test_net = pygt.init_testnet(test_net_file, trained_model=caffemodels[-1][1], test_device=test_device)
# Load the datasets
hdf5_raw_file = '../dataset_06/fibsem_medulla_7col/tstvol-520-1-h5/img_normalized.h5'
hdf5_gt_file = '../dataset_06/fibsem_medulla_7col/tstvol-520-1-h5/groundtruth_seg.h5'
hdf5_aff_file = '../dataset_06/fibsem_medulla_7col/tstvol-520-1-h5/groundtruth_aff.h5'
hdf5_raw = h5py.File(hdf5_raw_file, 'r')
hdf5_gt = h5py.File(hdf5_gt_file, 'r')
hdf5_aff = h5py.File(hdf5_aff_file, 'r')
hdf5_raw_ds = pygt.normalize(np.asarray(hdf5_raw[hdf5_raw.keys()[0]]).astype(float32), -1, 1)
hdf5_gt_ds = np.asarray(hdf5_gt[hdf5_gt.keys()[0]]).astype(float32)
hdf5_aff_ds = np.asarray(hdf5_aff[hdf5_aff.keys()[0]]).astype(float32)
datasets = []
for i in range(0,1):
dataset = {}
dataset['data'] = hdf5_raw_ds[None, i, :]
datasets += [dataset]
pred_array = pygt.process(test_net, datasets)
pygt.dump_tikzgraph_maps(test_net, 'dump')
outhdf5 = h5py.File('test_out.h5', 'w')
outdset = outhdf5.create_dataset('main', np.shape(pred_array)[1:], np.float32, data=pred_array)
outhdf5.close()
