def main(): i1, h1 = load(sys.argv[1]) i2, h2 = load(sys.argv[2]) # shift image to align origins origin_h1 = numpy.sign(h1.get_qform()[0:3,0:3]).dot(header.get_offset(h1)) origin_h2 = numpy.sign(h2.get_qform()[0:3,0:3]).dot(header.get_offset(h2)) origin_difference_pixel = (origin_h1 - origin_h2) / numpy.asarray(header.get_pixel_spacing(h1)) # negative values: shift image 1 by this upon inserting (which is the smae as cutting the output image) # positive values: cut image 1 by this at inserting and also cut right side by length of output image plus this value o = numpy.zeros(i2.shape, i2.dtype) o_slicer = [] i_slicer = [] for j, p in enumerate(origin_difference_pixel): if p >= 0: i_slicer.append(slice(0, min(i1.shape[j], o.shape[j] - abs(p)))) o_slicer.append(slice(abs(p), min(i1.shape[j] + abs(p), o.shape[j]))) else: i_slicer.append(slice(abs(p), min(i1.shape[j], o.shape[j] + abs(p)))) o_slicer.append(slice(0, min(i1.shape[j] - abs(p), o.shape[j]))) o[o_slicer] = i1[i_slicer] header.set_offset(h1, header.get_offset(h2)) save(o, sys.argv[3], h1)
def test03(img, hdr, idx, delta):
# TEST 03: DOES ANY META-INFORMATION GET LOST DURING FORMAT CONVERSION? AND IF YES; WHICH?
for tr in types_int: # reference type
print ''
oformat = tr
# create, save and load reference image
try:
name_ref = tmp_folder + '.'.join(['tmp_ref', tr])
save(img, name_ref, hdr, True)
img_ref, hdr_ref = load(name_ref)
except Exception as e:
print '\tERROR: Could not generate reference image for type {}: {}'.format(otype, e)
continue
# extract meta-data from reference image
mdata_ref = {'shape': img_ref.shape,
'dtype': img_ref.dtype,
'point': img_ref[idx],
'spacing': header.get_pixel_spacing(hdr_ref),
'offset': header.get_offset(hdr_ref),}
# print meta-data from reference image
# iterate of test images
for tt in types_int: # test type
print '{} => {}'.format(oformat, tt),
# create, save and load test images
try:
#print type(img_ref), type(hdr_ref)
#print type(img_test), type(hdr_test)
name_test = tmp_folder + '.'.join(['tmp_test', tt])
save(img_ref, name_test, hdr_ref, True)
img_test, hdr_test = load(name_test)
except Exception as e:
print '\tERROR: Could not generate test image. {}'.format(e)
continue
# extract meta-data from test image
mdata_test = {'shape': img_test.shape,
'dtype': img_test.dtype,
'spacing': header.get_pixel_spacing(hdr_test),
'offset': header.get_offset(hdr_test),
'point': img_test[idx]}
# compare reference against meta-image
error = False
for k in mdata_ref.keys():
equal = _compare(mdata_ref[k], mdata_test[k], delta)
#print '\n\t{} ({}) : {} = {}'.format(equal, k, mdata_ref[k], mdata_test[k]),
if not equal:
error = True
print '\n\t{} ({}) : {} = {}'.format(equal, k, mdata_ref[k], mdata_test[k]),
if not error:
print '\t{}'.format(True)
else:
print '\n'
def __diff(self, hdr1, hdr2):
"""
Returns an error message if the meta-data of the supplied headers differ,
otherwise False.
