winnie22 / transformers_4_35_0 /image_utils.py
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# coding=utf-8
# Copyright 2021 The HuggingFace Inc. team.
#
# 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.
import base64
import os
from io import BytesIO
from typing import TYPE_CHECKING, Dict, Iterable, List, Optional, Tuple, Union
import numpy as np
import requests
from packaging import version
from .utils import (
ExplicitEnum,
is_jax_tensor,
is_tf_tensor,
is_torch_available,
is_torch_tensor,
is_vision_available,
requires_backends,
to_numpy,
)
from .utils.constants import ( # noqa: F401
IMAGENET_DEFAULT_MEAN,
IMAGENET_DEFAULT_STD,
IMAGENET_STANDARD_MEAN,
IMAGENET_STANDARD_STD,
OPENAI_CLIP_MEAN,
OPENAI_CLIP_STD,
)
if is_vision_available():
import PIL.Image
import PIL.ImageOps
if version.parse(version.parse(PIL.__version__).base_version) >= version.parse("9.1.0"):
PILImageResampling = PIL.Image.Resampling
else:
PILImageResampling = PIL.Image
if TYPE_CHECKING:
if is_torch_available():
import torch
ImageInput = Union[
"PIL.Image.Image", np.ndarray, "torch.Tensor", List["PIL.Image.Image"], List[np.ndarray], List["torch.Tensor"]
] # noqa
class ChannelDimension(ExplicitEnum):
FIRST = "channels_first"
LAST = "channels_last"
def is_pil_image(img):
return is_vision_available() and isinstance(img, PIL.Image.Image)
def is_valid_image(img):
return (
(is_vision_available() and isinstance(img, PIL.Image.Image))
or isinstance(img, np.ndarray)
or is_torch_tensor(img)
or is_tf_tensor(img)
or is_jax_tensor(img)
)
def valid_images(imgs):
# If we have an list of images, make sure every image is valid
if isinstance(imgs, (list, tuple)):
for img in imgs:
if not valid_images(img):
return False
# If not a list of tuple, we have been given a single image or batched tensor of images
elif not is_valid_image(imgs):
return False
return True
def is_batched(img):
if isinstance(img, (list, tuple)):
return is_valid_image(img[0])
return False
def is_scaled_image(image: np.ndarray) -> bool:
"""
Checks to see whether the pixel values have already been rescaled to [0, 1].
"""
if image.dtype == np.uint8:
return False
# It's possible the image has pixel values in [0, 255] but is of floating type
return np.min(image) >= 0 and np.max(image) <= 1
def make_list_of_images(images, expected_ndims: int = 3) -> List[ImageInput]:
"""
Ensure that the input is a list of images. If the input is a single image, it is converted to a list of length 1.
If the input is a batch of images, it is converted to a list of images.
Args:
images (`ImageInput`):
Image of images to turn into a list of images.
expected_ndims (`int`, *optional*, defaults to 3):
Expected number of dimensions for a single input image. If the input image has a different number of
dimensions, an error is raised.
"""
if is_batched(images):
return images
# Either the input is a single image, in which case we create a list of length 1
if isinstance(images, PIL.Image.Image):
# PIL images are never batched
return [images]
if is_valid_image(images):
if images.ndim == expected_ndims + 1:
# Batch of images
images = list(images)
elif images.ndim == expected_ndims:
# Single image
images = [images]
else:
raise ValueError(
f"Invalid image shape. Expected either {expected_ndims + 1} or {expected_ndims} dimensions, but got"
f" {images.ndim} dimensions."
)
return images
raise ValueError(
"Invalid image type. Expected either PIL.Image.Image, numpy.ndarray, torch.Tensor, tf.Tensor or "
f"jax.ndarray, but got {type(images)}."
)
def to_numpy_array(img) -> np.ndarray:
if not is_valid_image(img):
raise ValueError(f"Invalid image type: {type(img)}")
if is_vision_available() and isinstance(img, PIL.Image.Image):
return np.array(img)
return to_numpy(img)
def infer_channel_dimension_format(
image: np.ndarray, num_channels: Optional[Union[int, Tuple[int, ...]]] = None
) -> ChannelDimension:
"""
Infers the channel dimension format of `image`.
Args:
image (`np.ndarray`):
The image to infer the channel dimension of.
num_channels (`int` or `Tuple[int, ...]`, *optional*, defaults to `(1, 3)`):
The number of channels of the image.
