filesRaw = glob.glob(Raw_path)
# In[6]:
for fname in filesRaw:
if os.path.exists(fname) == True:
if os.path.exists(basedirResults2Dextended + '_' +
os.path.basename(fname)) == False:
print(basedirResults2Dextended + '_' + os.path.basename(fname))
print(fname)
y = imread(fname)
restored = RestorationModel.predict(y, axes, n_tiles=(1, 4, 4))
#restored = RestorationModel.predict(y, axes, n_tiles = (1,4,8)) #n_tiles is for the decomposition of the image in (z,y,x). (1,2,2) will work with light images. Less tiles we have, faster the calculation is
projection = ProjectionModel.predict(restored,
axes,
n_tiles=(1, 1, 2))
#projection = ProjectionModel.predict(restored, axes, n_tiles = (1,1,2)) #n_tiles is for the decomposition of the image in (z,y,x). There is overlapping in the decomposition wich is managed by the program itself
axes_restored = axes.replace(ProjectionModel.proj_params.axis, '')
#restored = restored.astype('uint8') # if prediction and projection running at the same time
#restored = restored.astype('uint16') # if projection training set creation or waiting for a future projection
projection = projection.astype('uint8')
#save_tiff_imagej_compatible((basedirResults3Dextended + os.path.basename(fname)) , restored, axes)
save_tiff_imagej_compatible(
(basedirResults2Dextended + '_' + os.path.basename(fname)),
projection, axes_restored)
# In[]:
from csbdeep.utils import Path
# In[6]:
for fname in filesRaw:
if os.path.exists(fname) == True:
if os.path.exists(basedirResults3Dextended +
os.path.basename(fname)) == False or os.path.exists(
basedirResults2Dextended + '_' +
os.path.basename(fname)) == False:
print(fname)
y = imread(fname)
restored = RestorationModel.predict(
y, axes, n_tiles=(1, 2, 4)
) #n_tiles is for the decomposition of the image in (z,y,x). (1,2,2) will work with light images. Less tiles we have, faster the calculation is
projection = ProjectionModel.predict(
restored, axes, n_tiles=(1, 1, 1)
) #n_tiles is for the decomposition of the image in (z,y,x). There is overlapping in the decomposition wich is managed by the program itself
axes_restored = axes.replace(ProjectionModel.proj_params.axis, '')
#restored = restored.astype('uint8') # if prediction and projection running at the same time
restored = restored.astype(
'uint16'
) # if projection training set creation or waiting for a future projection
projection = projection.astype('uint8')
save_tiff_imagej_compatible(
(basedirResults3Dextended + os.path.basename(fname)), restored,
axes)
save_tiff_imagej_compatible(
(basedirResults2Dextended + '_' + os.path.basename(fname)),
projection, axes_restored)
# In[]:
from csbdeep.utils import Path, download_and_extract_zip_file, plot_some
from csbdeep.io import save_tiff_imagej_compatible
from csbdeep.models import ProjectionCARE
# In[2]:
basedirLow = '/local/u934/private/v_kapoor/ProjectionTraining/MasterLow/NotsoLow/'
basedirResults = '/local/u934/private/v_kapoor/ProjectionTraining/MasterLow/NetworkProjections'
ModelName = 'DrosophilaDenoisingProjection'
BaseDir = '/local/u934/private/v_kapoor/CurieDeepLearningModels/'
# In[3]:
model = ProjectionCARE(config=None, name=ModelName, basedir=BaseDir)
# In[4]:
Raw_path = os.path.join(basedirLow, '*tif')
Path(basedirResults).mkdir(exist_ok=True)
axes = 'ZYX'
filesRaw = glob.glob(Raw_path)
filesRaw.sort
for fname in filesRaw:
x = imread(fname)
print('Saving file' + basedirResults + '%s_' + os.path.basename(fname))
restored = model.predict(x, axes, n_tiles=(1, 4, 4))
axes_restored = axes.replace(model.proj_params.axis, '')
save_tiff_imagej_compatible(
(basedirResults + '%s_' + os.path.basename(fname)) % model.name,
restored, axes_restored)