def main(args):
total_start = timeit.default_timer()
print('Starting Preibisch fusion', args.substack_id)
ss = SubStack(args.first_view_dir, args.substack_id)
minz = int(ss.info['Files'][0].split("/")[-1].split('_')[-1].split('.tif')[0])
prefix = '_'.join(ss.info['Files'][0].split("/")[-1].split('_')[0:-1])+'_'
np_tensor_3d_first_view,_ = imtensor.load_nearby(args.tensorimage_first_view, ss, args.size_patch)
sc_in=np_tensor_3d_first_view.shape
if args.transformation_file is not None:
R, t = parse_transformation_file(args.transformation_file)
np_tensor_3d_second_view = transform_substack(args.second_view_dir, args.tensorimage_second_view, args.substack_id, R, t, args.size_patch, invert=True)
else:
np_tensor_3d_second_view,_ = imtensor.load_nearby(args.tensorimage_second_view, ss, args.size_patch)
fused_image,entropy_mask__view,entropy_mask_second_view = do_content_based_fusion(np_tensor_3d_first_view,np_tensor_3d_second_view,args.size_patch, args.size_patch, speedup=1,fast_computation=True)
if args.extramargin>args.size_patch:
args.extramargin=args.size_patch
offset_margin=args.size_patch - args.extramargin
fused_image_output=fused_image[offset_margin:sc_in[0]-offset_margin,offset_margin:sc_in[1]-offset_margin,offset_margin:sc_in[2]-offset_margin]
atom = tables.UInt8Atom()
mkdir_p(args.outdir)
h5f = tables.openFile(args.outdir + '/' + args.substack_id + '.h5', 'w')
sc_out=fused_image_output.shape
ca = h5f.createCArray(h5f.root, 'full_image', atom, sc_out)
for z in xrange(0, sc_out[0], 1):
ca[z, :, :] = fused_image_output[z,:,:]
h5f.close()
imtensor.save_tensor_as_tif(fused_image_output, args.outdir+'/'+args.substack_id, minz,prefix=prefix)
print ("total time Preibisch fusion: %s" %(str(timeit.default_timer() - total_start)))
def main(args):
total_start = timeit.default_timer()
print('Starting Preibisch fusion', args.substack_id)
ss = SubStack(args.first_view_dir, args.substack_id)
minz = int(
ss.info['Files'][0].split("/")[-1].split('_')[-1].split('.tif')[0])
prefix = '_'.join(
ss.info['Files'][0].split("/")[-1].split('_')[0:-1]) + '_'
np_tensor_3d_first_view, _ = imtensor.load_nearby(
args.tensorimage_first_view, ss, args.size_patch)
sc_in = np_tensor_3d_first_view.shape
if args.transformation_file is not None:
R, t = parse_transformation_file(args.transformation_file)
np_tensor_3d_second_view = transform_substack(
args.second_view_dir,
args.tensorimage_second_view,
args.substack_id,
R,
t,
args.size_patch,
invert=True)
else:
np_tensor_3d_second_view, _ = imtensor.load_nearby(
args.tensorimage_second_view, ss, args.size_patch)
fused_image, entropy_mask__view, entropy_mask_second_view = do_content_based_fusion(
np_tensor_3d_first_view,
np_tensor_3d_second_view,
args.size_patch,
args.size_patch,
speedup=1,
fast_computation=True)
if args.extramargin > args.size_patch:
args.extramargin = args.size_patch
offset_margin = args.size_patch - args.extramargin
fused_image_output = fused_image[offset_margin:sc_in[0] - offset_margin,
offset_margin:sc_in[1] - offset_margin,
offset_margin:sc_in[2] - offset_margin]
atom = tables.UInt8Atom()
mkdir_p(args.outdir)
h5f = tables.openFile(args.outdir + '/' + args.substack_id + '.h5', 'w')
sc_out = fused_image_output.shape
