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# copied from ViTImageProcessor (https://github.com/huggingface/transformers/blob/v4.37.2/src/transformers/models/vit/image_processing_vit.py)
"""Image processor class for Manga Line Extraction."""
from typing import Optional, List, Dict, Union, Tuple
import numpy as np
import cv2
from PIL import Image
from transformers.image_processing_utils import (
BaseImageProcessor,
BatchFeature,
get_size_dict,
)
from transformers.image_transforms import (
rescale,
to_channel_dimension_format,
_rescale_for_pil_conversion,
to_pil_image,
)
from transformers.image_utils import (
IMAGENET_STANDARD_MEAN,
IMAGENET_STANDARD_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
infer_channel_dimension_format,
is_scaled_image,
make_list_of_images,
to_numpy_array,
valid_images,
)
from transformers.utils import TensorType, logging
logger = logging.get_logger(__name__)
def resize_by_factor(
image: np.ndarray,
resize_factor: int,
resample: PILImageResampling = None,
data_format: Optional[Union[str, ChannelDimension]] = None,
input_data_format: Optional[Union[str, ChannelDimension]] = None,
return_numpy: bool = True,
):
"""
Resizes `image` to `(height, width)` specified by `size` using the PIL library.
Args:
image (`np.ndarray`):
The image to resize.
resize_factor (`int`):
Value for padding the image to a multiple of the factor.
resample (`int`, *optional*, defaults to `PILImageResampling.BILINEAR`):
The filter to user for resampling.
data_format (`ChannelDimension`, *optional*):
The channel dimension format of the output image. If unset, will use the inferred format from the input.
return_numpy (`bool`, *optional*, defaults to `True`):
Whether or not to return the resized image as a numpy array. If False a `PIL.Image.Image` object is
returned.
input_data_format (`ChannelDimension`, *optional*):
The channel dimension format of the input image. If unset, will use the inferred format from the input.
Returns:
`np.ndarray`: The resized image.
"""
resample = resample if resample is not None else PILImageResampling.BILINEAR
# For all transformations, we want to keep the same data format as the input image unless otherwise specified.
# The resized image from PIL will always have channels last, so find the input format first.
if input_data_format is None:
input_data_format = infer_channel_dimension_format(image)
data_format = input_data_format if data_format is None else data_format
# To maintain backwards compatibility with the resizing done in previous image feature extractors, we use
# the pillow library to resize the image and then convert back to numpy
do_rescale = False
if not isinstance(image, Image.Image):
do_rescale = _rescale_for_pil_conversion(image)
image = to_pil_image(
image, do_rescale=do_rescale, input_data_format=input_data_format
)
assert isinstance(image, Image.Image)
width, height = (
int(np.ceil(image.size[0] // resize_factor) * resize_factor),
int(np.ceil(image.size[1] // resize_factor) * resize_factor),
)
# solid image
new_image = Image.new(image.mode, (width, height), "white")
# paste original image on top left
new_image.paste(image)
if return_numpy:
new_image = np.array(new_image)
# If the input image channel dimension was of size 1, then it is dropped when converting to a PIL image
# so we need to add it back if necessary.
new_image = (
np.expand_dims(new_image, axis=-1) if new_image.ndim == 2 else new_image
)
# The image is always in channels last format after converting from a PIL image
new_image = to_channel_dimension_format(
new_image, data_format, input_channel_dim=ChannelDimension.LAST
)
# If an image was rescaled to be in the range [0, 255] before converting to a PIL image, then we need to
# rescale it back to the original range.
new_image = rescale(new_image, 1 / 255) if do_rescale else new_image
return new_image
def greyscale(
image: np.ndarray,
data_format: Optional[Union[str, ChannelDimension]] = ChannelDimension.FIRST,
input_data_format: Optional[Union[str, ChannelDimension]] = ChannelDimension.FIRST,
return_numpy: bool = True,
):
"""
Convert `image` to `greyscale` using the PIL library.
Args:
image (`np.ndarray`):
The image to greyscale.
Returns:
`np.ndarray`: The greyscaled image.
