def test_resample_outvalue():
# Test resampling with different modes, constant values, datatypes, orders
def func(xyz):
return xyz + np.asarray([1, 0, 0])
coordmap = vox2mni(np.eye(4))
arr = np.arange(3 * 3 * 3).reshape(3, 3, 3)
aff = np.eye(4)
aff[0, 3] = 1. # x translation
for mapping, dt, order in product(
[aff, func],
[np.int8, np.intp, np.int32, np.int64, np.float32, np.float64],
[0, 1, 3]):
img = Image(arr.astype(dt), coordmap)
# Test constant value of 0
img2 = resample(img,
coordmap,
mapping,
img.shape,
order=order,
mode='constant',
cval=0.)
exp_arr = np.zeros(arr.shape)
exp_arr[:-1, :, :] = arr[1:, :, :]
assert_array_almost_equal(img2.get_data(), exp_arr)
# Test constant value of 1
img2 = resample(img,
coordmap,
mapping,
img.shape,
order=order,
mode='constant',
cval=1.)
exp_arr[-1, :, :] = 1
assert_array_almost_equal(img2.get_data(), exp_arr)
# Test nearest neighbor
img2 = resample(img,
coordmap,
mapping,
img.shape,
order=order,
mode='nearest')
exp_arr[-1, :, :] = arr[-1, :, :]
assert_array_almost_equal(img2.get_data(), exp_arr)
# Test img2img
target_coordmap = vox2mni(aff)
target = Image(arr, target_coordmap)
img2 = resample_img2img(img, target, 3, 'nearest')
assert_array_almost_equal(img2.get_data(), exp_arr)
img2 = resample_img2img(img, target, 3, 'constant', cval=1.)
exp_arr[-1, :, :] = 1
assert_array_almost_equal(img2.get_data(), exp_arr)
def randimg_in2out(rng, in_dtype, out_dtype, name):
in_dtype = np.dtype(in_dtype)
out_dtype = np.dtype(out_dtype)
shape = (2,3,4)
if in_dtype.kind in 'iu':
info = np.iinfo(in_dtype)
dmin, dmax = info.min, info.max
# Numpy bug for np < 1.6.0 allows overflow for range that does not fit
# into C long int (int32 on 32-bit, int64 on 64-bit)
try:
data = rng.randint(dmin, dmax, size=shape)
except ValueError:
from random import randint
vals = [randint(dmin, dmax) for v in range(np.prod(shape))]
data = np.array(vals).astype(in_dtype).reshape(shape)
elif in_dtype.kind == 'f':
info = np.finfo(in_dtype)
dmin, dmax = info.min, info.max
# set some value for scaling our data
scale = np.iinfo(np.uint16).max * 2.0
data = rng.normal(size=shape, scale=scale)
data[0,0,0] = dmin
data[1,0,0] = dmax
data = data.astype(in_dtype)
img = Image(data, vox2mni(np.eye(4)))
# The dtype_from dtype won't be visible until the image is loaded
newimg = save_image(img, name, dtype_from=out_dtype)
return newimg.get_data(), data
def randimg_in2out(rng, in_dtype, out_dtype, name):
in_dtype = np.dtype(in_dtype)
out_dtype = np.dtype(out_dtype)
shape = (2, 3, 4)
if in_dtype.kind in 'iu':
info = np.iinfo(in_dtype)
dmin, dmax = info.min, info.max
# Numpy bug for np < 1.6.0 allows overflow for range that does not fit
# into C long int (int32 on 32-bit, int64 on 64-bit)
try:
data = rng.randint(dmin, dmax, size=shape)
except ValueError:
from random import randint
vals = [randint(dmin, dmax) for v in range(np.prod(shape))]
data = np.array(vals).astype(in_dtype).reshape(shape)
elif in_dtype.kind == 'f':
info = np.finfo(in_dtype)
dmin, dmax = info.min, info.max
# set some value for scaling our data
scale = np.iinfo(np.uint16).max * 2.0
data = rng.normal(size=shape, scale=scale)
data[0, 0, 0] = dmin
data[1, 0, 0] = dmax
data = data.astype(in_dtype)
img = Image(data, vox2mni(np.eye(4)))
# The dtype_from dtype won't be visible until the image is loaded
newimg = save_image(img, name, dtype_from=out_dtype)
return newimg.get_data(), data
def save_to_image(data,
template_file=DEFAULT_template,
output_file=DEFAULT_output):
template = load_image(template_file)
newimg = Image(data, vox2mni(template.affine))
save_image(newimg, output_file)
