Source code for monai.transforms.utils

# 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.

from __future__ import annotations

import itertools
import random
import warnings
from collections.abc import Callable, Hashable, Iterable, Mapping, Sequence
from contextlib import contextmanager
from functools import lru_cache, wraps
from inspect import getmembers, isclass
from typing import Any

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
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,
    GridSamplePadMode,
    InterpolateMode,
    NdimageMode,
    NumpyPadMode,
    PostFix,
    PytorchPadMode,
    SplineMode,
    TraceKeys,
    TraceStatusKeys,
    deprecated_arg_default,
    ensure_tuple,
    ensure_tuple_rep,
    ensure_tuple_size,
    fall_back_tuple,
    get_equivalent_dtype,
    issequenceiterable,
    look_up_option,
    min_version,
    optional_import,
    pytorch_after,
)
from monai.utils.enums import TransformBackends
from monai.utils.type_conversion import (
    convert_data_type,
    convert_to_cupy,
    convert_to_dst_type,
    convert_to_numpy,
    convert_to_tensor,
)

measure, has_measure = optional_import("skimage.measure", "0.14.2", min_version)
morphology, has_morphology = optional_import("skimage.morphology")
ndimage, has_ndimage = optional_import("scipy.ndimage")
cp, has_cp = optional_import("cupy")
cp_ndarray, _ = optional_import("cupy", name="ndarray")
exposure, has_skimage = optional_import("skimage.exposure")

__all__ = [
    "allow_missing_keys_mode",
    "check_boundaries",
    "compute_divisible_spatial_size",
    "convert_applied_interp_mode",
    "copypaste_arrays",
    "check_non_lazy_pending_ops",
    "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",
    "resolves_modes",
    "has_status_keys",
    "distance_transform_edt",
]


[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: 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: float | None = 0.0, maxv: float | None = 1.0, dtype: 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: maximum 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: float | None = 0.0, maxv: float | None = 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[int | None] ) -> 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: int | None, 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 check_non_lazy_pending_ops( input_array: NdarrayOrTensor, name: None | str = None, raise_error: bool = False ) -> None: """ Check whether the input array has pending operations, raise an error or warn when it has. Args: input_array: input array to be checked. name: an optional name to be included in the error message. raise_error: whether to raise an error, default to False, a warning message will be issued instead. """ if isinstance(input_array, monai.data.MetaTensor) and input_array.pending_operations: msg = ( "The input image is a MetaTensor and has pending operations,\n" f"but the function {name or ''} assumes non-lazy input, result may be incorrect." ) if raise_error: raise ValueError(msg) warnings.warn(msg)
[docs] def map_binary_to_indices( label: NdarrayOrTensor, image: NdarrayOrTensor | None = 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. """ check_non_lazy_pending_ops(label, name="map_binary_to_indices") # 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: check_non_lazy_pending_ops(image, name="map_binary_to_indices") img_flat = ravel(any_np_pt(image > image_threshold, 0)) img_flat, *_ = convert_to_dst_type(img_flat, label, dtype=bool) 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: int | None = None, image: NdarrayOrTensor | None = None, image_threshold: float = 0.0, max_samples_per_class: int | None = None, ) -> 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. max_samples_per_class: maximum length of indices in each class to reduce memory consumption. Default is None, no subsampling. """ check_non_lazy_pending_ops(label, name="map_classes_to_indices") img_flat: NdarrayOrTensor | None = None if image is not None: check_non_lazy_pending_ops(image, name="map_classes_to_indices") img_flat = ravel((image > image_threshold).any(0)) # assuming the first dimension is channel channels = len(label) num_classes_: int = channels if channels == 1: if num_classes is None: raise ValueError("channels==1 indicates not using One-Hot format label, must provide ``num_classes``.") num_classes_ = num_classes indices: list[NdarrayOrTensor] = [] for c in range(num_classes_): if channels > 1: label_flat = ravel(convert_data_type(label[c], dtype=bool)[0]) else: label_flat = ravel(label == c) if img_flat is not None: label_flat = img_flat & label_flat # no need to save the indices in GPU, otherwise, still need to move to CPU at runtime when crop by indices output_type = torch.Tensor if isinstance(label, monai.data.MetaTensor) else None cls_indices: NdarrayOrTensor = convert_data_type( nonzero(label_flat), output_type=output_type, device=torch.device("cpu") )[0] if max_samples_per_class and len(cls_indices) > max_samples_per_class and len(cls_indices) > 1: sample_id = np.round(np.linspace(0, len(cls_indices) - 1, max_samples_per_class)).astype(int) indices.append(cls_indices[sample_id]) else: indices.append(cls_indices) return indices
[docs] def weighted_patch_samples( spatial_size: int | Sequence[int], w: NdarrayOrTensor, n_samples: int = 1, r_state: np.random.RandomState | None = 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. """ check_non_lazy_pending_ops(w, name="weighted_patch_samples") if w is None: raise ValueError("w must be an ND array, got None.") 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) # always copy 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: Sequence[int] | int, label_spatial_shape: Sequence[int], allow_smaller: bool = False, ) -> tuple[Any]: """ 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, " f"got ROI size {spatial_size} and label image size {label_spatial_shape} respectively." ) 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 ensure_tuple(valid_centers) # type: ignore
[docs] def generate_pos_neg_label_crop_centers( spatial_size: Sequence[int] | int, num_samples: int, pos_ratio: float, label_spatial_shape: Sequence[int], fg_indices: NdarrayOrTensor, bg_indices: NdarrayOrTensor, rand_state: np.random.RandomState | None = None, allow_smaller: bool = False, ) -> tuple[tuple]: """ 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: pos_ratio = 0 if len(fg_indices) == 0 else 1 warnings.warn( f"Num foregrounds {len(fg_indices)}, Num backgrounds {len(bg_indices)}, " f"unable to generate class balanced samples, setting `pos_ratio` to {pos_ratio}." ) 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 ensure_tuple(centers) # type: ignore
[docs] def generate_label_classes_crop_centers( spatial_size: Sequence[int] | int, num_samples: int, label_spatial_shape: Sequence[int], indices: Sequence[NdarrayOrTensor], ratios: list[float | int] | None = None, rand_state: np.random.RandomState | None = None, allow_smaller: bool = False, warn: bool = True, ) -> tuple[tuple]: """ 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). warn: if `True` prints a warning if a class is not present in the label. """ if rand_state is None: rand_state = np.random.random.__self__ # type: ignore if num_samples < 1: raise ValueError(f"num_samples must be an int number and greater than 0, got {num_samples}.") ratios_: list[float | int] = list(ensure_tuple([1] * len(indices) if ratios is None else ratios)) if len(ratios_) != len(indices): raise ValueError( f"random crop ratios must match the number of indices of classes, got {len(ratios_)} and {len(indices)}." ) if any(i < 0 for i in ratios_): raise ValueError(f"ratios should not contain negative number, got {ratios_}.") for i, array in enumerate(indices): if len(array) == 0: ratios_[i] = 0 if warn: warnings.warn(f"no available indices of class {i} to crop, set the crop ratio of this class to zero.") 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 ensure_tuple(centers) # type: ignore
[docs] def create_grid( spatial_size: Sequence[int], spacing: Sequence[float] | None = None, homogeneous: bool = True, dtype: DtypeLike | torch.dtype = float, device: torch.device | None = 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: Sequence[float] | None = None, homogeneous: bool = True, dtype: 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: Sequence[float] | None = None, homogeneous: bool = True, dtype=torch.float32, device: torch.device | None = 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: torch.device | None = 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: Sequence[float] | float, device: torch.device | None = 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: 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: Sequence[float] | float, device: torch.device | None = 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: 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: Sequence[float] | float, device: torch.device | str | None = 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: 