ca = h5f.createCArray(h5f.root, 'full_image', atom, sc_out)
for z in xrange(0, sc_out[0], 1):
ca[z, :, :] = fused_image_output[z, :, :]
h5f.close()
imtensor.save_tensor_as_tif(fused_image_output,
args.outdir + '/' + args.substack_id,
minz,
prefix=prefix)
print("total time Preibisch fusion: %s" %
(str(timeit.default_timer() - total_start)))
def main(args):
total_start = timeit.default_timer()
print('Starting reconstruction of volume %s ...' % (args.substack_id))
ss = SubStack(args.first_view_dir, args.substack_id)
minz = int(
ss.info['Files'][0].split("/")[-1].split('_')[-1].split('.tif')[0])
prefix = '_'.join(
ss.info['Files'][0].split("/")[-1].split('_')[0:-1]) + '_'
np_tensor_3d_first_view, _ = imtensor.load_nearby(
args.tensorimage_first_view, ss, args.extramargin)
if args.transformation_file is not None:
R, t = parse_transformation_file(args.transformation_file)
np_tensor_3d_second_view = transform_substack(
args.second_view_dir,
args.tensorimage_second_view,
args.substack_id,
R,
t,
args.extramargin,
invert=True)
else:
np_tensor_3d_second_view, _ = imtensor.load_nearby(
args.tensorimage_second_view, ss, args.extramargin)
print('Loading model...')
model = pickle.load(open(args.model))
if not args.local_mean_std:
h5 = tables.openFile(args.trainfile)
Xmean = h5.root.Xmean[:].astype(np.float32)
Xstd = h5.root.Xstd[:].astype(np.float32)
h5.close()
else:
Xmean = None
Xstd = None
reconstruction = deconvolver.filter_volume(
[np_tensor_3d_first_view, np_tensor_3d_second_view],
Xmean,
Xstd,
args.extramargin,
model,
args.speedup,
do_cython=args.do_cython,
trainfile=args.trainfile)
pair_id = basename(args.first_view_dir) + '_' + basename(
args.second_view_dir)
outdir = args.outdir + '/' + args.substack_id + '/' + pair_id
imtensor.save_tensor_as_tif(reconstruction, outdir, minz, prefix='slice_')
print("total time reconstruction: %s" %
(str(timeit.default_timer() - total_start)))
def main(args):
total_start = timeit.default_timer()
print('Starting reconstruction of volume %s ...'%(args.substack_id))
substack = SubStack(args.indir,args.substack_id)
np_tensor_3d, minz = imtensor.load_nearby(args.tensorimage, substack, args.extramargin)
if not args.local_mean_std:
print('Reading standardization data from', args.trainfile)
h5 = tables.openFile(args.trainfile)
Xmean = h5.root.Xmean[:].astype(np.float32)
Xstd = h5.root.Xstd[:].astype(np.float32)
h5.close()
else:
Xmean=None
Xstd=None
print('Starting semantic devonvolution of volume', args.substack_id)
model = pickle.load(open(args.model))
minz = int(re.split('[a-zA-z0-9]*_',substack.info['Files'][0])[1].split('.tif')[0])
reconstruction = deconvolver.filter_volume(np_tensor_3d, Xmean, Xstd,
args.extramargin, model, args.speedup, do_cython=args.do_cython, trainfile=args.trainfile)
imtensor.save_tensor_as_tif(reconstruction, args.outdir+'/'+args.substack_id, minz)
print ("total time reconstruction: %s" %(str(timeit.default_timer() - total_start)))
def main(args):
total_start = timeit.default_timer()
print('Starting transformation and fusion of views of volume %s ...'%(args.substack_id))
ss = SubStack(args.first_view_dir, args.substack_id)
minz = int(ss.info['Files'][0].split("/")[-1].split('_')[-1].split('.tif')[0])
