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)))