"""
if not isinstance(image, Image.Image):
do_rescale = _rescale_for_pil_conversion(image)
image = to_pil_image(
image, do_rescale=do_rescale, input_data_format=input_data_format
)
assert isinstance(image, Image.Image)
# do greyscale
image = image.convert("L")
if return_numpy:
image = np.array(image)
# If the input image channel dimension was of size 1, then it is dropped when converting to a PIL image
# so we need to add it back if necessary.
image = np.expand_dims(image, axis=-1) if image.ndim == 2 else image
# The image is always in channels last format after converting from a PIL image
image = to_channel_dimension_format(
image, data_format, input_channel_dim=ChannelDimension.LAST
)
# If an image was rescaled to be in the range [0, 255] before converting to a PIL image, then we need to
# rescale it back to the original range.
image = rescale(image, 1 / 255) if do_rescale else image
return image
class MLEImageProcessor(BaseImageProcessor):
r"""
Constructs a MLE image processor.
Args:
do_resize (`bool`, *optional*, defaults to `True`):
Whether to resize the image's (height, width) dimensions to the specified `(size["height"],
size["width"])`. Can be overridden by the `do_resize` parameter in the `preprocess` method.
resize_factor (`int`, *optional*, defaults to `16`):
Value for padding the image to a multiple of the factor.
resample (`PILImageResampling`, *optional*, defaults to `Resampling.BILINEAR`):
Resampling filter to use if resizing the image. Can be overridden by the `resample` parameter in the
`preprocess` method.
do_rescale (`bool`, *optional*, defaults to `False`):
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. Can be overridden by the `rescale_factor` parameter in the
`preprocess` method.
do_normalize (`bool`, *optional*, defaults to `False`):
Whether to normalize the image. 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.
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.
"""
model_input_names = ["pixel_values"]
def __init__(
self,
do_resize: bool = True,
resize_factor: int = 16,
do_greyscale: bool = True,
resample: PILImageResampling = PILImageResampling.BILINEAR,
do_rescale: bool = True,
rescale_factor: Union[int, float] = 1.0,
do_normalize: bool = False,
image_mean: Optional[Union[float, List[float]]] = None,
image_std: Optional[Union[float, List[float]]] = None,
**kwargs,
) -> None:
super().__init__(**kwargs)
self.do_resize = do_resize
self.resize_factor = resize_factor
self.do_greyscale = do_greyscale
self.do_rescale = do_rescale
self.do_normalize = do_normalize
self.resample = resample
self.rescale_factor = rescale_factor
self.image_mean = (
image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN[0]
)
self.image_std = (
image_std if image_std is not None else IMAGENET_STANDARD_STD[0]
)
def resize(
self,
image: np.ndarray,
resize_factor: int,
resample: PILImageResampling = PILImageResampling.BILINEAR,
data_format: Optional[Union[str, ChannelDimension]] = None,
input_data_format: Optional[Union[str, ChannelDimension]] = None,
**kwargs,
) -> np.ndarray:
"""
Resize an image to `(size["height"], size["width"])`.
Args:
image (`np.ndarray`):
Image to resize.
resize_factor (`int`):
Value for padding the image to a multiple of the factor.
resample (`PILImageResampling`, *optional*, defaults to `PILImageResampling.BILINEAR`):
`PILImageResampling` filter to use when resizing the image e.g. `PILImageResampling.BILINEAR`.
data_format (`ChannelDimension` or `str`, *optional*):
The channel dimension format for the output image. If unset, the channel dimension format of the input
image is used. Can be one of:
- `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
- `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
- `"none"` or `ChannelDimension.NONE`: image in (height, width) format.
input_data_format (`ChannelDimension` or `str`, *optional*):
The channel dimension format for the input image. If unset, the channel dimension format is inferred
from the input image. Can be one of:
- `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
- `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
- `"none"` or `ChannelDimension.NONE`: image in (height, width) format.
Returns:
`np.ndarray`: The resized image.
