preprocessing.py

import tensorflow as tf

_R_MEAN = 123.68
_G_MEAN = 116.78
_B_MEAN = 103.94

_RESIZE_SIDE_MIN = 256
_RESIZE_SIDE_MAX = 512


def _crop(image, offset_height, offset_width, crop_height, crop_width):
    """Crops the given image using the provided offsets and sizes.
  Note that the method doesn't assume we know the input image size but it does
  assume we know the input image rank.
  Args:
    image: an image of shape [height, width, channels].
    offset_height: a scalar tensor indicating the height offset.
    offset_width: a scalar tensor indicating the width offset.
    crop_height: the height of the cropped image.
    crop_width: the width of the cropped image.
  Returns:
    the cropped (and resized) image.
  Raises:
    InvalidArgumentError: if the rank is not 3 or if the image dimensions are
      less than the crop size.
  """
    original_shape = tf.shape(image)

    rank_assertion = tf.Assert(
        tf.equal(tf.rank(image), 3), ['Rank of image must be equal to 3.']
    )
    with tf.control_dependencies([rank_assertion]):
        cropped_shape = tf.stack([crop_height, crop_width, original_shape[2]])

    size_assertion = tf.Assert(
        tf.logical_and(
            tf.greater_equal(original_shape[0], crop_height),
            tf.greater_equal(original_shape[1], crop_width)
        ), ['Crop size greater than the image size.']
    )

    offsets = tf.cast(tf.stack([offset_height, offset_width, 0]), tf.int32)

    # Use tf.slice instead of crop_to_bounding box as it accepts tensors to
    # define the crop size.
    with tf.control_dependencies([size_assertion]):
        image = tf.slice(image, offsets, cropped_shape)
    return tf.reshape(image, cropped_shape)


def _central_crop(image_list, crop_height, crop_width):
    """Performs central crops of the given image list.
  Args:
    image_list: a list of image tensors of the same dimension but possibly
      varying channel.
    crop_height: the height of the image following the crop.
    crop_width: the width of the image following the crop.
  Returns:
    the list of cropped images.
  """
    outputs = []
    for image in image_list:
        image_height = tf.shape(image)[0]
        image_width = tf.shape(image)[1]

        offset_height = (image_height-crop_height) / 2
        offset_width = (image_width-crop_width) / 2

        outputs.append(
            _crop(image, offset_height, offset_width, crop_height, crop_width)
        )
    return outputs


def _mean_image_subtraction(image, means):
    """Subtracts the given means from each image channel.
  For example:
    means = [123.68, 116.779, 103.939]
    image = _mean_image_subtraction(image, means)
  Note that the rank of `image` must be known.
  Args:
    image: a tensor of size [height, width, C].
    means: a C-vector of values to subtract from each channel.
  Returns:
    the centered image.
  Raises:
    ValueError: If the rank of `image` is unknown, if `image` has a rank other
      than three or if the number of channels in `image` doesn't match the
      number of values in `means`.
  """
    if image.get_shape().ndims != 3:
        raise ValueError('Input must be of size [height, width, C>0]')
    num_channels = image.get_shape().as_list()[-1]
    if len(means) != num_channels:
        raise ValueError('len(means) must match the number of channels')

    channels = tf.split(axis=2, num_or_size_splits=num_channels, value=image)
    for i in range(num_channels):
        channels[i] -= means[i]
    return tf.concat(axis=2, values=channels)


def _aspect_preserving_resize(image, smallest_side):
    """Resize images preserving the original aspect ratio.
  Args:
    image: A 3-D image `Tensor`.
    smallest_side: A python integer or scalar `Tensor` indicating the size of
      the smallest side after resize.
  Returns:
    resized_image: A 3-D tensor containing the resized image.
  """
    smallest_side = tf.convert_to_tensor(smallest_side, dtype=tf.int32)

    shape = tf.shape(image)
    height = shape[0]
    width = shape[1]
    new_height, new_width = _smallest_size_at_least(
        height, width, smallest_side
    )
    image = tf.expand_dims(image, 0)
    resized_image = tf.image.resize(image, [new_height, new_width])
    resized_image = tf.squeeze(resized_image)
    resized_image.set_shape([None, None, 3])
    return resized_image


def _smallest_size_at_least(height, width, smallest_side):
    """Computes new shape with the smallest side equal to `smallest_side`.
  Computes new shape with the smallest side equal to `smallest_side` while
  preserving the original aspect ratio.
  Args:
    height: an int32 scalar tensor indicating the current height.
    width: an int32 scalar tensor indicating the current width.
    smallest_side: A python integer or scalar `Tensor` indicating the size of
      the smallest side after resize.
  Returns:
    new_height: an int32 scalar tensor indicating the new height.
    new_width: and int32 scalar tensor indicating the new width.
  """
    smallest_side = tf.convert_to_tensor(smallest_side, dtype=tf.int32)

