def main(args):
if args.outdir == 'GT':
marker_files = map(
string.strip,
os.popen('ls ' + args.indir + '*-GT.marker').readlines())
substack_ids = [f.split('/')[-1].split('-')[0] for f in marker_files]
else:
marker_files = map(
string.strip,
os.popen('ls ' + args.outdir + '/*/ms.marker').readlines())
substack_ids = [f.split('/')[-2] for f in marker_files]
C_final = set()
plist = None
hue = 0.0
# FIXME: This is way too slow, should use divide & conquer using a binary tree
for marker_file, substack_id in zip(marker_files, substack_ids):
substack = SubStack(args.indir, substack_id, plist)
plist = substack.plist
print('Merging', marker_file, substack_id)
C = substack.load_markers(marker_file, args.outdir == 'GT')
# I had forgotten that markers are shifted by 1 in save_markers()
# that was because Vaa3D coordinates starts from 1 rather than 0
# ==> repair original values
for c in C:
c.x -= 1
c.y -= 1
c.z -= 1
hue = hue + 0.31
if hue > 1:
hue = hue - 1
merge(C_final, C, substack, hue, args.verbose)
substack.save_markers(args.outfile, C_final)
def main(args):
if args.outdir=='GT':
marker_files = map(string.strip,os.popen('ls '+args.indir+'*-GT.marker').readlines())
substack_ids = [f.split('/')[-1].split('-')[0] for f in marker_files]
else:
marker_files = map(string.strip,os.popen('ls '+args.outdir+'/*/ms.marker').readlines())
substack_ids = [f.split('/')[-2] for f in marker_files]
C_final=set()
plist = None
hue = 0.0
# FIXME: This is way too slow, should use divide & conquer using a binary tree
for marker_file,substack_id in zip(marker_files,substack_ids):
substack=SubStack(args.indir,substack_id, plist)
plist = substack.plist
print('Merging', marker_file, substack_id)
C=substack.load_markers(marker_file,args.outdir=='GT')
# I had forgotten that markers are shifted by 1 in save_markers()
# that was because Vaa3D coordinates starts from 1 rather than 0
# ==> repair original values
for c in C:
c.x -= 1
c.y -= 1
c.z -= 1
hue = hue + 0.31
if hue > 1:
hue = hue -1
merge(C_final, C, substack, hue, args.verbose)
substack.save_markers(args.outfile, C_final)
def main(args):
substack = SubStack(args.indir,args.substack_id)
patch = substack.get_volume()
histogram = np.histogram(patch, bins=256,range=(0,256))[0]
thresholds = threshold.multi_kapur(histogram, 2)
outfile=args.outdir+'/'+args.substack_id+'/'+basename(args.indir)
mkdir_p(args.outdir+'/'+args.substack_id)
f=open(outfile,'w')
f.write(str(thresholds[0])+','+str(thresholds[1])+'\n')
f.close()
def main(args):
substack = SubStack(args.indir, args.substack_id)
patch = substack.get_volume()
histogram = np.histogram(patch, bins=256, range=(0, 256))[0]
thresholds = threshold.multi_kapur(histogram, 2)
outfile = args.outdir + '/' + args.substack_id + '/' + basename(args.indir)
mkdir_p(args.outdir + '/' + args.substack_id)
f = open(outfile, 'w')
f.write(str(thresholds[0]) + ',' + str(thresholds[1]) + '\n')
f.close()
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 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)
substack.load_volume()
tensor = substack.get_volume()
# Changing the tensor so that it has a 6 pixel black padding. Hopefully it won't frick things up too much. Else, mail time.
print("The shape of the tensor before padding: " + str(np.shape(tensor)))
tensor = pad(tensor, 6)
print("The shape of the tensor after padding: " + str(np.shape(tensor)))
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)
# Importing here to have a clean --help
from keras.models import model_from_json
model = model_from_json(open(args.model + '/architecture.json').read())
model.load_weights(args.model + '/weights.h5')
minz = int(re.split('[a-zA-z0-9]*_',substack.info['Files'][0])[1].split('.tif')[0])
# Remove the margin, I have changed deconvolver to use a fized number instead of the extramargin. Hope it works.
reconstruction = deconvolver.filter_volume(tensor, 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):
try:
C_firstview = m_load_markers(args.first_view,from_vaa3d=True)
except IOError:
print('Warning: first view marker file',args.first_view,'not found.')
