# Copyright 2020 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 random
import warnings
import numpy as np
from monai.utils.misc import ensure_tuple
[docs]def rand_choice(prob=0.5):
"""Returns True if a randomly chosen number is less than or equal to `prob`, by default this is a 50/50 chance."""
return random.random() <= prob
[docs]def img_bounds(img):
"""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, y, margin, maxx, maxy):
"""Returns True if (x,y) is within the rectangle (margin, margin, maxx-margin, maxy-margin)."""
return margin <= x < (maxx - margin) and margin <= y < (maxy - margin)
[docs]def is_empty(img):
"""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 zero_margins(img, margin):
"""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
if np.any(img[:, :margin, :]) or np.any(img[:, -margin:, :]):
return False
return True
[docs]def rescale_array(arr, minv=0.0, maxv=1.0, dtype=np.float32):
"""Rescale the values of numpy array `arr` to be from `minv` to `maxv`."""
if dtype is not None:
arr = arr.astype(dtype)
mina = np.min(arr)
maxa = np.max(arr)
if mina == maxa:
return arr * minv
norm = (arr - mina) / (maxa - mina) # normalize the array first
return (norm * (maxv - minv)) + minv # rescale by minv and maxv, which is the normalized array by default
[docs]def rescale_instance_array(arr, minv=0.0, maxv=1.0, dtype=np.float32):
"""Rescale each array slice along the first dimension of `arr` independently."""
out = np.zeros(arr.shape, 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, dtype=np.uint16):
"""Rescale the array `arr` to be between the minimum and maximum values of the type `dtype`."""
info = np.iinfo(dtype)
return rescale_array(arr, info.min, info.max).astype(dtype)
[docs]def copypaste_arrays(src, dest, srccenter, destcenter, dims):
"""
Calculate the slices to copy a sliced area of array `src` into array `dest`. 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 = np.random.randint(0,10,(6,6))
dest = np.zeros_like(src)
srcslices, destslices = copypaste_arrays(src, dest, (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]]
"""
srcslices = [slice(None)] * src.ndim
destslices = [slice(None)] * dest.ndim
for i, ss, ds, sc, dc, dim in zip(range(src.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, *resize_dims, fill_value=0):
"""
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 = tuple(resize_dims[i] or img.shape[i] for i in range(len(resize_dims)))
dest = np.full(resize_dims, fill_value, img.dtype)
half_img_shape = np.asarray(img.shape) // 2
half_dest_shape = np.asarray(dest.shape) // 2
srcslices, destslices = copypaste_arrays(img, dest, half_img_shape, half_dest_shape, resize_dims)
dest[destslices] = img[srcslices]
return dest
[docs]def one_hot(labels, num_classes):
"""
Converts label image `labels` to a one-hot vector with `num_classes` number of channels as last dimension.
"""
labels = labels % num_classes
y = np.eye(num_classes)
onehot = y[labels.flatten()]
return onehot.reshape(tuple(labels.shape) + (num_classes,)).astype(labels.dtype)
[docs]def generate_pos_neg_label_crop_centers(label, size, num_samples, pos_ratio, image=None,
image_threshold=0, rand_state=np.random):
"""Generate valid sample locations based on image with option for specifying foreground ratio
Valid: samples sitting entirely within image, expected input shape: [C, H, W, D] or [C, H, W]
Args:
label (numpy.ndarray): use the label data to get the foreground/background information.
size (list or tuple): size of the ROIs to be sampled.
num_samples (int): total sample centers to be generated.
pos_ratio (float): ratio of total locations generated that have center being foreground.
image (numpy.ndarray): if image is not None, use ``label = 0 & image > image_threshold``
to select background. so the crop center will only exist on valid image area.
image_threshold (int or float): if enabled image_key, use ``image > image_threshold`` to
determine the valid image content area.
rand_state (random.RandomState): numpy randomState object to align with other modules.
"""
max_size = label.shape[1:]
assert len(max_size) == len(size), 'expected size does not match label dim.'
assert (np.subtract(max_size, size) >= 0).all(), 'proposed roi is larger than image itself.'
# Select subregion to assure valid roi
valid_start = np.floor_divide(size, 2)
valid_end = np.subtract(max_size + np.array(1), size / np.array(2)).astype(np.uint16) # add 1 for random
# int generation to have full range on upper side, but subtract unfloored size/2 to prevent rounded range
# from being too high
for i in range(len(valid_start)): # need this because np.random.randint does not work with same start and end
if valid_start[i] == valid_end[i]:
valid_end[i] += 1
# Prepare fg/bg indices
label_flat = np.any(label, axis=0).ravel() # in case label has multiple dimensions
fg_indices = np.nonzero(label_flat)[0]
if image is not None:
img_flat = np.any(image > image_threshold, axis=0).ravel()
bg_indices = np.nonzero(np.logical_and(img_flat, ~label_flat))[0]
else:
bg_indices = np.nonzero(~label_flat)[0]
if not len(fg_indices) or not len(bg_indices):
if not len(fg_indices) and not len(bg_indices):
raise ValueError('no sampling location available.')
warnings.warn('N foreground {}, N background {}, unable to generate class balanced samples.'.format(
len(fg_indices), len(bg_indices)))
pos_ratio = 0 if not len(fg_indices) else 1
centers = []
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))
center = np.unravel_index(indices_to_use[random_int], label.shape)
center = center[1:]
# shift center to range of valid centers
center_ori = [c for c in center]
for i, c in enumerate(center):
center_i = c
if c < valid_start[i]:
center_i = valid_start[i]
if c >= valid_end[i]:
center_i = valid_end[i] - 1
center_ori[i] = center_i
centers.append(center_ori)
return centers
[docs]def create_grid(spatial_size, spacing=None, homogeneous=True, dtype=float):
"""
compute a `spatial_size` mesh.
