image_processing_bridgetower.py

# coding=utf-8
# Copyright 2023 The Intel Labs Team Authors, The Microsoft Research Team Authors and HuggingFace Inc. team. 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.
"""Image processor class for BridgeTower."""

import warnings
from typing import Any, Dict, Iterable, List, Optional, Tuple, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import PaddingMode, center_crop, normalize, pad, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    get_image_size,
    infer_channel_dimension_format,
    is_batched,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
    import PIL

logger = logging.get_logger(__name__)


# Copied from transformers.models.vilt.image_processing_vilt.max_across_indices
def max_across_indices(values: Iterable[Any]) -> List[Any]:
    """
    Return the maximum value across all indices of an iterable of values.
    """
    return [max(values_i) for values_i in zip(*values)]


# Copied from transformers.models.vilt.image_processing_vilt.make_pixel_mask
def make_pixel_mask(image: np.ndarray, output_size: Tuple[int, int]) -> np.ndarray:
    """
    Make a pixel mask for the image, where 1 indicates a valid pixel and 0 indicates padding.

    Args:
        image (`np.ndarray`):
            Image to make the pixel mask for.
        output_size (`Tuple[int, int]`):
            Output size of the mask.
    """
    input_height, input_width = get_image_size(image)
    mask = np.zeros(output_size, dtype=np.int64)
    mask[:input_height, :input_width] = 1
    return mask


# Copied from transformers.models.vilt.image_processing_vilt.get_max_height_width
def get_max_height_width(images: List[np.ndarray]) -> List[int]:
    """
    Get the maximum height and width across all images in a batch.
    """
    input_channel_dimension = infer_channel_dimension_format(images[0])

    if input_channel_dimension == ChannelDimension.FIRST:
        _, max_height, max_width = max_across_indices([img.shape for img in images])
    elif input_channel_dimension == ChannelDimension.LAST:
        max_height, max_width, _ = max_across_indices([img.shape for img in images])
    else:
        raise ValueError(f"Invalid channel dimension format: {input_channel_dimension}")
    return (max_height, max_width)


# Copied from transformers.models.vilt.image_processing_vilt.get_resize_output_image_size
def get_resize_output_image_size(
    input_image: np.ndarray, shorter: int = 800, longer: int = 1333, size_divisor: int = 32
) -> Tuple[int, int]:
    input_height, input_width = get_image_size(input_image)
    min_size, max_size = shorter, longer

    scale = min_size / min(input_height, input_width)

    if input_height < input_width:
        new_height = min_size
        new_width = scale * input_width
    else:
        new_height = scale * input_height
        new_width = min_size

    if max(new_height, new_width) > max_size:
        scale = max_size / max(new_height, new_width)
        new_height = scale * new_height
        new_width = scale * new_width

    new_height, new_width = int(new_height + 0.5), int(new_width + 0.5)
    new_height = new_height // size_divisor * size_divisor
    new_width = new_width // size_divisor * size_divisor

    return new_height, new_width


class BridgeTowerImageProcessor(BaseImageProcessor):
    r"""
    Constructs a BridgeTower image processor.

