#%%############################################################################
# Load the model
###############################################################################
if load_model == True:
import_path = os.path.join(os.getcwd(), 'model_export',
'2019-06-12_21-46-36')
#cnn.load_model_single_file(import_path, 'model_single')
cnn.load_model_json(import_path, 'model_json', 'model_weights')
#%%############################################################################
# Predict some data
###############################################################################
rand_int = np.random.randint(low=0, high=np.size(X_test_data, axis=0))
X_test = X_test_data[rand_int, ]
y_test = y_test_data[rand_int, ]
y_pred = cnn.predict_sample(X_test)
plt_slice = 8
if border == None:
plt.imshow(X_test[8, :, :])
else:
X_inner = impro.get_inner_slice(X_test, border)
plt.imshow(X_inner[plt_slice, :, :])
plt.imshow(y_test[plt_slice, :, :])
plt.imshow(y_pred[plt_slice, :, :])
print('Number of cells (ground truth): ', np.sum(y_test))
print('Number of cells (predicted): ', np.sum(y_pred))
padding='VALID')
#p = patches[2,15,6,15,:,:]
#plt.imshow(p)
#predictions = np.zeros_like(patches, dtype=np.float32)
#predictions = np.zeros((patches.shape[0], patches.shape[1], patches.shape[2], stride_z, stride_y, stride_x), dtype=np.float32)
predictions = np.zeros((patches.shape[0], patches.shape[1], patches.shape[2],
size_z, size_y, size_x),
dtype=np.float32)
# Predict the density-patches
for zslice in range(patches.shape[0]):
for row in range(patches.shape[1]):
for col in range(patches.shape[2]):
X = patches[zslice, row, col, :]
prediction = cnn.predict_sample(X)
predictions[zslice, row, col, :] = prediction
plt.imshow(predictions[8, 5, 7, 12, :, :])
## Restore the volumes from the patches
nuclei = impro.restore_volume(patches=patches,
border=(8, 8, 8),
output_dim_order='XYZ')
density_map = impro.restore_volume(patches=predictions,
border=(8, 8, 8),
output_dim_order='XYZ')
#
## Plot patch
#pz = 0