil_image = None
    pil_image_resampling = None


if pil_image_resampling is not None:
    _PIL_INTERPOLATION_METHODS = {
        "nearest": pil_image_resampling.NEAREST,
        "bilinear": pil_image_resampling.BILINEAR,
        "bicubic": pil_image_resampling.BICUBIC,
        "hamming": pil_image_resampling.HAMMING,
        "box": pil_image_resampling.BOX,
        "lanczos": pil_image_resampling.LANCZOS,
    }

ResizeMethod = tf.image.ResizeMethod

_TF_INTERPOLATION_METHODS = {
    "bilinear": ResizeMethod.BILINEAR,
    "nearest": ResizeMethod.NEAREST_NEIGHBOR,
    "bicubic": ResizeMethod.BICUBIC,
    "area": ResizeMethod.AREA,
    "lanczos3": ResizeMethod.LANCZOS3,
    "lanczos5": ResizeMethod.LANCZOS5,
    "gaussian": ResizeMethod.GAUSSIAN,
    "mitchellcubic": ResizeMethod.MITCHELLCUBIC,
}


@keras_export("keras.preprocessing.image.smart_resize", v1=[])
def smart_resize(x, size, interpolation="bilinear"):
    """Resize images to a target size without aspect ratio distortion.

    Warning: `tf.keras.preprocessing.image.smart_resize` is not recommended for
    new code. Prefer `tf.keras.layers.Resizing`, which provides the same
    functionality as a preprocessing layer and adds `tf.RaggedTensor` support.
    See the [preprocessing layer guide](
    https://www.tensorflow.org/guide/keras/preprocessing_layers)
    for an overview of preprocessing layers.

    TensorFlow image datasets typically yield images that have each a different
    size. However, these images need to be batched before they can be
    processed by Keras layers. To be batched, images need to share the same
    height and width.

    You could simply do:

    ```python
    size = (200, 200)
    ds = ds.map(lambda img: tf.image.resize(img, size))
    ```

    However, if you do this, you distort the aspect ratio of your images, since
    in general they do not all have the same aspect ratio as `size`. This is
    fine in many cases, but not always (e.g. for GANs this can be a problem).

    Note that passing the argument `preserve_aspect_ratio=True` to `resize`
    will preserve the aspect ratio, but at the cost of no longer respecting the
    provided target size. Because `tf.image.resize` doesn't crop images,
    your output images will still have different sizes.

    This calls for:

    ```python
    size = (200, 200)
    ds = ds.map(lambda img: smart_resize(img, size))
    ```

    Your output images will actually be `(200, 200)`, and will not be distorted.
    Instead, the parts of the image that do not fit within the target size
    get cropped out.

    The resizing process is:

    1. Take the largest centered crop of the image that has the same aspect
    ratio as the target size. For instance, if `size=(200, 200)` and the input
    image has size `(340, 500)`, we take a crop of `(340, 340)` centered along
    the width.
    2. Resize the cropped image to the target size. In the example above,
    we resize the `(340, 340)` crop to `(200, 200)`.

    Args:
      x: Input image or batch of images (as a tensor or NumPy array). Must be in
        format `(height, width, channels)` or `(batch_size, height, width,
        channels)`.
      size: Tuple of `(height, width)` integer. Target size.
      interpolation: String, interpolation to use for resizing. Defaults to
        `'bilinear'`. Supports `bilinear`, `nearest`, `bicubic`, `area`,
        `lanczos3`, `lanczos5`, `gaussian`, `mitchellcubic`.