    Args:
        do_resize (`bool`, *optional*, defaults to `True`):
            Whether to resize the image's (height, width) dimensions to the specified `size`. Can be overridden by the
            `do_resize` parameter in the `preprocess` method.
        size (`Dict[str, int]` *optional*, defaults to `288`):
            Resize the shorter side of the input to `size["shortest_edge"]`. The longer side will be limited to under
            `int((1333 / 800) * size["shortest_edge"])` while preserving the aspect ratio. Only has an effect if
            `do_resize` is set to `True`. Can be overridden by the `size` parameter in the `preprocess` method.
        size_divisor (`int`, *optional*, defaults to `32`):
            The size by which to make sure both the height and width can be divided. Only has an effect if `do_resize`
            is set to `True`. Can be overridden by the `size_divisor` parameter in the `preprocess` method.
        resample (`PILImageResampling`, *optional*, defaults to `PILImageResampling.BICUBIC`):
            Resampling filter to use if resizing the image. Only has an effect if `do_resize` is set to `True`. Can be
            overridden by the `resample` parameter in the `preprocess` method.
        do_rescale (`bool`, *optional*, defaults to `True`):
            Whether to rescale the image by the specified scale `rescale_factor`. Can be overridden by the `do_rescale`
            parameter in the `preprocess` method.
        rescale_factor (`int` or `float`, *optional*, defaults to `1/255`):
            Scale factor to use if rescaling the image. Only has an effect if `do_rescale` is set to `True`. Can be
            overridden by the `rescale_factor` parameter in the `preprocess` method.
        do_normalize (`bool`, *optional*, defaults to `True`):
            Whether to normalize the image. Can be overridden by the `do_normalize` parameter in the `preprocess`
            method. Can be overridden by the `do_normalize` parameter in the `preprocess` method.
        image_mean (`float` or `List[float]`, *optional*, defaults to `IMAGENET_STANDARD_MEAN`):
            Mean to use if normalizing the image. This is a float or list of floats the length of the number of
            channels in the image. Can be overridden by the `image_mean` parameter in the `preprocess` method. Can be
            overridden by the `image_mean` parameter in the `preprocess` method.
        image_std (`float` or `List[float]`, *optional*, defaults to `IMAGENET_STANDARD_STD`):
            Standard deviation to use if normalizing the image. This is a float or list of floats the length of the
            number of channels in the image. Can be overridden by the `image_std` parameter in the `preprocess` method.
            Can be overridden by the `image_std` parameter in the `preprocess` method.
        do_center_crop (`bool`, *optional*, defaults to `True`):
            Whether to center crop the image. Can be overridden by the `do_center_crop` parameter in the `preprocess`
            method.
        do_pad (`bool`, *optional*, defaults to `True`):
            Whether to pad the image to the `(max_height, max_width)` of the images in the batch. Can be overridden by
            the `do_pad` parameter in the `preprocess` method.
    """

    model_input_names = ["pixel_values"]

    def __init__(
        self,
        do_resize: bool = True,
        size: Dict[str, int] = 288,
        size_divisor: int = 32,
        resample: PILImageResampling = PILImageResampling.BICUBIC,
        do_rescale: bool = True,
        rescale_factor: Union[int, float] = 1 / 255,
        do_normalize: bool = True,
        image_mean: Optional[Union[float, List[float]]] = None,
        image_std: Optional[Union[float, List[float]]] = None,
        do_center_crop: bool = True,
        do_pad: bool = True,
        **kwargs,
    ) -> None:
        if "pad_and_return_pixel_mask" in kwargs:
            do_pad = kwargs.pop("pad_and_return_pixel_mask")

        super().__init__(**kwargs)
        size = size if size is not None else {"shortest_edge": 288}
        size = get_size_dict(size, default_to_square=False)

        self.do_resize = do_resize
        self.size = size
        self.size_divisor = size_divisor
        self.resample = resample
        self.do_rescale = do_rescale
        self.rescale_factor = rescale_factor
        self.do_normalize = do_normalize
        self.image_mean = image_mean if image_mean is not None else OPENAI_CLIP_MEAN
        self.image_std = image_std if image_std is not None else OPENAI_CLIP_STD
        self.do_pad = do_pad
        self.do_center_crop = do_center_crop

    # Copied from transformers.models.vilt.image_processing_vilt.ViltImageProcessor.resize
    def resize(
        self,
        image: np.ndarray,
        size: Dict[str, int],
        size_divisor: int = 32,
        resample: PILImageResampling = PILImageResampling.BICUBIC,
        data_format: Optional[Union[str, ChannelDimension]] = None,
        **kwargs,
    ) -> np.ndarray:
        """
        Resize an image.

        Resizes the shorter side of the image to `size["shortest_edge"]` while preserving the aspect ratio. If the
        longer side is larger than the max size `(int(`size["shortest_edge"]` * 1333 / 800))`, the longer side is then
        resized to the max size while preserving the aspect ratio.