Args:
spatial_size (sequence of ints): spatial size of the grid.
spacing (sequence of ints): same len as ``spatial_size``, defaults to 1.0 (dense grid).
homogeneous (bool): whether to make homogeneous coordinates.
dtype (type): output grid data type.
"""
spacing = spacing or tuple(1.0 for _ in spatial_size)
ranges = [np.linspace(-(d - 1.) / 2. * s, (d - 1.) / 2. * s, int(d)) for d, s in zip(spatial_size, spacing)]
coords = np.asarray(np.meshgrid(*ranges, indexing='ij'), dtype=dtype)
if not homogeneous:
return coords
return np.concatenate([coords, np.ones_like(coords[:1])])
[docs]def create_control_grid(spatial_shape, spacing, homogeneous=True, dtype=float):
"""
control grid with two additional point in each direction
"""
grid_shape = []
for d, s in zip(spatial_shape, spacing):
d = int(d)
if d % 2 == 0:
grid_shape.append(np.ceil((d - 1.) / (2. * s) + 0.5) * 2. + 2.)
else:
grid_shape.append(np.ceil((d - 1.) / (2. * s)) * 2. + 3.)
return create_grid(grid_shape, spacing, homogeneous, dtype)
[docs]def create_rotate(spatial_dims, radians):
"""
create a 2D or 3D rotation matrix
Args:
spatial_dims (2|3): spatial rank
radians (float or a sequence of floats): rotation radians
when spatial_dims == 3, the `radians` sequence corresponds to
rotation in the 1st, 2nd, and 3rd dim respectively.
"""
radians = ensure_tuple(radians)
if spatial_dims == 2:
if len(radians) >= 1:
sin_, cos_ = np.sin(radians[0]), np.cos(radians[0])
return np.array([[cos_, -sin_, 0.], [sin_, cos_, 0.], [0., 0., 1.]])
if spatial_dims == 3:
affine = None
if len(radians) >= 1:
sin_, cos_ = np.sin(radians[0]), np.cos(radians[0])
affine = np.array([
[1., 0., 0., 0.],
[0., cos_, -sin_, 0.],
[0., sin_, cos_, 0.],
[0., 0., 0., 1.],
])
if len(radians) >= 2:
sin_, cos_ = np.sin(radians[1]), np.cos(radians[1])
affine = affine @ np.array([
[cos_, 0.0, sin_, 0.],
[0., 1., 0., 0.],
[-sin_, 0., cos_, 0.],
[0., 0., 0., 1.],
])
if len(radians) >= 3:
sin_, cos_ = np.sin(radians[2]), np.cos(radians[2])
affine = affine @ np.array([
[cos_, -sin_, 0., 0.],
[sin_, cos_, 0., 0.],
[0., 0., 1., 0.],
[0., 0., 0., 1.],
])
return affine
raise ValueError('create_rotate got spatial_dims={}, radians={}.'.format(spatial_dims, radians))
[docs]def create_shear(spatial_dims, coefs):
"""
create a shearing matrix
Args:
spatial_dims (int): spatial rank
coefs (floats): shearing factors, defaults to 0.
"""
coefs = list(ensure_tuple(coefs))
if spatial_dims == 2:
while len(coefs) < 2:
coefs.append(0.0)
return np.array([
[1, coefs[0], 0.],
[coefs[1], 1., 0.],
[0., 0., 1.],
])
if spatial_dims == 3:
while len(coefs) < 6:
coefs.append(0.0)
return np.array([
[1., coefs[0], coefs[1], 0.],
[coefs[2], 1., coefs[3], 0.],
[coefs[4], coefs[5], 1., 0.],
[0., 0., 0., 1.],
])
raise NotImplementedError
[docs]def create_scale(spatial_dims, scaling_factor):
"""
create a scaling matrix
Args:
spatial_dims (int): spatial rank
scaling_factor (floats): scaling factors, defaults to 1.
"""
scaling_factor = list(ensure_tuple(scaling_factor))
while len(scaling_factor) < spatial_dims:
scaling_factor.append(1.)
return np.diag(scaling_factor[:spatial_dims] + [1.])
[docs]def create_translate(spatial_dims, shift):
"""
create a translation matrix
Args:
spatial_dims (int): spatial rank
shift (floats): translate factors, defaults to 0.
"""
shift = ensure_tuple(shift)
affine = np.eye(spatial_dims + 1)
for i, a in enumerate(shift[:spatial_dims]):
affine[i, spatial_dims] = a
return affine
[docs]def generate_spatial_bounding_box(img, select_fn=lambda x: x > 0, channel_indexes=None, margin=0):
"""
generate the spatial bounding box of foreground in the image with start-end positions.
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.
Args:
img (ndarrary): source image to generate bounding box from.
select_fn (Callable): function to select expected foreground, default is to select values > 0.
channel_indexes (int, tuple or list): if defined, select foregound only on the specified channels
of image. if None, select foreground on the whole image.
margin (int): add margin to all dims of the bounding box.
"""
assert isinstance(margin, int), 'margin must be int type.'
data = img[[*(ensure_tuple(channel_indexes))]] if channel_indexes is not None else img
data = np.any(select_fn(data), axis=0)
nonzero_idx = np.nonzero(data)
box_start = list()
box_end = list()
for i in range(data.ndim):
assert len(nonzero_idx[i]) > 0, 'did not find nonzero index at spatial dim {}'.format(i)
box_start.append(max(0, np.min(nonzero_idx[i]) - margin))
box_end.append(min(data.shape[i], np.max(nonzero_idx[i]) + margin + 1))
return box_start, box_end