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: Sequence[float] | float, device: torch.device | None = 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) spatial_dims = int(spatial_dims) 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: 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] @deprecated_arg_default("allow_smaller", old_default=True, new_default=False, since="1.2", replaced="1.5") def generate_spatial_bounding_box( img: NdarrayOrTensor, select_fn: Callable = is_positive, channel_indices: IndexSelection | None = None, margin: 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] 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 to allow the image edges to be smaller than the final box edges. If `True`, the bounding boxes edges are aligned with the input image edges, if `False`, the bounding boxes edges are aligned with the final box edges. Default to `True`. """ check_non_lazy_pending_ops(img, name="generate_spatial_bounding_box") 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(f"margin value should not be negative number, got {margin}.") 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: int | None = 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 skimage, has_cucim = optional_import("cucim.skimage") 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 = 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: int | Iterable[int] | None = 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(f"If input not one-hotted, should only be 1 channel, got {n_channels}.") 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: Iterable[int] | None = None, connectivity: int | None = 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: np.random.RandomState | None = 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. """ check_non_lazy_pending_ops(img, name="get_extreme_points") 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) if rand_state is not None else idx pt = [] for j in range(img.ndim): # add +- pert to each dimension val = int(indices[j][idx] + 2.0 * pert * (rand_state.rand() if rand_state is not None else 0.5 - 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: 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: Sequence[int] | int | None = 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: 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: bool | None = 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 isinstance(current_mode, int) or current_mode in _interp_modes: trans_info["mode"] = mode elif isinstance(current_mode[0], int) or current_mode[0] in _interp_modes: trans_info["mode"] = [mode for _ in range(len(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 ``Tracekeys.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: 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 tuple(new_size)
[docs] def equalize_hist( img: np.ndarray, mask: np.ndarray | None = 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: int | None = 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: Hashable | None = 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. """ from monai.transforms.compose import OneOf 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 get_transform_backends(): """Get the backends of all MONAI transforms. Returns: Dictionary, where each key is a transform, and its corresponding values are a boolean list, stating whether that transform supports (1) `torch.Tensor`, and (2) `np.ndarray` as input without needing to convert. """ backends = {} unique_transforms = [] for n, obj in getmembers(monai.transforms): # skip aliases if obj in unique_transforms: continue unique_transforms.append(obj) if ( isclass(obj) and issubclass(obj, Transform) and n not in [ "BatchInverseTransform", "Compose", "CuCIM", "CuCIMD", "Decollated", "InvertD", "InvertibleTransform", "Lambda", "LambdaD", "MapTransform", "OneOf", "RandCuCIM", "RandCuCIMD", "RandomOrder", "PadListDataCollate", "RandLambda", "RandLambdaD", "RandTorchVisionD", "RandomizableTransform", "TorchVisionD", "Transform", ] ): backends[n] = [TransformBackends.TORCH in obj.backend, TransformBackends.NUMPY in obj.backend] return backends
[docs] def convert_pad_mode(dst: NdarrayOrTensor, mode: str | None): """ 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" elif mode == "edge": mode = "replicate" return look_up_option(mode, PytorchPadMode) if isinstance(dst, np.ndarray): if mode == "circular": mode = "wrap" elif mode == "replicate": mode = "edge" return look_up_option(mode, NumpyPadMode) raise ValueError(f"unsupported data type: {type(dst)}.")