C_firstview = []
try:
C_secondview = m_load_markers(args.second_view,from_vaa3d=True)
except IOError:
print('Warning: second view marker file',args.second_view,'not found.')
C_secondview = []
mkdir_p(os.path.dirname(args.output_marker_file))
substack = SubStack(args.indir,args.substack_id)
if args.do_icp:
C_merged, C_onlyfirstview, C_onlyfirstview, _ = do_fuse_with_icp(substack,C_firstview,C_secondview,args.max_distance,match_distance=args.match_distance,verbose=args.verbose)
else:
C_merged, C_onlyfirstview, C_onlyfirstview, _ = do_fuse(substack,C_firstview,C_secondview,args.max_distance, args.verbose)
save_fused_markers(substack,C_merged,C_onlyfirstview,C_onlysecondview,output_marker_file,first_view_id,second_view_id,verbose)
def get_visible_pairs(base_indir,substack_id,view_ids,lower_threshold=30.):
num_vis=0
for view_id in view_ids:
substack = SubStack(base_indir+'/'+view_id,args.substack_id)
patch = substack.get_volume()
histogram = np.histogram(patch, bins=256,range=(0,256))[0]
thresholds = threshold.multi_kapur(histogram, 2)
if thresholds[1]>=lower_threshold:
num_vis+=1
threshold_0=thresholds[0]
threshold_1=thresholds[1]
threshold_2=thresholds[2]
threshold_3=thresholds[3]
list_views=[]
if num_vis==0:
return list_views
elif num_vis==1:
if threshold_0>=lower_threshold:
list_views.append(tuple((view_ids[0],view_ids[1])))
list_views.append(tuple((view_ids[0],view_ids[3])))
if threshold_1>=lower_threshold:
list_views.append(tuple((view_ids[0],view_ids[1])))
list_views.append(tuple((view_ids[2],view_ids[1])))
if threshold_2>=lower_threshold:
list_views.append(tuple((view_ids[2],view_ids[1])))
list_views.append(tuple((view_ids[2],view_ids[3])))
if threshold_3>=lower_threshold:
list_views.append(tuple((view_ids[0],view_ids[3])))
list_views.append(tuple((view_ids[2],view_ids[3])))
elif num_vis==2:
if (threshold_0>=lower_threshold and threshold_2>=lower_threshold) or (threshold_1>=lower_threshold and threshold_3>=lower_threshold):
list_views.append(tuple((view_ids[0],view_ids[1])))
list_views.append(tuple((view_ids[0],view_ids[3])))
list_views.append(tuple((view_ids[2],view_ids[1])))
list_views.append(tuple((view_ids[2],view_ids[3])))
elif threshold_0>=lower_threshold and threshold_1>=lower_threshold:
list_views.append(tuple((view_ids[0],view_ids[1])))
elif threshold_0>=lower_threshold and threshold_3>=lower_threshold:
list_views.append(tuple((view_ids[0],view_ids[3])))
elif threshold_2>=lower_threshold and threshold_1>=lower_threshold:
list_views.append(tuple((view_ids[2],view_ids[1])))
elif threshold_2>=lower_threshold and threshold_3>=lower_threshold:
list_views.append(tuple((view_ids[2],view_ids[3])))
elif num_vis==3:
if threshold_0 < lower_threshold:
list_views.append(tuple((view_ids[2],view_ids[1])))
list_views.append(tuple((view_ids[2],view_ids[3])))
elif threshold_2 < lower_threshold:
list_views.append(tuple((view_ids[0],view_ids[1])))
list_views.append(tuple((view_ids[0],view_ids[3])))
elif threshold_1 < lower_threshold or threshold_3 < lower_threshold:
list_views.append(tuple((view_ids[0],view_ids[1])))
list_views.append(tuple((view_ids[0],view_ids[3])))
list_views.append(tuple((view_ids[2],view_ids[1])))
list_views.append(tuple((view_ids[2],view_ids[3])))
elif num_vis==4:
list_views.append(tuple((view_ids[0],view_ids[1])))
list_views.append(tuple((view_ids[0],view_ids[3])))
list_views.append(tuple((view_ids[2],view_ids[1])))
list_views.append(tuple((view_ids[2],view_ids[3])))
return list_views
def merge_views(args):
view_ids=args.view_ids
max_len=0
hue=0.
