def reg(stack):
sr = StackReg(StackReg.TRANSLATION)
t_mats = sr.register_stack(gauss(stack), reference='first', n_frames=1)
t_mats[:, 0, -1] = 0
reg_stack = sr.transform_stack(stack, tmats=t_mats)
return reg_stack
def process(pth):
print(f'\nProcessing {pth}')
save_pth = pth / 'reg_stacks'
#tmat_pth = pth / 'transformation_matrices'
try:
save_pth.mkdir()
#tmat_pth.mkdir()
except FileExistsError:
ex('Save File for reg stacks or tmats already exists. Delete and re-run.'
)
#tell pystack reg that we will use a translational transformation
#there shouldn't be intra-volume rotation or shear (there might be for rod blink)
sr = StackReg(StackReg.TRANSLATION)
#register to the first slice and output the transfomation matrix without transforming
#iterate through the files in the stacks folder
files = pth.glob('*.tif')
loop_starts = time.time()
for i, file in enumerate(files):
#start_time = time.time()
print(f'Processing: {file}')
#Intravolume registration of the fixed volume
#load first stack and do a 3d gaussian blur with a sigma=1
#str needed for imread, otherwise only one frame is loaded
fixed = io.imread(str(file)) #had gauss(), testing
t_mats = sr.register_stack(gauss(fixed), reference='first', n_frames=1)
#remove the x shift from all the matrices - horizontal movement isn't relevant here,
#the volume should be acquired quickly enough that this isn't a problem.
t_mats[:, 0, -1] = 0
fixed = sr.transform_stack(fixed, tmats=t_mats)
#Using previous to see if I could get rid of wigge. Didn't seem to work.
#t_mats = sr.register_stack(gauss(fixed), reference='previous')
#t_mats[:,1,-1] = 0
#fixed = sr.transform_stack(fixed, tmats=t_mats)
#save the register fixed volume in the parent directory for reference
save_name = save_pth / f'reg_{file.name}'
#io.imsave(arr=img_as_uint(fixed), fname=str(save_name))
io.imsave(arr=img_as_float32(fixed), fname=str(save_name))
#get fixed out of memory - may not be worth the time?
print(f'Intravolume registration complete. File saved at {save_name}')
#end_time = time.time()
#print(f'{file} was processed in {(end_time - start_time):.2f}s. \
#\n{((end_time - loop_starts)/60):.2f} minutes have elapsed.')
#de;ete emumerate
#if i==4:
#ex('auto break')
end_time = time.time()
print(f'Run took {(end_time-loop_starts)/60:.2f} minutes')
def process(pth):
print(f'\nProcessing {pth}')
save_pth = pth / 'vol_reg_stacks'
tmat_pth = pth / 'vol_transformation_matrices'
try:
save_pth.mkdir()
tmat_pth.mkdir()
except FileExistsError:
ex('Save File for reg stacks or tmats already exists. Delete and re-run.')
#Intravolume registration of the fixed volume
print('Doing intravolume registration.')
#load first stack and do a 3d gaussian blur with a sigma=1
#str needed for imread, otherwise only one frame is loaded
fixed = io.imread(str(pth/'stack0.tif')) #had gauss(), testing
#tell pystack reg that we will use a translational transformation
#there shouldn't be intra-volume rotation or shear (there might be for rod blink)
sr = StackReg(StackReg.TRANSLATION)
#register to the first slice and output the transfomation matrix without transforming
t_mats = sr.register_stack(gauss(fixed), reference='first', n_frames=1)
#remove the x shift from all the matrices - horizontal movement isn't relevant here,
#the volume should be acquired quickly enough that this isn't a problem.
t_mats[:,0,-1] = 0
fixed = sr.transform_stack(fixed, tmats=t_mats)
#save the register fixed volume in the parent directory for reference
save_name = pth.parent / 'fixed.tif'
#io.imsave(arr=img_as_uint(fixed), fname=str(save_name))
io.imsave(arr=fixed.astype('float32'), fname=str(save_name))
#get fixed out of memory - may not be worth the time?
del fixed
print(f'Intravolume registration complete. File saved at {save_name}')
#Volume registration
#switch to tmat folder because i dont know how to set the output folder to save
#the transformation mats. elastix.Execute() automatically saves
#TransformParameters.0.txt in the current directory
os.chdir(tmat_pth)
#reload fixed image with sitk so it is in the right format
fixed = sitk.ReadImage(str(save_name))
#set up for multivolume registration
parameterMap = sitk.GetDefaultParameterMap('affine')
elastixImageFilter = sitk.ElastixImageFilter()
elastixImageFilter.SetFixedImage(fixed)
elastixImageFilter.SetParameterMap(parameterMap)
#test 11-8-2019
parameterMap['Image Sampler'] = ['Full']
#parameterMap['Metric'] = ['AdvancedMeanSquares']
parameterMap['Optimiser'] = ['FullSearch']
#iterate through the files in the stacks folder
files = sorted(list(pth.glob('*.tif')), key=sort_key)
loop_starts = time.time()
for i,file in enumerate(files):
#i=0 #use first stack
#register to previous stack
if i > 1:
fixed = sitk.ReadImage(str(save_name))
elastixImageFilter.SetFixedImage(fixed)
start_time = time.time()
print(f'Processing: {file}')
#load the volume
#Do affine registration so that volume can shear to do the intravolume registration
#this code is equivalent to the lines below, but allows the export of the
#transformation matrices
#moving = sitk.ReadImage(file)
#reg_volume = sitk.Elastix(fixed, moving, "affine")
elastixImageFilter.SetMovingImage(sitk.ReadImage(str(file)))
elastixImageFilter.Execute()
reg_volume = elastixImageFilter.GetResultImage()
#can get the transform parameters, but have no idea how to save them
#maybe just through normal python file manipulation?
#transformParameterMap = elastixImageFilter.GetTransformParameterMap()
#save volume
save_name = save_pth / f'reg_{file.name}'
sitk.WriteImage(reg_volume, str(save_name))
#transformation was saved on execute, now rename.
#tmat_name = tmat_pth / f'tmat_{file.name}'
os.rename('TransformParameters.0.txt', f'tmat_{file.stem}.txt')
end_time = time.time()
print(f'{file} was processed in {(end_time - start_time):.2f}s. \
\n{((end_time - loop_starts)/60):.2f} minutes have elapsed.')
print('Unzipping the database...')
zip_Archivo = zipfile.ZipFile(cwd + '\Hybrid_Lab4.zip', 'r')
zip_Archivo.extractall(cwd)
zip_Archivo.close()
print('Unzipping done.')
A = ndimage.imread("parte6.jpg", flatten=False)
B = ndimage.imread("parte8.jpg", flatten=False)
test = numpy.concatenate((A[:, 0:512, :], B[:, 512:, :]), axis=1)
misc.imsave("Blended_NotPyr.jpg", numpy.real(test))
plt.imshow(test)
#downsampling:
x1_A = misc.imresize(gauss(A,
sigma=0.1,
multichannel=True,
preserve_range=True), (512, 512),
interp='bicubic',
mode=None)
s1_A = cv2.pyrUp(x1_A)
A_fl = A.astype(float)
s1_A_fl = s1_A.astype(float)
l1_A_fl = numpy.absolute(A_fl - s1_A_fl)
l1_A = l1_A_fl.astype(numpy.uint8)
plt.imshow(x1_A)
x2_A = misc.imresize(gauss(x1_A,
sigma=0.1,
multichannel=True,
preserve_range=True), (256, 256),
interp='bicubic',