    Returns:
      Array with shape `(size[0], size[1], channels)`. If the input image was a
      NumPy array, the output is a NumPy array, and if it was a TF tensor,
      the output is a TF tensor.
    """
    if len(size) != 2:
        raise ValueError(
            f"Expected `size` to be a tuple of 2 integers, but got: {size}."
        )
    img = tf.convert_to_tensor(x)
    if img.shape.rank is not None:
        if img.shape.rank < 3 or img.shape.rank > 4:
            raise ValueError(
                "Expected an image array with shape `(height, width, "
                "channels)`, or `(batch_size, height, width, channels)`, but "
                f"got input with incorrect rank, of shape {img.shape}."
            )
    shape = tf.shape(img)
    height, width = shape[-3], shape[-2]
    target_height, target_width = size
    if img.shape.rank is not None:
        static_num_channels = img.shape[-1]
    else:
        static_num_channels = None

    crop_height = tf.cast(
        tf.cast(width * target_height, "float32") / target_width, "int32"
    )
    crop_width = tf.cast(
        tf.cast(height * target_width, "float32") / target_height, "int32"
    )

    # Set back to input height / width if crop_height / crop_width is not
    # smaller.
    crop_height = tf.minimum(height, crop_height)
    crop_width = tf.minimum(width, crop_width)

    crop_box_hstart = tf.cast(
        tf.cast(height - crop_height, "float32") / 2, "int32"
    )
    crop_box_wstart = tf.cast(
        tf.cast(width - crop_width, "float32") / 2, "int32"
    )

    if img.shape.rank == 4:
        crop_box_start = tf.stack([0, crop_box_hstart, crop_box_wstart, 0])
        crop_box_size = tf.stack([-1, crop_height, crop_width, -1])
    else:
        crop_box_start = tf.stack([crop_box_hstart, crop_box_wstart, 0])
        crop_box_size = tf.stack([crop_height, crop_width, -1])

    img = tf.slice(img, crop_box_start, crop_box_size)
    img = tf.image.resize(images=img, size=size, method=interpolation)
    # Apparent bug in resize_images_v2 may cause shape to be lost
    if img.shape.rank is not None:
        if img.shape.rank == 4:
            img.set_shape((None, None, None, static_num_channels))
        if img.shape.rank == 3:
            img.set_shape((None, None, static_num_channels))
    if isinstance(x, np.ndarray):
        return img.numpy()
    return img


def get_interpolation(interpolation):
    interpolation = interpolation.lower()
    if interpolation not in _TF_INTERPOLATION_METHODS:
        raise NotImplementedError(
            "Value not recognized for `interpolation`: {}. Supported values "
            "are: {}".format(interpolation, _TF_INTERPOLATION_METHODS.keys())
        )
    return _TF_INTERPOLATION_METHODS[interpolation]


@keras_export(
    "keras.utils.array_to_img", "keras.preprocessing.image.array_to_img"
)
def array_to_img(x, data_format=None, scale=True, dtype=None):
    """Converts a 3D Numpy array to a PIL Image instance.

    Usage:

    ```python
    from PIL import Image
    img = np.random.random(size=(100, 100, 3))
    pil_img = tf.keras.utils.array_to_img(img)
    ```


    Args:
        x: Input data, in any form that can be converted to a Numpy array.
        data_format: Image data format, can be either `"channels_first"` or
          `"channels_last"`. Defaults to `None`, in which case the global
          setting `tf.keras.backend.image_data_format()` is used (unless you
          changed it, it defaults to `"channels_last"`).
        scale: Whether to rescale the image such that minimum and maximum values
          are 0 and 255 respectively. Defaults to `True`.
        dtype: Dtype to use. Default to `None`, in which case the global setting
          `tf.keras.backend.floatx()` is used (unless you changed it, it
          defaults to `"float32"`)

    Returns:
        A PIL Image instance.

    Raises:
        ImportError: if PIL is not available.
        ValueError: if invalid `x` or `data_format` is passed.
    """

    if data_format is None:
        data_format = backend.image_data_format()
    if dtype is None:
        dtype = backend.floatx()
    if pil_image is None:
        raise ImportError(
            "Could not import PIL.Image. "
            "The use of `array_to_img` requires PIL."
        )
    x = np.asarray(x, dtype=dtype)
    if x.ndim != 3:
        raise ValueError(
            "Expected image array to have rank 3 (single image). "
            f"Got array with shape: {x.shape}"
        )

    if data_format not in {"channels_first", "channels_last"}:
        raise ValueError(f"Invalid data_format: {data_format}")

