__all__ = [
"matrix_to_images",
"images_from_matrix",
"image_list_to_matrix",
"images_to_matrix",
"matrix_from_images",
"timeseries_to_matrix",
"matrix_to_timeseries"
]
import os
import json
import numpy as np
import warnings
import ants
from ants.decorators import image_method
@image_method
def matrix_to_timeseries(image, matrix, mask=None):
"""
converts a matrix to a ND image.
ANTsR function: `matrix2timeseries`
Arguments
---------
image: reference ND image
matrix: matrix to convert to image
mask: mask image defining voxels of interest
Returns
-------
ANTsImage
Example
-------
>>> import ants
>>> img = ants.make_image( (10,10,10,5 ) )
>>> mask = ants.ndimage_to_list( img )[0] * 0
>>> mask[ 4:8, 4:8, 4:8 ] = 1
>>> mat = ants.timeseries_to_matrix( img, mask = mask )
>>> img2 = ants.matrix_to_timeseries( img, mat, mask)
"""
if mask is None:
mask = temp[0] * 0 + 1
temp = matrix_to_images(matrix, mask)
newImage = ants.list_to_ndimage(image, temp)
ants.copy_image_info(image, newImage)
return newImage
[docs]def matrix_to_images(data_matrix, mask):
"""
Unmasks rows of a matrix and writes as images
ANTsR function: `matrixToImages`
Arguments
---------
data_matrix : numpy.ndarray
each row corresponds to an image
array should have number of columns equal to non-zero voxels in the mask
mask : ANTsImage
image containing a binary mask. Rows of the matrix are
unmasked and written as images. The mask defines the output image space
Returns
-------
list of ANTsImage types
Example
-------
>>> import ants
>>> img = ants.image_read(ants.get_ants_data('r16'))
>>> msk = ants.get_mask( img )
>>> img2 = ants.image_read(ants.get_ants_data('r16'))
>>> img3 = ants.image_read(ants.get_ants_data('r16'))
>>> mat = ants.image_list_to_matrix([img,img2,img3], msk )
>>> ilist = ants.matrix_to_images( mat, msk )
"""
if data_matrix.ndim > 2:
data_matrix = data_matrix.reshape(data_matrix.shape[0], -1)
numimages = len(data_matrix)
numVoxelsInMatrix = data_matrix.shape[1]
numVoxelsInMask = (mask >= 0.5).sum()
if numVoxelsInMask != numVoxelsInMatrix:
raise ValueError(
"Num masked voxels %i must match data matrix %i"
% (numVoxelsInMask, numVoxelsInMatrix)
)
imagelist = []
for i in range(numimages):
img = mask.clone()
img[mask >= 0.5] = data_matrix[i, :]
imagelist.append(img)
return imagelist
images_from_matrix = matrix_to_images
[docs]def images_to_matrix(image_list, mask=None, sigma=None, epsilon=0.5):
"""
Read images into rows of a matrix, given a mask - much faster for
large datasets as it is based on C++ implementations.
ANTsR function: `imagesToMatrix`
Arguments
---------
image_list : list of ANTsImage types
images to convert to ndarray
mask : ANTsImage (optional)
Mask image, voxels in the mask (>= epsilon) are placed in the matrix. If None,
the first image in image_list is thresholded at its mean value to create a mask.
sigma : scaler (optional)
smoothing factor
epsilon : scalar
threshold for mask, values >= epsilon are included in the mask.
Returns
-------
ndarray
array with a row for each image
shape = (N_IMAGES, N_VOXELS)
Example
-------
>>> import ants
>>> img = ants.image_read(ants.get_ants_data('r16'))
>>> img2 = ants.image_read(ants.get_ants_data('r16'))
>>> img3 = ants.image_read(ants.get_ants_data('r16'))
>>> mat = ants.image_list_to_matrix([img,img2,img3])
"""
if mask is None:
mask = ants.get_mask(image_list[0])
num_images = len(image_list)
mask_thresh = mask.clone() >= epsilon
mask_arr = mask.numpy() >= epsilon
num_voxels = np.sum(mask_arr)
data_matrix = np.empty((num_images, num_voxels))
do_smooth = sigma is not None
for i, img in enumerate(image_list):
if do_smooth:
img = ants.smooth_image(img, sigma, sigma_in_physical_coordinates=True)
if np.sum(np.array(img.shape) - np.array(mask_thresh.shape)) != 0:
img = ants.resample_image_to_target(img, mask_thresh, 2)
data_matrix[i, :] = img[mask_thresh]
return data_matrix
image_list_to_matrix = images_to_matrix
matrix_from_images = images_to_matrix
@image_method
def timeseries_to_matrix(image, mask=None):
"""
Convert a timeseries image into a matrix.
ANTsR function: `timeseries2matrix`
Arguments
---------
image : image whose slices we convert to a matrix. E.g. a 3D image of size
x by y by z will convert to a z by x*y sized matrix
mask : ANTsImage (optional)
image containing binary mask. voxels in the mask are placed in the matrix
Returns
-------
ndarray
array with a row for each image
shape = (N_IMAGES, N_VOXELS)
Example
-------
>>> import ants
>>> img = ants.make_image( (10,10,10,5 ) )
>>> mat = ants.timeseries_to_matrix( img )
"""
temp = ants.ndimage_to_list(image)
if mask is None:
mask = temp[0] * 0 + 1
return image_list_to_matrix(temp, mask)
