"""
Modified from https://github.com/pytorch/vision/blob/main/torchvision/transforms/functional.py
"""
import warnings
import numbers
from enum import Enum
from typing import List, Any, Tuple, Optional
import numpy as np
from PIL import Image
import math
try:
import accimage
except ImportError:
accimage = None
import oneflow as flow
from oneflow.framework.tensor import Tensor
from . import functional_pil as F_pil
from . import functional_tensor as F_t
try:
from flowvision.transforms.functional import InterpolationMode
has_interpolation_mode = True
except ImportError:
has_interpolation_mode = False
[docs]class InterpolationMode(Enum):
r"""Interpolation modes
"""
NEAREST = "nearest"
BILINEAR = "bilinear"
BICUBIC = "bicubic"
# For PIL compatibility
BOX = "box"
HAMMING = "hamming"
LANCZOS = "lanczos"
def _interpolation_modes_from_int(i: int) -> InterpolationMode:
inverse_modes_mapping = {
0: InterpolationMode.NEAREST,
2: InterpolationMode.BILINEAR,
3: InterpolationMode.BICUBIC,
4: InterpolationMode.BOX,
5: InterpolationMode.HAMMING,
1: InterpolationMode.LANCZOS,
}
return inverse_modes_mapping[i]
pil_modes_mapping = {
InterpolationMode.NEAREST: 0,
InterpolationMode.BILINEAR: 2,
InterpolationMode.BICUBIC: 3,
InterpolationMode.BOX: 4,
InterpolationMode.HAMMING: 5,
InterpolationMode.LANCZOS: 1,
}
_pil_interpolation_to_str = {
Image.NEAREST: "nearest",
Image.BILINEAR: "bilinear",
Image.BICUBIC: "bicubic",
Image.BOX: "box",
Image.HAMMING: "hamming",
Image.LANCZOS: "lanczos",
}
_str_to_pil_interpolation = {b: a for a, b in _pil_interpolation_to_str.items()}
if has_interpolation_mode:
_flow_interpolation_to_str = {
InterpolationMode.NEAREST: "nearest",
InterpolationMode.BILINEAR: "bilinear",
InterpolationMode.BICUBIC: "bicubic",
InterpolationMode.BOX: "box",
InterpolationMode.HAMMING: "hamming",
InterpolationMode.LANCZOS: "lanczos",
}
_str_to_flow_interpolation = {b: a for a, b in _flow_interpolation_to_str.items()}
else:
_pil_interpolation_to_flow = {}
_flow_interpolation_to_str = {}
def str_to_pil_interp(mode_str):
return _str_to_pil_interpolation[mode_str]
def str_to_interp_mode(mode_str):
if has_interpolation_mode:
return _str_to_flow_interpolation[mode_str]
else:
return _str_to_pil_interpolation[mode_str]
def get_dimensions(img: Tensor) -> List[int]:
"""Returns the dimensions of an image as [channels, height, width].
Args:
img (PIL Image or Tensor): The image to be checked.
Returns:
List[int]: The image dimensions.
"""
if isinstance(img, flow.Tensor):
return F_t.get_dimensions(img)
return F_pil.get_dimensions(img)
def _get_image_size(img: Tensor) -> List[int]:
"""Returns image size as [w, h]
"""
if isinstance(img, flow.Tensor):
return F_t._get_image_size(img)
return F_pil._get_image_size(img)
def _get_image_num_channels(img: Tensor) -> int:
"""Returns number of image channels
"""
if isinstance(img, flow.Tensor):
return F_t._get_image_num_channels(img)
return F_pil._get_image_num_channels(img)
def _is_pil_image(img: Any) -> bool:
if accimage is not None:
return isinstance(img, (Image.Image, accimage.Image))
else:
return isinstance(img, Image.Image)
def _is_numpy(img: Any) -> bool:
return isinstance(img, np.ndarray)
def _is_numpy_image(img: Any) -> bool:
return img.ndim in {2, 3}
def to_tensor(pic):
"""Convert a ``PIL Image`` or ``numpy.ndarray`` to tensor.
See :class:`~transforms.ToTensor` for more details.
Args:
pic (PIL Image or numpy.ndarray): Image to be converted to tensor.
Returns:
Tensor: Converted image.
"""
if not (_is_pil_image(pic) or _is_numpy(pic)):
raise TypeError("pic should be PIL Image or ndarray. Got {}".format(type(pic)))
if _is_numpy(pic) and not _is_numpy_image(pic):
raise ValueError(
"pic should be 2/3 dimensional. Got {} dimensions.".format(pic.ndim)
)
# default_float_dtype = flow.get_default_dtype()
default_float_dtype = flow.float32
if isinstance(pic, np.ndarray):
# handle numpy array
if pic.ndim == 2:
pic = pic[:, :, None]
img = flow.tensor(np.ascontiguousarray(pic.transpose((2, 0, 1))))
# backward compatibility
if img.dtype == flow.uint8:
return flow._C.cast(img, dtype=default_float_dtype).div(255)
else:
return img
if accimage is not None and isinstance(pic, accimage.Image):
nppic = np.zeros([pic.channels, pic.height, pic.width], dtype=np.float32)
pic.copyto(nppic)
return flow.tensor(nppic, dtype=default_float_dtype)
# handle PIL Image
mode_to_nptype = {"I": np.int32, "I;16": np.int16, "F": np.float32}
np_arr = np.array(pic, mode_to_nptype.get(pic.mode, np.uint8), copy=True)
img = flow.from_numpy(np_arr)
if pic.mode == "1":
img = 255 * img
img = flow._C.reshape(img, shape=(pic.size[1], pic.size[0], len(pic.getbands())))
# put it from HWC to CHW format
res = flow._C.transpose(img, perm=[2, 0, 1])
if img.dtype == flow.uint8:
res = flow._C.cast(res, dtype=default_float_dtype).div(255)
return res
def pil_to_tensor(pic):
"""Convert a ``PIL Image`` to a tensor of the same type.
