def test_model_projection_predict(tmpdir, config):
rng = np.random.RandomState(42)
K.clear_session()
model = ProjectionCARE(config, basedir=None)
axes = config.axes
proj_axis = model.proj_params.axis
def _predict(imdims, axes):
img = rng.uniform(size=imdims)
n_tiles = [1] * len(axes)
ax = axes_dict(axes)
if config.probabilistic:
prob = model.predict_probabilistic(img, axes, None, None)
mean, scale = prob.mean(), prob.scale()
assert mean.shape == scale.shape
else:
mean = model.predict(img, axes, None, None)
n_tiles[ax['X']] = 3
n_tiles[ax['Y']] = 2
mean_tiled = model.predict(img, axes, None, None, n_tiles=n_tiles)
error_max = np.max(np.abs(mean - mean_tiled))
# print(n_tiles, error_max)
assert error_max < 1e-3
with pytest.raises(ValueError):
n_tiles[ax[proj_axis]] = 2
model.predict(img, axes, None, None, n_tiles=n_tiles)
shape_out = list(imdims)
if 'C' in axes:
shape_out[ax['C']] = config.n_channel_out
elif config.n_channel_out > 1:
shape_out.append(config.n_channel_out)
del shape_out[ax[proj_axis]]
assert tuple(shape_out) == mean.shape
imdims = list(rng.randint(30, 50, size=config.n_dim))
# imdims = [10,1024,1024]
imdims = [(d // div_n) * div_n
for d, div_n in zip(imdims, model._axes_div_by(axes))]
if config.n_channel_in == 1:
_predict(imdims, axes=axes.replace('C', ''))
channel = rng.randint(0, config.n_dim)
imdims.insert(channel, config.n_channel_in)
_axes = axes.replace('C', '')
_axes = _axes[:channel] + 'C' + _axes[channel:]
_predict(imdims, axes=_axes)
basedirResults3D = basedir + '/Restored'
basedirResults2D = basedir + '/Projected'
basedirResults3Dextended = basedirResults3D + '/Restored'
basedirResults2Dextended = basedirResults2D + '/Projected'
Model_Dir = '/run/media/sancere/DATA/Lucas_Model_to_use/CARE/'
# In[3]:
RestorationModel = 'CARE_restoration_Borealis_Bin1'
ProjectionModel = 'CARE_projection_Borealis_Bin1'
RestorationModel = CARE(config=None, name=RestorationModel, basedir=Model_Dir)
ProjectionModel = ProjectionCARE(config=None,
name=ProjectionModel,
basedir=Model_Dir)
# In[5]:
Path(basedirResults3D).mkdir(exist_ok=True)
Path(basedirResults2D).mkdir(exist_ok=True)
Raw_path = os.path.join(basedir, '*TIF') #tif or TIF be careful
axes = 'ZYX' #projection axes : 'YX'
filesRaw = glob.glob(Raw_path)
# In[6]:
n_channel_out,
unet_n_depth=4,
train_epochs=50,
train_steps_per_epoch=400,
train_batch_size=16,
train_reduce_lr={
'patience': 5,
'factor': 0.5
})
print(config)
vars(config)
# In[5]:
model = ProjectionCARE(
config,
'DrosophilaDenoisingProjection',
basedir='/local/u934/private/v_kapoor/CurieDeepLearningModels')
# In[6]:
history = model.train(X, Y, validation_data=(X_val, Y_val))
# In[7]:
# In[8]:
# In[9]:
model.export_TF()
basedirResults3D = basedir + '/Restored'
basedirResults2D = basedir + '/Projected'
basedirResults3Dextended = basedirResults3D + '/Restored'
basedirResults2Dextended = basedirResults2D + '/Projected'
Model_Dir = '/run/media/sancere/DATA/Lucas_Model_to_use/CARE/'
#Test_change_email_adress_to_see_commit
# In[3]:
RestorationModel = 'CARE_restoration_SpinWideFRAP4_Bin1_3Gfp'
ProjectionModel = 'CARE_projection_SpinWideFRAP4_Bin1_3Gfp'
RestorationModel = CARE(config=None, name=RestorationModel, basedir=Model_Dir)
ProjectionModel = ProjectionCARE(config=None,
name=ProjectionModel,
basedir=Model_Dir)
# In[5]:
#Path(basedirResults3D).mkdir(exist_ok = True)
Path(basedirResults2D).mkdir(exist_ok=True)
Raw_path = os.path.join(basedir, '*TIF') #tif or TIF be careful
axes = 'ZYX' #projection axes : 'YX'
filesRaw = glob.glob(Raw_path)
# In[6]:
n_channel_in,
n_channel_out,
unet_n_depth=4,
train_epochs=150,
train_steps_per_epoch=120,
train_batch_size=100,
train_reduce_lr={
'patience': 5,
'factor': 0.5
})
print(config)
vars(config)
# In[5]:
model = ProjectionCARE(config=config, name=ModelName, basedir=BaseDir)
# model.load_weights(ModelDir + 'CARE_projection_Borealis_Bin2_AudeData_Second' + '/' + 'weights_best.h5')
# In[6]:
history = model.train(X, Y, validation_data=(X_val, Y_val))
# In[7]:
# In[8]:
# In[9]:
model.export_TF()
# In[ ]:
from tifffile import imread
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,