return output_file
def test_output_dtypes():
shape = (4, 2, 3)
rng = np.random.RandomState(19441217) # IN-S BD
data = rng.normal(4, 20, size=shape)
aff = np.diag([2.2, 3.3, 4.1, 1])
cmap = vox2mni(aff)
img = Image(data, cmap)
fname_root = 'my_file'
with InTemporaryDirectory():
for ext in 'img', 'nii':
out_fname = fname_root + '.' + ext
# Default is for data to come from data dtype
save_image(img, out_fname)
img_back = load_image(out_fname)
hdr = img_back.metadata['header']
assert_dt_no_end_equal(hdr.get_data_dtype(), np.float)
del img_back # lets window re-use the file
# All these types are OK for both output formats
for out_dt in 'i2', 'i4', np.int16, '<f4', '>f8':
# Specified output dtype
save_image(img, out_fname, out_dt)
img_back = load_image(out_fname)
hdr = img_back.metadata['header']
assert_dt_no_end_equal(hdr.get_data_dtype(), out_dt)
del img_back # windows file re-use
# Output comes from data by default
data_typed = data.astype(out_dt)
img_again = Image(data_typed, cmap)
save_image(img_again, out_fname)
img_back = load_image(out_fname)
hdr = img_back.metadata['header']
assert_dt_no_end_equal(hdr.get_data_dtype(), out_dt)
del img_back
# Even if header specifies otherwise
in_hdr = Nifti1Header()
in_hdr.set_data_dtype(np.dtype('c8'))
img_more = Image(data_typed, cmap, metadata={'header': in_hdr})
save_image(img_more, out_fname)
img_back = load_image(out_fname)
hdr = img_back.metadata['header']
assert_dt_no_end_equal(hdr.get_data_dtype(), out_dt)
del img_back
# But can come from header if specified
save_image(img_more, out_fname, dtype_from='header')
img_back = load_image(out_fname)
hdr = img_back.metadata['header']
assert_dt_no_end_equal(hdr.get_data_dtype(), 'c8')
del img_back
# u2 only OK for nifti
save_image(img, 'my_file.nii', 'u2')
img_back = load_image('my_file.nii')
hdr = img_back.metadata['header']
assert_dt_no_end_equal(hdr.get_data_dtype(), 'u2')
# Check analyze can't save u2 datatype
assert_raises(HeaderDataError, save_image, img, 'my_file.img', 'u2')
del img_back
def test_output_dtypes():
shape = (4, 2, 3)
rng = np.random.RandomState(19441217) # IN-S BD
data = rng.normal(4, 20, size=shape)
aff = np.diag([2.2, 3.3, 4.1, 1])
cmap = vox2mni(aff)
img = Image(data, cmap)
fname_root = 'my_file'
with InTemporaryDirectory():
for ext in 'img', 'nii':
out_fname = fname_root + '.' + ext
# Default is for data to come from data dtype
save_image(img, out_fname)
img_back = load_image(out_fname)
hdr = img_back.metadata['header']
assert_dt_no_end_equal(hdr.get_data_dtype(), np.float)
del img_back # lets window re-use the file
# All these types are OK for both output formats
for out_dt in 'i2', 'i4', np.int16, '<f4', '>f8':
# Specified output dtype
save_image(img, out_fname, out_dt)
img_back = load_image(out_fname)
hdr = img_back.metadata['header']
assert_dt_no_end_equal(hdr.get_data_dtype(), out_dt)
del img_back # windows file re-use
# Output comes from data by default
data_typed = data.astype(out_dt)
img_again = Image(data_typed, cmap)
save_image(img_again, out_fname)
img_back = load_image(out_fname)
hdr = img_back.metadata['header']
assert_dt_no_end_equal(hdr.get_data_dtype(), out_dt)
del img_back
# Even if header specifies otherwise
in_hdr = Nifti1Header()
in_hdr.set_data_dtype(np.dtype('c8'))
img_more = Image(data_typed, cmap, metadata={'header': in_hdr})
save_image(img_more, out_fname)
img_back = load_image(out_fname)
hdr = img_back.metadata['header']
assert_dt_no_end_equal(hdr.get_data_dtype(), out_dt)
del img_back
# But can come from header if specified
save_image(img_more, out_fname, dtype_from='header')