See :class:`~vision.transforms.PILToTensor` for more details.
Args:
pic (PIL Image): Image to be converted to tensor.
Returns:
Tensor: Converted image.
"""
if not F_pil._is_pil_image(pic):
raise TypeError("pic should be PIL Image. Got {}".format(type(pic)))
if accimage is not None and isinstance(pic, accimage.Image):
# accimage format is always uint8 internally, so always return uint8 here
nppic = np.zeros([pic.channels, pic.height, pic.width], dtype=np.uint8)
pic.copyto(nppic)
return flow.tensor(nppic)
# handle PIL Image
img = flow.tensor(np.asarray(pic))
img = img.view(pic.size[1], pic.size[0], len(pic.getbands()))
# put it from HWC to CHW format
return img.permute((2, 0, 1))
def convert_image_dtype(
image: flow.Tensor, dtype: flow.dtype = flow.float
) -> flow.Tensor:
"""Convert a tensor image to the given ``dtype`` and scale the values accordingly
This function does not support PIL Image.
Args:
image (flow.Tensor): Image to be converted
dtype (flow.dtype): Desired data type of the output
Returns:
Tensor: Converted image
.. note::
When converting from a smaller to a larger integer ``dtype`` the maximum values are **not** mapped exactly.
If converted back and forth, this mismatch has no effect.
Raises:
RuntimeError: When trying to cast :class:`flow.float32` to :class:`flow.int32` or :class:`flow.int64` as
well as for trying to cast :class:`flow.float64` to :class:`flow.int64`. These conversions might lead to
overflow errors since the floating point ``dtype`` cannot store consecutive integers over the whole range
of the integer ``dtype``.
"""
if not isinstance(image, flow.Tensor):
raise TypeError("Input img should be Tensor Image")
return F_t.convert_image_dtype(image, dtype)
def to_pil_image(pic, mode=None):
"""Convert a tensor or an ndarray to PIL Image.
See :class:`~flowvision.transforms.ToPILImage` for more details.
Args:
pic (Tensor or numpy.ndarray): Image to be converted to PIL Image.
mode (`PIL.Image mode`_): color space and pixel depth of input data (optional).
.. _PIL.Image mode: https://pillow.readthedocs.io/en/latest/handbook/concepts.html#concept-modes
Returns:
PIL Image: Image converted to PIL Image.
"""
if not (isinstance(pic, flow.Tensor) or isinstance(pic, np.ndarray)):
raise TypeError("pic should be Tensor or ndarray. Got {}".format(type(pic)))
elif isinstance(pic, flow.Tensor):
if pic.ndimension() not in {2, 3}:
raise ValueError(
"pic should be 2/3 dimensional. Got {} dimensions.".format(
pic.ndimension()
)
)
elif pic.ndimension() == 2:
# if 2D image, add channel dimension (CHW)
pic = pic.unsqueeze(0)
# check number of channels
if pic.shape[-3] > 4:
raise ValueError(
"pic should not have > 4 channels. Got {} channels.".format(
pic.shape[-3]
)
)
elif isinstance(pic, np.ndarray):
if pic.ndim not in {2, 3}:
raise ValueError(
"pic should be 2/3 dimensional. Got {} dimensions.".format(pic.ndim)
)
elif pic.ndim == 2:
# if 2D image, add channel dimension (HWC)
pic = np.expand_dims(pic, 2)
# check number of channels
if pic.shape[-1] > 4:
raise ValueError(
"pic should not have > 4 channels. Got {} channels.".format(
pic.shape[-1]
)
)
npimg = pic
if isinstance(pic, flow.Tensor):
if pic.is_floating_point() and mode != "F":
pic = pic.mul(255).to(flow.uint8)
npimg = np.transpose(pic.cpu().numpy(), (1, 2, 0))
if not isinstance(npimg, np.ndarray):
raise TypeError(
"Input pic must be a flow.Tensor or NumPy ndarray, "
+ "not {}".format(type(npimg))
)
if npimg.shape[2] == 1:
expected_mode = None
npimg = npimg[:, :, 0]
if npimg.dtype == np.uint8:
expected_mode = "L"
elif npimg.dtype == np.int16:
expected_mode = "I;16"
elif npimg.dtype == np.int32:
expected_mode = "I"
elif npimg.dtype == np.float32:
expected_mode = "F"
if mode is not None and mode != expected_mode:
raise ValueError(
"Incorrect mode ({}) supplied for input type {}. Should be {}".format(
mode, np.dtype, expected_mode
)
)
mode = expected_mode
elif npimg.shape[2] == 2:
permitted_2_channel_modes = ["LA"]
if mode is not None and mode not in permitted_2_channel_modes:
raise ValueError(
"Only modes {} are supported for 2D inputs".format(
permitted_2_channel_modes
)
)
if mode is None and npimg.dtype == np.uint8:
mode = "LA"
elif npimg.shape[2] == 4:
permitted_4_channel_modes = ["RGBA", "CMYK", "RGBX"]
if mode is not None and mode not in permitted_4_channel_modes:
raise ValueError(
"Only modes {} are supported for 4D inputs".format(
permitted_4_channel_modes
)
)
if mode is None and npimg.dtype == np.uint8:
mode = "RGBA"
else:
permitted_3_channel_modes = ["RGB", "YCbCr", "HSV"]
if mode is not None and mode not in permitted_3_channel_modes:
raise ValueError(
"Only modes {} are supported for 3D inputs".format(
permitted_3_channel_modes
)
)
if mode is None and npimg.dtype == np.uint8:
mode = "RGB"
if mode is None:
raise TypeError("Input type {} is not supported".format(npimg.dtype))
return Image.fromarray(npimg, mode=mode)
def normalize(
tensor: Tensor, mean: List[float], std: List[float], inplace: bool = False
) -> Tensor:
"""Normalize a float tensor image with mean and standard deviation.
