# Copyright (c) MONAI Consortium
# 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 itertools
import random
import warnings
from contextlib import contextmanager
from functools import wraps
from inspect import getmembers, isclass
from typing import Any, Callable, Hashable, Iterable, List, Mapping, Optional, Sequence, Set, Tuple, Union
import numpy as np
import torch
import monai
from monai.config import DtypeLike, IndexSelection
from monai.config.type_definitions import NdarrayOrTensor, NdarrayTensor
from monai.networks.layers import GaussianFilter
from monai.networks.utils import meshgrid_ij
from monai.transforms.compose import Compose, OneOf
from monai.transforms.transform import MapTransform, Transform, apply_transform
from monai.transforms.utils_pytorch_numpy_unification import (
any_np_pt,
ascontiguousarray,
cumsum,
isfinite,
nonzero,
ravel,
searchsorted,
unique,
unravel_index,
where,
)
from monai.utils import (
GridSampleMode,
InterpolateMode,
NumpyPadMode,
PostFix,
PytorchPadMode,
TraceKeys,
ensure_tuple,
ensure_tuple_rep,
ensure_tuple_size,
fall_back_tuple,
get_equivalent_dtype,
issequenceiterable,
look_up_option,
min_version,
optional_import,
)
from monai.utils.enums import TransformBackends
from monai.utils.type_conversion import convert_data_type, convert_to_cupy, convert_to_dst_type, convert_to_tensor
measure, has_measure = optional_import("skimage.measure", "0.14.2", min_version)
morphology, has_morphology = optional_import("skimage.morphology")
ndimage, _ = optional_import("scipy.ndimage")
cp, has_cp = optional_import("cupy")
cp_ndarray, _ = optional_import("cupy", name="ndarray")
cucim, has_cucim = optional_import("cucim")
exposure, has_skimage = optional_import("skimage.exposure")
__all__ = [
"allow_missing_keys_mode",
"check_boundaries",
"compute_divisible_spatial_size",
"convert_applied_interp_mode",
"copypaste_arrays",
"create_control_grid",
"create_grid",
"create_rotate",
"create_scale",
"create_shear",
"create_translate",
"extreme_points_to_image",
"fill_holes",
"Fourier",
"generate_label_classes_crop_centers",
"generate_pos_neg_label_crop_centers",
"generate_spatial_bounding_box",
"get_extreme_points",
"get_largest_connected_component_mask",
"remove_small_objects",
"img_bounds",
"in_bounds",
"is_empty",
"is_positive",
"map_binary_to_indices",
"map_classes_to_indices",
"map_spatial_axes",
"rand_choice",
"rescale_array",
"rescale_array_int_max",
"rescale_instance_array",
"resize_center",
"weighted_patch_samples",
"zero_margins",
"equalize_hist",
"get_number_image_type_conversions",
"get_transform_backends",
"print_transform_backends",
"convert_pad_mode",
"convert_to_contiguous",
"get_unique_labels",
"scale_affine",
"attach_hook",
"sync_meta_info",
"reset_ops_id",
]
[docs]def rand_choice(prob: float = 0.5) -> bool:
"""
Returns True if a randomly chosen number is less than or equal to `prob`, by default this is a 50/50 chance.
"""
return bool(random.random() <= prob)
[docs]def img_bounds(img: np.ndarray):
"""
Returns the minimum and maximum indices of non-zero lines in axis 0 of `img`, followed by that for axis 1.
"""
ax0 = np.any(img, axis=0)
ax1 = np.any(img, axis=1)
return np.concatenate((np.where(ax0)[0][[0, -1]], np.where(ax1)[0][[0, -1]]))
[docs]def in_bounds(x: float, y: float, margin: float, maxx: float, maxy: float) -> bool:
"""
Returns True if (x,y) is within the rectangle (margin, margin, maxx-margin, maxy-margin).
"""
return bool(margin <= x < (maxx - margin) and margin <= y < (maxy - margin))
[docs]def is_empty(img: Union[np.ndarray, torch.Tensor]) -> bool:
"""
Returns True if `img` is empty, that is its maximum value is not greater than its minimum.
"""
return not (img.max() > img.min()) # use > instead of <= so that an image full of NaNs will result in True
[docs]def is_positive(img):
"""
Returns a boolean version of `img` where the positive values are converted into True, the other values are False.
"""
return img > 0
[docs]def zero_margins(img: np.ndarray, margin: int) -> bool:
"""
Returns True if the values within `margin` indices of the edges of `img` in dimensions 1 and 2 are 0.
"""
if np.any(img[:, :, :margin]) or np.any(img[:, :, -margin:]):
return False
return not np.any(img[:, :margin, :]) and not np.any(img[:, -margin:, :])
[docs]def rescale_array(
arr: NdarrayOrTensor,
minv: Optional[float] = 0.0,
maxv: Optional[float] = 1.0,
dtype: Union[DtypeLike, torch.dtype] = np.float32,
) -> NdarrayOrTensor:
"""
Rescale the values of numpy array `arr` to be from `minv` to `maxv`.
If either `minv` or `maxv` is None, it returns `(a - min_a) / (max_a - min_a)`.
Args:
arr: input array to rescale.
minv: minimum value of target rescaled array.
maxv: maxmum value of target rescaled array.
dtype: if not None, convert input array to dtype before computation.
"""
if dtype is not None:
arr, *_ = convert_data_type(arr, dtype=dtype)
mina = arr.min()
maxa = arr.max()
if mina == maxa:
return arr * minv if minv is not None else arr
norm = (arr - mina) / (maxa - mina) # normalize the array first
if (minv is None) or (maxv is None):
return norm
return (norm * (maxv - minv)) + minv # rescale by minv and maxv, which is the normalized array by default
[docs]def rescale_instance_array(
arr: np.ndarray, minv: Optional[float] = 0.0, maxv: Optional[float] = 1.0, dtype: DtypeLike = np.float32
) -> np.ndarray:
"""
Rescale each array slice along the first dimension of `arr` independently.
"""
out: np.ndarray = np.zeros(arr.shape, dtype or arr.dtype)
for i in range(arr.shape[0]):
out[i] = rescale_array(arr[i], minv, maxv, dtype)
return out
[docs]def rescale_array_int_max(arr: np.ndarray, dtype: DtypeLike = np.uint16) -> np.ndarray:
"""
Rescale the array `arr` to be between the minimum and maximum values of the type `dtype`.
"""
info: np.iinfo = np.iinfo(dtype or arr.dtype)
return np.asarray(rescale_array(arr, info.min, info.max), dtype=dtype or arr.dtype)
[docs]def copypaste_arrays(
src_shape, dest_shape, srccenter: Sequence[int], destcenter: Sequence[int], dims: Sequence[Optional[int]]
) -> Tuple[Tuple[slice, ...], Tuple[slice, ...]]:
"""
Calculate the slices to copy a sliced area of array in `src_shape` into array in `dest_shape`.
The area has dimensions `dims` (use 0 or None to copy everything in that dimension),
the source area is centered at `srccenter` index in `src` and copied into area centered at `destcenter` in `dest`.
The dimensions of the copied area will be clipped to fit within the
source and destination arrays so a smaller area may be copied than expected. Return value is the tuples of slice
objects indexing the copied area in `src`, and those indexing the copy area in `dest`.