Returns:
The channel dimension of the image.
"""
num_channels = num_channels if num_channels is not None else (1, 3)
num_channels = (num_channels,) if isinstance(num_channels, int) else num_channels
if image.ndim == 3:
first_dim, last_dim = 0, 2
elif image.ndim == 4:
first_dim, last_dim = 1, 3
else:
raise ValueError(f"Unsupported number of image dimensions: {image.ndim}")
if image.shape[first_dim] in num_channels:
return ChannelDimension.FIRST
elif image.shape[last_dim] in num_channels:
return ChannelDimension.LAST
raise ValueError("Unable to infer channel dimension format")
def get_channel_dimension_axis(
image: np.ndarray, input_data_format: Optional[Union[ChannelDimension, str]] = None
) -> int:
"""
Returns the channel dimension axis of the image.
Args:
image (`np.ndarray`):
The image to get the channel dimension axis of.
input_data_format (`ChannelDimension` or `str`, *optional*):
The channel dimension format of the image. If `None`, will infer the channel dimension from the image.
Returns:
The channel dimension axis of the image.
"""
if input_data_format is None:
input_data_format = infer_channel_dimension_format(image)
if input_data_format == ChannelDimension.FIRST:
return image.ndim - 3
elif input_data_format == ChannelDimension.LAST:
return image.ndim - 1
raise ValueError(f"Unsupported data format: {input_data_format}")
def get_image_size(image: np.ndarray, channel_dim: ChannelDimension = None) -> Tuple[int, int]:
"""
Returns the (height, width) dimensions of the image.
Args:
image (`np.ndarray`):
The image to get the dimensions of.
channel_dim (`ChannelDimension`, *optional*):
Which dimension the channel dimension is in. If `None`, will infer the channel dimension from the image.
Returns:
A tuple of the image's height and width.
"""
if channel_dim is None:
channel_dim = infer_channel_dimension_format(image)
if channel_dim == ChannelDimension.FIRST:
return image.shape[-2], image.shape[-1]
elif channel_dim == ChannelDimension.LAST:
return image.shape[-3], image.shape[-2]
else:
raise ValueError(f"Unsupported data format: {channel_dim}")
def is_valid_annotation_coco_detection(annotation: Dict[str, Union[List, Tuple]]) -> bool:
if (
isinstance(annotation, dict)
and "image_id" in annotation
and "annotations" in annotation
and isinstance(annotation["annotations"], (list, tuple))
and (
# an image can have no annotations
len(annotation["annotations"]) == 0
or isinstance(annotation["annotations"][0], dict)
)
):
return True
return False
def is_valid_annotation_coco_panoptic(annotation: Dict[str, Union[List, Tuple]]) -> bool:
if (
isinstance(annotation, dict)
and "image_id" in annotation
and "segments_info" in annotation
and "file_name" in annotation
and isinstance(annotation["segments_info"], (list, tuple))
and (
# an image can have no segments
len(annotation["segments_info"]) == 0
or isinstance(annotation["segments_info"][0], dict)
)
):
return True
return False
def valid_coco_detection_annotations(annotations: Iterable[Dict[str, Union[List, Tuple]]]) -> bool:
return all(is_valid_annotation_coco_detection(ann) for ann in annotations)
def valid_coco_panoptic_annotations(annotations: Iterable[Dict[str, Union[List, Tuple]]]) -> bool:
return all(is_valid_annotation_coco_panoptic(ann) for ann in annotations)
def load_image(image: Union[str, "PIL.Image.Image"], timeout: Optional[float] = None) -> "PIL.Image.Image":
"""
Loads `image` to a PIL Image.
Args:
image (`str` or `PIL.Image.Image`):
The image to convert to the PIL Image format.
timeout (`float`, *optional*):
The timeout value in seconds for the URL request.
Returns:
`PIL.Image.Image`: A PIL Image.