img_back = load_image(out_fname)
hdr = img_back.metadata['header']
assert_dt_no_end_equal(hdr.get_data_dtype(), 'c8')
del img_back
# u2 only OK for nifti
save_image(img, 'my_file.nii', 'u2')
img_back = load_image('my_file.nii')
hdr = img_back.metadata['header']
assert_dt_no_end_equal(hdr.get_data_dtype(), 'u2')
# Check analyze can't save u2 datatype
assert_raises(HeaderDataError, save_image, img, 'my_file.img', 'u2')
del img_back
def test_interpolator():
arr = np.arange(24).reshape((2, 3, 4))
coordmap = vox2mni(np.eye(4))
img = Image(arr, coordmap)
# Interpolate off top right corner with different modes
interp = ImageInterpolator(img, mode='nearest')
assert_almost_equal(interp.evaluate([0, 0, 4]), arr[0, 0, -1])
interp = ImageInterpolator(img, mode='constant', cval=0)
assert_array_equal(interp.evaluate([0, 0, 4]), 0)
interp = ImageInterpolator(img, mode='constant', cval=1)
assert_array_equal(interp.evaluate([0, 0, 4]), 1)
def test_interpolator():
arr = np.arange(24).reshape((2, 3, 4))
coordmap = vox2mni(np.eye(4))
img = Image(arr, coordmap)
# Interpolate off top right corner with different modes
interp = ImageInterpolator(img, mode='nearest')
assert_almost_equal(interp.evaluate([0, 0, 4]), arr[0, 0, -1])
interp = ImageInterpolator(img, mode='constant', cval=0)
assert_array_equal(interp.evaluate([0, 0, 4]), 0)
interp = ImageInterpolator(img, mode='constant', cval=1)
assert_array_equal(interp.evaluate([0, 0, 4]), 1)
def test_resample_outvalue():
# Test resampling with different modes, constant values, datatypes, orders
def func(xyz):
return xyz + np.asarray([1,0,0])
coordmap = vox2mni(np.eye(4))
arr = np.arange(3 * 3 * 3).reshape(3, 3, 3)
aff = np.eye(4)
aff[0, 3] = 1. # x translation
for mapping, dt, order in product(
[aff, func],
[np.int8, np.intp, np.int32, np.int64, np.float32, np.float64],
[0, 1, 3]):
img = Image(arr.astype(dt), coordmap)
# Test constant value of 0
img2 = resample(img, coordmap, mapping, img.shape,
order=order, mode='constant', cval=0.)
exp_arr = np.zeros(arr.shape)
exp_arr[:-1, :, :] = arr[1:, :, :]
assert_array_almost_equal(img2.get_data(), exp_arr)
# Test constant value of 1
img2 = resample(img, coordmap, mapping, img.shape,
order=order, mode='constant', cval=1.)
exp_arr[-1, :, :] = 1
assert_array_almost_equal(img2.get_data(), exp_arr)
# Test nearest neighbor
img2 = resample(img, coordmap, mapping, img.shape,
order=order, mode='nearest')
exp_arr[-1, :, :] = arr[-1, :, :]
assert_array_almost_equal(img2.get_data(), exp_arr)
# Test img2img
target_coordmap = vox2mni(aff)
target = Image(arr, target_coordmap)
img2 = resample_img2img(img, target, 3, 'nearest')
assert_array_almost_equal(img2.get_data(), exp_arr)
img2 = resample_img2img(img, target, 3, 'constant', cval=1.)
exp_arr[-1, :, :] = 1
assert_array_almost_equal(img2.get_data(), exp_arr)
def test_save2b():
# A test to ensure that when a file is saved, the affine and the
# data agree. This image comes from a NIFTI file. This example has a
# non-diagonal affine matrix for the spatial part, but is 'diagonal' for the
# space part.
#
# make a 5x5 transformation (for 4d image)
step = np.array([3.45, 2.3, 4.5, 6.9])
A = np.random.standard_normal((3,3))
B = np.diag(list(step)+[1])
B[:3, :3] = A
shape = (13,5,7,3)
cmap = api.vox2mni(B)
data = np.random.standard_normal(shape)
img = api.Image(data, cmap)
with InTemporaryDirectory():
save_image(img, TMP_FNAME)
img2 = load_image(TMP_FNAME)
assert_array_almost_equal(img.affine, img2.affine)
assert_equal(img.shape, img2.shape)
assert_array_almost_equal(img2.get_data(), img.get_data())
del img2
def test_save2b():