Example
.. code-block:: python
src_shape = (6,6)
src = np.random.randint(0,10,src_shape)
dest = np.zeros_like(src)
srcslices, destslices = copypaste_arrays(src_shape, dest.shape, (3, 2),(2, 1),(3, 4))
dest[destslices] = src[srcslices]
print(src)
print(dest)
>>> [[9 5 6 6 9 6]
[4 3 5 6 1 2]
[0 7 3 2 4 1]
[3 0 0 1 5 1]
[9 4 7 1 8 2]
[6 6 5 8 6 7]]
[[0 0 0 0 0 0]
[7 3 2 4 0 0]
[0 0 1 5 0 0]
[4 7 1 8 0 0]
[0 0 0 0 0 0]
[0 0 0 0 0 0]]
"""
s_ndim = len(src_shape)
d_ndim = len(dest_shape)
srcslices = [slice(None)] * s_ndim
destslices = [slice(None)] * d_ndim
for i, ss, ds, sc, dc, dim in zip(range(s_ndim), src_shape, dest_shape, srccenter, destcenter, dims):
if dim:
# dimension before midpoint, clip to size fitting in both arrays
d1 = np.clip(dim // 2, 0, min(sc, dc))
# dimension after midpoint, clip to size fitting in both arrays
d2 = np.clip(dim // 2 + 1, 0, min(ss - sc, ds - dc))
srcslices[i] = slice(sc - d1, sc + d2)
destslices[i] = slice(dc - d1, dc + d2)
return tuple(srcslices), tuple(destslices)
[docs]def resize_center(img: np.ndarray, *resize_dims: Optional[int], fill_value: float = 0.0, inplace: bool = True):
"""
Resize `img` by cropping or expanding the image from the center. The `resize_dims` values are the output dimensions
(or None to use original dimension of `img`). If a dimension is smaller than that of `img` then the result will be
cropped and if larger padded with zeros, in both cases this is done relative to the center of `img`. The result is
a new image with the specified dimensions and values from `img` copied into its center.
"""
resize_dims = fall_back_tuple(resize_dims, img.shape)
half_img_shape = (np.asarray(img.shape) // 2).tolist()
half_dest_shape = (np.asarray(resize_dims) // 2).tolist()
srcslices, destslices = copypaste_arrays(img.shape, resize_dims, half_img_shape, half_dest_shape, resize_dims)
if not inplace:
dest = np.full(resize_dims, fill_value, img.dtype) # type: ignore
dest[destslices] = img[srcslices]
return dest
return img[srcslices]
[docs]def map_binary_to_indices(
label: NdarrayOrTensor, image: Optional[NdarrayOrTensor] = None, image_threshold: float = 0.0
) -> Tuple[NdarrayOrTensor, NdarrayOrTensor]:
"""
Compute the foreground and background of input label data, return the indices after fattening.
For example:
``label = np.array([[[0, 1, 1], [1, 0, 1], [1, 1, 0]]])``
``foreground indices = np.array([1, 2, 3, 5, 6, 7])`` and ``background indices = np.array([0, 4, 8])``
Args:
label: use the label data to get the foreground/background information.
image: if image is not None, use ``label = 0 & image > image_threshold``
to define background. so the output items will not map to all the voxels in the label.
image_threshold: if enabled `image`, use ``image > image_threshold`` to
determine the valid image content area and select background only in this area.
"""
# Prepare fg/bg indices
if label.shape[0] > 1:
label = label[1:] # for One-Hot format data, remove the background channel
label_flat = ravel(any_np_pt(label, 0)) # in case label has multiple dimensions
fg_indices = nonzero(label_flat)
if image is not None:
img_flat = ravel(any_np_pt(image > image_threshold, 0))
img_flat, *_ = convert_to_dst_type(img_flat, label, dtype=img_flat.dtype)
bg_indices = nonzero(img_flat & ~label_flat)
else:
bg_indices = nonzero(~label_flat)
# no need to save the indices in GPU, otherwise, still need to move to CPU at runtime when crop by indices
fg_indices, *_ = convert_data_type(fg_indices, device=torch.device("cpu"))
bg_indices, *_ = convert_data_type(bg_indices, device=torch.device("cpu"))
return fg_indices, bg_indices
[docs]def map_classes_to_indices(
label: NdarrayOrTensor,
num_classes: Optional[int] = None,
image: Optional[NdarrayOrTensor] = None,
image_threshold: float = 0.0,
) -> List[NdarrayOrTensor]:
"""
Filter out indices of every class of the input label data, return the indices after fattening.
It can handle both One-Hot format label and Argmax format label, must provide `num_classes` for
Argmax label.
For example:
``label = np.array([[[0, 1, 2], [2, 0, 1], [1, 2, 0]]])`` and `num_classes=3`, will return a list
which contains the indices of the 3 classes:
``[np.array([0, 4, 8]), np.array([1, 5, 6]), np.array([2, 3, 7])]``
Args:
label: use the label data to get the indices of every class.
num_classes: number of classes for argmax label, not necessary for One-Hot label.
image: if image is not None, only return the indices of every class that are within the valid
region of the image (``image > image_threshold``).
image_threshold: if enabled `image`, use ``image > image_threshold`` to
determine the valid image content area and select class indices only in this area.
"""
img_flat: Optional[NdarrayOrTensor] = None
if image is not None:
img_flat = ravel((image > image_threshold).any(0))
indices: List[NdarrayOrTensor] = []
# assuming the first dimension is channel
channels = len(label)
num_classes_: int = channels
if channels == 1:
if num_classes is None:
raise ValueError("if not One-Hot format label, must provide the num_classes.")
num_classes_ = num_classes
for c in range(num_classes_):
label_flat = ravel(any_np_pt(label[c : c + 1] if channels > 1 else label == c, 0))
label_flat = img_flat & label_flat if img_flat is not None else label_flat
# no need to save the indices in GPU, otherwise, still need to move to CPU at runtime when crop by indices
cls_indices: NdarrayOrTensor = convert_data_type(nonzero(label_flat), device=torch.device("cpu"))[0]
indices.append(cls_indices)
return indices
[docs]def weighted_patch_samples(
spatial_size: Union[int, Sequence[int]],
w: NdarrayOrTensor,
n_samples: int = 1,
r_state: Optional[np.random.RandomState] = None,
) -> List:
"""
Computes `n_samples` of random patch sampling locations, given the sampling weight map `w` and patch `spatial_size`.
Args:
spatial_size: length of each spatial dimension of the patch.
w: weight map, the weights must be non-negative. each element denotes a sampling weight of the spatial location.
0 indicates no sampling.
The weight map shape is assumed ``(spatial_dim_0, spatial_dim_1, ..., spatial_dim_n)``.
n_samples: number of patch samples
r_state: a random state container
Returns:
a list of `n_samples` N-D integers representing the spatial sampling location of patches.
"""
if w is None:
raise ValueError("w must be an ND array.")
if r_state is None:
r_state = np.random.RandomState()
img_size = np.asarray(w.shape, dtype=int)
win_size = np.asarray(fall_back_tuple(spatial_size, img_size), dtype=int)
s = tuple(slice(w // 2, m - w + w // 2) if m > w else slice(m // 2, m // 2 + 1) for w, m in zip(win_size, img_size))
v = w[s] # weight map in the 'valid' mode
v_size = v.shape
v = ravel(v)
if (v < 0).any():
v -= v.min() # shifting to non-negative
v = cumsum(v)
if not v[-1] or not isfinite(v[-1]) or v[-1] < 0: # uniform sampling
idx = r_state.randint(0, len(v), size=n_samples)
else:
r, *_ = convert_to_dst_type(r_state.random(n_samples), v)
idx = searchsorted(v, r * v[-1], right=True) # type: ignore
idx, *_ = convert_to_dst_type(idx, v, dtype=torch.int) # type: ignore
# compensate 'valid' mode
diff = np.minimum(win_size, img_size) // 2
diff, *_ = convert_to_dst_type(diff, v) # type: ignore
return [unravel_index(i, v_size) + diff for i in idx]
def correct_crop_centers(
centers: List[int],
spatial_size: Union[Sequence[int], int],
label_spatial_shape: Sequence[int],
allow_smaller: bool = False,
):
"""
Utility to correct the crop center if the crop size and centers are not compatible with the image size.