"""
requires_backends(load_image, ["vision"])
if isinstance(image, str):
if image.startswith("http://") or image.startswith("https://"):
# We need to actually check for a real protocol, otherwise it's impossible to use a local file
# like http_huggingface_co.png
image = PIL.Image.open(requests.get(image, stream=True, timeout=timeout).raw)
elif os.path.isfile(image):
image = PIL.Image.open(image)
else:
if image.startswith("data:image/"):
image = image.split(",")[1]
# Try to load as base64
try:
b64 = base64.b64decode(image, validate=True)
image = PIL.Image.open(BytesIO(b64))
except Exception as e:
raise ValueError(
f"Incorrect image source. Must be a valid URL starting with `http://` or `https://`, a valid path to an image file, or a base64 encoded string. Got {image}. Failed with {e}"
)
elif isinstance(image, PIL.Image.Image):
image = image
else:
raise ValueError(
"Incorrect format used for image. Should be an url linking to an image, a base64 string, a local path, or a PIL image."
)
image = PIL.ImageOps.exif_transpose(image)
image = image.convert("RGB")
return image
# In the future we can add a TF implementation here when we have TF models.
class ImageFeatureExtractionMixin:
"""
Mixin that contain utilities for preparing image features.
"""
def _ensure_format_supported(self, image):
if not isinstance(image, (PIL.Image.Image, np.ndarray)) and not is_torch_tensor(image):
raise ValueError(
f"Got type {type(image)} which is not supported, only `PIL.Image.Image`, `np.array` and "
"`torch.Tensor` are."
)
def to_pil_image(self, image, rescale=None):
"""
Converts `image` to a PIL Image. Optionally rescales it and puts the channel dimension back as the last axis if
needed.
Args:
image (`PIL.Image.Image` or `numpy.ndarray` or `torch.Tensor`):
The image to convert to the PIL Image format.
rescale (`bool`, *optional*):
Whether or not to apply the scaling factor (to make pixel values integers between 0 and 255). Will
default to `True` if the image type is a floating type, `False` otherwise.
"""
self._ensure_format_supported(image)
if is_torch_tensor(image):
image = image.numpy()
if isinstance(image, np.ndarray):
if rescale is None:
# rescale default to the array being of floating type.
rescale = isinstance(image.flat[0], np.floating)
# If the channel as been moved to first dim, we put it back at the end.
if image.ndim == 3 and image.shape[0] in [1, 3]:
image = image.transpose(1, 2, 0)
if rescale:
image = image * 255
image = image.astype(np.uint8)
return PIL.Image.fromarray(image)
return image
def convert_rgb(self, image):
"""
Converts `PIL.Image.Image` to RGB format.
Args:
image (`PIL.Image.Image`):
The image to convert.
"""
self._ensure_format_supported(image)
if not isinstance(image, PIL.Image.Image):
return image
return image.convert("RGB")
def rescale(self, image: np.ndarray, scale: Union[float, int]) -> np.ndarray:
"""
Rescale a numpy image by scale amount
"""
self._ensure_format_supported(image)
return image * scale
def to_numpy_array(self, image, rescale=None, channel_first=True):
"""
Converts `image` to a numpy array. Optionally rescales it and puts the channel dimension as the first
dimension.
Args:
image (`PIL.Image.Image` or `np.ndarray` or `torch.Tensor`):
The image to convert to a NumPy array.
rescale (`bool`, *optional*):
Whether or not to apply the scaling factor (to make pixel values floats between 0. and 1.). Will
default to `True` if the image is a PIL Image or an array/tensor of integers, `False` otherwise.
channel_first (`bool`, *optional*, defaults to `True`):
Whether or not to permute the dimensions of the image to put the channel dimension first.
"""
self._ensure_format_supported(image)
if isinstance(image, PIL.Image.Image):
image = np.array(image)
if is_torch_tensor(image):
image = image.numpy()
rescale = isinstance(image.flat[0], np.integer) if rescale is None else rescale
if rescale:
image = self.rescale(image.astype(np.float32), 1 / 255.0)
if channel_first and image.ndim == 3:
image = image.transpose(2, 0, 1)
return image
def expand_dims(self, image):
"""
Expands 2-dimensional `image` to 3 dimensions.
Args:
image (`PIL.Image.Image` or `np.ndarray` or `torch.Tensor`):
The image to expand.
"""
self._ensure_format_supported(image)
# Do nothing if PIL image
if isinstance(image, PIL.Image.Image):
return image
if is_torch_tensor(image):
image = image.unsqueeze(0)
else:
image = np.expand_dims(image, axis=0)
return image
def normalize(self, image, mean, std, rescale=False):
"""
Normalizes `image` with `mean` and `std`. Note that this will trigger a conversion of `image` to a NumPy array
if it's a PIL Image.