# A test to ensure that when a file is saved, the affine and the
# data agree. This image comes from a NIFTI file. This example has a
# non-diagonal affine matrix for the spatial part, but is 'diagonal' for the
# space part.
#
# make a 5x5 transformation (for 4d image)
step = np.array([3.45, 2.3, 4.5, 6.9])
A = np.random.standard_normal((3, 3))
B = np.diag(list(step) + [1])
B[:3, :3] = A
shape = (13, 5, 7, 3)
cmap = api.vox2mni(B)
data = np.random.standard_normal(shape)
img = api.Image(data, cmap)
with InTemporaryDirectory():
save_image(img, TMP_FNAME)
img2 = load_image(TMP_FNAME)
assert_array_almost_equal(img.affine, img2.affine)
assert_equal(img.shape, img2.shape)
assert_array_almost_equal(img2.get_data(), img.get_data())
del img2
#!/usr/bin/env python
# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*-
# vi: set ft=python sts=4 ts=4 sw=4 et:
"""This example shows how to create a temporary image to use during processing.
The array is filled with zeros.
"""
import numpy as np
from nipy import load_image, save_image
from nipy.core.api import Image, vox2mni
# create an array of zeros, the shape of your data array
zero_array = np.zeros((91,109,91))
# create an image from our array. The image will be in MNI space
img = Image(zero_array, vox2mni(np.diag([2, 2, 2, 1])))
# save the image to a file
newimg = save_image(img, 'tempimage.nii.gz')
# Example of creating a temporary image file from an existing image with a
# matching coordinate map.
img = load_image('tempimage.nii.gz')
zeroarray = np.zeros(img.shape)
zeroimg = Image(zeroarray, img.coordmap)
newimg = save_image(zeroimg, 'another_tempimage.nii.gz')
def save_nii(image, file_path):
nipy.save_image(Image(image, vox2mni(np.eye(4))), file_path)
rot[:3, :3] = euler.euler2mat(0, 0, -angle)
# downsample to make smaller output image
downsamp = 1/3
epi_scale = np.diag([downsamp, downsamp, downsamp, 1])
# template voxels to epi box image voxels
vox2epi_vox = epi_scale.dot(rot.dot(epi_trans))
# epi image voxels to mm
epi_vox2mm = t2_img.affine.dot(npl.inv(vox2epi_vox))
# downsampled image shape
epi_vox_shape = np.array([data.shape[0], epi_x_len, epi_y_len]) * downsamp
# Make sure dimensions are odd by rounding up or down
# This makes the voxel center an integer index, which is convenient
epi_vox_shape = [np.floor(d) if np.floor(d) % 2 else np.ceil(d)
for d in epi_vox_shape]
# resample, preserving affine
epi_cmap = nca.vox2mni(epi_vox2mm)
epi = rsm.resample(t2_img, epi_cmap, np.eye(4), epi_vox_shape)
epi_data = epi.get_data()
# Do the same kind of thing for the anatomical scan
anat_vox_sizes = [2.75, 2.75, 2.75]
anat_scale = npl.inv(np.diag(anat_vox_sizes + [1]))
anat_trans = np.eye(4)
anat_trans[:3, 3] = -np.array([0, anat_box[0, 0], anat_box[0, 1]])
vox2anat_vox = anat_scale.dot(anat_trans)
anat_vox2mm = t1_img.affine.dot(npl.inv(vox2anat_vox))
anat_vox_shape = np.round(np.divide(
[data.shape[0], anat_x_len, anat_y_len], anat_vox_sizes))
anat_cmap = nca.vox2mni(anat_vox2mm)
anat = rsm.resample(t1_img, anat_cmap, np.eye(4), anat_vox_shape)
anat_data = anat.get_data()
#-*- coding: utf-8 -*-
from nipy import save_image
import numpy as np
from nipy.core.api import Image, vox2mni
rawarray = np.ones((43,128,128), dtype=np.uint8)
arr_img = Image(rawarray, vox2mni(np.eye(4))) ##affine
print arr_img.shape
newimg = save_image(arr_img, 'G:\\Userworkspace\\brain-pca\\niwe.nii.gz')
#用affine，将image转 成3d的东西 nipy.pdf在brain里面
#print save_image.func_doc
#cmap = AffineTransform('kji', 'zxy', np.eye(4))
#img = Image(data, cmap)