prefix = '_'.join(ss.info['Files'][0].split("/")[-1].split('_')[0:-1])+'_'
np_tensor_3d_first_view,_ = imtensor.load_nearby(args.tensorimage_first_view, ss, 0)
if args.transformation_file is not None:
R, t = parse_transformation_file(args.transformation_file)
np_tensor_3d_second_view = transform_substack(args.second_view_dir, args.tensorimage_second_view, args.substack_id, R, t, 0, invert=True)
else:
np_tensor_3d_second_view,_ = imtensor.load_nearby(args.tensorimage_second_view, ss, 0)
fuse_tensors(args.outdir, np_tensor_3d_first_view,np_tensor_3d_second_view,np.zeros_like(np_tensor_3d_first_view).astype(np.uint8))
print ("total time transformation and fusion: %s" %(str(timeit.default_timer() - total_start)))
def main(args):
total_start = timeit.default_timer()
print('Starting reconstruction of volume %s ...'%(args.substack_id))
ss = SubStack(args.first_view_dir, args.substack_id)
minz = int(ss.info['Files'][0].split("/")[-1].split('_')[-1].split('.tif')[0])
prefix = '_'.join(ss.info['Files'][0].split("/")[-1].split('_')[0:-1])+'_'
np_tensor_3d_first_view,_ = imtensor.load_nearby(args.tensorimage_first_view, ss, args.extramargin)
if args.transformation_file is not None:
R, t = parse_transformation_file(args.transformation_file)
np_tensor_3d_second_view = transform_substack(args.second_view_dir, args.tensorimage_second_view, args.substack_id, R, t, args.extramargin, invert=True)
else:
np_tensor_3d_second_view,_ = imtensor.load_nearby(args.tensorimage_second_view, ss, args.extramargin)
print('Loading model...')
model = pickle.load(open(args.model))
if not args.local_mean_std:
h5 = tables.openFile(args.trainfile)
Xmean = h5.root.Xmean[:].astype(np.float32)
Xstd = h5.root.Xstd[:].astype(np.float32)
h5.close()
else:
Xmean=None
Xstd=None
reconstruction = deconvolver.filter_volume([np_tensor_3d_first_view,np_tensor_3d_second_view], Xmean, Xstd,
args.extramargin, model, args.speedup, do_cython=args.do_cython,trainfile=args.trainfile)
pair_id = basename(args.first_view_dir)+ '_' +basename(args.second_view_dir)
outdir=args.outdir+'/'+args.substack_id+'/'+pair_id
imtensor.save_tensor_as_tif(reconstruction, outdir, minz, prefix='slice_')
print ("total time reconstruction: %s" %(str(timeit.default_timer() - total_start)))
def main(args):
substack = SubStack(args.indir,args.substack_id)
np_tensor_3d, minz = imtensor.load_nearby(args.tensorimage, substack, args.extramargin)
# Standardize volume according to mean and std found in the training set
print('Reading standardization data from', args.trainfile)
h5=tables.openFile(args.trainfile)
Xmean = h5.root.Xmean[:]
Xstd = h5.root.Xstd[:]
h5.close()
print('Starting semantic devonvolution of volume', args.substack_id)
model = pickle.load(open(args.model))
minz = int(substack.info['Files'][0].split('full_')[1].split('.tif')[0])
reconstruction = deconvolver.filter_volume(np_tensor_3d, Xmean, Xstd,
args.extramargin, model, args.speedup)
imtensor.save_tensor_as_tif(reconstruction, args.outdir+'/'+args.substack_id, minz)
def main(args):
patchlen = (1+2*args.size_patch) ** 3
X_sup = np.array([]).reshape(0, 2*patchlen).astype(np.float32)
y_sup = np.array([]).reshape(0, patchlen).astype(np.float32)