This transform does not support PIL Image.
.. note::
This transform acts out of place by default, i.e., it does not mutates the input tensor.
See :class:`~transforms.Normalize` for more details.
Args:
tensor (Tensor): Float tensor image of size (C, H, W) or (B, C, H, W) to be normalized.
mean (sequence): Sequence of means for each channel.
std (sequence): Sequence of standard deviations for each channel.
inplace(bool,optional): Bool to make this operation inplace.
Returns:
Tensor: Normalized Tensor image.
"""
if isinstance(tensor, Image.Image):
tensor = tensor.convert("RGB")
im = np.array(tensor).astype(np.float32)
im = im / 255.0
im = (im - mean) / std
return im
if not isinstance(tensor, flow.Tensor):
raise TypeError(
"Input tensor should be a oneflow tensor. Got {}.".format(type(tensor))
)
if not tensor.dtype == flow.float:
raise TypeError(
"Input tensor should be a float tensor. Got {}.".format(tensor.dtype)
)
if tensor.ndim < 3:
raise ValueError(
"Expected tensor to be a tensor image of size (..., C, H, W). Got tensor.size() = "
"{}.".format(tensor.size())
)
if not inplace:
tensor = tensor.clone()
dtype = tensor.dtype
# NOTE: np array cannot be used as flow.as_tensor argument because of oneflow bug
np_dtype = flow.framework.dtype.convert_oneflow_dtype_to_numpy_dtype(dtype)
if (np.array(std, dtype=np_dtype) == 0).any():
raise ValueError(
"std evaluated to zero after conversion to {}, leading to division by zero.".format(
dtype
)
)
mean = flow.as_tensor(mean, dtype=dtype, device=tensor.device)
std = flow.as_tensor(std, dtype=dtype, device=tensor.device)
if mean.ndim == 1:
mean = flow._C.reshape(mean, shape=(-1, 1, 1))
if std.ndim == 1:
std = flow._C.reshape(std, shape=(-1, 1, 1))
tensor.sub_(mean).div_(std)
return tensor
def resize(
img: Tensor,
size: List[int],
interpolation: InterpolationMode = InterpolationMode.BILINEAR,
) -> Tensor:
r"""Resize the input image to the given size.
If the image is oneflow Tensor, it is expected
to have [..., H, W] shape, where ... means an arbitrary number of leading dimensions
Args:
img (PIL Image or Tensor): Image to be resized.
size (sequence or int): Desired output size. If size is a sequence like
(h, w), the output size will be matched to this. If size is an int,
the smaller edge of the image will be matched to this number maintaining
the aspect ratio. i.e, if height > width, then image will be rescaled to
:math:`\left(\text{size} \times \frac{\text{height}}{\text{width}}, \text{size}\right)`.
interpolation (InterpolationMode): Desired interpolation enum defined by
:class:`flowvision.transforms.InterpolationMode`.
Default is ``InterpolationMode.BILINEAR``. If input is Tensor, only ``InterpolationMode.NEAREST``,
``InterpolationMode.BILINEAR`` and ``InterpolationMode.BICUBIC`` are supported.
For backward compatibility integer values (e.g. ``PIL.Image.NEAREST``) are still acceptable.
Returns:
PIL Image or Tensor: Resized image.
"""
# Backward compatibility with integer value
if isinstance(interpolation, int):
warnings.warn(
"Argument interpolation should be of type InterpolationMode instead of int. "
"Please, use InterpolationMode enum."
)
interpolation = _interpolation_modes_from_int(interpolation)
if not isinstance(interpolation, InterpolationMode):
raise TypeError("Argument interpolation should be a InterpolationMode")
if not isinstance(img, (flow.Tensor, flow._oneflow_internal.Tensor)):
pil_interpolation = pil_modes_mapping[interpolation]
return F_pil.resize(img, size=size, interpolation=pil_interpolation)
return F_t.resize(img, size=size, interpolation=interpolation.value)
def scale(*args, **kwargs):
warnings.warn(
"The use of the transforms.Scale transform is deprecated, "
+ "please use transforms.Resize instead."
)
return resize(*args, **kwargs)
def pad(
img: Tensor, padding: List[int], fill: int = 0, padding_mode: str = "constant"
) -> Tensor:
r"""Pad the given image on all sides with the given "pad" value.
