import numpy as np
import Utility_Functions.file_io as fio
import Utility_Functions.stack as stack
from Visualisation.grid_img import viz_grid
import pylab as plt
import Registration.registration_new as registration
from skimage.exposure import rescale_intensity
import Unzipping.unzip as uzip
import scipy.io as spio
import os
dataset_folder = '../../Data/Holly/czifile_test_tiff'
out_view_aligned_folder = os.path.join(dataset_folder, 'view_aligned-t')
fio.mkdir(out_view_aligned_folder)
"""
Load dataset and pair up data.
"""
dataset_files = fio.load_dataset(
dataset_folder, ext='.tif', split_key='_',
split_position=3) # load in the just aligned files.
view_pair_files = fio.pair_views(dataset_files,
ext='.tif',
split_key='_',
split_position=3,
view_by=2)
"""
Do Sift3D registration to align the sequence of paired views.
"""
processfiles = np.hstack(view_pair_files)
return np.hstack([min_x, min_y, min_z, max_x, max_y, max_z])
if __name__ == "__main__":
import numpy as np
import Utility_Functions.file_io as fio
import Utility_Functions.stack as stack
from skimage.exposure import rescale_intensity
import Unzipping.unzip as uzip
import time
infolder = '../../Data/Holly/czifile_test'
outfolder = '../../Data/Holly/czifile_test_tiff'
fio.mkdir(outfolder)
datasets = fio.load_dataset(infolder,
ext='.czi',
split_position=3,
split_key='_')
pad_num = 11
for datafile in datasets[:]:
t1 = time.time()
im = fio.read_czifile(datafile)
im = im[:, ::-1]
"""
Rescale to uint8
"""
import time
import pylab as plt
from tqdm import tqdm
import scipy.io as spio
import Utility_Functions.stack as stack
#==============================================================================
# load in the dataset
#==============================================================================
dataset_folder = '/media/felix/Elements/Shankar LightSheet/Example Timelapse/test'
"""
Create output folders.
"""
out_pre_folder = os.path.join(dataset_folder, 'preprocess_full')
fio.mkdir(out_pre_folder)
# out_pre_folder = os.path.join(dataset_folder, 'enhance_scaled'); fio.mkdir(out_pre_folder)
# out_aligned_folder = os.path.join(dataset_folder, 'aligned_full'); fio.mkdir(out_aligned_folder)
out_aligned_folder = os.path.join(dataset_folder,
'aligned_sift-enh-test-correct1')
fio.mkdir(out_aligned_folder)
# out_aligned_folder2 = os.path.join(dataset_folder, 'aligned2_full'); fio.mkdir(out_aligned_folder2)
# out_flow_folder = os.path.join(dataset_folder, 'optflow_full'); fio.mkdir(out_flow_folder)
# out_track_folder = os.path.join(dataset_folder, 'tracks_full'); fio.mkdir(out_track_folder)
##==============================================================================
## Preprocessing: Rescale and repad the frames in the dataset (for speed)
##==============================================================================
#
# dataset_files = fio.load_dataset(dataset_folder, ext='.tif', split_position=1)
## downsample_factor = 5
import Unzipping.unzip_backup as uzip
import numpy as np
import pylab as plt
from skimage.exposure import rescale_intensity, equalize_adapthist
import Geometry.transforms as tf
import Registration.registration_new as registration
# load in a set of files.
dataset_folder = '/media/felix/Elements/Shankar LightSheet/Example Timelapse/test'
out_aligned_folder = os.path.join(dataset_folder, 'aligned2')
dataset_files = fio.load_dataset(
out_aligned_folder, ext='.tif', split_key='TP_',
split_position=1) # load in the just aligned files.
out_folder = 'test_non-rigid-registered'
fio.mkdir(out_folder)
from scipy.misc import imsave
from tqdm import tqdm
import Visualisation.volume_img as vol_img
import Geometry.meshtools as meshtools
# test the parametrization approach again? o
for i in tqdm(range(len(dataset_files))[1:-1]):
reg_config = {'alpha': 0.1, 'levels': [8, 4, 2, 1], 'warps': [4, 2, 0, 0]}
infile1 = dataset_files[1] # pick one reference don't do sequential !.
infile2 = dataset_files[i + 1]
# savefile='test_nonrigid.tif'
def register3D_SIFT_wrapper(dataset_files, in_folder, out_folder, reg_config):
"""
Registers the similarity transformation exploiting the sift3D library.
https://github.com/bbrister/SIFT3D
returns transforms for each time point which subsequently applied to each image?