Args:
centers: pre-computed crop centers of every dim, will correct based on the valid region.
spatial_size: spatial size of the ROIs to be sampled.
label_spatial_shape: spatial shape of the original label data to compare with ROI.
allow_smaller: if `False`, an exception will be raised if the image is smaller than
the requested ROI in any dimension. If `True`, any smaller dimensions will be set to
match the cropped size (i.e., no cropping in that dimension).
"""
spatial_size = fall_back_tuple(spatial_size, default=label_spatial_shape)
if any(np.subtract(label_spatial_shape, spatial_size) < 0):
if not allow_smaller:
raise ValueError("The size of the proposed random crop ROI is larger than the image size.")
spatial_size = tuple(min(l, s) for l, s in zip(label_spatial_shape, spatial_size))
# Select subregion to assure valid roi
valid_start = np.floor_divide(spatial_size, 2)
# add 1 for random
valid_end = np.subtract(label_spatial_shape + np.array(1), spatial_size / np.array(2)).astype(np.uint16)
# int generation to have full range on upper side, but subtract unfloored size/2 to prevent rounded range
# from being too high
for i, valid_s in enumerate(valid_start):
# need this because np.random.randint does not work with same start and end
if valid_s == valid_end[i]:
valid_end[i] += 1
valid_centers = []
for c, v_s, v_e in zip(centers, valid_start, valid_end):
center_i = min(max(c, v_s), v_e - 1)
valid_centers.append(int(center_i))
return valid_centers
[docs]def generate_pos_neg_label_crop_centers(
spatial_size: Union[Sequence[int], int],
num_samples: int,
pos_ratio: float,
label_spatial_shape: Sequence[int],
fg_indices: NdarrayOrTensor,
bg_indices: NdarrayOrTensor,
rand_state: Optional[np.random.RandomState] = None,
allow_smaller: bool = False,
) -> List[List[int]]:
"""
Generate valid sample locations based on the label with option for specifying foreground ratio
Valid: samples sitting entirely within image, expected input shape: [C, H, W, D] or [C, H, W]
Args:
spatial_size: spatial size of the ROIs to be sampled.
num_samples: total sample centers to be generated.
pos_ratio: ratio of total locations generated that have center being foreground.
label_spatial_shape: spatial shape of the original label data to unravel selected centers.
fg_indices: pre-computed foreground indices in 1 dimension.
bg_indices: pre-computed background indices in 1 dimension.
rand_state: numpy randomState object to align with other modules.
allow_smaller: if `False`, an exception will be raised if the image is smaller than
the requested ROI in any dimension. If `True`, any smaller dimensions will be set to
match the cropped size (i.e., no cropping in that dimension).
Raises:
ValueError: When the proposed roi is larger than the image.
ValueError: When the foreground and background indices lengths are 0.
"""
if rand_state is None:
rand_state = np.random.random.__self__ # type: ignore
centers = []
fg_indices = np.asarray(fg_indices) if isinstance(fg_indices, Sequence) else fg_indices
bg_indices = np.asarray(bg_indices) if isinstance(bg_indices, Sequence) else bg_indices
if len(fg_indices) == 0 and len(bg_indices) == 0:
raise ValueError("No sampling location available.")
if len(fg_indices) == 0 or len(bg_indices) == 0:
warnings.warn(
f"N foreground {len(fg_indices)}, N background {len(bg_indices)},"
"unable to generate class balanced samples."
)
pos_ratio = 0 if fg_indices.size == 0 else 1
for _ in range(num_samples):
indices_to_use = fg_indices if rand_state.rand() < pos_ratio else bg_indices
random_int = rand_state.randint(len(indices_to_use))
idx = indices_to_use[random_int]
center = unravel_index(idx, label_spatial_shape).tolist()
# shift center to range of valid centers
centers.append(correct_crop_centers(center, spatial_size, label_spatial_shape, allow_smaller))
return centers
[docs]def generate_label_classes_crop_centers(
spatial_size: Union[Sequence[int], int],
num_samples: int,
label_spatial_shape: Sequence[int],
indices: Sequence[NdarrayOrTensor],
ratios: Optional[List[Union[float, int]]] = None,
rand_state: Optional[np.random.RandomState] = None,
allow_smaller: bool = False,
) -> List[List[int]]:
"""
Generate valid sample locations based on the specified ratios of label classes.
Valid: samples sitting entirely within image, expected input shape: [C, H, W, D] or [C, H, W]
Args:
spatial_size: spatial size of the ROIs to be sampled.
num_samples: total sample centers to be generated.
label_spatial_shape: spatial shape of the original label data to unravel selected centers.
indices: sequence of pre-computed foreground indices of every class in 1 dimension.
ratios: ratios of every class in the label to generate crop centers, including background class.
if None, every class will have the same ratio to generate crop centers.
rand_state: numpy randomState object to align with other modules.
allow_smaller: if `False`, an exception will be raised if the image is smaller than
the requested ROI in any dimension. If `True`, any smaller dimensions will be set to
match the cropped size (i.e., no cropping in that dimension).
"""
if rand_state is None:
rand_state = np.random.random.__self__ # type: ignore
if num_samples < 1:
raise ValueError("num_samples must be an int number and greater than 0.")
ratios_: List[Union[float, int]] = ([1] * len(indices)) if ratios is None else ratios
if len(ratios_) != len(indices):
raise ValueError("random crop ratios must match the number of indices of classes.")
if any(i < 0 for i in ratios_):
raise ValueError("ratios should not contain negative number.")
for i, array in enumerate(indices):
if len(array) == 0:
warnings.warn(f"no available indices of class {i} to crop, set the crop ratio of this class to zero.")
ratios_[i] = 0
centers = []
classes = rand_state.choice(len(ratios_), size=num_samples, p=np.asarray(ratios_) / np.sum(ratios_))
for i in classes:
# randomly select the indices of a class based on the ratios
indices_to_use = indices[i]
random_int = rand_state.randint(len(indices_to_use))
center = unravel_index(indices_to_use[random_int], label_spatial_shape).tolist()
# shift center to range of valid centers
centers.append(correct_crop_centers(center, spatial_size, label_spatial_shape, allow_smaller))
return centers
[docs]def create_grid(
spatial_size: Sequence[int],
spacing: Optional[Sequence[float]] = None,
homogeneous: bool = True,
dtype: Union[DtypeLike, torch.dtype] = float,
device: Optional[torch.device] = None,
backend=TransformBackends.NUMPY,
) -> NdarrayOrTensor:
"""
compute a `spatial_size` mesh.
- when ``homogeneous=True``, the output shape is (N+1, dim_size_1, dim_size_2, ..., dim_size_N)
- when ``homogeneous=False``, the output shape is (N, dim_size_1, dim_size_2, ..., dim_size_N)
Args:
spatial_size: spatial size of the grid.
spacing: same len as ``spatial_size``, defaults to 1.0 (dense grid).
homogeneous: whether to make homogeneous coordinates.
dtype: output grid data type, defaults to `float`.
device: device to compute and store the output (when the backend is "torch").
backend: APIs to use, ``numpy`` or ``torch``.
"""
_backend = look_up_option(backend, TransformBackends)
_dtype = dtype or float
if _backend == TransformBackends.NUMPY:
return _create_grid_numpy(spatial_size, spacing, homogeneous, _dtype) # type: ignore
if _backend == TransformBackends.TORCH:
return _create_grid_torch(spatial_size, spacing, homogeneous, _dtype, device) # type: ignore
raise ValueError(f"backend {backend} is not supported")
def _create_grid_numpy(
spatial_size: Sequence[int],
spacing: Optional[Sequence[float]] = None,
homogeneous: bool = True,
dtype: Union[DtypeLike, torch.dtype] = float,
):
"""
compute a `spatial_size` mesh with the numpy API.