C_substack=[]
if args.thresholds_dir is None:
print(view_ids)
list_views = get_visible_pairs(args.base_indir,args.substack_id,view_ids)
else:
list_views = get_visible_pairs_from_dir(args.thresholds_dir, args.substack_id, view_ids)
substack = SubStack(args.base_indir+'/'+view_ids[0],args.substack_id)
valid_pairs = filter_valid_pairs(args,substack,list_views)
if len(valid_pairs.keys())==1:
C_substack+=valid_pairs.values()[0]
else:
keys_view_1=[]
keys_view_3=[]
for view_key in valid_pairs.keys():
if view_key[0] == view_ids[2]:
keys_view_3.append(view_key)
elif view_key[0] == view_ids[0]:
keys_view_1.append(view_key)
if len(valid_pairs)==2:
if len(keys_view_1) == 2 or len(keys_view_3) == 2:
total_list=compute_fusion(substack,valid_pairs.values()[0],valid_pairs.values()[1],args.max_distance,match_distance=args.match_distance,verbose=args.verbose)
C_substack+=total_list
elif len(keys_view_1) == 1 and len(keys_view_3) == 1:
C_view_3_t=transform_markers(args,substack,valid_pairs[keys_view_3[0]], view_ids)
total_list=compute_fusion(substack,valid_pairs[keys_view_1[0]],C_view_3_t,args.max_distance,match_distance=args.match_distance,verbose=args.verbose)
C_substack+=total_list
else:
raise Exception('not valid list of views %s'%(str(valid_pairs)))
elif len(valid_pairs)==3:
if len(keys_view_1) == 2:
C_view_1=compute_fusion(substack,valid_pairs[keys_view_1[0]],valid_pairs[keys_view_1[1]],args.max_distance,match_distance=args.match_distance,verbose=args.verbose)
C_view_3_t=transform_markers(args,substack,valid_pairs[keys_view_3[0]], view_ids)
total_list=compute_fusion(substack,C_view_1,C_view_3_t,args.max_distance,match_distance=args.match_distance,verbose=args.verbose)
C_substack+=total_list
elif len(keys_view_3) == 2:
C_view_3=compute_fusion(substack,valid_pairs[keys_view_3[0]],valid_pairs[keys_view_3[1]],args.max_distance,match_distance=args.match_distance,verbose=args.verbose)
C_view_3_t=transform_markers(args,substack,C_view_3, view_ids)
C_view_1=valid_pairs[keys_view_1[0]]
total_list=compute_fusion(substack,C_view_1,C_view_3_t,args.max_distance,match_distance=args.match_distance,verbose=args.verbose)
C_substack+=total_list
else:
raise Exception('not valid list of views %s'%(str(valid_pairs)))
elif len(valid_pairs)==4:
if len(keys_view_1) == 2 and len(keys_view_3) == 2:
C_view_1=compute_fusion(substack,valid_pairs[keys_view_1[0]],valid_pairs[keys_view_1[1]],args.max_distance,match_distance=args.match_distance,verbose=args.verbose)
C_view_3=compute_fusion(substack,valid_pairs[keys_view_3[0]],valid_pairs[keys_view_3[1]],args.max_distance,match_distance=args.match_distance,verbose=args.verbose)
C_view_3_t=transform_markers(args,substack,C_view_3, view_ids)
total_list=compute_fusion(substack,C_view_1,C_view_3_t,args.max_distance,match_distance=args.match_distance,verbose=args.verbose)
C_substack+=total_list
else:
raise Exception('not valid list of views %s'%(str(valid_pairs)))
mkdir_p(args.outdir+'/'+args.substack_id)
if len(C_substack)>0:
C_substack = filter_outside_markers(C_substack,substack)
substack.save_markers(args.outdir+'/'+args.substack_id+'/ms.marker', C_substack, floating_point=True)
def get_visible_pairs(base_indir, substack_id, view_ids, lower_threshold=30.):
num_vis = 0
for view_id in view_ids:
substack = SubStack(base_indir + '/' + view_id, args.substack_id)
patch = substack.get_volume()
histogram = np.histogram(patch, bins=256, range=(0, 256))[0]
thresholds = threshold.multi_kapur(histogram, 2)
if thresholds[1] >= lower_threshold:
num_vis += 1
threshold_0 = thresholds[0]
threshold_1 = thresholds[1]
threshold_2 = thresholds[2]
threshold_3 = thresholds[3]
list_views = []