If the image is oneflow Tensor, it is expected
to have [..., H, W] shape, where ... means at most 2 leading dimensions for mode reflect and symmetric,
at most 3 leading dimensions for mode edge,
and an arbitrary number of leading dimensions for mode constant
Args:
img (PIL Image or Tensor): Image to be padded.
padding (int or sequence): Padding on each border. If a single int is provided this
is used to pad all borders. If sequence of length 2 is provided this is the padding
on left/right and top/bottom respectively. If a sequence of length 4 is provided
this is the padding for the left, top, right and bottom borders respectively.
fill (number or str or tuple): Pixel fill value for constant fill. Default is 0.
If a tuple of length 3, it is used to fill R, G, B channels respectively.
This value is only used when the padding_mode is constant.
Only number is supported for oneflow Tensor.
Only int or str or tuple value is supported for PIL Image.
padding_mode (str): Type of padding. Should be: constant, edge, reflect or symmetric.
Default is constant.
- constant: pads with a constant value, this value is specified with fill
- edge: pads with the last value at the edge of the image.
If input a 5D oneflow Tensor, the last 3 dimensions will be padded instead of the last 2
- reflect: pads with reflection of image without repeating the last value on the edge.
For example, padding [1, 2, 3, 4] with 2 elements on both sides in reflect mode
will result in [3, 2, 1, 2, 3, 4, 3, 2]
- symmetric: pads with reflection of image repeating the last value on the edge.
For example, padding [1, 2, 3, 4] with 2 elements on both sides in symmetric mode
will result in [2, 1, 1, 2, 3, 4, 4, 3]
Returns:
PIL Image or Tensor: Padded image.
"""
if not isinstance(img, flow.Tensor):
return F_pil.pad(img, padding=padding, fill=fill, padding_mode=padding_mode)
return F_t.pad(img, padding=padding, fill=fill, padding_mode=padding_mode)
def crop(img: Tensor, top: int, left: int, height: int, width: int) -> Tensor:
"""Crop the given image at specified location and output size.
If the image is oneflow Tensor, it is expected
to have [..., H, W] shape, where ... means an arbitrary number of leading dimensions.
If image size is smaller than output size along any edge, image is padded with 0 and then cropped.
Args:
img (PIL Image or Tensor): Image to be cropped. (0,0) denotes the top left corner of the image.
top (int): Vertical component of the top left corner of the crop box.
left (int): Horizontal component of the top left corner of the crop box.
height (int): Height of the crop box.
width (int): Width of the crop box.
Returns:
PIL Image or Tensor: Cropped image.
"""
if not isinstance(img, flow.Tensor):
return F_pil.crop(img, top, left, height, width)
return F_t.crop(img, top, left, height, width)
def center_crop(img: Tensor, output_size: List[int]) -> Tensor:
"""Crops the given image at the center.
If the image is oneflow Tensor, it is expected
to have [..., H, W] shape, where ... means an arbitrary number of leading dimensions.
If image size is smaller than output size along any edge, image is padded with 0 and then center cropped.
Args:
img (PIL Image or Tensor): Image to be cropped.
output_size (sequence or int): (height, width) of the crop box. If int or sequence with single int,
it is used for both directions.
Returns:
PIL Image or Tensor: Cropped image.
"""
if isinstance(output_size, numbers.Number):
output_size = (int(output_size), int(output_size))
elif isinstance(output_size, (tuple, list)) and len(output_size) == 1:
output_size = (output_size[0], output_size[0])
image_width, image_height = _get_image_size(img)
crop_height, crop_width = output_size
if crop_width > image_width or crop_height > image_height:
padding_ltrb = [
(crop_width - image_width) // 2 if crop_width > image_width else 0,
(crop_height - image_height) // 2 if crop_height > image_height else 0,
(crop_width - image_width + 1) // 2 if crop_width > image_width else 0,
(crop_height - image_height + 1) // 2 if crop_height > image_height else 0,
]
img = pad(img, padding_ltrb, fill=0) # PIL uses fill value 0
image_width, image_height = _get_image_size(img)
if crop_width == image_width and crop_height == image_height:
return img
crop_top = int(round((image_height - crop_height) / 2.0))
crop_left = int(round((image_width - crop_width) / 2.0))
return crop(img, crop_top, crop_left, crop_height, crop_width)
def resized_crop(
img: Tensor,
top: int,
left: int,
height: int,
width: int,
size: List[int],
interpolation: InterpolationMode = InterpolationMode.BILINEAR,
) -> Tensor:
"""Crop the given image and resize it to desired size.
If the image is oneflow Tensor, it is expected
to have [..., H, W] shape, where ... means an arbitrary number of leading dimensions
Notably used in :class:`~vision.transforms.RandomResizedCrop`.
Args:
img (PIL Image or Tensor): Image to be cropped. (0,0) denotes the top left corner of the image.
top (int): Vertical component of the top left corner of the crop box.
left (int): Horizontal component of the top left corner of the crop box.
height (int): Height of the crop box.
width (int): Width of the crop box.
size (sequence or int): Desired output size. Same semantics as ``resize``.
interpolation (InterpolationMode): Desired interpolation enum defined by
:class:`vision.transforms.InterpolationMode`.
Default is ``InterpolationMode.BILINEAR``. If input is Tensor, only ``InterpolationMode.NEAREST``,
``InterpolationMode.BILINEAR`` and ``InterpolationMode.BICUBIC`` are supported.
For backward compatibility integer values (e.g. ``PIL.Image.NEAREST``) are still acceptable.
Returns:
PIL Image or Tensor: Cropped image.