"""
return resize_by_factor(
image,
resize_factor=resize_factor,
resample=resample,
data_format=data_format,
input_data_format=input_data_format,
**kwargs,
)
def greyscale(
self,
image: np.ndarray,
data_format: Optional[Union[str, ChannelDimension]] = ChannelDimension.FIRST,
input_data_format: Optional[
Union[str, ChannelDimension]
] = ChannelDimension.FIRST,
**kwargs,
):
"""
Convert an image to greyscale.
Args:
image (`np.ndarray`):
Image to greyscale
Returns:
`np.ndarray`: The greyscaled image.
"""
return greyscale(
image,
data_format=data_format,
input_data_format=input_data_format,
**kwargs,
)
def preprocess(
self,
images: ImageInput,
do_resize: Optional[bool] = None,
resize_factor: Optional[int] = None,
do_greyscale: Optional[bool] = 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,
return_tensors: Optional[Union[str, TensorType]] = None,
data_format: Union[str, ChannelDimension] = ChannelDimension.FIRST,
input_data_format: Optional[Union[str, ChannelDimension]] = None,
**kwargs,
):
"""
Preprocess an image or batch of images.
Args:
images (`ImageInput`):
Image to preprocess. Expects a single or batch of images with pixel values ranging from 0 to 255. If
passing in images with pixel values between 0 and 1, set `do_rescale=False`.
do_resize (`bool`, *optional*, defaults to `self.do_resize`):
Whether to resize the image.
resize_factor (`int`, *optional*, defaults to `self.resize_factor`):
Value for padding the image to a multiple of the factor.
resample (`PILImageResampling` filter, *optional*, defaults to `self.resample`):
`PILImageResampling` filter to use if resizing the image e.g. `PILImageResampling.BILINEAR`. 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.
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:
- `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
- `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
- Unset: Use the channel dimension format of the input image.
input_data_format (`ChannelDimension` or `str`, *optional*):
The channel dimension format for the input image. If unset, the channel dimension format is inferred
from the input image. Can be one of:
- `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
- `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
- `"none"` or `ChannelDimension.NONE`: image in (height, width) format.
"""
do_resize = do_resize if do_resize is not None else self.do_resize
do_rescale = do_rescale if do_rescale is not None else self.do_rescale
do_normalize = do_normalize if do_normalize is not None else self.do_normalize
do_greyscale = do_greyscale if do_greyscale is not None else self.do_greyscale
resample = resample if resample is not None else self.resample
rescale_factor = (
rescale_factor if rescale_factor is not None else self.rescale_factor
)
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
resize_factor = (
resize_factor if resize_factor is not None else self.resize_factor
)
images = make_list_of_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 resize_factor is None:
raise ValueError("Resize factor 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.")
# All transformations expect numpy arrays.
images = [to_numpy_array(image) for image in images]
if is_scaled_image(images[0]) and do_rescale:
logger.warning_once(
"It looks like you are trying to rescale already rescaled images. If the input"
" images have pixel values between 0 and 1, set `do_rescale=False` to avoid rescaling them again."
)
if input_data_format is None:
# We assume that all images have the same channel dimension format.
input_data_format = infer_channel_dimension_format(images[0])
if do_resize:
images = [
self.resize(
image=image,
resize_factor=resize_factor,
resample=resample,
input_data_format=input_data_format,
)
for image in images
]
if do_greyscale:
images = [
self.greyscale(
image=image,
data_format=data_format,
input_data_format=input_data_format,
)
for image in images
]
# the channel would be set to 1, so input data format could't be estimated
input_data_format = ChannelDimension.FIRST
if do_rescale:
images = [
self.rescale(
image=image,
scale=rescale_factor,
input_data_format=input_data_format,
)
for image in images
]
if do_normalize:
images = [
self.normalize(
image=image,
mean=image_mean,
std=image_std,
input_data_format=input_data_format,
)
for image in images
]
images = [
to_channel_dimension_format(
image, data_format, input_channel_dim=input_data_format
)
for image in images
]
data = {"pixel_values": images}
return BatchFeature(data=data, tensor_type=return_tensors)
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