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 main():
parser = argparse.ArgumentParser()
parser.add_argument('-1',
'--roi1',
type=str,
help='Filename of the first ROI image')
parser.add_argument('-2',
'--roi2',
type=str,
help='Filename of the second ROI image')
parser.add_argument('-c',
'--coord-image',
type=str,
help='Filename of the coordinates image')
parser.add_argument('-o',
'--output-file',
type=str,
help='Filename to use for combined ROIs')
args = parser.parse_args()
print(args)
# check that both images exist
if not os.path.exists(args.roi1):
print("File '" + args.roi1 + "' does not exist")
if not os.path.exists(args.roi2):
print("File '" + args.roi2 + "' does not exist")
if not os.path.exists(args.coord_image):
print("File '" + args.coord_image)
# load both images for the purpose of checking coordinates
roi1, coords1 = mil.loadBOLD(args.roi1)
roi2, coords2 = mil.loadBOLD(args.roi2)
img, imgCoords = mil.loadBOLD(args.coord_image)
print("roi1", roi1.shape)
print("roi2", roi2.shape)
print("coords", img.shape)
# perform the OR-ing of the 2 rois
addingImgs = BinaryMaths()
addingImgs.inputs.in_file = args.roi1
addingImgs.inputs.operand_file = args.roi2
addingImgs.inputs.operation = "max"
addingImgs.inputs.out_file = args.output_file
addingImgs.run()
# resample the new roi to be in the coordinate frame of the coordinate image
jointRoi = load_image(args.output_file)
coordsImg = load_image(args.coord_image)
print("joint rois", jointRoi.get_data().shape)
newRoiImg = resample_img2img(jointRoi, coordsImg, order=0)
save_image(newRoiImg, args.output_file)
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 run(self, masterFile, inputFiles, outputDirectory, spacing, dimensions,
likefreesurfer, nii):
'''
Performs the equalization
'''
# sanity checks
outputDirectory = os.path.normpath(outputDirectory)
# prepare the output directory
if os.path.exists(outputDirectory):
c.error('The output directory already exists!')
c.error('Aborting..')
sys.exit(2)
# create the output directory
os.mkdir(outputDirectory)
# MASTER
masterFile = os.path.normpath(masterFile)
# read the master
master = io.readImage(masterFile)
c.info('MASTER IMAGE: ' + str(masterFile))
# INPUTS
for i in range(len(inputFiles)):
inputFiles[i] = os.path.normpath(inputFiles[i])
c.info('INPUT IMAGE ' + str(i + 1) + ': ' + str(inputFiles[i]))
# print more info
c.info('OUTPUT DIRECTORY: ' + str(outputDirectory))
if likefreesurfer:
spacing = '1,1,1'
dimensions = '256,256,256'
if spacing != 'no':
c.info('SET SPACINGS: ' + str(spacing))
if dimensions != 'no':
c.info('SET DIMENSIONS: ' + str(dimensions))
# re-sample master to obtain an isotropic dataset
master = self.aniso2iso(master, spacing, dimensions)
masterFileBasename = os.path.split(masterFile)[1]
masterFileBasenameWithoutExt = os.path.splitext(masterFileBasename)[0]
if not nii:
masterOutputFileName = os.path.join(outputDirectory,
masterFileBasename)
else:
masterOutputFileName = os.path.join(
outputDirectory, masterFileBasenameWithoutExt) + '.nii'
io.saveImage(masterOutputFileName, master)
# equalize all images to the master
for i in range(len(inputFiles)):
currentInputFile = inputFiles[i]
c.info('Equalizing ' + str(currentInputFile) + ' to ' +
str(masterFile) + "...")
# load the image
currentImage = io.readImage(currentInputFile)
currentImageHeader = currentImage.header
c.info(' old spacing: ' + str(currentImageHeader.get_zooms()))
c.info(' old dimensions: ' + str(currentImage.shape[:3]))
# now resample
resampledImage = resampler.resample_img2img(currentImage, master)
# .. and save it
currentInputFileBasename = os.path.split(currentInputFile)[1]
currentInputFileBasenameWithoutExt = os.path.splitext(
currentInputFileBasename)[0]
if not nii:
outputFileName = os.path.join(outputDirectory,
currentInputFileBasename)
else:
outputFileName = os.path.join(
outputDirectory, currentInputFileBasenameWithoutExt)
savedImage = io.saveImage(outputFileName, resampledImage)
#c.info( ' new spacing: ' + str( savedImageHeader.get_zooms() ) )
c.info(' new dimensions: ' + str(savedImage.shape[:3]))
c.info('All done!')
def test_resample_img2img():
fimg = load_image(funcfile)
aimg = load_image(anatfile)
resimg = resample_img2img(fimg, fimg)
yield assert_true, np.allclose(resimg.get_data(), fimg.get_data())
yield assert_raises, ValueError, resample_img2img, fimg, aimg
def test_resample_img2img():
fimg = load_image(funcfile)
aimg = load_image(anatfile)
resimg = resample_img2img(fimg, fimg)
yield assert_true, np.allclose(np.asarray(resimg), np.asarray(fimg))
yield assert_raises, ValueError, resample_img2img, fimg, aimg
