'..', '04-conv_net', 'model_export', 'dataset_mix',
'2019-08-13_22-25-12_1_3_train_samples_fiji_and_mathematica_segmentations')
# Specify the standardization mode
standardization_mode = 'per_sample'
# Specify the linear output scaling factor !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
linear_output_scaling_factor = 1 #1e11#409600000000
# Specify if the results are saved
save_results = False
#%%############################################################################
# Initialize the CNN
###############################################################################
cnn = CNN(linear_output_scaling_factor=linear_output_scaling_factor,
standardization_mode=standardization_mode)
cnn.load_model_json(model_import_path, 'model_json', 'best_weights')
#%%############################################################################
# Predict the density-map
###############################################################################
table = []
table.append([
'Category', 'Spheroid',
'Ground-Truth number of cells (cell-volumes > 3um^3)',
'Number of cells (predicted)', 'Absolute difference',
'Percentual difference'
])
subdirs1 = get_immediate_subdirectories(path_to_data)
for subdir1 in subdirs1:
#category = 'NPC1'
#spheroid_name = 'C3-7.nrrd'
#path_to_spheroid = os.path.join('..', '..', '..', 'Datensaetze', 'Aufnahmen_und_Segmentierungen', 'Datensatz2', category, spheroid_name)
category = 'none'
spheroid_name = 'X_scaled.nrrd'
path_to_spheroid = os.path.join('..', '..', '..', 'Datensaetze',
'OpenSegSPIM_Beispieldaten', 'Neurosphere',
spheroid_name)
#path_to_spheroid = os.path.join('Skalierung', 'NPC1', 'C3-2-1_1-3_upper.nrrd')
#%%############################################################################
# Initialize the CNN
###############################################################################
cnn = CNN(linear_output_scaling_factor=linear_output_scaling_factor,
standardization_mode=standardization_mode)
cnn.load_model_json(model_import_path, 'model_json', 'best_weights')
#%%############################################################################
# Predict the density-map
###############################################################################
spheroid_new, density_map, num_of_cells = cnn.predict_density_map(
path_to_spheroid=path_to_spheroid,
patch_sizes=patch_sizes,
strides=strides,
border=cut_border,
padding=padding)
plt.figure()
plt.imshow(spheroid_new[int(spheroid_new.shape[0] / 2), ])
plt.figure()
plt.imshow(density_map[int(density_map.shape[0] / 2), ])
# Specify which model is used
model_import_path = os.path.join('..', '04-conv_net', 'model_export', 'dataset1', '2019-08-10_09-13-46_1_3_train_samples_fiji_SEGMENTATIONS_crossentropy_256_epochs')
# Specify the standardization mode
standardization_mode = 'per_sample'
# Specify the linear output scaling factor !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
linear_output_scaling_factor = 1#1e11#409600000000
colocalization_threshold = 10.0
#%%############################################################################
# Initialize the CNN
###############################################################################
cnn = CNN(linear_output_scaling_factor=linear_output_scaling_factor,
standardization_mode=standardization_mode)
cnn.load_model_json(model_import_path, 'model_json', 'best_weights')
table = []
table.append(['Cultivation-period', 'Spheroid', 'Ground-Truth number of cells (cell-volumes > 3um^3)', 'Number of cells (predicted)', 'Absolute difference', 'Percentual difference', 'Number of colocalized cells', 'Percentual number of colocalized cells'])
for directory in os.listdir(path_to_data):
data_dir = os.path.join(path_to_data, directory, 'untreated')
if os.path.exists(data_dir):
for filename in os.listdir(data_dir):
if filename.endswith('.nrrd'):
spheroid_name = os.path.splitext(filename)[0]
#print('Actual file: ', spheroid_name)
for subdir in os.listdir(data_dir):
res_dir = os.path.join(data_dir, subdir)
if(os.path.isdir(res_dir)):
#%%############################################################################
# Plot the dataset distributions
###############################################################################
if plt_hist == True:
plot_dataset_histogram(path_to_dataset=path_to_dataset,
data_list=train_list)
plot_dataset_histogram(path_to_dataset=path_to_dataset, data_list=val_list)
plot_dataset_histogram(path_to_dataset=path_to_dataset,