[docs] def convert_to_contiguous( data: NdarrayOrTensor | str | bytes | Mapping | Sequence[Any], **kwargs ) -> NdarrayOrTensor | Mapping | Sequence[Any]: """ 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) elif isinstance(data, Mapping): return {k: convert_to_contiguous(v, **kwargs) for k, v in data.items()} elif isinstance(data, Sequence): return type(data)(convert_to_contiguous(i, **kwargs) for i in data) # type: ignore else: return data
[docs] def scale_affine(spatial_size, new_spatial_size, centered: bool = True): """ Compute the scaling matrix according to the new spatial size Args: 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: the scaling matrix. """ r = max(len(new_spatial_size), len(spatial_size)) if spatial_size == new_spatial_size: return np.eye(r + 1) s = np.array([float(o) / float(max(n, 1)) for o, n in zip(spatial_size, new_spatial_size)], dtype=float) scale = create_scale(r, s.tolist()) if centered: scale[:r, -1] = (np.diag(scale)[:r] - 1) / 2.0 # type: ignore return scale
[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 sync_meta_info(key, data_dict, t: bool = True): """ Given the key, sync up between metatensor `data_dict[key]` and meta_dict `data_dict[key_transforms/meta_dict]`. t=True: the one with more applied_operations in metatensor vs meta_dict is the output, False: less is the output. """ if not isinstance(data_dict, Mapping): return data_dict d = dict(data_dict) # update meta dicts meta_dict_key = PostFix.meta(key) if meta_dict_key not in d: d[meta_dict_key] = monai.data.MetaTensor.get_default_meta() if not isinstance(d[key], monai.data.MetaTensor): d[key] = monai.data.MetaTensor(data_dict[key]) d[key].meta = d[meta_dict_key] d[meta_dict_key].update(d[key].meta) # prefer metatensor's data # update xform info xform_key = monai.transforms.TraceableTransform.trace_key(key) if xform_key not in d: d[xform_key] = monai.data.MetaTensor.get_default_applied_operations() from_meta, from_dict = d[key].applied_operations, d[xform_key] if not from_meta: # avoid [] d[key].applied_operations = d[xform_key] = from_dict return d if not from_dict: d[key].applied_operations = d[xform_key] = from_meta return d if t: # larger transform info stack is used as the result ref = from_meta if len(from_meta) > len(from_dict) else from_dict else: # smaller transform info stack is used as the result ref = from_dict if len(from_meta) > len(from_dict) else from_meta d[key].applied_operations = d[xform_key] = ref return d
[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[-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 def _to_numpy_resample_interp_mode(interp_mode): ret = look_up_option(str(interp_mode), SplineMode, default=None) if ret is not None: return int(ret) _mapping = { InterpolateMode.NEAREST: SplineMode.ZERO, InterpolateMode.NEAREST_EXACT: SplineMode.ZERO, InterpolateMode.LINEAR: SplineMode.ONE, InterpolateMode.BILINEAR: SplineMode.ONE, InterpolateMode.TRILINEAR: SplineMode.ONE, InterpolateMode.BICUBIC: SplineMode.THREE, InterpolateMode.AREA: SplineMode.ZERO, } ret = look_up_option(str(interp_mode), _mapping, default=None) if ret is not None: return ret return look_up_option(str(interp_mode), list(_mapping) + list(SplineMode)) # for better error msg def _to_torch_resample_interp_mode(interp_mode): ret = look_up_option(str(interp_mode), InterpolateMode, default=None) if ret is not None: return ret _mapping = { SplineMode.ZERO: InterpolateMode.NEAREST_EXACT if pytorch_after(1, 11) else InterpolateMode.NEAREST, SplineMode.ONE: InterpolateMode.LINEAR, SplineMode.THREE: InterpolateMode.BICUBIC, } ret = look_up_option(str(interp_mode), _mapping, default=None) if ret is not None: return ret return look_up_option(str(interp_mode), list(_mapping) + list(InterpolateMode)) def _to_numpy_resample_padding_mode(m): ret = look_up_option(str(m), NdimageMode, default=None) if ret is not None: return ret _mapping = { GridSamplePadMode.ZEROS: NdimageMode.CONSTANT, GridSamplePadMode.BORDER: NdimageMode.NEAREST, GridSamplePadMode.REFLECTION: NdimageMode.REFLECT, } ret = look_up_option(str(m), _mapping, default=None) if ret is not None: return ret return look_up_option(str(m), list(_mapping) + list(NdimageMode)) def _to_torch_resample_padding_mode(m): ret = look_up_option(str(m), GridSamplePadMode, default=None) if ret is not None: return ret _mapping = { NdimageMode.CONSTANT: GridSamplePadMode.ZEROS, NdimageMode.GRID_CONSTANT: GridSamplePadMode.ZEROS, NdimageMode.NEAREST: GridSamplePadMode.BORDER, NdimageMode.REFLECT: GridSamplePadMode.REFLECTION, NdimageMode.WRAP: GridSamplePadMode.REFLECTION, NdimageMode.GRID_WRAP: GridSamplePadMode.REFLECTION, NdimageMode.GRID_MIRROR: GridSamplePadMode.REFLECTION, } ret = look_up_option(str(m), _mapping, default=None) if ret is not None: return ret return look_up_option(str(m), list(_mapping) + list(GridSamplePadMode))
[docs] @lru_cache(None) def resolves_modes( interp_mode: str | None = "constant", padding_mode="zeros", backend=TransformBackends.TORCH, **kwargs ): """ Automatically adjust the resampling interpolation mode and padding mode, so that they are compatible with the corresponding API of the `backend`. Depending on the availability of the backends, when there's no exact equivalent, a similar mode is returned. Args: interp_mode: interpolation mode. padding_mode: padding mode. backend: optional backend of `TransformBackends`. If None, the backend will be decided from `interp_mode`. kwargs: additional keyword arguments. currently support ``torch_interpolate_spatial_nd``, to provide additional information to determine ``linear``, ``bilinear`` and ``trilinear``; ``use_compiled`` to use MONAI's precompiled backend (pytorch c++ extensions), default to ``False``. """ _interp_mode, _padding_mode, _kwargs = None, None, (kwargs or {}).copy() if backend is None: # infer backend backend = ( TransformBackends.NUMPY if look_up_option(str(interp_mode), SplineMode, default=None) is not None else TransformBackends.TORCH ) if backend == TransformBackends.NUMPY: _interp_mode = _to_numpy_resample_interp_mode(interp_mode) _padding_mode = _to_numpy_resample_padding_mode(padding_mode) return backend, _interp_mode, _padding_mode, _kwargs _interp_mode = _to_torch_resample_interp_mode(interp_mode) _padding_mode = _to_torch_resample_padding_mode(padding_mode) if str(_interp_mode).endswith("linear"): nd = _kwargs.pop("torch_interpolate_spatial_nd", 2) if nd == 1: _interp_mode = InterpolateMode.LINEAR elif nd == 3: _interp_mode = InterpolateMode.TRILINEAR else: _interp_mode = InterpolateMode.BILINEAR # torch grid_sample bilinear is trilinear in 3D if not _kwargs.pop("use_compiled", False): return backend, _interp_mode, _padding_mode, _kwargs _padding_mode = 1 if _padding_mode == "reflection" else _padding_mode if _interp_mode == "bicubic": _interp_mode = 3 elif str(_interp_mode).endswith("linear"): _interp_mode = 1 else: _interp_mode = GridSampleMode(_interp_mode) return backend, _interp_mode, _padding_mode, _kwargs
def check_applied_operations(entry: list | dict, status_key: str, default_message: str = "No message provided"): """ Check the operations of a MetaTensor to determine whether there are any statuses Args: entry: a dictionary that may contain TraceKey.STATUS entries, or a list of such dictionaries status_key: the status key to search for. This must be an entry in `TraceStatusKeys`_ default_message: The message to provide if no messages are provided for the given status key entry Returns: A list of status messages matching the providing status key """ if isinstance(entry, list): results = list() for sub_entry in entry: results.extend(check_applied_operations(sub_entry, status_key, default_message)) return results else: status_key_ = TraceStatusKeys(status_key) if TraceKeys.STATUSES in entry: if status_key_ in entry[TraceKeys.STATUSES]: reason = entry[TraceKeys.STATUSES][status_key_] if reason is None: return [default_message] return reason if isinstance(reason, list) else [reason] return []