"""
img = crop(img, top, left, height, width)
img = resize(img, size, interpolation)
return img
def hflip(img: Tensor) -> Tensor:
"""Horizontally flip the given image.
Args:
img (PIL Image or Tensor): Image to be flipped. If img
is a Tensor, it is expected to be in [..., H, W] format,
where ... means it can have an arbitrary number of leading
dimensions.
Returns:
PIL Image or Tensor: Horizontally flipped image.
"""
if not isinstance(img, flow.Tensor):
return F_pil.hflip(img)
return F_t.hflip(img)
def vflip(img: Tensor) -> Tensor:
"""Vertically flip the given image.
Args:
img (PIL Image or Tensor): Image to be flipped. If img
is a Tensor, it is expected to be in [..., H, W] format,
where ... means it can have an arbitrary number of leading
dimensions.
Returns:
PIL Image or Tensor: Vertically flipped image.
"""
if not isinstance(img, flow.Tensor):
return F_pil.vflip(img)
return F_t.vflip(img)
def five_crop(
img: Tensor, size: List[int]
) -> Tuple[Tensor, Tensor, Tensor, Tensor, Tensor]:
"""Crop the given image into four corners and the central crop.
If the image is oneflow Tensor, it is expected
to have [..., H, W] shape, where ... means an arbitrary number of leading dimensions
.. Note::
This transform returns a tuple of images and there may be a
mismatch in the number of inputs and targets your ``Dataset`` returns.
Args:
img (PIL Image or Tensor): Image to be cropped.
size (sequence or int): Desired output size of the crop. If size is an
int instead of sequence like (h, w), a square crop (size, size) is
made. If provided a sequence of length 1, it will be interpreted as (size[0], size[0]).
Returns:
tuple: tuple (tl, tr, bl, br, center)
Corresponding top left, top right, bottom left, bottom right and center crop.
"""
if isinstance(size, numbers.Number):
size = (int(size), int(size))
elif isinstance(size, (tuple, list)) and len(size) == 1:
size = (size[0], size[0])
if len(size) != 2:
raise ValueError("Please provide only two dimensions (h, w) for size.")
image_width, image_height = _get_image_size(img)
crop_height, crop_width = size
if crop_width > image_width or crop_height > image_height:
msg = "Requested crop size {} is bigger than input size {}"
raise ValueError(msg.format(size, (image_height, image_width)))
tl = crop(img, 0, 0, crop_height, crop_width)
tr = crop(img, 0, image_width - crop_width, crop_height, crop_width)
bl = crop(img, image_height - crop_height, 0, crop_height, crop_width)
br = crop(
img,
image_height - crop_height,
image_width - crop_width,
crop_height,
crop_width,
)
center = center_crop(img, [crop_height, crop_width])
return tl, tr, bl, br, center
def ten_crop(img: Tensor, size: List[int], vertical_flip: bool = False) -> List[Tensor]:
"""Generate ten cropped images from the given image.
Crop the given image into four corners and the central crop plus the
flipped version of these (horizontal flipping is used by default).
If the image is oneflow Tensor, it is expected
to have [..., H, W] shape, where ... means an arbitrary number of leading dimensions
.. Note::
This transform returns a tuple of images and there may be a
mismatch in the number of inputs and targets your ``Dataset`` returns.
Args:
img (PIL Image or Tensor): Image to be cropped.
size (sequence or int): Desired output size of the crop. If size is an
int instead of sequence like (h, w), a square crop (size, size) is
made. If provided a sequence of length 1, it will be interpreted as (size[0], size[0]).
vertical_flip (bool): Use vertical flipping instead of horizontal
Returns:
tuple: tuple (tl, tr, bl, br, center, tl_flip, tr_flip, bl_flip, br_flip, center_flip)
Corresponding top left, top right, bottom left, bottom right and
center crop and same for the flipped image.
"""
if isinstance(size, numbers.Number):
size = (int(size), int(size))
elif isinstance(size, (tuple, list)) and len(size) == 1:
size = (size[0], size[0])
if len(size) != 2:
raise ValueError("Please provide only two dimensions (h, w) for size.")
first_five = five_crop(img, size)
if vertical_flip:
img = vflip(img)
else:
img = hflip(img)
second_five = five_crop(img, size)
return first_five + second_five
def adjust_brightness(img: Tensor, brightness_factor: float) -> Tensor:
"""Adjust brightness of an image.
Args:
img (PIL Image or Tensor): Image to be adjusted.
If img is flow Tensor, it is expected to be in [..., 1 or 3, H, W] format,
where ... means it can have an arbitrary number of leading dimensions.
brightness_factor (float): How much to adjust the brightness. Can be
any non negative number. 0 gives a black image, 1 gives the
original image while 2 increases the brightness by a factor of 2.
Returns:
PIL Image or Tensor: Brightness adjusted image.
"""
if not isinstance(img, flow.Tensor):
return F_pil.adjust_brightness(img, brightness_factor)
return F_t.adjust_brightness(img, brightness_factor)
def adjust_contrast(img: Tensor, contrast_factor: float) -> Tensor:
"""Adjust contrast of an image.
Args:
img (PIL Image or Tensor): Image to be adjusted.
If img is flow Tensor, it is expected to be in [..., 3, H, W] format,
where ... means it can have an arbitrary number of leading dimensions.
contrast_factor (float): How much to adjust the contrast. Can be any
non negative number. 0 gives a solid gray image, 1 gives the
original image while 2 increases the contrast by a factor of 2.