Args:
image (`PIL.Image.Image` or `np.ndarray` or `torch.Tensor`):
The image to normalize.
mean (`List[float]` or `np.ndarray` or `torch.Tensor`):
The mean (per channel) to use for normalization.
std (`List[float]` or `np.ndarray` or `torch.Tensor`):
The standard deviation (per channel) to use for normalization.
rescale (`bool`, *optional*, defaults to `False`):
Whether or not to rescale the image to be between 0 and 1. If a PIL image is provided, scaling will
happen automatically.
"""
self._ensure_format_supported(image)
if isinstance(image, PIL.Image.Image):
image = self.to_numpy_array(image, rescale=True)
# If the input image is a PIL image, it automatically gets rescaled. If it's another
# type it may need rescaling.
elif rescale:
if isinstance(image, np.ndarray):
image = self.rescale(image.astype(np.float32), 1 / 255.0)
elif is_torch_tensor(image):
image = self.rescale(image.float(), 1 / 255.0)
if isinstance(image, np.ndarray):
if not isinstance(mean, np.ndarray):
mean = np.array(mean).astype(image.dtype)
if not isinstance(std, np.ndarray):
std = np.array(std).astype(image.dtype)
elif is_torch_tensor(image):
import torch
if not isinstance(mean, torch.Tensor):
mean = torch.tensor(mean)
if not isinstance(std, torch.Tensor):
std = torch.tensor(std)
if image.ndim == 3 and image.shape[0] in [1, 3]:
return (image - mean[:, None, None]) / std[:, None, None]
else:
return (image - mean) / std
def resize(self, image, size, resample=None, default_to_square=True, max_size=None):
"""
Resizes `image`. Enforces conversion of input to PIL.Image.
Args:
image (`PIL.Image.Image` or `np.ndarray` or `torch.Tensor`):
The image to resize.
size (`int` or `Tuple[int, int]`):
The size to use for resizing the image. If `size` is a sequence like (h, w), output size will be
matched to this.
If `size` is an int and `default_to_square` is `True`, then image will be resized to (size, size). If
`size` is an int and `default_to_square` is `False`, then smaller edge of the image will be matched to
this number. i.e, if height > width, then image will be rescaled to (size * height / width, size).
resample (`int`, *optional*, defaults to `PILImageResampling.BILINEAR`):
The filter to user for resampling.
default_to_square (`bool`, *optional*, defaults to `True`):
How to convert `size` when it is a single int. If set to `True`, the `size` will be converted to a
square (`size`,`size`). If set to `False`, will replicate
[`torchvision.transforms.Resize`](https://pytorch.org/vision/stable/transforms.html#torchvision.transforms.Resize)
with support for resizing only the smallest edge and providing an optional `max_size`.
max_size (`int`, *optional*, defaults to `None`):
The maximum allowed for the longer edge of the resized image: if the longer edge of the image is
greater than `max_size` after being resized according to `size`, then the image is resized again so
that the longer edge is equal to `max_size`. As a result, `size` might be overruled, i.e the smaller
edge may be shorter than `size`. Only used if `default_to_square` is `False`.
Returns:
image: A resized `PIL.Image.Image`.
"""
resample = resample if resample is not None else PILImageResampling.BILINEAR
self._ensure_format_supported(image)
if not isinstance(image, PIL.Image.Image):
image = self.to_pil_image(image)
if isinstance(size, list):
size = tuple(size)
if isinstance(size, int) or len(size) == 1:
if default_to_square:
size = (size, size) if isinstance(size, int) else (size[0], size[0])
else:
width, height = image.size
# specified size only for the smallest edge
short, long = (width, height) if width <= height else (height, width)
requested_new_short = size if isinstance(size, int) else size[0]
if short == requested_new_short:
return image
new_short, new_long = requested_new_short, int(requested_new_short * long / short)
if max_size is not None:
if max_size <= requested_new_short:
raise ValueError(
f"max_size = {max_size} must be strictly greater than the requested "
f"size for the smaller edge size = {size}"
)
if new_long > max_size:
new_short, new_long = int(max_size * new_short / new_long), max_size
size = (new_short, new_long) if width <= height else (new_long, new_short)
return image.resize(size, resample=resample)
def center_crop(self, image, size):
"""
Crops `image` to the given size using a center crop. Note that if the image is too small to be cropped to the
size given, it will be padded (so the returned result has the size asked).