    # Original Numpy array x has format (height, width, channel)
    # or (channel, height, width)
    # but target PIL image has format (width, height, channel)
    if data_format == "channels_first":
        x = x.transpose(1, 2, 0)
    if scale:
        x = x - np.min(x)
        x_max = np.max(x)
        if x_max != 0:
            x /= x_max
        x *= 255
    if x.shape[2] == 4:
        # RGBA
        return pil_image.fromarray(x.astype("uint8"), "RGBA")
    elif x.shape[2] == 3:
        # RGB
        return pil_image.fromarray(x.astype("uint8"), "RGB")
    elif x.shape[2] == 1:
        # grayscale
        if np.max(x) > 255:
            # 32-bit signed integer grayscale image. PIL mode "I"
            return pil_image.fromarray(x[:, :, 0].astype("int32"), "I")
        return pil_image.fromarray(x[:, :, 0].astype("uint8"), "L")
    else:
        raise ValueError(f"Unsupported channel number: {x.shape[2]}")


@keras_export(
    "keras.utils.img_to_array", "keras.preprocessing.image.img_to_array"
)
def img_to_array(img, data_format=None, dtype=None):
    """Converts a PIL Image instance to a Numpy array.

    Usage:

    ```python
    from PIL import Image
    img_data = np.random.random(size=(100, 100, 3))
    img = tf.keras.utils.array_to_img(img_data)
    array = tf.keras.utils.image.img_to_array(img)
    ```


    Args:
        img: Input PIL Image instance.
        data_format: Image data format, can be either `"channels_first"` or
          `"channels_last"`. Defaults to `None`, in which case the global
          setting `tf.keras.backend.image_data_format()` is used (unless you
          changed it, it defaults to `"channels_last"`).
        dtype: Dtype to use. Default to `None`, in which case the global setting
          `tf.keras.backend.floatx()` is used (unless you changed it, it
          defaults to `"float32"`).

    Returns:
        A 3D Numpy array.

    Raises:
        ValueError: if invalid `img` or `data_format` is passed.
    """

    if data_format is None:
        data_format = backend.image_data_format()
    if dtype is None:
        dtype = backend.floatx()
    if data_format not in {"channels_first", "channels_last"}:
        raise ValueError(f"Unknown data_format: {data_format}")
    # Numpy array x has format (height, width, channel)
    # or (channel, height, width)
    # but original PIL image has format (width, height, channel)
    x = np.asarray(img, dtype=dtype)
    if len(x.shape) == 3:
        if data_format == "channels_first":
            x = x.transpose(2, 0, 1)
    elif len(x.shape) == 2:
        if data_format == "channels_first":
            x = x.reshape((1, x.shape[0], x.shape[1]))
        else:
            x = x.reshape((x.shape[0], x.shape[1], 1))
    else:
        raise ValueError(f"Unsupported image shape: {x.shape}")
    return x


@keras_export("keras.utils.save_img", "keras.preprocessing.image.save_img")
def save_img(path, x, data_format=None, file_format=None, scale=True, **kwargs):
    """Saves an image stored as a Numpy array to a path or file object.

    Args:
        path: Path or file object.
        x: Numpy array.
        data_format: Image data format, either `"channels_first"` or
          `"channels_last"`.
        file_format: Optional file format override. If omitted, the format to
          use is determined from the filename extension. If a file object was
          used instead of a filename, this parameter should always be used.
        scale: Whether to rescale image values to be within `[0, 255]`.
        **kwargs: Additional keyword arguments passed to `PIL.Image.save()`.
    """
    if data_format is None:
        data_format = backend.image_data_format()
    img = array_to_img(x, data_format=data_format, scale=scale)
    if img.mode == "RGBA" and (file_format == "jpg" or file_format == "jpeg"):
        warnings.warn(
            "The JPG format does not support RGBA images, converting to RGB."
        )
        img = img.convert("RGB")
    img.save(path, format=file_format, **kwargs)


@keras_export("keras.utils.load_img", "keras.preprocessing.image.load_img")
def load_img(
    path,
    grayscale=False,
    color_mode="rgb",
    target_size=None,
    interpolation="nearest",
    keep_aspect_ratio=False,
):
    """Loads an image into PIL format.