if num_vis == 0:
return list_views
elif num_vis == 1:
if threshold_0 >= lower_threshold:
list_views.append(tuple((view_ids[0], view_ids[1])))
list_views.append(tuple((view_ids[0], view_ids[3])))
if threshold_1 >= lower_threshold:
list_views.append(tuple((view_ids[0], view_ids[1])))
list_views.append(tuple((view_ids[2], view_ids[1])))
if threshold_2 >= lower_threshold:
list_views.append(tuple((view_ids[2], view_ids[1])))
list_views.append(tuple((view_ids[2], view_ids[3])))
if threshold_3 >= lower_threshold:
list_views.append(tuple((view_ids[0], view_ids[3])))
list_views.append(tuple((view_ids[2], view_ids[3])))
elif num_vis == 2:
if (threshold_0 >= lower_threshold and threshold_2 >= lower_threshold
) or (threshold_1 >= lower_threshold
and threshold_3 >= lower_threshold):
list_views.append(tuple((view_ids[0], view_ids[1])))
list_views.append(tuple((view_ids[0], view_ids[3])))
list_views.append(tuple((view_ids[2], view_ids[1])))
list_views.append(tuple((view_ids[2], view_ids[3])))
elif threshold_0 >= lower_threshold and threshold_1 >= lower_threshold:
list_views.append(tuple((view_ids[0], view_ids[1])))
elif threshold_0 >= lower_threshold and threshold_3 >= lower_threshold:
list_views.append(tuple((view_ids[0], view_ids[3])))
elif threshold_2 >= lower_threshold and threshold_1 >= lower_threshold:
list_views.append(tuple((view_ids[2], view_ids[1])))
elif threshold_2 >= lower_threshold and threshold_3 >= lower_threshold:
list_views.append(tuple((view_ids[2], view_ids[3])))
elif num_vis == 3:
if threshold_0 < lower_threshold:
list_views.append(tuple((view_ids[2], view_ids[1])))
list_views.append(tuple((view_ids[2], view_ids[3])))
elif threshold_2 < lower_threshold:
list_views.append(tuple((view_ids[0], view_ids[1])))
list_views.append(tuple((view_ids[0], view_ids[3])))
elif threshold_1 < lower_threshold or threshold_3 < lower_threshold:
list_views.append(tuple((view_ids[0], view_ids[1])))
list_views.append(tuple((view_ids[0], view_ids[3])))
list_views.append(tuple((view_ids[2], view_ids[1])))
list_views.append(tuple((view_ids[2], view_ids[3])))
elif num_vis == 4:
list_views.append(tuple((view_ids[0], view_ids[1])))
list_views.append(tuple((view_ids[0], view_ids[3])))
list_views.append(tuple((view_ids[2], view_ids[1])))
list_views.append(tuple((view_ids[2], view_ids[3])))
return list_views
def merge_views(args):
view_ids = args.view_ids
max_len = 0
hue = 0.
C_substack = []
if args.thresholds_dir is None:
print(view_ids)
list_views = get_visible_pairs(args.base_indir, args.substack_id,
view_ids)
else:
list_views = get_visible_pairs_from_dir(args.thresholds_dir,
args.substack_id, view_ids)
substack = SubStack(args.base_indir + '/' + view_ids[0], args.substack_id)
valid_pairs = filter_valid_pairs(args, substack, list_views)
if len(valid_pairs.keys()) == 1:
C_substack += valid_pairs.values()[0]
else:
keys_view_1 = []
keys_view_3 = []
for view_key in valid_pairs.keys():
if view_key[0] == view_ids[2]:
keys_view_3.append(view_key)
elif view_key[0] == view_ids[0]:
keys_view_1.append(view_key)
if len(valid_pairs) == 2:
if len(keys_view_1) == 2 or len(keys_view_3) == 2:
total_list = compute_fusion(substack,
valid_pairs.values()[0],
valid_pairs.values()[1],
args.max_distance,
match_distance=args.match_distance,
verbose=args.verbose)
C_substack += total_list
elif len(keys_view_1) == 1 and len(keys_view_3) == 1:
C_view_3_t = transform_markers(args, substack,
valid_pairs[keys_view_3[0]],
view_ids)
total_list = compute_fusion(substack,
valid_pairs[keys_view_1[0]],
C_view_3_t,
args.max_distance,
match_distance=args.match_distance,
verbose=args.verbose)
C_substack += total_list
else:
raise Exception('not valid list of views %s' % (str(valid_pairs)))
elif len(valid_pairs) == 3:
if len(keys_view_1) == 2:
C_view_1 = compute_fusion(substack,
valid_pairs[keys_view_1[0]],
valid_pairs[keys_view_1[1]],
args.max_distance,
match_distance=args.match_distance,
verbose=args.verbose)
C_view_3_t = transform_markers(args, substack,
valid_pairs[keys_view_3[0]],
view_ids)
total_list = compute_fusion(substack,
C_view_1,
C_view_3_t,
args.max_distance,
match_distance=args.match_distance,
verbose=args.verbose)
C_substack += total_list
elif len(keys_view_3) == 2:
C_view_3 = compute_fusion(substack,
valid_pairs[keys_view_3[0]],
valid_pairs[keys_view_3[1]],
args.max_distance,
match_distance=args.match_distance,
verbose=args.verbose)
C_view_3_t = transform_markers(args, substack, C_view_3, view_ids)
C_view_1 = valid_pairs[keys_view_1[0]]
total_list = compute_fusion(substack,
C_view_1,
C_view_3_t,
args.max_distance,
match_distance=args.match_distance,
verbose=args.verbose)
C_substack += total_list
else:
raise Exception('not valid list of views %s' % (str(valid_pairs)))
elif len(valid_pairs) == 4:
if len(keys_view_1) == 2 and len(keys_view_3) == 2:
C_view_1 = compute_fusion(substack,
valid_pairs[keys_view_1[0]],
valid_pairs[keys_view_1[1]],
args.max_distance,
match_distance=args.match_distance,
verbose=args.verbose)
C_view_3 = compute_fusion(substack,
valid_pairs[keys_view_3[0]],
valid_pairs[keys_view_3[1]],
args.max_distance,
match_distance=args.match_distance,
verbose=args.verbose)
C_view_3_t = transform_markers(args, substack, C_view_3, view_ids)
total_list = compute_fusion(substack,
C_view_1,
C_view_3_t,
args.max_distance,
match_distance=args.match_distance,
verbose=args.verbose)
C_substack += total_list
else:
raise Exception('not valid list of views %s' % (str(valid_pairs)))
mkdir_p(args.outdir + '/' + args.substack_id)
if len(C_substack) > 0:
C_substack = filter_outside_markers(C_substack, substack)
substack.save_markers(args.outdir + '/' + args.substack_id +
'/ms.marker',
C_substack,
floating_point=True)
def transform_substack(indir, tensorimage, substack_id, R, t, extramargin, outdir=None, invert=False, save_tiff=False, save_hdf5=False):
"""
Method that applies a previously estimated rigid transformation to a specific substack.
Parameters
----------
indir : str
substack dir of the input view
tensorimage : str
the whole tensor in hdf5 format
substack_id : str
id of the substack that will be transformed
R : numpy array of shape (3, 3)
rotational component of the estimated rigid transformation
t : numpy array of shape (3)
translational component of the estimated rigid transformation
extramargin : int
extramargin used to extract the transformed substack from tensorimage
outdir : str
output directory where the transformed substack will be saved (Default: None)
invert : boolean
if True the tranformation is inverted (Default: False)
save_tiff : boolean
save the transformed substack in a stack of tiff slices (Default: False)
save_hdf5 : boolean
save the transformed substack as a hdf5 tensor (Default: False)
Returns
-------
pixels_transformed_input: numpy tensor
tensor of the transformed substack
"""
ss = SubStack(indir, substack_id)
input_stack_file = tensorimage
hf5 = tables.openFile(input_stack_file, 'r')
full_D, full_H, full_W = hf5.root.full_image.shape
X0,Y0,Z0 = ss.info['X0'], ss.info['Y0'], ss.info['Z0']
origin = (Z0, Y0, X0)
H,W,D = ss.info['Height'], ss.info['Width'], ss.info['Depth']
or_ss_shape = (D,H,W)
offset_W=int((3**(1/2.0)*W + W/2.0 - W)/2.0)
offset_H=int((3**(1/2.0)*H + H/2.0 - H)/2.0)
offset_D=int((3**(1/2.0)*D + D/2.0 - D)/2.0)
if offset_W < extramargin:
offset_W = extramargin
if offset_H < extramargin:
offset_H = extramargin
if offset_D < extramargin:
offset_D = extramargin
offset_D_left = offset_D if int(origin[0] - offset_D) > 0 else origin[0]
offset_H_left = offset_H if int(origin[1] - offset_H) > 0 else origin[1]
offset_W_left = offset_W if int(origin[2] - offset_W) > 0 else origin[2]
offset_D_right = offset_D if int(origin[0] + or_ss_shape[0] + offset_D) <= full_D else full_D - (origin[0] + or_ss_shape[0])
offset_H_right = offset_H if int(origin[1] + or_ss_shape[1] + offset_H) <= full_H else full_H - (origin[1] + or_ss_shape[1])
offset_W_right = offset_W if int(origin[2] + or_ss_shape[2] + offset_W) <= full_W else full_W - (origin[2] + or_ss_shape[2])
pixels_input = hf5.root.full_image[origin[0] - offset_D_left:origin[0] + or_ss_shape[0] + offset_D_right,
origin[1] - offset_H_left:origin[1] + or_ss_shape[1] + offset_H_right,
origin[2] - offset_W_left:origin[2] + or_ss_shape[2] + offset_W_right]
exmar_D_left = 0 if offset_D_left == origin[0] else extramargin
exmar_H_left = 0 if offset_H_left == origin[1] else extramargin
exmar_W_left = 0 if offset_W_left == origin[2] else extramargin
depth_target, height_target, width_target = or_ss_shape[0] + 2 * extramargin, or_ss_shape[1] + 2 * extramargin, or_ss_shape[2] + 2 * extramargin #new
depth_input, height_input, width_input = pixels_input.shape[0], pixels_input.shape[1], pixels_input.shape[2]
pixels_transformed_input = np.zeros((depth_target,height_target,width_target), dtype=np.uint8)
total_start = timeit.default_timer()
coords_2d_target = np.vstack(np.indices((width_target,height_target)).swapaxes(0,2).swapaxes(0,1))
invR = R.T
if invert:
t = -np.dot(invR, t)
invR = R
invR_2d_transpose = np.transpose(np.dot(invR[:, 0:2], np.transpose(coords_2d_target - t[0:2])))
offset_coords = np.array([[offset_W_left - exmar_W_left, offset_H_left - exmar_H_left, offset_D_left - exmar_D_left]]*invR_2d_transpose.shape[0])#new
for z in xrange(0, depth_target, 1):
R_t_3d = np.transpose(invR_2d_transpose + invR[:, 2] * (z - t[2]) + offset_coords)
good_indices = np.array(range(R_t_3d.shape[1]))
good_indices = good_indices[(R_t_3d[0, :] > 0) * (R_t_3d[1, :] > 0) * (R_t_3d[2, :] > 0) * (R_t_3d[0, :] < (width_input - 1)) * (R_t_3d[1, :] < (height_input - 1)) * (R_t_3d[2, :] < (depth_input - 1))]
R_t_3d = R_t_3d.take(good_indices,axis=1)
R_t_3d = np.round(R_t_3d).astype(int)
coords_2d_target_tmp = coords_2d_target.take(good_indices, axis=0)
coords_3d_target_tmp = np.hstack((coords_2d_target_tmp, np.ones((coords_2d_target_tmp.shape[0], 1)).astype(int)*z))
pixels_transformed_input[coords_3d_target_tmp[:, 2], coords_3d_target_tmp[:, 1], coords_3d_target_tmp[:, 0]] = pixels_input[R_t_3d[2, :], R_t_3d[1, :], R_t_3d[0, :]]
total_stop = timeit.default_timer()
print ("total time transformation stack:%s "%(str(total_stop - total_start)))
pixels_transformed_input = np.array(pixels_transformed_input, dtype=np.uint8)
if save_tiff or save_hdf5:
mkdir_p(outdir)
if save_tiff:
minz = int(ss.info['Files'][0].split("/")[-1].split('_')[-1].split('.tif')[0])
_prefix = '_'.join(ss.info['Files'][0].split("/")[-1].split('_')[0:-1])+'_'
substack_outdir = outdir + '/' + substack_id
imtensor.save_tensor_as_tif(pixels_transformed_input, substack_outdir, minz, prefix=_prefix)
if save_hdf5:
target_shape = (depth_target, height_target, width_target)
atom = tables.UInt8Atom()
h5f = tables.openFile(outdir + '/' + ss.substack_id + '.h5', 'w')
ca = h5f.createCArray(h5f.root, 'full_image', atom, target_shape)
for z in xrange(0, depth_target, 1):
ca[z, :, :] = pixels_transformed_input[z,:,:]
h5f.close()
return pixels_transformed_input