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 count_proportion():
id2zyxd = utils_lung.read_luna_annotations(pathfinder.LUNA_LABELS_PATH)
luna_data_paths = utils_lung.get_patient_data_paths(
pathfinder.LUNA_DATA_PATH)
luna_data_paths = [p for p in luna_data_paths if '.mhd' in p]
n_white = 0
n_black = 0
for k, p in enumerate(luna_data_paths):
img, origin, pixel_spacing = utils_lung.read_mhd(p)
img = data_transforms.hu2normHU(img)
id = os.path.basename(p).replace('.mhd', '')
print id
annotations = id2zyxd[id]
img_out, annotations_out = data_transforms.transform_scan3d(
img,
pixel_spacing=pixel_spacing,
p_transform=config().p_transform,
p_transform_augment=None,
# config().p_transform_augment,
luna_annotations=annotations,
luna_origin=origin)
mask = data_transforms.make_3d_mask_from_annotations(img_out.shape,
annotations_out,
shape='sphere')
n_white += np.sum(mask)
n_black += mask.shape[0] * mask.shape[1] * mask.shape[2] - np.sum(mask)
print 'white', n_white
print 'black', n_black
def count_proportion():
id2zyxd = utils_lung.read_luna_annotations(pathfinder.LUNA_LABELS_PATH)
luna_data_paths = utils_lung.get_patient_data_paths(pathfinder.LUNA_DATA_PATH)
luna_data_paths = [p for p in luna_data_paths if '.mhd' in p]
n_white = 0
n_black = 0
for k, p in enumerate(luna_data_paths):
img, origin, pixel_spacing = utils_lung.read_mhd(p)
img = data_transforms.hu2normHU(img)
id = os.path.basename(p).replace('.mhd', '')
print id
annotations = id2zyxd[id]
img_out, annotations_out = data_transforms.transform_scan3d(img,
pixel_spacing=pixel_spacing,
p_transform=config().p_transform,
p_transform_augment=None,
# config().p_transform_augment,
luna_annotations=annotations,
luna_origin=origin)
mask = data_transforms.make_3d_mask_from_annotations(img_out.shape, annotations_out, shape='sphere')
n_white += np.sum(mask)
n_black += mask.shape[0] * mask.shape[1] * mask.shape[2] - np.sum(mask)
print 'white', n_white
print 'black', n_black
def data_prep_function(data, patch_center, luna_annotations, pixel_spacing, luna_origin, p_transform,
p_transform_augment, **kwargs):
x, patch_annotation_tf, annotations_tf = data_transforms.transform_patch3d(data=data,
luna_annotations=luna_annotations,
patch_center=patch_center,
p_transform=p_transform,
p_transform_augment=p_transform_augment,
pixel_spacing=pixel_spacing,
luna_origin=luna_origin)
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
def data_prep_function(data, patch_center, luna_annotations, pixel_spacing, luna_origin, p_transform,
p_transform_augment, **kwargs):
x, patch_annotation_tf, annotations_tf = data_transforms.transform_patch3d(data=data,
luna_annotations=luna_annotations,
patch_center=patch_center,
p_transform=p_transform,
p_transform_augment=p_transform_augment,
pixel_spacing=pixel_spacing,
luna_origin=luna_origin)
x = data_transforms.hu2normHU(x)
y = data_transforms.make_3d_mask_from_annotations(img_shape=x.shape, annotations=annotations_tf, shape='sphere')
return x, y
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
x, annotations_tf, tf_matrix = 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)
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, 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 THAT DATA IS PREPROCESSED THE SAME WAY
x, annotations_tf, tf_matrix = 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)
x = data_transforms.hu2normHU(x)
y = data_transforms.make_3d_mask_from_annotations(img_shape=x.shape, annotations=annotations_tf, shape='sphere')
return x, y, 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
x, annotations_tf, tf_matrix = 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)
x = data_transforms.hu2normHU(x)
y = data_transforms.make_3d_mask_from_annotations(img_shape=x.shape, annotations=annotations_tf, shape='sphere')
return x, y, 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_frederic(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