Returns:
PIL Image or Tensor: Contrast adjusted image.
"""
if not isinstance(img, flow.Tensor):
return F_pil.adjust_contrast(img, contrast_factor)
return F_t.adjust_contrast(img, contrast_factor)
def adjust_saturation(img: Tensor, saturation_factor: float) -> Tensor:
"""Adjust color saturation of an image.
Args:
img (PIL Image or Tensor): Image to be adjusted.
If img is flow Tensor, it is expected to be in [..., 3, H, W] format,
where ... means it can have an arbitrary number of leading dimensions.
saturation_factor (float): How much to adjust the saturation. 0 will
give a black and white image, 1 will give the original image while
2 will enhance the saturation by a factor of 2.
Returns:
PIL Image or Tensor: Saturation adjusted image.
"""
if not isinstance(img, flow.Tensor):
return F_pil.adjust_saturation(img, saturation_factor)
return F_t.adjust_saturation(img, saturation_factor)
def adjust_hue(img: Tensor, hue_factor: float) -> Tensor:
"""Adjust hue of an image.
The image hue is adjusted by converting the image to HSV and
cyclically shifting the intensities in the hue channel (H).
The image is then converted back to original image mode.
`hue_factor` is the amount of shift in H channel and must be in the
interval `[-0.5, 0.5]`.
See `Hue`_ for more details.
.. _Hue: https://en.wikipedia.org/wiki/Hue
Args:
img (PIL Image or Tensor): Image to be adjusted.
If img is flow Tensor, it is expected to be in [..., 3, H, W] format,
where ... means it can have an arbitrary number of leading dimensions.
If img is PIL Image mode "1", "L", "I", "F" and modes with transparency (alpha channel) are not supported.
hue_factor (float): How much to shift the hue channel. Should be in
[-0.5, 0.5]. 0.5 and -0.5 give complete reversal of hue channel in
HSV space in positive and negative direction respectively.
0 means no shift. Therefore, both -0.5 and 0.5 will give an image
with complementary colors while 0 gives the original image.
Returns:
PIL Image or Tensor: Hue adjusted image.
"""
if not isinstance(img, flow.Tensor):
return F_pil.adjust_hue(img, hue_factor)
return F_t.adjust_hue(img, hue_factor)
def _get_inverse_affine_matrix(
center: List[float],
angle: float,
translate: List[float],
scale: float,
shear: List[float],
) -> List[float]:
# Helper method to compute inverse matrix for affine transformation
# As it is explained in PIL.Image.rotate
# We need compute INVERSE of affine transformation matrix: M = T * C * RSS * C^-1
# where T is translation matrix: [1, 0, tx | 0, 1, ty | 0, 0, 1]
# C is translation matrix to keep center: [1, 0, cx | 0, 1, cy | 0, 0, 1]
# RSS is rotation with scale and shear matrix
# RSS(a, s, (sx, sy)) =
# = R(a) * S(s) * SHy(sy) * SHx(sx)
# = [ s*cos(a - sy)/cos(sy), s*(-cos(a - sy)*tan(x)/cos(y) - sin(a)), 0 ]
# [ s*sin(a + sy)/cos(sy), s*(-sin(a - sy)*tan(x)/cos(y) + cos(a)), 0 ]
# [ 0 , 0 , 1 ]
#
# where R is a rotation matrix, S is a scaling matrix, and SHx and SHy are the shears:
# SHx(s) = [1, -tan(s)] and SHy(s) = [1 , 0]
# [0, 1 ] [-tan(s), 1]
#
# Thus, the inverse is M^-1 = C * RSS^-1 * C^-1 * T^-1
rot = math.radians(angle)
sx, sy = [math.radians(s) for s in shear]
cx, cy = center
tx, ty = translate
# RSS without scaling
a = math.cos(rot - sy) / math.cos(sy)
b = -math.cos(rot - sy) * math.tan(sx) / math.cos(sy) - math.sin(rot)
c = math.sin(rot - sy) / math.cos(sy)
d = -math.sin(rot - sy) * math.tan(sx) / math.cos(sy) + math.cos(rot)
# Inverted rotation matrix with scale and shear
# det([[a, b], [c, d]]) == 1, since det(rotation) = 1 and det(shear) = 1
matrix = [d, -b, 0.0, -c, a, 0.0]
matrix = [x / scale for x in matrix]
# Apply inverse of translation and of center translation: RSS^-1 * C^-1 * T^-1
matrix[2] += matrix[0] * (-cx - tx) + matrix[1] * (-cy - ty)
matrix[5] += matrix[3] * (-cx - tx) + matrix[4] * (-cy - ty)
# Apply center translation: C * RSS^-1 * C^-1 * T^-1
matrix[2] += cx
matrix[5] += cy
return matrix
def rotate(
img: Tensor,
angle: float,
interpolation: InterpolationMode = InterpolationMode.NEAREST,
expand: bool = False,
center: Optional[List[int]] = None,
fill: Optional[List[float]] = None,
resample: Optional[int] = None,
) -> Tensor:
"""Rotate the image by angle.
If the image is oneflow Tensor, it is expected
to have [..., H, W] shape, where ... means an arbitrary number of leading dimensions.
Args:
img (PIL Image or Tensor): image to be rotated.
angle (number): rotation angle value in degrees, counter-clockwise.
interpolation (InterpolationMode): Desired interpolation enum defined by
:class:`flowvision.transforms.InterpolationMode`. Default is ``InterpolationMode.NEAREST``.