        Args:
            image (`np.ndarray`):
                Image to resize.
            size (`Dict[str, int]`):
                Controls the size of the output image. Should be of the form `{"shortest_edge": int}`.
            size_divisor (`int`, defaults to 32):
                The image is resized to a size that is a multiple of this value.
            resample (`PILImageResampling` filter, *optional*, defaults to `PILImageResampling.BICUBIC`):
                Resampling filter to use when resiizing the image.
            data_format (`str` or `ChannelDimension`, *optional*):
                The channel dimension format of the image. If not provided, it will be the same as the input image.
        """
        size = get_size_dict(size, default_to_square=False)
        if "shortest_edge" not in size:
            raise ValueError(f"The `size` dictionary must contain the key `shortest_edge`. Got {size.keys()}")
        shorter = size["shortest_edge"]
        longer = int(1333 / 800 * shorter)
        output_size = get_resize_output_image_size(image, shorter=shorter, longer=longer, size_divisor=size_divisor)
        return resize(image, size=output_size, resample=resample, data_format=data_format, **kwargs)

    # Copied from transformers.models.vilt.image_processing_vilt.ViltImageProcessor.rescale
    def rescale(
        self,
        image: np.ndarray,
        scale: Union[int, float],
        data_format: Optional[Union[str, ChannelDimension]] = None,
        **kwargs,
    ):
        """
        Rescale an image by a scale factor. image = image * scale.

        Args:
            image (`np.ndarray`):
                Image to rescale.
            scale (`int` or `float`):
                Scale to apply to the image.
            data_format (`str` or `ChannelDimension`, *optional*):
                The channel dimension format of the image. If not provided, it will be the same as the input image.
        """
        return rescale(image, scale=scale, data_format=data_format, **kwargs)

    def center_crop(
        self,
        image: np.ndarray,
        size: Dict[str, int],
        data_format: Optional[Union[str, ChannelDimension]] = None,
        **kwargs,
    ) -> np.ndarray:
        """
        Center crop an image to (size["height"], size["width"]). If the input size is smaller than `size` along any
        edge, the image is padded with 0's and then center cropped.

        Args:
            image (`np.ndarray`):
                Image to center crop.
            size (`Dict[str, int]`):
                Size of the output image.
            data_format (`str` or `ChannelDimension`, *optional*):
                The channel dimension format of the image. If not provided, it will be the same as the input image.
        """
        output_size = size["shortest_edge"]
        return center_crop(image, size=(output_size, output_size), data_format=data_format, **kwargs)

    # Copied from transformers.models.vilt.image_processing_vilt.ViltImageProcessor.normalize
    def normalize(
        self,
        image: np.ndarray,
        mean: Union[float, List[float]],
        std: Union[float, List[float]],
        data_format: Optional[Union[str, ChannelDimension]] = None,
        **kwargs,
    ) -> np.ndarray:
        """
        Normalize an image. image = (image - image_mean) / image_std.

        Args:
            image (`np.ndarray`):
                Image to normalize.
            mean (`float` or `List[float]`):
                Image mean.
            std (`float` or `List[float]`):
                Image standard deviation.
            data_format (`str` or `ChannelDimension`, *optional*):
                The channel dimension format of the image. If not provided, it will be the same as the input image.
        """
        return normalize(image, mean=mean, std=std, data_format=data_format, **kwargs)

    def _pad_image(
        self,
        image: np.ndarray,
        output_size: Tuple[int, int],
        constant_values: Union[float, Iterable[float]] = 0,
        data_format: Optional[ChannelDimension] = None,
    ) -> np.ndarray:
        """
        Pad an image with zeros to the given size.
        """
        input_height, input_width = get_image_size(image)
        output_height, output_width = output_size

        pad_bottom = output_height - input_height
        pad_right = output_width - input_width
        padding = ((0, pad_bottom), (0, pad_right))
        padded_image = pad(
            image, padding, mode=PaddingMode.CONSTANT, constant_values=constant_values, data_format=data_format
        )
        return padded_image

    def pad(
        self,
        images: List[np.ndarray],
        return_pixel_mask: bool = True,
        return_tensors: Optional[Union[str, TensorType]] = None,
        data_format: Optional[ChannelDimension] = None,
    ) -> BatchFeature:
        """
        Pads a batch of images with zeros to the size of largest height and width in the batch and optionally returns
        their corresponding pixel mask.