[docs] def has_status_keys(data: torch.Tensor, status_key: Any, default_message: str = "No message provided"): """ Checks whether a given tensor is has a particular status key message on any of its applied operations. If it doesn't, it returns the tuple `(False, None)`. If it does it returns a tuple of True and a list of status messages for that status key. Status keys are defined in :class:`TraceStatusKeys<monai.utils.enums.TraceStatusKeys>`. This function also accepts: * dictionaries of tensors * lists or tuples of tensors * list or tuples of dictionaries of tensors In any of the above scenarios, it iterates through the collections and executes itself recursively until it is operating on tensors. Args: data: a `torch.Tensor` or `MetaTensor` or collections of torch.Tensor or MetaTensor, as described above status_key: the status key to look for, from `TraceStatusKeys` default_message: a default message to use if the status key entry doesn't have a message set Returns: A tuple. The first entry is `False` or `True`. The second entry is the status messages that can be used for the user to help debug their pipelines. """ status_key_occurrences = list() if isinstance(data, (list, tuple)): for d in data: _, reasons = has_status_keys(d, status_key, default_message) if reasons is not None: status_key_occurrences.extend(reasons) elif isinstance(data, monai.data.MetaTensor): for op in data.applied_operations: status_key_occurrences.extend(check_applied_operations(op, status_key, default_message)) elif isinstance(data, dict): for d in data.values(): _, reasons = has_status_keys(d, status_key, default_message) if reasons is not None: status_key_occurrences.extend(reasons) if len(status_key_occurrences) > 0: return False, status_key_occurrences return True, None
[docs] def distance_transform_edt( img: NdarrayOrTensor, sampling: None | float | list[float] = None, return_distances: bool = True, return_indices: bool = False, distances: NdarrayOrTensor | None = None, indices: NdarrayOrTensor | None = None, *, block_params: tuple[int, int, int] | None = None, float64_distances: bool = False, ) -> None | NdarrayOrTensor | tuple[NdarrayOrTensor, NdarrayOrTensor]: """ Euclidean distance transform, either GPU based with CuPy / cuCIM or CPU based with scipy. To use the GPU implementation, make sure cuCIM is available and that the data is a `torch.tensor` on a GPU device. Note that the results of the libraries can differ, so stick to one if possible. For details, check out the `SciPy`_ and `cuCIM`_ documentation. .. _SciPy: https://docs.scipy.org/doc/scipy/reference/generated/scipy.ndimage.distance_transform_edt.html .. _cuCIM: https://docs.rapids.ai/api/cucim/nightly/api/#cucim.core.operations.morphology.distance_transform_edt Args: img: Input image on which the distance transform shall be run. Has to be a channel first array, must have shape: (num_channels, H, W [,D]). Can be of any type but will be converted into binary: 1 wherever image equates to True, 0 elsewhere. Input gets passed channel-wise to the distance-transform, thus results from this function will differ from directly calling ``distance_transform_edt()`` in CuPy or SciPy. sampling: Spacing of elements along each dimension. If a sequence, must be of length equal to the input rank -1; if a single number, this is used for all axes. If not specified, a grid spacing of unity is implied. return_distances: Whether to calculate the distance transform. return_indices: Whether to calculate the feature transform. distances: An output array to store the calculated distance transform, instead of returning it. `return_distances` must be True. indices: An output array to store the calculated feature transform, instead of returning it. `return_indicies` must be True. block_params: This parameter is specific to cuCIM and does not exist in SciPy. For details, look into `cuCIM`_. float64_distances: This parameter is specific to cuCIM and does not exist in SciPy. If True, use double precision in the distance computation (to match SciPy