X_neg = np.array([]).reshape(0, 2*patchlen).astype(np.float32)
y_neg = np.array([]).reshape(0, patchlen).astype(np.float32)
X_original = np.array([]).reshape(0, 2*patchlen).astype(np.float32)
data_frame_markers = pd.read_csv(args.list_trainset, dtype={'view1': str, 'view2': str, 'ss_id': str })
for row in data_frame_markers.index:
row_data=data_frame_markers.iloc[row]
first_view_dir=args.substacks_base_path+'/'+row_data['view1']
substack = SubStack(first_view_dir, row_data['ss_id'])
markers = args.mergedmarkers_folder + '/' + row_data['view1']+'_'+row_data['view2']+ '/'+row_data['ss_id']+'-GT.marker'
print('Loading ground truth markers from', markers)
C = substack.load_markers(markers, from_vaa3d=True)
for c in C:
c.x -= 1
c.y -= 1
c.z -= 1
substack.load_volume(h5filename=args.tensors_base_path+'/'+row_data['view1'] + '.h5')
tensor_first_view,_ = imtensor.load_nearby(args.tensors_base_path+'/'+row_data['view1'] + '.h5', substack, 0)
second_view_dir=args.substacks_base_path+'/'+row_data['view2']
R, t = parse_transformation_file(args.transformations_path+'/'+row_data['ss_id']+'/'+row_data['view1']+'_'+row_data['view2'])
tensor_second_view = transform_substack(second_view_dir, args.tensors_base_path+'/'+row_data['view2']+'.h5', row_data['ss_id'], R, t, 0, invert=True)
temp_X_sup, temp_y_sup, temp_X_neg, temp_y_neg = make_pos_neg_dataset(tensor_first_view, tensor_second_view, substack, C,row_data['view1'],row_data['view2'], default_sigma=args.sigma,size=args.size_patch, save_tiff_files=False ,find_negative=args.negatives)
X_original = np.vstack((X_original, temp_X_sup))
X_original = np.vstack((X_original, temp_X_neg))
if args.local_standardization:
print('Do local standardization')
Xmean = np.vstack((temp_X_sup,temp_X_neg)).mean(axis=0)
Xstd = np.vstack((temp_X_sup,temp_X_neg)).std(axis=0)
temp_X_sup = (temp_X_sup - Xmean) / Xstd
temp_X_neg = (temp_X_neg - Xmean) / Xstd
X_sup = np.vstack((X_sup, temp_X_sup))
y_sup = np.vstack((y_sup, temp_y_sup))
X_neg = np.vstack((X_neg, temp_X_neg))
y_neg = np.vstack((y_neg, temp_y_neg))
print('Negative Data set shape:', X_neg.shape, 'size:', X_neg.nbytes / (1024 * 1024), 'MBytes')
print('Negative target shape:', y_neg.shape, 'size:', y_neg.nbytes / (1024 * 1024), 'MBytes')
print('Positive Data set shape:', X_sup.shape, 'size:', X_sup.nbytes / (1024 * 1024), 'MBytes')
print('Positive target shape:', y_sup.shape, 'size:', y_sup.nbytes / (1024 * 1024), 'MBytes')
ratio_positive_negative = float(y_sup.sum())/float(y_neg.shape[0]*y_neg.shape[1] + y_sup.shape[0]*y_sup.shape[1])
print('ratio positive-negative:', ratio_positive_negative)
X = np.vstack((X_sup,X_neg))
y = np.vstack((y_sup,y_neg))
print('Total Data set shape:', X.shape, 'size:', X.nbytes / (1024 * 1024), 'MBytes')
print('Total target shape:', y.shape, 'size:', y.nbytes / (1024 * 1024), 'MBytes')
print('Compute global mean and std')
Xmean = X_original.mean(axis=0)
Xstd = X_original.std(axis=0)
print('Saving training data to', args.outfile)
h5file = tables.openFile(args.outfile, mode='w', title="Training set")
root = h5file.root
h5file.createArray(root, "X", X)
h5file.createArray(root, "y", y)
h5file.createArray(root, "Xmean", Xmean)
h5file.createArray(root, "Xstd", Xstd)
h5file.close()