"""
spacing = spacing or tuple(1.0 for _ in spatial_size)
ranges = [np.linspace(-(d - 1.0) / 2.0 * s, (d - 1.0) / 2.0 * s, int(d)) for d, s in zip(spatial_size, spacing)]
coords = np.asarray(np.meshgrid(*ranges, indexing="ij"), dtype=get_equivalent_dtype(dtype, np.ndarray))
if not homogeneous:
return coords
return np.concatenate([coords, np.ones_like(coords[:1])])
def _create_grid_torch(
spatial_size: Sequence[int],
spacing: Optional[Sequence[float]] = None,
homogeneous: bool = True,
dtype=torch.float32,
device: Optional[torch.device] = None,
):
"""
compute a `spatial_size` mesh with the torch API.
"""
spacing = spacing or tuple(1.0 for _ in spatial_size)
ranges = [
torch.linspace(
-(d - 1.0) / 2.0 * s,
(d - 1.0) / 2.0 * s,
int(d),
device=device,
dtype=get_equivalent_dtype(dtype, torch.Tensor),
)
for d, s in zip(spatial_size, spacing)
]
coords = meshgrid_ij(*ranges)
if not homogeneous:
return torch.stack(coords)
return torch.stack([*coords, torch.ones_like(coords[0])])
[docs]def create_control_grid(
spatial_shape: Sequence[int],
spacing: Sequence[float],
homogeneous: bool = True,
dtype: DtypeLike = float,
device: Optional[torch.device] = None,
backend=TransformBackends.NUMPY,
):
"""
control grid with two additional point in each direction
"""
torch_backend = look_up_option(backend, TransformBackends) == TransformBackends.TORCH
ceil_func: Callable = torch.ceil if torch_backend else np.ceil # type: ignore
grid_shape = []
for d, s in zip(spatial_shape, spacing):
d = torch.as_tensor(d, device=device) if torch_backend else int(d) # type: ignore
if d % 2 == 0:
grid_shape.append(ceil_func((d - 1.0) / (2.0 * s) + 0.5) * 2.0 + 2.0)
else:
grid_shape.append(ceil_func((d - 1.0) / (2.0 * s)) * 2.0 + 3.0)
return create_grid(
spatial_size=grid_shape, spacing=spacing, homogeneous=homogeneous, dtype=dtype, device=device, backend=backend
)
[docs]def create_rotate(
spatial_dims: int,
radians: Union[Sequence[float], float],
device: Optional[torch.device] = None,
backend: str = TransformBackends.NUMPY,
) -> NdarrayOrTensor:
"""
create a 2D or 3D rotation matrix
Args:
spatial_dims: {``2``, ``3``} spatial rank
radians: rotation radians
when spatial_dims == 3, the `radians` sequence corresponds to
rotation in the 1st, 2nd, and 3rd dim respectively.
device: device to compute and store the output (when the backend is "torch").
backend: APIs to use, ``numpy`` or ``torch``.
Raises:
ValueError: When ``radians`` is empty.
ValueError: When ``spatial_dims`` is not one of [2, 3].
"""
_backend = look_up_option(backend, TransformBackends)
if _backend == TransformBackends.NUMPY:
return _create_rotate(
spatial_dims=spatial_dims, radians=radians, sin_func=np.sin, cos_func=np.cos, eye_func=np.eye
)
if _backend == TransformBackends.TORCH:
return _create_rotate(
spatial_dims=spatial_dims,
radians=radians,
sin_func=lambda th: torch.sin(torch.as_tensor(th, dtype=torch.float32, device=device)),
cos_func=lambda th: torch.cos(torch.as_tensor(th, dtype=torch.float32, device=device)),
eye_func=lambda rank: torch.eye(rank, device=device),
)
raise ValueError(f"backend {backend} is not supported")
def _create_rotate(
spatial_dims: int,
radians: Union[Sequence[float], float],
sin_func: Callable = np.sin,
cos_func: Callable = np.cos,
eye_func: Callable = np.eye,
) -> NdarrayOrTensor:
radians = ensure_tuple(radians)
if spatial_dims == 2:
if len(radians) >= 1:
sin_, cos_ = sin_func(radians[0]), cos_func(radians[0])
out = eye_func(3)
out[0, 0], out[0, 1] = cos_, -sin_
out[1, 0], out[1, 1] = sin_, cos_
return out # type: ignore
raise ValueError("radians must be non empty.")
if spatial_dims == 3:
affine = None
if len(radians) >= 1:
sin_, cos_ = sin_func(radians[0]), cos_func(radians[0])
affine = eye_func(4)
affine[1, 1], affine[1, 2] = cos_, -sin_
affine[2, 1], affine[2, 2] = sin_, cos_
if len(radians) >= 2:
sin_, cos_ = sin_func(radians[1]), cos_func(radians[1])
if affine is None:
raise ValueError("Affine should be a matrix.")
_affine = eye_func(4)
_affine[0, 0], _affine[0, 2] = cos_, sin_
_affine[2, 0], _affine[2, 2] = -sin_, cos_
affine = affine @ _affine
if len(radians) >= 3:
sin_, cos_ = sin_func(radians[2]), cos_func(radians[2])
if affine is None:
raise ValueError("Affine should be a matrix.")
_affine = eye_func(4)
_affine[0, 0], _affine[0, 1] = cos_, -sin_
_affine[1, 0], _affine[1, 1] = sin_, cos_
affine = affine @ _affine
if affine is None:
raise ValueError("radians must be non empty.")
return affine # type: ignore
raise ValueError(f"Unsupported spatial_dims: {spatial_dims}, available options are [2, 3].")
[docs]def create_shear(
spatial_dims: int,
coefs: Union[Sequence[float], float],
device: Optional[torch.device] = None,
backend=TransformBackends.NUMPY,
) -> NdarrayOrTensor:
"""
create a shearing matrix
Args:
spatial_dims: spatial rank
coefs: shearing factors, a tuple of 2 floats for 2D, a tuple of 6 floats for 3D),
take a 3D affine as example::
[
[1.0, coefs[0], coefs[1], 0.0],
[coefs[2], 1.0, coefs[3], 0.0],
[coefs[4], coefs[5], 1.0, 0.0],
[0.0, 0.0, 0.0, 1.0],
]
device: device to compute and store the output (when the backend is "torch").
backend: APIs to use, ``numpy`` or ``torch``.
Raises:
NotImplementedError: When ``spatial_dims`` is not one of [2, 3].
"""
_backend = look_up_option(backend, TransformBackends)
if _backend == TransformBackends.NUMPY:
return _create_shear(spatial_dims=spatial_dims, coefs=coefs, eye_func=np.eye)
if _backend == TransformBackends.TORCH:
return _create_shear(
spatial_dims=spatial_dims, coefs=coefs, eye_func=lambda rank: torch.eye(rank, device=device)
)
raise ValueError(f"backend {backend} is not supported")
def _create_shear(spatial_dims: int, coefs: Union[Sequence[float], float], eye_func=np.eye) -> NdarrayOrTensor:
if spatial_dims == 2:
coefs = ensure_tuple_size(coefs, dim=2, pad_val=0.0)
out = eye_func(3)
out[0, 1], out[1, 0] = coefs[0], coefs[1]
return out # type: ignore
if spatial_dims == 3:
coefs = ensure_tuple_size(coefs, dim=6, pad_val=0.0)
out = eye_func(4)
out[0, 1], out[0, 2] = coefs[0], coefs[1]
out[1, 0], out[1, 2] = coefs[2], coefs[3]
out[2, 0], out[2, 1] = coefs[4], coefs[5]
return out # type: ignore
raise NotImplementedError("Currently only spatial_dims in [2, 3] are supported.")
