def test_luna3d_2():
image_dir = utils.get_dir_path('analysis', pathfinder.METADATA_PATH)
image_dir = image_dir + '/test_luna/'
utils.auto_make_dir(image_dir)
id2zyxd = utils_lung.read_luna_annotations(pathfinder.LUNA_LABELS_PATH)
luna_data_paths = [
'/mnt/sda3/data/kaggle-lung/luna_test_patient/1.3.6.1.4.1.14519.5.2.1.6279.6001.943403138251347598519939390311.mhd']
for k, p in enumerate(luna_data_paths):
id = os.path.basename(p).replace('.mhd', '')
print id
img, origin, pixel_spacing = utils_lung.read_mhd(p)
lung_mask = lung_segmentation.segment_HU_scan(img)
annotations = id2zyxd[id]
x, annotations_tf, tf_matrix, lung_mask_out = data_transforms.transform_scan3d(data=img,
pixel_spacing=pixel_spacing,
p_transform=p_transform,
luna_annotations=annotations,
p_transform_augment=None,
luna_origin=origin,
lung_mask=lung_mask,
world_coord_system=True)
y = data_transforms.make_3d_mask_from_annotations(img_shape=x.shape, annotations=annotations_tf, shape='sphere')
for zyxd in annotations_tf:
plot_slice_3d_3(x, lung_mask_out, y, 0, id, idx=zyxd)
plot_slice_3d_3(x, lung_mask_out, y, 1, id, idx=zyxd)
plot_slice_3d_3(x, lung_mask_out, y, 2, id, idx=zyxd)
def data_prep_function(data, pixel_spacing, p_transform=p_transform):
# TODO: MAKE SURE THAT DATA IS PREPROCESSED THE SAME WAY
lung_mask = lung_segmentation.segment_HU_scan(data)
x, tf_matrix, lung_mask_out = data_transforms.transform_scan3d(data=data,
pixel_spacing=pixel_spacing,
p_transform=p_transform,
lung_mask=lung_mask,
p_transform_augment=None)
x = data_transforms.pixelnormHU(x)
return x, lung_mask_out, tf_matrix
def data_prep_function(data, pixel_spacing, p_transform=p_transform):
# TODO: MAKE SURE THAT DATA IS PREPROCESSED THE SAME WAY
lung_mask = lung_segmentation.segment_HU_scan(data)
x, tf_matrix, lung_mask_out = data_transforms.transform_scan3d(
data=data,
pixel_spacing=pixel_spacing,
p_transform=p_transform,
lung_mask=lung_mask,
p_transform_augment=None)
x = data_transforms.pixelnormHU(x)
return x, lung_mask_out, tf_matrix
def data_prep_function(data, luna_annotations, pixel_spacing, luna_origin,
p_transform=p_transform,
p_transform_augment=None):
# make sure the data is processed the same way
lung_mask = lung_segmentation.segment_HU_scan(data)
x, annotations_tf, tf_matrix, lung_mask_out = data_transforms.transform_scan3d(data=data,
pixel_spacing=pixel_spacing,
p_transform=p_transform,
luna_annotations=luna_annotations,
p_transform_augment=None,
luna_origin=luna_origin,
lung_mask=lung_mask)
x = data_transforms.pixelnormHU(x)
y = data_transforms.make_3d_mask_from_annotations(img_shape=x.shape, annotations=annotations_tf, shape='sphere')
return x, y, lung_mask_out, annotations_tf, tf_matrix
def data_prep_function(data, luna_annotations, pixel_spacing, luna_origin,
p_transform=p_transform,
p_transform_augment=None):
# make sure the data is processed the same way
lung_mask = lung_segmentation.segment_HU_scan(data)
x, annotations_tf, tf_matrix, lung_mask_out = data_transforms.transform_scan3d(data=data,
pixel_spacing=pixel_spacing,
p_transform=p_transform,
luna_annotations=luna_annotations,
p_transform_augment=None,
luna_origin=luna_origin,
lung_mask=lung_mask)
x = data_transforms.pixelnormHU(x)
y = data_transforms.make_3d_mask_from_annotations(img_shape=x.shape, annotations=annotations_tf, shape='sphere')
return x, y, lung_mask_out, annotations_tf, tf_matrix