data_list=test_list)
#%%############################################################################
# Define the model
###############################################################################
cnn = CNN(linear_output_scaling_factor=linear_output_scaling_factor,
standardization_mode=standardization_mode)
cnn.define_model(input_shape=input_shape,
filters_exp=filters_exp,
kernel_size=kernel_size,
pool_size=pool_size,
hidden_layer_activation=hidden_layer_activation,
output_layer_activation=output_layer_activation,
padding=padding)
#cnn.define_unet(input_shape=input_shape, n_filters=8, kernel_size=3,
# batchnorm=batchnorm, hidden_layer_activation=hidden_layer_activation,
# output_layer_activation=None, pool_size=2, padding=padding)
#cnn.define_unet(input_shape=input_shape, n_filters=n_filters, kernel_size=kernel_size, kernel_initializer=kernel_initializer,
# pool_size=pool_size, hidden_layer_activation=hidden_layer_activation, alpha=alpha, batchnorm=batchnorm,
data_list=train_files,
hist_export_file=os.path.join(
model_export_path, 'train_hist.png'))
plot_dataset_histogram(path_to_dataset=path_to_dataset,
data_list=val_files,
hist_export_file=os.path.join(
model_export_path, 'val_hist.png'))
plot_dataset_histogram(path_to_dataset=path_to_dataset,
data_list=test_files,
hist_export_file=os.path.join(
model_export_path, 'test_hist.png'))
###############################################################################
# Define the model
###############################################################################
cnn = CNN(linear_output_scaling_factor=linear_output_scaling_factor,
standardization_mode=standardization_mode)
cnn.define_unet(input_shape=input_shape,
n_filters=n_filters,
kernel_size=kernel_size,
pool_size=pool_size,
kernel_initializer=kernel_initializer,
hidden_layer_activation=hidden_layer_activation,
alpha=alpha,
batchnorm_encoder=batchnorm_encoder,
batchnorm_decoder=batchnorm_decoder,
regularization_rate=regularization_rate,
dropout_rate=dropout_rate,
output_layer_activation=output_layer_activation,
upsampling_method=upsampling_method,
padding=padding)
train_spheroids=test_spheroids,
val_spheroids=test_spheroids,
test_spheroids=test_spheroids)
# Load unstandardized test data
X_test_data, y_test_data = datatools.load_data(path_to_dataset=path_to_dataset,
data_list=test_files,
input_shape=data_shape,
standardization_mode=None,
border=border)
###############################################################################
# Load the model
###############################################################################
#cnn.load_model_single_file(import_path, 'model_single')
cnn = CNN(linear_output_scaling_factor=linear_output_scaling_factor,
standardization_mode=standardization_mode)
cnn.load_model_json(model_import_path, 'model_json', weights)
# Evaluate the model on the test data
summary = cnn.compile_model(loss=loss, optimizer=optimizer, metrics=metrics)
test_loss_best_weights = cnn.evaluate_model(X_test=X_test_data,
y_test=y_test_data,
batch_size=batch_size)
print(test_loss_best_weights)
# Export the value of the test-loss
test_loss_export_path = os.path.join('test_loss_best_weights.txt')
with open(test_loss_export_path, 'w') as file:
for l in range(len(test_loss_best_weights)):
file.write(str(test_loss_best_weights[l]) + '\n')
# Read the data
#path_to_nuclei = os.path.join('test_data', '48h-X-C2-untreated_3.nrrd')
path_to_nuclei = os.path.join('..', '..', '..', 'Daten', '72h', 'untreated',
'C2-untreated_4.nrrd')
data, header = nrrd.read(path_to_nuclei)
plt.imshow(data[:, :, 55])
# Load the CNN
# !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
# !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
linear_output_scaling_factor = 1e12
# !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
# !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
standardization_mode = 'per_sample'
cnn = CNN(linear_output_scaling_factor=linear_output_scaling_factor,
standardization_mode=standardization_mode)
import_path = os.path.join(os.getcwd(), 'model_export', '2019-07-19_18-50-39')
cnn.load_model_json(import_path, 'model_json', 'model_weights')
# Generate image patches
size_z = patch_slices = 32
size_y = patch_rows = 32
size_x = patch_cols = 32
stride_z = stride_slices = 16
stride_y = stride_rows = 16
stride_x = stride_cols = 16
session = tf.Session()
patches = impro.gen_patches(session=session,
data=data,
patch_slices=size_z,
patch_rows=size_y,