Args:
image (`PIL.Image.Image` or `np.ndarray` or `torch.Tensor` of shape (n_channels, height, width) or (height, width, n_channels)):
The image to resize.
size (`int` or `Tuple[int, int]`):
The size to which crop the image.
Returns:
new_image: A center cropped `PIL.Image.Image` or `np.ndarray` or `torch.Tensor` of shape: (n_channels,
height, width).
"""
self._ensure_format_supported(image)
if not isinstance(size, tuple):
size = (size, size)
# PIL Image.size is (width, height) but NumPy array and torch Tensors have (height, width)
if is_torch_tensor(image) or isinstance(image, np.ndarray):
if image.ndim == 2:
image = self.expand_dims(image)
image_shape = image.shape[1:] if image.shape[0] in [1, 3] else image.shape[:2]
else:
image_shape = (image.size[1], image.size[0])
top = (image_shape[0] - size[0]) // 2
bottom = top + size[0] # In case size is odd, (image_shape[0] + size[0]) // 2 won't give the proper result.
left = (image_shape[1] - size[1]) // 2
right = left + size[1] # In case size is odd, (image_shape[1] + size[1]) // 2 won't give the proper result.
# For PIL Images we have a method to crop directly.
if isinstance(image, PIL.Image.Image):
return image.crop((left, top, right, bottom))
# Check if image is in (n_channels, height, width) or (height, width, n_channels) format
channel_first = True if image.shape[0] in [1, 3] else False
# Transpose (height, width, n_channels) format images
if not channel_first:
if isinstance(image, np.ndarray):
image = image.transpose(2, 0, 1)
if is_torch_tensor(image):
image = image.permute(2, 0, 1)
# Check if cropped area is within image boundaries
if top >= 0 and bottom <= image_shape[0] and left >= 0 and right <= image_shape[1]:
return image[..., top:bottom, left:right]
# Otherwise, we may need to pad if the image is too small. Oh joy...
new_shape = image.shape[:-2] + (max(size[0], image_shape[0]), max(size[1], image_shape[1]))
if isinstance(image, np.ndarray):
new_image = np.zeros_like(image, shape=new_shape)
elif is_torch_tensor(image):
new_image = image.new_zeros(new_shape)
top_pad = (new_shape[-2] - image_shape[0]) // 2
bottom_pad = top_pad + image_shape[0]
left_pad = (new_shape[-1] - image_shape[1]) // 2
right_pad = left_pad + image_shape[1]
new_image[..., top_pad:bottom_pad, left_pad:right_pad] = image
top += top_pad
bottom += top_pad
left += left_pad
right += left_pad
new_image = new_image[
..., max(0, top) : min(new_image.shape[-2], bottom), max(0, left) : min(new_image.shape[-1], right)
]
return new_image
def flip_channel_order(self, image):
"""
Flips the channel order of `image` from RGB to BGR, or vice versa. Note that this will trigger a conversion of
`image` to a NumPy array if it's a PIL Image.
Args:
image (`PIL.Image.Image` or `np.ndarray` or `torch.Tensor`):
The image whose color channels to flip. If `np.ndarray` or `torch.Tensor`, the channel dimension should
be first.
"""
self._ensure_format_supported(image)
if isinstance(image, PIL.Image.Image):
image = self.to_numpy_array(image)
return image[::-1, :, :]
def rotate(self, image, angle, resample=None, expand=0, center=None, translate=None, fillcolor=None):
"""
Returns a rotated copy of `image`. This method returns a copy of `image`, rotated the given number of degrees
counter clockwise around its centre.
Args:
image (`PIL.Image.Image` or `np.ndarray` or `torch.Tensor`):
The image to rotate. If `np.ndarray` or `torch.Tensor`, will be converted to `PIL.Image.Image` before
rotating.
Returns:
image: A rotated `PIL.Image.Image`.
"""
resample = resample if resample is not None else PIL.Image.NEAREST
self._ensure_format_supported(image)
if not isinstance(image, PIL.Image.Image):
image = self.to_pil_image(image)
return image.rotate(
angle, resample=resample, expand=expand, center=center, translate=translate, fillcolor=fillcolor
)