[docs]def create_scale(
spatial_dims: int,
scaling_factor: Union[Sequence[float], float],
device: Optional[torch.device] = None,
backend=TransformBackends.NUMPY,
) -> NdarrayOrTensor:
"""
create a scaling matrix
Args:
spatial_dims: spatial rank
scaling_factor: scaling factors for every spatial dim, defaults to 1.
device: device to compute and store the output (when the backend is "torch").
backend: APIs to use, ``numpy`` or ``torch``.
"""
_backend = look_up_option(backend, TransformBackends)
if _backend == TransformBackends.NUMPY:
return _create_scale(spatial_dims=spatial_dims, scaling_factor=scaling_factor, array_func=np.diag)
if _backend == TransformBackends.TORCH:
return _create_scale(
spatial_dims=spatial_dims,
scaling_factor=scaling_factor,
array_func=lambda x: torch.diag(torch.as_tensor(x, device=device)),
)
raise ValueError(f"backend {backend} is not supported")
def _create_scale(
spatial_dims: int, scaling_factor: Union[Sequence[float], float], array_func=np.diag
) -> NdarrayOrTensor:
scaling_factor = ensure_tuple_size(scaling_factor, dim=spatial_dims, pad_val=1.0)
return array_func(scaling_factor[:spatial_dims] + (1.0,)) # type: ignore
[docs]def create_translate(
spatial_dims: int,
shift: Union[Sequence[float], float],
device: Optional[torch.device] = None,
backend=TransformBackends.NUMPY,
) -> NdarrayOrTensor:
"""
create a translation matrix
Args:
spatial_dims: spatial rank
shift: translate pixel/voxel for every spatial dim, defaults to 0.
device: device to compute and store the output (when the backend is "torch").
backend: APIs to use, ``numpy`` or ``torch``.
"""
_backend = look_up_option(backend, TransformBackends)
if _backend == TransformBackends.NUMPY:
return _create_translate(spatial_dims=spatial_dims, shift=shift, eye_func=np.eye, array_func=np.asarray)
if _backend == TransformBackends.TORCH:
return _create_translate(
spatial_dims=spatial_dims,
shift=shift,
eye_func=lambda x: torch.eye(torch.as_tensor(x), device=device), # type: ignore
array_func=lambda x: torch.as_tensor(x, device=device),
)
raise ValueError(f"backend {backend} is not supported")
def _create_translate(
spatial_dims: int, shift: Union[Sequence[float], float], eye_func=np.eye, array_func=np.asarray
) -> NdarrayOrTensor:
shift = ensure_tuple(shift)
affine = eye_func(spatial_dims + 1)
for i, a in enumerate(shift[:spatial_dims]):
affine[i, spatial_dims] = a
return array_func(affine) # type: ignore
[docs]def generate_spatial_bounding_box(
img: NdarrayOrTensor,
select_fn: Callable = is_positive,
channel_indices: Optional[IndexSelection] = None,
margin: Union[Sequence[int], int] = 0,
allow_smaller: bool = True,
) -> Tuple[List[int], List[int]]:
"""
Generate the spatial bounding box of foreground in the image with start-end positions (inclusive).
Users can define arbitrary function to select expected foreground from the whole image or specified channels.
And it can also add margin to every dim of the bounding box.
The output format of the coordinates is:
[1st_spatial_dim_start, 2nd_spatial_dim_start, ..., Nth_spatial_dim_start],
[1st_spatial_dim_end, 2nd_spatial_dim_end, ..., Nth_spatial_dim_end]
If `allow_smaller`, the bounding boxes edges are aligned with the input image edges.
This function returns [0, 0, ...], [0, 0, ...] if there's no positive intensity.
Args:
img: a "channel-first" image of shape (C, spatial_dim1[, spatial_dim2, ...]) to generate bounding box from.
select_fn: function to select expected foreground, default is to select values > 0.
channel_indices: if defined, select foreground only on the specified channels
of image. if None, select foreground on the whole image.
margin: add margin value to spatial dims of the bounding box, if only 1 value provided, use it for all dims.
allow_smaller: when computing box size with `margin`, whether allow the image size to be smaller
than box size, default to `True`.
"""
spatial_size = img.shape[1:]
data = img[list(ensure_tuple(channel_indices))] if channel_indices is not None else img
data = select_fn(data).any(0)
ndim = len(data.shape)
margin = ensure_tuple_rep(margin, ndim)
for m in margin:
if m < 0:
raise ValueError("margin value should not be negative number.")
box_start = [0] * ndim
box_end = [0] * ndim
for di, ax in enumerate(itertools.combinations(reversed(range(ndim)), ndim - 1)):
dt = data
if len(ax) != 0:
dt = any_np_pt(dt, ax)
if not dt.any():
# if no foreground, return all zero bounding box coords
return [0] * ndim, [0] * ndim
arg_max = where(dt == dt.max())[0]
min_d = arg_max[0] - margin[di]
max_d = arg_max[-1] + margin[di] + 1
if allow_smaller:
min_d = max(min_d, 0)
max_d = min(max_d, spatial_size[di])
box_start[di] = min_d.detach().cpu().item() if isinstance(min_d, torch.Tensor) else min_d
box_end[di] = max_d.detach().cpu().item() if isinstance(max_d, torch.Tensor) else max_d
return box_start, box_end
[docs]def get_largest_connected_component_mask(
img: NdarrayTensor, connectivity: Optional[int] = None, num_components: int = 1
) -> NdarrayTensor:
"""
Gets the largest connected component mask of an image.
Args:
img: Image to get largest connected component from. Shape is (spatial_dim1 [, spatial_dim2, ...])
connectivity: Maximum number of orthogonal hops to consider a pixel/voxel as a neighbor.
Accepted values are ranging from 1 to input.ndim. If ``None``, a full
connectivity of ``input.ndim`` is used. for more details:
https://scikit-image.org/docs/dev/api/skimage.measure.html#skimage.measure.label.
num_components: The number of largest components to preserve.
"""
# use skimage/cucim.skimage and np/cp depending on whether packages are
# available and input is non-cpu torch.tensor
use_cp = has_cp and has_cucim and isinstance(img, torch.Tensor) and img.device != torch.device("cpu")
if use_cp:
img_ = convert_to_cupy(img.short()) # type: ignore
label = cucim.skimage.measure.label
lib = cp
else:
if not has_measure:
raise RuntimeError("Skimage.measure required.")
img_, *_ = convert_data_type(img, np.ndarray)
label = measure.label
lib = np
# features will be an image -- 0 for background and then each different
# feature will have its own index.
features, num_features = label(img_, connectivity=connectivity, return_num=True)
# if num features less than max desired, nothing to do.
if num_features <= num_components:
out = img_.astype(bool)
else:
# ignore background
nonzeros = features[lib.nonzero(features)]
# get number voxels per feature (bincount). argsort[::-1] to get indices
# of largest components.
features_to_keep = lib.argsort(lib.bincount(nonzeros))[::-1]
# only keep the first n non-background indices
features_to_keep = features_to_keep[:num_components]
# generate labelfield. True if in list of features to keep
out = lib.isin(features, features_to_keep)
return convert_to_dst_type(out, dst=img, dtype=out.dtype)[0]
[docs]def remove_small_objects(
img: NdarrayTensor, min_size: int = 64, connectivity: int = 1, independent_channels: bool = True
) -> NdarrayTensor:
"""
Use `skimage.morphology.remove_small_objects` to remove small objects from images.
See: https://scikit-image.org/docs/dev/api/skimage.morphology.html#remove-small-objects.
Data should be one-hotted.