behavior). Otherwise, single precision will be used for efficiency. Returns: distances: The calculated distance transform. Returned only when `return_distances` is True and `distances` is not supplied. It will have the same shape and type as image. For cuCIM: Will have dtype torch.float64 if float64_distances is True, otherwise it will have dtype torch.float32. For SciPy: Will have dtype np.float64. indices: The calculated feature transform. It has an image-shaped array for each dimension of the image. The type will be equal to the type of the image. Returned only when `return_indices` is True and `indices` is not supplied. dtype np.float64. """ distance_transform_edt, has_cucim = optional_import( "cucim.core.operations.morphology", name="distance_transform_edt" ) use_cp = has_cp and has_cucim and isinstance(img, torch.Tensor) and img.device.type == "cuda" if not return_distances and not return_indices: raise RuntimeError("Neither return_distances nor return_indices True") if not (img.ndim >= 3 and img.ndim <= 4): raise RuntimeError("Wrong input dimensionality. Use (num_channels, H, W [,D])") distances_original, indices_original = distances, indices distances, indices = None, None if use_cp: distances_, indices_ = None, None if return_distances: dtype = torch.float64 if float64_distances else torch.float32 if distances is None: distances = torch.zeros_like(img, dtype=dtype) # type: ignore else: if not isinstance(distances, torch.Tensor) and distances.device != img.device: raise TypeError("distances must be a torch.Tensor on the same device as img") if not distances.dtype == dtype: raise TypeError("distances must be a torch.Tensor of dtype float32 or float64") distances_ = convert_to_cupy(distances) if return_indices: dtype = torch.int32 if indices is None: indices = torch.zeros((img.dim(),) + img.shape, dtype=dtype) # type: ignore else: if not isinstance(indices, torch.Tensor) and indices.device != img.device: raise TypeError("indices must be a torch.Tensor on the same device as img") if not indices.dtype == dtype: raise TypeError("indices must be a torch.Tensor of dtype int32") indices_ = convert_to_cupy(indices) img_ = convert_to_cupy(img) for channel_idx in range(img_.shape[0]): distance_transform_edt( img_[channel_idx], sampling=sampling, return_distances=return_distances, return_indices=return_indices, distances=distances_[channel_idx] if distances_ is not None else None, indices=indices_[channel_idx] if indices_ is not None else None, block_params=block_params, float64_distances=float64_distances, ) else: if not has_ndimage: raise RuntimeError("scipy.ndimage required if cupy is not available") img_ = convert_to_numpy(img) if return_distances: if distances is None: distances = np.zeros_like(img_, dtype=np.float64) else: if not isinstance(distances, np.ndarray): raise TypeError("distances must be a numpy.ndarray") if not distances.dtype == np.float64: raise TypeError("distances must be a numpy.ndarray of dtype float64") if return_indices: if indices is None: indices = np.zeros((img_.ndim,) + img_.shape, dtype=np.int32) else: if not isinstance(indices, np.ndarray): raise TypeError("indices must be a numpy.ndarray") if not indices.dtype == np.int32: raise TypeError("indices must be a numpy.ndarray of dtype int32") for channel_idx in range(img_.shape[0]): ndimage.distance_transform_edt( img_[channel_idx], sampling=sampling, return_distances=return_distances, return_indices=return_indices, distances=distances[channel_idx] if distances is not None else None, indices=indices[channel_idx] if indices is not None else None, ) r_vals = [] if return_distances and distances_original is None: r_vals.append(distances) if return_indices and indices_original is None: r_vals.append(indices) if not r_vals: return None device = img.device if isinstance(img, torch.Tensor) else None return convert_data_type(r_vals[0] if len(r_vals) == 1 else r_vals, output_type=type(img), device=device)[0] # type: ignore
if __name__ == "__main__": print_transform_backends()