        Args:
            images (`List[np.ndarray]`):
                Batch of images to pad.
            return_pixel_mask (`bool`, *optional*, defaults to `False`):
                Whether to return the pixel mask.
            return_tensors (`str` or `TensorType`, *optional*):
                The type of tensors to return. Can be one of:
                    - Unset: Return a list of `np.ndarray`.
                    - `TensorType.TENSORFLOW` or `'tf'`: Return a batch of type `tf.Tensor`.
                    - `TensorType.PYTORCH` or `'pt'`: Return a batch of type `torch.Tensor`.
                    - `TensorType.NUMPY` or `'np'`: Return a batch of type `np.ndarray`.
                    - `TensorType.JAX` or `'jax'`: Return a batch of type `jax.numpy.ndarray`.
            data_format (`str` or `ChannelDimension`, *optional*):
                The channel dimension format of the image. If not provided, it will be the same as the input image.
        """
        pad_size = get_max_height_width(images)
        padded_images = [
            self._pad_image(image=image, output_size=pad_size, data_format=data_format) for image in images
        ]
        data = {"pixel_values": padded_images}
        if return_pixel_mask:
            masks = [make_pixel_mask(image=image, output_size=pad_size) for image in images]
            data["pixel_mask"] = masks

        return BatchFeature(data=data, tensor_type=return_tensors)

    # Copied from transformers.models.vilt.image_processing_vilt.ViltImageProcessor.pad_and_create_pixel_mask
    def pad_and_create_pixel_mask(
        self,
        pixel_values_list: List[ImageInput],
        return_tensors: Optional[Union[str, TensorType]] = None,
        data_format: Optional[ChannelDimension] = None,
    ) -> BatchFeature:
        """
        Pads a batch of images with zeros to the size of largest height and width in the batch and returns their
        corresponding pixel mask.

        Args:
            images (`List[np.ndarray]`):
                Batch of images to pad.
            return_tensors (`str` or `TensorType`, *optional*):
                The type of tensors to return. Can be one of:
                    - Unset: Return a list of `np.ndarray`.
                    - `TensorType.TENSORFLOW` or `'tf'`: Return a batch of type `tf.Tensor`.
                    - `TensorType.PYTORCH` or `'pt'`: Return a batch of type `torch.Tensor`.
                    - `TensorType.NUMPY` or `'np'`: Return a batch of type `np.ndarray`.
                    - `TensorType.JAX` or `'jax'`: Return a batch of type `jax.numpy.ndarray`.
            data_format (`str` or `ChannelDimension`, *optional*):
                The channel dimension format of the image. If not provided, it will be the same as the input image.
        """
        warnings.warn(
            "This method is deprecated and will be removed in v4.26.0. Please use pad instead.", FutureWarning
        )
        # pad expects a list of np.ndarray, but the previous feature extractors expected torch tensors
        images = [to_numpy_array(image) for image in pixel_values_list]
        return self.pad(
            images=images,
            return_pixel_mask=True,
            return_tensors=return_tensors,
            data_format=data_format,
        )

    def preprocess(
        self,
        images: ImageInput,
        do_resize: Optional[bool] = None,
        size: Optional[Dict[str, int]] = None,
        size_divisor: Optional[int] = None,
        resample: PILImageResampling = None,
        do_rescale: Optional[bool] = None,
        rescale_factor: Optional[float] = None,
        do_normalize: Optional[bool] = None,
        image_mean: Optional[Union[float, List[float]]] = None,
        image_std: Optional[Union[float, List[float]]] = None,
        do_pad: Optional[bool] = None,
        do_center_crop: Optional[bool] = None,
        return_tensors: Optional[Union[str, TensorType]] = None,
        data_format: ChannelDimension = ChannelDimension.FIRST,
        **kwargs,
    ) -> PIL.Image.Image:
        """
        Preprocess an image or batch of images.