Args:
img: image to process. Expected shape: C, H,W,[D]. Expected to only have singleton channel dimension,
i.e., not be one-hotted. Converted to type int.
min_size: objects smaller than this size are removed.
connectivity: Maximum number of orthogonal hops to consider a pixel/voxel as a neighbor.
Accepted values are ranging from 1 to input.ndim. If ``None``, a full
connectivity of ``input.ndim`` is used. For more details refer to linked scikit-image
documentation.
independent_channels: Whether to consider each channel independently.
"""
# if all equal to one value, no need to call skimage
if len(unique(img)) == 1:
return img
if not has_morphology:
raise RuntimeError("Skimage required.")
img_np: np.ndarray
img_np, *_ = convert_data_type(img, np.ndarray)
# morphology.remove_small_objects assumes them to be independent by default
# else, convert to foreground vs background, remove small objects, then convert
# back by multiplying the output by the input
if not independent_channels:
img_np = img_np > 0
else:
# if binary, convert to boolean, else int
img_np = img_np.astype(bool if img_np.max() <= 1 else np.int32)
out_np = morphology.remove_small_objects(img_np, min_size, connectivity)
out, *_ = convert_to_dst_type(out_np, img)
# convert back by multiplying
if not independent_channels:
out = img * out # type: ignore
return out
[docs]def get_unique_labels(
img: NdarrayOrTensor, is_onehot: bool, discard: Optional[Union[int, Iterable[int]]] = None
) -> Set[int]:
"""Get list of non-background labels in an image.
Args:
img: Image to be processed. Shape should be [C, W, H, [D]] with C=1 if not onehot else `num_classes`.
is_onehot: Boolean as to whether input image is one-hotted. If one-hotted, only return channels with
discard: Can be used to remove labels (e.g., background). Can be any value, sequence of values, or
`None` (nothing is discarded).
Returns:
Set of labels
"""
applied_labels: Set[int]
n_channels = img.shape[0]
if is_onehot:
applied_labels = {i for i, s in enumerate(img) if s.sum() > 0}
else:
if n_channels != 1:
raise ValueError("If input not one-hotted, should only be 1 channel.")
applied_labels = set(unique(img).tolist())
if discard is not None:
for i in ensure_tuple(discard):
applied_labels.discard(i)
return applied_labels
[docs]def fill_holes(
img_arr: np.ndarray, applied_labels: Optional[Iterable[int]] = None, connectivity: Optional[int] = None
) -> np.ndarray:
"""
Fill the holes in the provided image.
The label 0 will be treated as background and the enclosed holes will be set to the neighboring class label.
What is considered to be an enclosed hole is defined by the connectivity.
Holes on the edge are always considered to be open (not enclosed).
Note:
The performance of this method heavily depends on the number of labels.
It is a bit faster if the list of `applied_labels` is provided.
Limiting the number of `applied_labels` results in a big decrease in processing time.
If the image is one-hot-encoded, then the `applied_labels` need to match the channel index.
Args:
img_arr: numpy array of shape [C, spatial_dim1[, spatial_dim2, ...]].
applied_labels: Labels for which to fill holes. Defaults to None,
that is filling holes for all labels.
connectivity: Maximum number of orthogonal hops to
consider a pixel/voxel as a neighbor. Accepted values are ranging from 1 to input.ndim.
Defaults to a full connectivity of ``input.ndim``.
Returns:
numpy array of shape [C, spatial_dim1[, spatial_dim2, ...]].
"""
channel_axis = 0
num_channels = img_arr.shape[channel_axis]
is_one_hot = num_channels > 1
spatial_dims = img_arr.ndim - 1
structure = ndimage.generate_binary_structure(spatial_dims, connectivity or spatial_dims)
# Get labels if not provided. Exclude background label.
applied_labels = set(applied_labels) if applied_labels is not None else get_unique_labels(img_arr, is_one_hot)
background_label = 0
applied_labels.discard(background_label)
for label in applied_labels:
tmp = np.zeros(img_arr.shape[1:], dtype=bool)
ndimage.binary_dilation(
tmp,
structure=structure,
iterations=-1,
mask=np.logical_not(img_arr[label]) if is_one_hot else img_arr[0] != label,
origin=0,
border_value=1,
output=tmp,
)
if is_one_hot:
img_arr[label] = np.logical_not(tmp)
else:
img_arr[0, np.logical_not(tmp)] = label
return img_arr
[docs]def get_extreme_points(
img: NdarrayOrTensor, rand_state: Optional[np.random.RandomState] = None, background: int = 0, pert: float = 0.0
) -> List[Tuple[int, ...]]:
"""
Generate extreme points from an image. These are used to generate initial segmentation
for annotation models. An optional perturbation can be passed to simulate user clicks.
Args:
img:
Image to generate extreme points from. Expected Shape is ``(spatial_dim1, [, spatial_dim2, ...])``.
rand_state: `np.random.RandomState` object used to select random indices.
background: Value to be consider as background, defaults to 0.
pert: Random perturbation amount to add to the points, defaults to 0.0.
Returns:
A list of extreme points, its length is equal to 2 * spatial dimension of input image.
The output format of the coordinates is:
[1st_spatial_dim_min, 1st_spatial_dim_max, 2nd_spatial_dim_min, ..., Nth_spatial_dim_max]
Raises:
ValueError: When the input image does not have any foreground pixel.
"""
if rand_state is None:
rand_state = np.random.random.__self__ # type: ignore
indices = where(img != background)
if np.size(indices[0]) == 0:
raise ValueError("get_extreme_points: no foreground object in mask!")
def _get_point(val, dim):
"""
Select one of the indices within slice containing val.
Args:
val : value for comparison
dim : dimension in which to look for value
"""
idx = where(indices[dim] == val)[0]
idx = idx.cpu() if isinstance(idx, torch.Tensor) else idx
idx = rand_state.choice(idx)
pt = []
for j in range(img.ndim):
# add +- pert to each dimension
val = int(indices[j][idx] + 2.0 * pert * (rand_state.rand() - 0.5))
val = max(val, 0)
val = min(val, img.shape[j] - 1)
pt.append(val)
return pt
points = []
for i in range(img.ndim):
points.append(tuple(_get_point(indices[i].min(), i)))
points.append(tuple(_get_point(indices[i].max(), i)))
return points
[docs]def extreme_points_to_image(
points: List[Tuple[int, ...]],
label: NdarrayOrTensor,
sigma: Union[Sequence[float], float, Sequence[torch.Tensor], torch.Tensor] = 0.0,
rescale_min: float = -1.0,
rescale_max: float = 1.0,
) -> torch.Tensor:
"""
Please refer to :py:class:`monai.transforms.AddExtremePointsChannel` for the usage.
Applies a gaussian filter to the extreme points image. Then the pixel values in points image are rescaled
to range [rescale_min, rescale_max].
Args:
points: Extreme points of the object/organ.
label: label image to get extreme points from. Shape must be
(1, spatial_dim1, [, spatial_dim2, ...]). Doesn't support one-hot labels.
sigma: if a list of values, must match the count of spatial dimensions of input data,
and apply every value in the list to 1 spatial dimension. if only 1 value provided,
use it for all spatial dimensions.
rescale_min: minimum value of output data.
rescale_max: maximum value of output data.
"""
# points to image
# points_image = torch.zeros(label.shape[1:], dtype=torch.float)
points_image = torch.zeros_like(torch.as_tensor(label[0]), dtype=torch.float)
for p in points:
points_image[p] = 1.0
if isinstance(sigma, Sequence):
sigma = [torch.as_tensor(s, device=points_image.device) for s in sigma]
else:
sigma = torch.as_tensor(sigma, device=points_image.device)
# add channel and add batch
points_image = points_image.unsqueeze(0).unsqueeze(0)
gaussian_filter = GaussianFilter(label.ndim - 1, sigma=sigma)
points_image = gaussian_filter(points_image).squeeze(0).detach()
# rescale the points image to [rescale_min, rescale_max]
min_intensity = points_image.min()
max_intensity = points_image.max()
points_image = (points_image - min_intensity) / (max_intensity - min_intensity)
return points_image * (rescale_max - rescale_min) + rescale_min
[docs]def map_spatial_axes(
img_ndim: int, spatial_axes: Optional[Union[Sequence[int], int]] = None, channel_first: bool = True
) -> List[int]:
"""
Utility to map the spatial axes to real axes in channel first/last shape.