    height = tf.cast(height, tf.float32)
    width = tf.cast(width, tf.float32)
    smallest_side = tf.cast(smallest_side, tf.float32)

    scale = tf.cond(
        tf.greater(height, width), lambda: smallest_side / width,
        lambda: smallest_side / height
    )
    new_height = tf.cast(tf.math.rint(height * scale), tf.int32)
    new_width = tf.cast(tf.math.rint(width * scale), tf.int32)
    return new_height, new_width


def inception_preprocess(
    image,
    height,
    width,
    central_fraction=0.875,
    scope=None,
    central_crop=False
):
    """Prepare one image for evaluation.
    If height and width are specified it would output an image with that size by
    applying resize_bilinear.
    If central_fraction is specified it would crop the central fraction of the
    input image.
    Args:
    image: 3-D Tensor of image. If dtype is tf.float32 then the range should be
      [0, 1], otherwise it would converted to tf.float32 assuming that the range
      is [0, MAX], where MAX is largest positive representable number for
      int(8/16/32) data type (see `tf.image.convert_image_dtype` for details).
    height: integer
    width: integer
    central_fraction: Optional Float, fraction of the image to crop.
    scope: Optional scope for name_scope.
    central_crop: Enable central cropping of images during preprocessing for
      evaluation.
    Returns:
    3-D float Tensor of prepared image.
    """
    if image.dtype != tf.float32:
        image = tf.image.convert_image_dtype(image, dtype=tf.float32)
        # Crop the central region of the image with an area containing 87.5% of
        # the original image.
    image = tf.image.central_crop(image, central_fraction=central_fraction)
    if height and width:
        # Resize the image to the specified height and width.
        image = tf.expand_dims(image, 0)
        image = tf.image.resize(image, [height, width])
        image = tf.squeeze(image, [0])
    image = tf.subtract(image, 0.5)
    image = tf.multiply(image, 2.0)
    return image


def vgg_preprocess(image, output_height, output_width):
    image = _aspect_preserving_resize(image, _RESIZE_SIDE_MIN)
    image = _central_crop([image], output_height, output_width)[0]
    image.set_shape([output_height, output_width, 3])
    image = tf.cast(image, tf.float32)
    return _mean_image_subtraction(image, [_R_MEAN, _G_MEAN, _B_MEAN])


def resnet50_v1_5_tf1_ngc_preprocess(
    image,
    height,
    width,
    central_fraction=0.875,
    scope=None,
    central_crop=False
):
    """Prepare one image for evaluation.
  If height and width are specified it would output an image with that size by
  applying resize_bilinear.
  If central_fraction is specified it would crop the central fraction of the
  input image.
  Args:
  image: 3-D Tensor of image. If dtype is tf.float32 then the range should be
    [0, 1], otherwise it would converted to tf.float32 assuming that the range
    is [0, MAX], where MAX is largest positive representable number for
    int(8/16/32) data type (see `tf.image.convert_image_dtype` for details).
  height: integer
  width: integer
  central_fraction: Optional Float, fraction of the image to crop.
  scope: Optional scope for name_scope.
  central_crop: Enable central cropping of images during preprocessing for
    evaluation.
  Returns:
  3-D float Tensor of prepared image.
  """
    if image.dtype != tf.float32:
        image = tf.image.convert_image_dtype(image, dtype=tf.float32)
        # Crop the central region of the image with an area containing 87.5% of
        # the original image.
    image = tf.image.central_crop(image, central_fraction=central_fraction)
    if height and width:
        # Resize the image to the specified height and width.
        image = tf.expand_dims(image, 0)
        image = tf.image.resize(image, [height, width])
        image = tf.squeeze(image, [0])
    image = image * 255
    return image


def vision_transformer_preprocess(image, height, width):
    image_resized = tf.image.resize(image, (height, width))
    image_resized = tf.cast(image_resized, tf.float32)
    image_resized = (image_resized-127.5) / 127.5
    return image_resized


def swin_transformer_preprocess(image, height, width):

    if height != width:
        raise UnimplementedError(
            "Preprocessed image must have the same height and width."
        )
    else:
        size = height

    crop_layer = tf.keras.layers.CenterCrop(size, size)
    norm_layer = tf.keras.layers.Normalization(
        mean=[0.485 * 255, 0.456 * 255, 0.406 * 255],
        variance=[(0.229 * 255)**2, (0.224 * 255)**2, (0.225 * 255)**2],
    )

    if size == 224:
        resize_size = int((256/224) * size)
        image = tf.image.resize(
            image, (resize_size, resize_size), method="bicubic"
        )
        image = crop_layer(image)
    else:
        image = tf.image.resize(image, (size, size), method="bicubic")

    return norm_layer(image)