"""
if not self.__same_seq(header.get_pixel_spacing(hdr1), header.get_pixel_spacing(hdr2)):
return 'the voxel spacing is not consistent: {} != {}'.format(header.get_pixel_spacing(hdr1), header.get_pixel_spacing(hdr2))
if not self.__same_seq(header.get_offset(hdr1), header.get_offset(hdr2)):
return 'the offset is not consistent: {} != {}'.format(header.get_offset(hdr1), header.get_offset(hdr2))
#return 'the offset is not consistent: {} != {}\n{} / {}\n{} / {}'.format(header.get_offset(hdr1), header.get_offset(hdr2), type(hdr1), type(hdr2), hdr2.NumberOfFrames if "NumberOfFrames" in hdr2 else "NONE", hdr2.ImagePositionPatient if "ImagePositionPatient" in hdr2 else 'NONE')
else: return False
def main():
args = getArguments(getParser())
# prepare logger
logger = Logger.getInstance()
if args.debug: logger.setLevel(logging.DEBUG)
elif args.verbose: logger.setLevel(logging.INFO)
# load input image
data_input, header_input = load(args.input)
logger.debug('Original shape = {}.'.format(data_input.shape))
# check if supplied dimension parameters is inside the images dimensions
if args.dimension1 >= data_input.ndim or args.dimension1 < 0:
raise ArgumentError('The first swap-dimension {} exceeds the number of input volume dimensions {}.'.format(args.dimension1, data_input.ndim))
elif args.dimension2 >= data_input.ndim or args.dimension2 < 0:
raise ArgumentError('The second swap-dimension {} exceeds the number of input volume dimensions {}.'.format(args.dimension2, data_input.ndim))
# swap axes
data_output = scipy.swapaxes(data_input, args.dimension1, args.dimension2)
# swap pixel spacing and offset
ps = list(header.get_pixel_spacing(header_input))
ps[args.dimension1], ps[args.dimension2] = ps[args.dimension2], ps[args.dimension1]
header.set_pixel_spacing(header_input, ps)
os = list(header.get_offset(header_input))
os[args.dimension1], os[args.dimension2] = os[args.dimension2], os[args.dimension1]
header.set_offset(header_input, os)
logger.debug('Resulting shape = {}.'.format(data_output.shape))
# save resulting volume
save(data_output, args.output, header_input, args.force)
logger.info("Successfully terminated.")
def __diff(self, hdr1, hdr2):
"""
Returns an error message if the meta-data of the supplied headers differ,
otherwise False.
"""
if not self.__same_seq(header.get_pixel_spacing(hdr1),
header.get_pixel_spacing(hdr2)):
return 'the voxel spacing is not consistent: {} != {}'.format(
header.get_pixel_spacing(hdr1), header.get_pixel_spacing(hdr2))
if not self.__same_seq(header.get_offset(hdr1),
header.get_offset(hdr2)):
return 'the offset is not consistent: {} != {}'.format(
header.get_offset(hdr1), header.get_offset(hdr2))
#return 'the offset is not consistent: {} != {}\n{} / {}\n{} / {}'.format(header.get_offset(hdr1), header.get_offset(hdr2), type(hdr1), type(hdr2), hdr2.NumberOfFrames if "NumberOfFrames" in hdr2 else "NONE", hdr2.ImagePositionPatient if "ImagePositionPatient" in hdr2 else 'NONE')
else:
return False
def main():
i, h = load(sys.argv[1])
print 'Image:\t{}'.format(sys.argv[1])
print 'Shape:\t{}'.format(i.shape)
print 'Spacing:{}'.format(header.get_pixel_spacing(h))
print 'Offset:\t{}'.format(header.get_offset(h))
if 0 == h.get_header()['qform_code']:
method = 'ANALYZE 7.5 (old)'
if h.get_header()['qform_code'] > 0:
method = 'Normal (qform)'
if h.get_header()['sform_code'] > 0:
method = 'Special space (sform)'
print
print 'Orientation and location in space:'
print 'Type:\t\t{}'.format(method)
print 'qform_code:\t{}'.format(h.get_header()['qform_code'])
print 'sform_code:\t{}'.format(h.get_header()['sform_code'])
print
print 'qform == sform?\t{} (max diff={})'.format(numpy.all(h.get_qform() == h.get_sform()), numpy.max(numpy.abs(h.get_qform() - h.get_sform())))
print 'affine = qform?\t{} (max diff={})'.format(numpy.all(h.get_affine() == h.get_qform()), numpy.max(numpy.abs(h.get_affine() - h.get_qform())))
print 'affine = sform?\t{} (max diff={})'.format(numpy.all(h.get_affine() == h.get_sform()), numpy.max(numpy.abs(h.get_affine() - h.get_sform())))
print
print 'qform:'
print h.get_qform()
print 'sform:'
print h.get_sform()
print 'affine:'
print h.get_affine()
def main():
args = getArguments(getParser())
# prepare logger
logger = Logger.getInstance()
if args.debug: logger.setLevel(logging.DEBUG)
elif args.verbose: logger.setLevel(logging.INFO)
# load input image
data_input, header_input = load(args.input)
logger.debug('Original shape = {}.'.format(data_input.shape))
# check if supplied dimension parameters is inside the images dimensions
if args.dimension1 >= data_input.ndim or args.dimension1 < 0:
raise ArgumentError(
'The first swap-dimension {} exceeds the number of input volume dimensions {}.'
.format(args.dimension1, data_input.ndim))
elif args.dimension2 >= data_input.ndim or args.dimension2 < 0:
raise ArgumentError(
'The second swap-dimension {} exceeds the number of input volume dimensions {}.'