For example:
If `channel_first` is True, and `img` has 3 spatial dims, map spatial axes to real axes as below:
None -> [1, 2, 3]
[0, 1] -> [1, 2]
[0, -1] -> [1, -1]
If `channel_first` is False, and `img` has 3 spatial dims, map spatial axes to real axes as below:
None -> [0, 1, 2]
[0, 1] -> [0, 1]
[0, -1] -> [0, -2]
Args:
img_ndim: dimension number of the target image.
spatial_axes: spatial axes to be converted, default is None.
The default `None` will convert to all the spatial axes of the image.
If axis is negative it counts from the last to the first axis.
If axis is a tuple of ints.
channel_first: the image data is channel first or channel last, default to channel first.
"""
if spatial_axes is None:
return list(range(1, img_ndim) if channel_first else range(img_ndim - 1))
spatial_axes_ = []
for a in ensure_tuple(spatial_axes):
if channel_first:
spatial_axes_.append(a % img_ndim if a < 0 else a + 1)
else:
spatial_axes_.append((a - 1) % (img_ndim - 1) if a < 0 else a)
return spatial_axes_
[docs]@contextmanager
def allow_missing_keys_mode(transform: Union[MapTransform, Compose, Tuple[MapTransform], Tuple[Compose]]):
"""Temporarily set all MapTransforms to not throw an error if keys are missing. After, revert to original states.
Args:
transform: either MapTransform or a Compose
Example:
.. code-block:: python
data = {"image": np.arange(16, dtype=float).reshape(1, 4, 4)}
t = SpatialPadd(["image", "label"], 10, allow_missing_keys=False)
_ = t(data) # would raise exception
with allow_missing_keys_mode(t):
_ = t(data) # OK!
"""
# If given a sequence of transforms, Compose them to get a single list
if issequenceiterable(transform):
transform = Compose(transform)
# Get list of MapTransforms
transforms = []
if isinstance(transform, MapTransform):
transforms = [transform]
elif isinstance(transform, Compose):
# Only keep contained MapTransforms
transforms = [t for t in transform.flatten().transforms if isinstance(t, MapTransform)]
if len(transforms) == 0:
raise TypeError(
"allow_missing_keys_mode expects either MapTransform(s) or Compose(s) containing MapTransform(s)"
)
# Get the state of each `allow_missing_keys`
orig_states = [t.allow_missing_keys for t in transforms]
try:
# Set all to True
for t in transforms:
t.allow_missing_keys = True
yield
finally:
# Revert
for t, o_s in zip(transforms, orig_states):
t.allow_missing_keys = o_s
_interp_modes = list(InterpolateMode) + list(GridSampleMode)
[docs]def convert_applied_interp_mode(trans_info, mode: str = "nearest", align_corners: Optional[bool] = None):
"""
Recursively change the interpolation mode in the applied operation stacks, default to "nearest".
See also: :py:class:`monai.transform.inverse.InvertibleTransform`
Args:
trans_info: applied operation stack, tracking the previously applied invertible transform.
mode: target interpolation mode to convert, default to "nearest" as it's usually used to save the mode output.
align_corners: target align corner value in PyTorch interpolation API, need to align with the `mode`.
"""
if isinstance(trans_info, (list, tuple)):
return [convert_applied_interp_mode(x, mode=mode, align_corners=align_corners) for x in trans_info]
if not isinstance(trans_info, Mapping):
return trans_info
trans_info = dict(trans_info)
if "mode" in trans_info:
current_mode = trans_info["mode"]
if current_mode[0] in _interp_modes:
trans_info["mode"] = [mode for _ in range(len(mode))]
elif current_mode in _interp_modes:
trans_info["mode"] = mode
if "align_corners" in trans_info:
_align_corners = TraceKeys.NONE if align_corners is None else align_corners
current_value = trans_info["align_corners"]
trans_info["align_corners"] = (
[_align_corners for _ in mode] if issequenceiterable(current_value) else _align_corners
)
if ("mode" not in trans_info) and ("align_corners" not in trans_info):
return {
k: convert_applied_interp_mode(trans_info[k], mode=mode, align_corners=align_corners) for k in trans_info
}
return trans_info
[docs]def reset_ops_id(data):
"""find MetaTensors in list or dict `data` and (in-place) set ``TraceKeys.ID`` to ``Tracekys.NONE``."""
if isinstance(data, (list, tuple)):
return [reset_ops_id(d) for d in data]
if isinstance(data, monai.data.MetaTensor):
data.applied_operations = reset_ops_id(data.applied_operations)
return data
if not isinstance(data, Mapping):
return data
data = dict(data)
if TraceKeys.ID in data:
data[TraceKeys.ID] = TraceKeys.NONE
return {k: reset_ops_id(v) for k, v in data.items()}
[docs]def compute_divisible_spatial_size(spatial_shape: Sequence[int], k: Union[Sequence[int], int]):
"""
Compute the target spatial size which should be divisible by `k`.
Args:
spatial_shape: original spatial shape.
k: the target k for each spatial dimension.
if `k` is negative or 0, the original size is preserved.
if `k` is an int, the same `k` be applied to all the input spatial dimensions.
"""
k = fall_back_tuple(k, (1,) * len(spatial_shape))
new_size = []
for k_d, dim in zip(k, spatial_shape):
new_dim = int(np.ceil(dim / k_d) * k_d) if k_d > 0 else dim
new_size.append(new_dim)
return new_size
[docs]def equalize_hist(
img: np.ndarray, mask: Optional[np.ndarray] = None, num_bins: int = 256, min: int = 0, max: int = 255
) -> np.ndarray:
"""
Utility to equalize input image based on the histogram.
If `skimage` installed, will leverage `skimage.exposure.histogram`, otherwise, use
`np.histogram` instead.
Args:
img: input image to equalize.
mask: if provided, must be ndarray of bools or 0s and 1s, and same shape as `image`.
only points at which `mask==True` are used for the equalization.
num_bins: number of the bins to use in histogram, default to `256`. for more details:
https://numpy.org/doc/stable/reference/generated/numpy.histogram.html.
min: the min value to normalize input image, default to `0`.
max: the max value to normalize input image, default to `255`.
"""
orig_shape = img.shape
hist_img = img[np.array(mask, dtype=bool)] if mask is not None else img
if has_skimage:
hist, bins = exposure.histogram(hist_img.flatten(), num_bins)
else:
hist, bins = np.histogram(hist_img.flatten(), num_bins)
bins = (bins[:-1] + bins[1:]) / 2
cum = hist.cumsum()
# normalize the cumulative result
cum = rescale_array(arr=cum, minv=min, maxv=max)
# apply linear interpolation
img = np.interp(img.flatten(), bins, cum)
return img.reshape(orig_shape)
[docs]class Fourier:
"""
Helper class storing Fourier mappings
"""
[docs] @staticmethod
def shift_fourier(x: NdarrayOrTensor, spatial_dims: int) -> NdarrayOrTensor:
"""
Applies fourier transform and shifts the zero-frequency component to the
center of the spectrum. Only the spatial dimensions get transformed.
Args:
x: Image to transform.
spatial_dims: Number of spatial dimensions.
Returns
k: K-space data.