If input is Tensor, only ``InterpolationMode.NEAREST``, ``InterpolationMode.BILINEAR`` are supported.
For backward compatibility integer values (e.g. ``PIL.Image.NEAREST``) are still acceptable.
expand (bool, optional): Optional expansion flag.
If true, expands the output image to make it large enough to hold the entire rotated image.
If false or omitted, make the output image the same size as the input image.
Note that the expand flag assumes rotation around the center and no translation.
center (sequence, optional): Optional center of rotation. Origin is the upper left corner.
Default is the center of the image.
fill (sequence or number, optional): Pixel fill value for the area outside the transformed
image. If given a number, the value is used for all bands respectively.
Returns:
PIL Image or Tensor: Rotated image.
.. _filters: https://pillow.readthedocs.io/en/latest/handbook/concepts.html#filters
"""
if resample is not None:
warnings.warn(
"Argument resample is deprecated and will be removed since v0.10.0. Please, use interpolation instead"
)
interpolation = _interpolation_modes_from_int(resample)
# Backward compatibility with integer value
if isinstance(interpolation, int):
warnings.warn(
"Argument interpolation should be of type InterpolationMode instead of int. "
"Please, use InterpolationMode enum."
)
interpolation = _interpolation_modes_from_int(interpolation)
if not isinstance(angle, (int, float)):
raise TypeError("Argument angle should be int or float")
if center is not None and not isinstance(center, (list, tuple)):
raise TypeError("Argument center should be a sequence")
if not isinstance(interpolation, InterpolationMode):
raise TypeError("Argument interpolation should be a InterpolationMode")
if not isinstance(img, flow.Tensor):
pil_interpolation = pil_modes_mapping[interpolation]
return F_pil.rotate(
img,
angle=angle,
interpolation=pil_interpolation,
expand=expand,
center=center,
fill=fill,
)
center_f = [0.0, 0.0]
if center is not None:
img_size = _get_image_size(img)
# Center values should be in pixel coordinates but translated such that (0, 0) corresponds to image center.
center_f = [1.0 * (c - s * 0.5) for c, s in zip(center, img_size)]
# due to current incoherence of rotation angle direction between affine and rotate implementations
# we need to set -angle.
matrix = _get_inverse_affine_matrix(center_f, -angle, [0.0, 0.0], 1.0, [0.0, 0.0])
return F_t.rotate(
img, matrix=matrix, interpolation=interpolation.value, expand=expand, fill=fill
)
def affine(
img: Tensor,
angle: float,
translate: List[int],
scale: float,
shear: List[float],
interpolation: InterpolationMode = InterpolationMode.NEAREST,
fill: Optional[List[float]] = None,
center: Optional[List[int]] = None,
) -> Tensor:
"""Apply affine transformation on the image keeping image center invariant.
If the image is flow Tensor, it is expected
to have [..., H, W] shape, where ... means an arbitrary number of leading dimensions.
Args:
img (PIL Image or Tensor): image to transform.
angle (number): rotation angle in degrees between -180 and 180, clockwise direction.
translate (sequence of integers): horizontal and vertical translations (post-rotation translation)
scale (float): overall scale
shear (float or sequence): shear angle value in degrees between -180 to 180, clockwise direction.
If a sequence is specified, the first value corresponds to a shear parallel to the x axis, while
the second value corresponds to a shear parallel to the y axis.
interpolation (InterpolationMode): Desired interpolation enum defined by
:class:`flowvision.transforms.InterpolationMode`. Default is ``InterpolationMode.NEAREST``.
If input is Tensor, only ``InterpolationMode.NEAREST``, ``InterpolationMode.BILINEAR`` are supported.
For backward compatibility integer values (e.g. ``PIL.Image[.Resampling].NEAREST``) are still accepted,
but deprecated since 0.13 and will be removed in 0.15. Please use InterpolationMode enum.
fill (sequence or number, optional): Pixel fill value for the area outside the transformed
image. If given a number, the value is used for all bands respectively.
center (sequence, optional): Optional center of rotation. Origin is the upper left corner.
Default is the center of the image.
Returns:
PIL Image or Tensor: Transformed image.
"""
# Backward compatibility with integer value
if isinstance(interpolation, int):
warnings.warn(
"Argument 'interpolation' of type int is deprecated since 0.13 and will be removed in 0.15. "
"Please use InterpolationMode enum."