        Args:
            images (`ImageInput`):
                Image to preprocess.
            do_resize (`bool`, *optional*, defaults to `self.do_resize`):
                Whether to resize the image.
            size (`Dict[str, int]`, *optional*, defaults to `self.size`):
                Controls the size of the image after `resize`. The shortest edge of the image is resized to
                `size["shortest_edge"]` whilst preserving the aspect ratio. If the longest edge of this resized image
                is > `int(size["shortest_edge"] * (1333 / 800))`, then the image is resized again to make the longest
                edge equal to `int(size["shortest_edge"] * (1333 / 800))`.
            size_divisor (`int`, *optional*, defaults to `self.size_divisor`):
                The image is resized to a size that is a multiple of this value.
            resample (`PILImageResampling`, *optional*, defaults to `self.resample`):
                Resampling filter to use if resizing the image. Only has an effect if `do_resize` is set to `True`.
            do_rescale (`bool`, *optional*, defaults to `self.do_rescale`):
                Whether to rescale the image values between [0 - 1].
            rescale_factor (`float`, *optional*, defaults to `self.rescale_factor`):
                Rescale factor to rescale the image by if `do_rescale` is set to `True`.
            do_normalize (`bool`, *optional*, defaults to `self.do_normalize`):
                Whether to normalize the image.
            image_mean (`float` or `List[float]`, *optional*, defaults to `self.image_mean`):
                Image mean to normalize the image by if `do_normalize` is set to `True`.
            image_std (`float` or `List[float]`, *optional*, defaults to `self.image_std`):
                Image standard deviation to normalize the image by if `do_normalize` is set to `True`.
            do_pad (`bool`, *optional*, defaults to `self.do_pad`):
                Whether to pad the image to the (max_height, max_width) in the batch. If `True`, a pixel mask is also
                created and returned.
            do_center_crop (`bool`, *optional*, defaults to `self.do_center_crop`):
                Whether to center crop the image. If the input size is smaller than `crop_size` along any edge, the
                image is padded with 0's and then center cropped.
            return_tensors (`str` or `TensorType`, *optional*):
                The type of tensors to return. Can be one of:
                    - Unset: Return a list of `np.ndarray`.
                    - `TensorType.TENSORFLOW` or `'tf'`: Return a batch of type `tf.Tensor`.
                    - `TensorType.PYTORCH` or `'pt'`: Return a batch of type `torch.Tensor`.
                    - `TensorType.NUMPY` or `'np'`: Return a batch of type `np.ndarray`.
                    - `TensorType.JAX` or `'jax'`: Return a batch of type `jax.numpy.ndarray`.
            data_format (`ChannelDimension` or `str`, *optional*, defaults to `ChannelDimension.FIRST`):
                The channel dimension format for the output image. Can be one of:
                    - `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
                    - `ChannelDimension.LAST`: image in (height, width, num_channels) format.
        """
        do_resize = do_resize if do_resize is not None else self.do_resize
        size_divisor = size_divisor if size_divisor is not None else self.size_divisor
        resample = resample if resample is not None else self.resample
        do_rescale = do_rescale if do_rescale is not None else self.do_rescale
        rescale_factor = rescale_factor if rescale_factor is not None else self.rescale_factor
        do_normalize = do_normalize if do_normalize is not None else self.do_normalize
        image_mean = image_mean if image_mean is not None else self.image_mean
        image_std = image_std if image_std is not None else self.image_std
        do_pad = do_pad if do_pad is not None else self.do_pad
        do_center_crop if do_center_crop is not None else self.do_center_crop

        size = size if size is not None else self.size
        size = get_size_dict(size, default_to_square=False)

        if not is_batched(images):
            images = [images]

        if not valid_images(images):
            raise ValueError(
                "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
                "torch.Tensor, tf.Tensor or jax.ndarray."
            )

        if do_resize and size is None or resample is None:
            raise ValueError("Size and resample must be specified if do_resize is True.")

        if do_rescale and rescale_factor is None:
            raise ValueError("Rescale factor must be specified if do_rescale is True.")

        if do_normalize and (image_mean is None or image_std is None):
            raise ValueError("Image mean and std must be specified if do_normalize is True.")

        # All transformations expect numpy arrays.
        images = [to_numpy_array(image) for image in images]

        if do_resize:
            images = [
                self.resize(image=image, size=size, size_divisor=size_divisor, resample=resample) for image in images
            ]

        if do_center_crop:
            images = [self.center_crop(image=image, size=size) for image in images]

        if do_rescale:
            images = [self.rescale(image=image, scale=rescale_factor) for image in images]

        if do_normalize:
            images = [self.normalize(image=image, mean=image_mean, std=image_std) for image in images]

        images = [to_channel_dimension_format(image, data_format) for image in images]

        if do_pad:
            encoded_outputs = self.pad(images, return_pixel_mask=True, return_tensors=return_tensors)
        else:
            encoded_outputs = BatchFeature(data={"pixel_values": images}, tensor_type=return_tensors)

        return encoded_outputs