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)
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 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 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