)
interpolation = _interpolation_modes_from_int(interpolation)
if not isinstance(angle, (int, float)):
raise TypeError("Argument angle should be int or float")
if not isinstance(translate, (list, tuple)):
raise TypeError("Argument translate should be a sequence")
if len(translate) != 2:
raise ValueError("Argument translate should be a sequence of length 2")
if scale <= 0.0:
raise ValueError("Argument scale should be positive")
if not isinstance(shear, (numbers.Number, (list, tuple))):
raise TypeError(
"Shear should be either a single value or a sequence of two values"
)
if not isinstance(interpolation, InterpolationMode):
raise TypeError("Argument interpolation should be a InterpolationMode")
if isinstance(angle, int):
angle = float(angle)
if isinstance(translate, tuple):
translate = list(translate)
if isinstance(shear, numbers.Number):
shear = [shear, 0.0]
if isinstance(shear, tuple):
shear = list(shear)
if len(shear) == 1:
shear = [shear[0], shear[0]]
if len(shear) != 2:
raise ValueError(
f"Shear should be a sequence containing two values. Got {shear}"
)
if center is not None and not isinstance(center, (list, tuple)):
raise TypeError("Argument center should be a sequence")
_, height, width = get_dimensions(img)
if not isinstance(img, flow.Tensor):
# center = (width * 0.5 + 0.5, height * 0.5 + 0.5)
# it is visually better to estimate the center without 0.5 offset
# otherwise image rotated by 90 degrees is shifted vs output image of torch.rot90 or F_t.affine
if center is None:
center = [width * 0.5, height * 0.5]
matrix = _get_inverse_affine_matrix(center, angle, translate, scale, shear)
pil_interpolation = pil_modes_mapping[interpolation]
return F_pil.affine(
img, matrix=matrix, interpolation=pil_interpolation, fill=fill
)
center_f = [0.0, 0.0]
if center is not None:
_, height, width = get_dimensions(img)
# Center values should be in pixel coordinates but translated such that (0, 0) corresponds to image center.
center_f = [1.0 * (c - s * 0.5) for c, s in zip(center, [width, height])]
translate_f = [1.0 * t for t in translate]
matrix = _get_inverse_affine_matrix(center_f, angle, translate_f, scale, shear)
return F_t.affine(img, matrix=matrix, interpolation=interpolation.value, fill=fill)
def rgb_to_grayscale(img: Tensor, num_output_channels: int = 1) -> Tensor:
"""Convert RGB image to grayscale version of image.
If the image is flow Tensor, it is expected
to have [..., 3, H, W] shape, where ... means an arbitrary number of leading dimensions
Note:
Please, note that this method supports only RGB images as input. For inputs in other color spaces,
please, consider using meth:`~flowvision.transforms.functional.to_grayscale` with PIL Image.
Args:
img (PIL Image or Tensor): RGB Image to be converted to grayscale.
num_output_channels (int): number of channels of the output image. Value can be 1 or 3. Default, 1.
Returns:
PIL Image or Tensor: Grayscale version of the image.
- if num_output_channels = 1 : returned image is single channel
- if num_output_channels = 3 : returned image is 3 channel with r = g = b
"""
if not isinstance(img, flow.Tensor):
return F_pil.to_grayscale(img, num_output_channels)
return F_t.rgb_to_grayscale(img, num_output_channels)
def gaussian_blur(
img: Tensor, kernel_size: List[int], sigma: Optional[List[float]] = None
) -> Tensor:
"""Performs Gaussian blurring on the image by given kernel.
If the image is oneflow Tensor, it is expected
to have [..., H, W] shape, where ... means an arbitrary number of leading dimensions.
Args:
img (PIL Image or Tensor): Image to be blurred
kernel_size (sequence of ints or int): Gaussian kernel size. Can be a sequence of integers
like ``(kx, ky)`` or a single integer for square kernels.
.. note::
In torchscript mode kernel_size as single int is not supported, use a sequence of
length 1: ``[ksize, ]``.
sigma (sequence of floats or float, optional): Gaussian kernel standard deviation. Can be a
sequence of floats like ``(sigma_x, sigma_y)`` or a single float to define the
same sigma in both X/Y directions. If None, then it is computed using
``kernel_size`` as ``sigma = 0.3 * ((kernel_size - 1) * 0.5 - 1) + 0.8``.
Default, None.
.. note::
In torchscript mode sigma as single float is
not supported, use a sequence of length 1: ``[sigma, ]``.
Returns:
PIL Image or Tensor: Gaussian Blurred version of the image.
"""
# TODO: implement the following code with oneflow
# if not flow.jit.is_scripting() and not flow.jit.is_tracing():
# _log_api_usage_once(gaussian_blur)
if not isinstance(kernel_size, (int, list, tuple)):
raise TypeError(
f"kernel_size should be int or a sequence of integers. Got {type(kernel_size)}"
)
if isinstance(kernel_size, int):
kernel_size = [kernel_size, kernel_size]
if len(kernel_size) != 2:
raise ValueError(
f"If kernel_size is a sequence its length should be 2. Got {len(kernel_size)}"
)
for ksize in kernel_size:
if ksize % 2 == 0 or ksize < 0:
raise ValueError(
f"kernel_size should have odd and positive integers. Got {kernel_size}"
)
if sigma is None:
sigma = [ksize * 0.15 + 0.35 for ksize in kernel_size]
if sigma is not None and not isinstance(sigma, (int, float, list, tuple)):
raise TypeError(
f"sigma should be either float or sequence of floats. Got {type(sigma)}"
)
if isinstance(sigma, (int, float)):
sigma = [float(sigma), float(sigma)]
if isinstance(sigma, (list, tuple)) and len(sigma) == 1:
sigma = [sigma[0], sigma[0]]
if len(sigma) != 2:
raise ValueError(
f"If sigma is a sequence, its length should be 2. Got {len(sigma)}"
)
for s in sigma:
if s <= 0.0:
raise ValueError(f"sigma should have positive values. Got {sigma}")
t_img = img
if not isinstance(img, flow.Tensor):
if not F_pil._is_pil_image(img):
raise TypeError(f"img should be PIL Image or Tensor. Got {type(img)}")
t_img = to_tensor(img)
output = F_t.gaussian_blur(t_img, kernel_size, sigma)
if not isinstance(img, flow.Tensor):
output = to_pil_image(output)
return output