"""
import matlab.engine
import scipy.io as spio
import os
import shutil
import pylab as plt
from tqdm import tqdm
import time
import pylab as plt
import Visualisation.imshowpair as imshowpair
from skimage.exposure import rescale_intensity
# start the python matlab engine.
eng = matlab.engine.start_matlab()
fio.mkdir(
out_folder
) # check that the output folder exists, create if does not exist.
print 'registration'
if reg_config['mode'] == 1:
tforms = []
translate_matrixs = []
datasetsave_files = np.hstack([
dataset_files[2 * i + 1].replace(in_folder, out_folder)
for i in range(len(dataset_files) // 2)
])
for i in tqdm(range(len(dataset_files) // 2)):
t1 = time.time()
im1file = dataset_files[2 * i]
im2file = dataset_files[2 * i + 1]
print 'registering SIFT'
tmatrix = eng.register3D_SIFT_wrapper(im1file, im2file,
datasetsave_files[i],
reg_config['downsample'],
reg_config['lib_path'],
reg_config['return_img'])
tmatrix = np.asarray(tmatrix)
tforms.append(tmatrix)
print tmatrix
"""
if matlab doesn't save matrix then we use python to do so.
"""
if reg_config['return_img'] != 1:
print 'reading image'
im1 = fio.read_multiimg_PIL(im1file)
im2 = fio.read_multiimg_PIL(im2file)
print 'finished reading image'
# restrict the tranformation to affine.
affine = np.zeros((4, 4))
affine[:-1, :] = tmatrix.copy()
affine[-1] = np.array([0, 0, 0, 1])
# decomposition to zero out scaling and shear (rigid transformation)
T, R, Z, S = decompose44(affine)
affine = compose(T, R, np.ones(3),
np.zeros(3)) # no scaling, no shears.
print 'affine matrix'
print affine
im2 = np.uint8(
tf.apply_affine_tform(im2.transpose(1, 2, 0), affine,
np.array(im1.shape)[[1, 2, 0]]))
im2 = im2.transpose(2, 0, 1)
# realign and correct the translation artifact.
im2, translate_matrix = align_centers(im1, im2)
translate_matrixs.append(translate_matrix)
print 'saving'
fio.save_multipage_tiff(im2, datasetsave_files[i])
t2 = time.time()
print 'elapsed time: ', t2 - t1
if reg_config['mode'] == 2:
print 'running sequential registration'
datasetsave_files = np.hstack(
[f.replace(in_folder, out_folder) for f in dataset_files])
translate_matrixs = []
if reg_config['return_img'] == 1:
tforms = eng.register3D_SIFT_wrapper_batch(
dataset_files, datasetsave_files, reg_config['downsample'],
reg_config['lib_path'], reg_config['mode'])
else:
print 'running python mode'
tforms = []
# set the fixed image.
fixed = fio.read_multiimg_PIL(dataset_files[0])
fio.save_multipage_tiff(fixed, datasetsave_files[0])
fixed_file = datasetsave_files[0]
for i in tqdm(range(len(dataset_files) - 1)):
moving_file = dataset_files[i + 1]
print 'registering SIFT'
tmatrix = eng.register3D_SIFT_wrapper(fixed_file, moving_file,
datasetsave_files[i + 1],
reg_config['downsample'],
reg_config['lib_path'],
reg_config['return_img'])
tmatrix = np.asarray(tmatrix)
tforms.append(tmatrix)
"""
Apply the transforms.
"""
im1 = fio.read_multiimg_PIL(fixed_file)
im2 = fio.read_multiimg_PIL(moving_file)
affine = np.zeros((4, 4))
affine[:-1, :] = tmatrix.copy()
affine[-1] = np.array([0, 0, 0, 1])
# decomposition
T, R, Z, S = decompose44(affine) # S is shear!
affine = compose(T, R, np.ones(3), np.zeros(
3)) # constant scale, allow shear.(since there is wiggle?)
print 'compose affine'
print affine
print 'applying transform'
im2 = np.uint8(
tf.apply_affine_tform(im2.transpose(1, 2, 0), affine,
np.array(im1.shape)[[1, 2, 0]]))
im2 = im2.transpose(2, 0, 1)
# realign and correct the translation artifact.
print 'realigning centers'
im2, translate_matrix = align_centers(im1, im2)
translate_matrixs.append(translate_matrix)
# translate_matrixs.append(affine)
fio.save_multipage_tiff(im2, datasetsave_files[i + 1])
comb = imshowpair.checkerboard_imgs(im1[:, 1000],
im2[:, 1000],
grid=(10, 10))
plt.figure()
plt.imshow(rescale_intensity(comb / 255.))
plt.show()
# update the savefile.
fixed_file = datasetsave_files[i + 1]
tforms = np.array(tforms)
translate_matrixs = np.array(translate_matrixs)
spio.savemat(
os.path.join(out_folder, 'tforms_view_align.mat'), {
'files': dataset_files,
'config': reg_config,
'view_tforms': tforms,
'translate_tforms': translate_matrixs
})
"""
Stop the matlab engine? to prevent hangups.
"""
# eng.quit()
return (tforms, translate_matrixs)