"""
dims = tuple(range(-spatial_dims, 0))
k: NdarrayOrTensor
if isinstance(x, torch.Tensor):
if hasattr(torch.fft, "fftshift"): # `fftshift` is new in torch 1.8.0
k = torch.fft.fftshift(torch.fft.fftn(x, dim=dims), dim=dims)
else:
# if using old PyTorch, will convert to numpy array and return
k = np.fft.fftshift(np.fft.fftn(x.cpu().numpy(), axes=dims), axes=dims)
else:
k = np.fft.fftshift(np.fft.fftn(x, axes=dims), axes=dims)
return k
[docs] @staticmethod
def inv_shift_fourier(k: NdarrayOrTensor, spatial_dims: int, n_dims: Optional[int] = None) -> NdarrayOrTensor:
"""
Applies inverse shift and fourier transform. Only the spatial
dimensions are transformed.
Args:
k: K-space data.
spatial_dims: Number of spatial dimensions.
Returns:
x: Tensor in image space.
"""
dims = tuple(range(-spatial_dims, 0))
out: NdarrayOrTensor
if isinstance(k, torch.Tensor):
if hasattr(torch.fft, "ifftshift"): # `ifftshift` is new in torch 1.8.0
out = torch.fft.ifftn(torch.fft.ifftshift(k, dim=dims), dim=dims, norm="backward").real
else:
# if using old PyTorch, will convert to numpy array and return
out = np.fft.ifftn(np.fft.ifftshift(k.cpu().numpy(), axes=dims), axes=dims).real
else:
out = np.fft.ifftn(np.fft.ifftshift(k, axes=dims), axes=dims).real
return out
[docs]def get_number_image_type_conversions(transform: Compose, test_data: Any, key: Optional[Hashable] = None) -> int:
"""
Get the number of times that the data need to be converted (e.g., numpy to torch).
Conversions between different devices are also counted (e.g., CPU to GPU).
Args:
transform: composed transforms to be tested
test_data: data to be used to count the number of conversions
key: if using dictionary transforms, this key will be used to check the number of conversions.
"""
def _get_data(obj, key):
return obj if key is None else obj[key]
# if the starting point is a string (e.g., input to LoadImage), start
# at -1 since we don't want to count the string -> image conversion.
num_conversions = 0 if not isinstance(_get_data(test_data, key), str) else -1
tr = transform.flatten().transforms
if isinstance(transform, OneOf) or any(isinstance(i, OneOf) for i in tr):
raise RuntimeError("Not compatible with `OneOf`, as the applied transform is deterministically chosen.")
for _transform in tr:
prev_data = _get_data(test_data, key)
prev_type = type(prev_data)
prev_device = prev_data.device if isinstance(prev_data, torch.Tensor) else None
test_data = apply_transform(_transform, test_data, transform.map_items, transform.unpack_items)
# every time the type or device changes, increment the counter
curr_data = _get_data(test_data, key)
curr_device = curr_data.device if isinstance(curr_data, torch.Tensor) else None
if not isinstance(curr_data, prev_type) or curr_device != prev_device:
num_conversions += 1
return num_conversions
[docs]def convert_pad_mode(dst: NdarrayOrTensor, mode: Optional[str]):
"""
Utility to convert padding mode between numpy array and PyTorch Tensor.
Args:
dst: target data to convert padding mode for, should be numpy array or PyTorch Tensor.
mode: current padding mode.
"""
if isinstance(dst, torch.Tensor):
if mode == "wrap":
mode = "circular"
if mode == "edge":
mode = "replicate"
return look_up_option(mode, PytorchPadMode)
if isinstance(dst, np.ndarray):
if mode == "circular":
mode = "wrap"
if mode == "replicate":
mode = "edge"
return look_up_option(mode, NumpyPadMode)
raise ValueError(f"unsupported data type: {type(dst)}.")
[docs]def convert_to_contiguous(data, **kwargs):
"""
Check and ensure the numpy array or PyTorch Tensor in data to be contiguous in memory.
Args:
data: input data to convert, will recursively convert the numpy array or PyTorch Tensor in dict and sequence.
kwargs: if `x` is PyTorch Tensor, additional args for `torch.contiguous`, more details:
https://pytorch.org/docs/stable/generated/torch.Tensor.contiguous.html#torch.Tensor.contiguous.
"""
if isinstance(data, (np.ndarray, torch.Tensor, str, bytes)):
return ascontiguousarray(data, **kwargs)
if isinstance(data, Mapping):
return {k: convert_to_contiguous(v, **kwargs) for k, v in data.items()}
if isinstance(data, Sequence):
return [convert_to_contiguous(i, **kwargs) for i in data]
return data
[docs]def scale_affine(affine, spatial_size, new_spatial_size, centered: bool = True):
"""
Scale the affine matrix according to the new spatial size.
Args:
affine: affine matrix to scale.
spatial_size: original spatial size.
new_spatial_size: new spatial size.
centered: whether the scaling is with respect to
the image center (True, default) or corner (False).
Returns:
Scaled affine matrix.
"""
if spatial_size == new_spatial_size:
return affine
r = len(affine) - 1
s = np.array([float(o) / float(max(n, 1)) for o, n in zip(spatial_size, new_spatial_size)])
scale = create_scale(r, s.tolist())
if centered:
scale[:r, -1] = (np.diag(scale)[:r] - 1) / 2 # type: ignore
return affine @ convert_to_dst_type(scale, affine)[0]
[docs]def attach_hook(func, hook, mode="pre"):
"""
Adds `hook` before or after a `func` call. If mode is "pre", the wrapper will call hook then func.
If the mode is "post", the wrapper will call func then hook.
"""
supported = {"pre", "post"}
if look_up_option(mode, supported) == "pre":
_hook, _func = hook, func
else:
_hook, _func = func, hook
@wraps(func)
def wrapper(inst, data):
data = _hook(inst, data)
return _func(inst, data)
return wrapper
[docs]def check_boundaries(boundaries) -> None:
"""
Check boundaries for Signal transforms
"""
if not (
isinstance(boundaries, Sequence) and len(boundaries) == 2 and all(isinstance(i, float) for i in boundaries)
):
raise ValueError("Incompatible values: boundaries needs to be a list of float.")
def paste_slices(tup):
"""
given a tuple (pos,w,max_w), return a tuple of slices
"""
pos, w, max_w = tup
max_w = max_w.shape[len(max_w.shape) - 1]
orig_min = max(pos, 0)
orig_max = min(pos + w, max_w)
block_min = -min(pos, 0)
block_max = max_w - max(pos + w, max_w)
block_max = block_max if block_max != 0 else None
return slice(orig_min, orig_max), slice(block_min, block_max)
def paste(orig, block, loc):
"""
given a location (loc) and an original array (orig), paste a block array into it
"""
loc_zip = zip(loc, block.shape, orig)
orig_slices, block_slices = zip(*map(paste_slices, loc_zip))
orig[:, orig_slices[0]] = block[block_slices[0]]
if orig.shape[0] == 1:
orig = orig.squeeze()
return orig
def squarepulse(sig, duty: float = 0.5):
"""
compute squarepulse using pytorch
equivalent to numpy implementation from
https://docs.scipy.org/doc/scipy/reference/generated/scipy.signal.square.html
"""
t, w = convert_to_tensor(sig), convert_to_tensor(duty)
w = convert_to_tensor(w)
t = convert_to_tensor(t)
y = torch.zeros(t.shape)
mask1 = (w > 1) | (w < 0)
tmod = torch.remainder(t, 2 * torch.pi)
mask2 = (~mask1) & (tmod < w * 2 * torch.pi)
y[mask2] = 1
mask3 = (~mask1) & (~mask2)
y[mask3] = -1
return y
if __name__ == "__main__":
print_transform_backends()