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 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):
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):
# The data for the substack proper are readable from the info.json (or .plist)
z = args.z
dz = args.dz
maxX = 3662
maxY = 8249
maxZ = 3646
start_timer = timeit.default_timer()
print('Extracting subtensor')
if (z + dz > maxZ):
print("Z coordinate is out of bound for target file! Exiting.")
return
big_tensor = extract_level(args.infile, z, dz + args.extramargin, args.fps)
# Now for an interactive loop to help catch volumes
quit = False
while (quit == False):
#x, y, dx, dy = getCoords()
x, y, dx, dy = 964, 256, 275, 239
if (x + dx > maxX or y + dy > maxY):
print("Coordinates are out of bound for target file! Exiting.")
return
np_tensor_3d = big_tensor[:, y:y + dy + args.extramargin,
x:x + dx + args.extramargin]
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()
extract_timer = timeit.default_timer()
print("Extraction time: " + str(extract_timer - start_timer))
print('Starting semantic devonvolution of a (' +
str(dx + args.extramargin) + ',' + str(dy + args.extramargin) +
',' + str(dz + args.extramargin) + ') volume starting at (' +
str(x) + ',' + str(y) + ',' + str(z) + ')')
# Importing here to have a clean --help
from keras.models import model_from_json
net_model = model_from_json(
open(args.model + '/architecture.json').read())
net_model.load_weights(args.model + '/weights.h5')
reconstruction = deconvolver.filter_volume(pad(np_tensor_3d, 6),
Xmean,
Xstd,
args.extramargin,
net_model,
4,
do_cython=args.do_cython,
trainfile=args.trainfile)
reconstruct_timer = timeit.default_timer()
print("Reconstruction time: " + str(reconstruct_timer - extract_timer))
volume = simpleSubStack.simpleSubStack()
volume.load_volume(reconstruction)
# save_image requires the full SubStack item, not my rough copy
args.save_image = False
# Without changing mscd.ms, right now, there is no way of controlling the file it goes to write.
# at each iteration it would overwrite the old ms.marker
mscd.ms(volume, args)
meanshift_timer = timeit.default_timer()
print("Mean Shift time: " + str(meanshift_timer - reconstruct_timer))
print("Total time: " + str(meanshift_timer - start_timer))
cont = raw_input(
"Do you want to get another volume in the same z-dz range? [y]/n: "
)
if (cont == 'n' or cont == 'no'):
quit = True