.format(args.dimension2, data_input.ndim))
# swap axes
data_output = scipy.swapaxes(data_input, args.dimension1, args.dimension2)
# swap pixel spacing and offset
ps = list(header.get_pixel_spacing(header_input))
ps[args.dimension1], ps[args.dimension2] = ps[args.dimension2], ps[
args.dimension1]
header.set_pixel_spacing(header_input, ps)
os = list(header.get_offset(header_input))
os[args.dimension1], os[args.dimension2] = os[args.dimension2], os[
args.dimension1]
header.set_offset(header_input, os)
logger.debug('Resulting shape = {}.'.format(data_output.shape))
# save resulting volume
save(data_output, args.output, header_input, args.force)
logger.info("Successfully terminated.")
def main(interest_list, train_only, proportion, save_name):
if train_only == False:
train_indicator = [False, True]
else:
train_indicator = [True]
for indicator in train_indicator:
isTrain = indicator
if isTrain == True:
datadir_to_load = '../input/submission/pred_result/train/'
data_name = 'train'
datadir_to_save = '../input/submission/pred_result_ensemble/train/'
else:
datadir_to_load = '../input/submission/pred_result/test/'
data_name = 'test'
datadir_to_save = '../input/submission/pred_result_ensemble/test/'
if os.path.isdir(datadir_to_load) is not True:
raise
if os.path.isdir(datadir_to_save) is not True:
os.makedirs(datadir_to_save)
file_list = []
interest_model_list = list(interest_list)
for model_num in interest_model_list:
file_list = file_list + glob(
os.path.join(datadir_to_load,
'*model_{}*.nii'.format(model_num)))
file_dict = dict()
for file in file_list:
name = str.split(file, '.')[-2]
if name in file_dict.keys():
file_dict[name].append(file)
else:
file_dict[name] = []
file_dict[name].append(file)
record = []
for name in file_dict.keys():
N = len(file_dict[name])
img_list = []
#NEED CHANGE !!!
if isTrain == True:
file_path = glob(
'../input/train_data/*/*/*{}*'.format(name))[0]
else:
file_path = glob('../input/test_data/*/*/*{}*'.format(name))[0]
mtt, mtt_header = load(file_path)
adc_path = glob('/'.join(str.split(file_path, '/')[:4]) +
'/*/*ADC*.nii')[0]
_, adc_header = load(adc_path)
# print('name: {}, number of images: {}'.format(name, N))
for j in xrange(N):
img, img_header = load(file_dict[name][j])
if header.get_pixel_spacing(
adc_header) != header.get_pixel_spacing(img_header):
print("ERROR!!!!")
if header.get_offset(adc_header) != header.get_offset(
img_header):
print("ERROR!!!!")
if mtt.shape != img.shape:
print("ERROR!!!!")
img_list.append(img)
arr = np.sum(np.array(img_list), axis=0)
arr = np.array(arr > N * proportion, dtype=np.uint16)
arr_name = datadir_to_save + 'VSD.yong_{}_{}.'.format(
save_name, data_name) + name + '.nii'
# print('proportion of GT: {}, dimension: {}'.format(np.mean(arr), arr.shape))
save(arr, arr_name, adc_header)
if isTrain == True:
gt_path = glob('../input/train_data/{}/*/*OT*'.format(
str.split(file_path, '/')[-3]))[0]
gt, gt_header = load(gt_path)
f1 = 2.0 * np.sum(gt * arr) / (np.sum(gt) + np.sum(arr))
record.append(f1)
if isTrain == True:
record = np.array(record)
print(record)
print(np.mean(record))
print(np.std(record))
numpy.bool)
delta = 0.0001 # maximum difference between meta data allowed (since rounding errors occure)
# load original input image
img, hdr = load(input_image)
# random index
idx = tuple([random.randint(0, img.shape[i] - 1) for i in range(len(img.shape))])
# extract information from original image
otype = input_image.split('.')[-1]
mdata = {'shape': img.shape,
'dtype': img.dtype,
'spacing': header.get_pixel_spacing(hdr),
'offset': header.get_offset(hdr),
'point': img[idx]}
print "ORIGINAL"
print "Type: {}".format(otype)
print "MData: {}".format(mdata)
def test01(img):
# TEST 01: WHAT DATATYPES CAN THE FORMATS SAVE?
for dt in dtypes:
print '\n:::{}:::'.format(dt).upper()
for t in types_int:
print t.upper(), '\t->',
try:
img1 = img.astype(dt)
