def segment_2d(model_fname, contrast_type, input_size, im_in):
"""Segment data using 2D convolutions."""
seg_model = nn_architecture_seg(height=input_size[0],
width=input_size[1],
depth=2 if contrast_type != 't2' else 3,
features=32,
batchnorm=False,
dropout=0.0)
seg_model.load_weights(model_fname)
seg_crop = zeros_like(im_in, dtype=np.uint8)
data_norm = im_in.data
x_cOm, y_cOm = None, None
for zz in range(im_in.dim[2]):
pred_seg = seg_model.predict(np.expand_dims(
np.expand_dims(data_norm[:, :, zz], -1), 0),
batch_size=BATCH_SIZE)[0, :, :, 0]
pred_seg_th = (pred_seg > 0.5).astype(int)
pred_seg_pp = post_processing_slice_wise(pred_seg_th, x_cOm, y_cOm)
seg_crop.data[:, :, zz] = pred_seg_pp
if 1 in pred_seg_pp:
x_cOm, y_cOm = center_of_mass(pred_seg_pp)
x_cOm, y_cOm = np.round(x_cOm), np.round(y_cOm)
return seg_crop.data
def segment_2d(model_fname, contrast_type, input_size, im_in):
"""Segment data using 2D convolutions."""
seg_model = nn_architecture_seg(height=input_size[0],
width=input_size[1],
depth=2 if contrast_type != 't2' else 3,
features=32,
batchnorm=False,
dropout=0.0)
seg_model.load_weights(model_fname)
seg_crop = zeros_like(im_in, dtype=np.uint8)
data_norm = im_in.data
x_cOm, y_cOm = None, None
for zz in range(im_in.dim[2]):
pred_seg = seg_model.predict(np.expand_dims(np.expand_dims(data_norm[:, :, zz], -1), 0),
batch_size=BATCH_SIZE)[0, :, :, 0]
pred_seg_th = (pred_seg > 0.5).astype(int)
pred_seg_pp = post_processing_slice_wise(pred_seg_th, x_cOm, y_cOm)
seg_crop.data[:, :, zz] = pred_seg_pp
if 1 in pred_seg_pp:
x_cOm, y_cOm = center_of_mass(pred_seg_pp)
x_cOm, y_cOm = np.round(x_cOm), np.round(y_cOm)
return seg_crop.data
def segment_2d(model_fname, contrast_type, input_size, fname_in, fname_out):
"""Segment data using 2D convolutions."""
seg_model = nn_architecture_seg(height=input_size[0],
width=input_size[1],
depth=2 if contrast_type != 't2' else 3,
features=32,
batchnorm=False,
dropout=0.0)
seg_model.load_weights(model_fname)
image_normalized = Image(fname_in)
seg_crop = image_normalized.copy()
seg_crop.data *= 0
seg_crop.changeType('uint8')
data_norm = image_normalized.data
x_cOm, y_cOm = None, None
# for zz in list(reversed(range(image_normalized.dim[2]))):
for zz in range(image_normalized.dim[2]):
pred_seg = seg_model.predict(
np.expand_dims(np.expand_dims(data_norm[:, :, zz], -1),
0))[0, :, :, 0]
pred_seg_th = (pred_seg > 0.5).astype(int)
pred_seg_pp = post_processing_slice_wise(pred_seg_th, x_cOm, y_cOm)
seg_crop.data[:, :, zz] = pred_seg_pp
if 1 in pred_seg_pp:
x_cOm, y_cOm = center_of_mass(pred_seg_pp)
x_cOm, y_cOm = np.round(x_cOm), np.round(y_cOm)
seg_crop.setFileName(fname_out)
seg_crop.save()
return seg_crop.data
parser.add_argument("binary_seg")
args = parser.parse_args()
input_dir = args.input_dir
output_dir = args.output_dir
seg_model_fname = args.seg_model_fname
contrast_type = args.contrast_type
binary_seg = True if args.binary_seg == 'True' else False
# build model
BATCH_SIZE = 4
input_size = (64, 64)
seg_model = nn_architecture_seg(height=input_size[0],
width=input_size[1],
depth=2,
features=32,
batchnorm=True,
dropout=0.0)
seg_model.load_weights(seg_model_fname)
# segment image
for name in os.listdir(input_dir):
if not '.DS_Store' in name:
fname_image = input_dir + name
parent, stem, ext = sct.extract_fname(fname_image)
# import image
image = Image(fname_image)
image.save(output_dir + stem + '_im' + ext)
# crop image