    Usage:

    ```python
    image = tf.keras.utils.load_img(image_path)
    input_arr = tf.keras.utils.img_to_array(image)
    input_arr = np.array([input_arr])  # Convert single image to a batch.
    predictions = model.predict(input_arr)
    ```

    Args:
        path: Path to image file.
        grayscale: DEPRECATED use `color_mode="grayscale"`.
        color_mode: One of `"grayscale"`, `"rgb"`, `"rgba"`. Default: `"rgb"`.
          The desired image format.
        target_size: Either `None` (default to original size) or tuple of ints
          `(img_height, img_width)`.
        interpolation: Interpolation method used to resample the image if the
          target size is different from that of the loaded image. Supported
          methods are `"nearest"`, `"bilinear"`, and `"bicubic"`. If PIL version
          1.1.3 or newer is installed, `"lanczos"` is also supported. If PIL
          version 3.4.0 or newer is installed, `"box"` and `"hamming"` are also
          supported. By default, `"nearest"` is used.
        keep_aspect_ratio: Boolean, whether to resize images to a target
                size without aspect ratio distortion. The image is cropped in
                the center with target aspect ratio before resizing.

    Returns:
        A PIL Image instance.

    Raises:
        ImportError: if PIL is not available.
        ValueError: if interpolation method is not supported.
    """
    if grayscale:
        warnings.warn(
            'grayscale is deprecated. Please use color_mode = "grayscale"'
        )
        color_mode = "grayscale"
    if pil_image is None:
        raise ImportError(
            "Could not import PIL.Image. The use of `load_img` requires PIL."
        )
    if isinstance(path, io.BytesIO):
        img = pil_image.open(path)
    elif isinstance(path, (pathlib.Path, bytes, str)):
        if isinstance(path, pathlib.Path):
            path = str(path.resolve())
        with open(path, "rb") as f:
            img = pil_image.open(io.BytesIO(f.read()))
    else:
        raise TypeError(
            f"path should be path-like or io.BytesIO, not {type(path)}"
        )

    if color_mode == "grayscale":
        # if image is not already an 8-bit, 16-bit or 32-bit grayscale image
        # convert it to an 8-bit grayscale image.
        if img.mode not in ("L", "I;16", "I"):
            img = img.convert("L")
    elif color_mode == "rgba":
        if img.mode != "RGBA":
            img = img.convert("RGBA")
    elif color_mode == "rgb":
        if img.mode != "RGB":
            img = img.convert("RGB")
    else:
        raise ValueError('color_mode must be "grayscale", "rgb", or "rgba"')
    if target_size is not None:
        width_height_tuple = (target_size[1], target_size[0])
        if img.size != width_height_tuple:
            if interpolation not in _PIL_INTERPOLATION_METHODS:
                raise ValueError(
                    "Invalid interpolation method {} specified. Supported "
                    "methods are {}".format(
                        interpolation,
                        ", ".join(_PIL_INTERPOLATION_METHODS.keys()),
                    )
                )
            resample = _PIL_INTERPOLATION_METHODS[interpolation]

            if keep_aspect_ratio:
                width, height = img.size
                target_width, target_height = width_height_tuple

                crop_height = (width * target_height) // target_width
                crop_width = (height * target_width) // target_height

                # Set back to input height / width
                # if crop_height / crop_width is not smaller.
                crop_height = min(height, crop_height)
                crop_width = min(width, crop_width)

                crop_box_hstart = (height - crop_height) // 2
                crop_box_wstart = (width - crop_width) // 2
                crop_box_wend = crop_box_wstart + crop_width
                crop_box_hend = crop_box_hstart + crop_height
                crop_box = [
                    crop_box_wstart,
                    crop_box_hstart,
                    crop_box_wend,
                    crop_box_hend,
                ]
                img = img.resize(width_height_tuple, resample, box=crop_box)
            else:
                img = img.resize(width_height_tuple, resample)
    return img

