def test_luna3d():
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/1.3.6.1.4.1.14519.5.2.1.6279.6001.877026508860018521147620598474.mhd'
]
candidates = utils.load_pkl(
'/mnt/sda3/data/kaggle-lung/luna_test_patient/1.3.6.1.4.1.14519.5.2.1.6279.6001.877026508860018521147620598474.pkl'
)
candidates = candidates[:4]
print(candidates)
print('--------------')
print(id2zyxd[
'1.3.6.1.4.1.14519.5.2.1.6279.6001.877026508860018521147620598474'])
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_ira(img)
print(np.min(lung_mask), np.max(lung_mask))
x, annotations_tf, tf_matrix, lung_mask_out = data_transforms.transform_scan3d(
data=img,
pixel_spacing=pixel_spacing,
p_transform=p_transform,
luna_annotations=candidates,
p_transform_augment=None,
luna_origin=origin,
lung_mask=lung_mask,
world_coord_system=False)
print(np.min(lung_mask_out), np.max(lung_mask_out))
plot_slice_3d_2(x, lung_mask_out, 0, id)
plot_slice_3d_2(x, lung_mask_out, 1, id)
plot_slice_3d_2(x, lung_mask_out, 2, id)
# for zyxd in annotations_tf:
# plot_slice_3d_2(x, lung_mask_out, 0, id, idx=zyxd)
# plot_slice_3d_2(x, lung_mask_out, 1, id, idx=zyxd)
# plot_slice_3d_2(x, lung_mask_out, 2, id, idx=zyxd)
for i in range(136, x.shape[1]):
plot_slice_3d_2(x,
lung_mask_out,
1,
str(id) + str(i),
idx=np.array([200, i, 200]))
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_ira(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_ira(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_ira(data)
annotatations_out = []
for zyxd in luna_annotations:
zyx = np.array(zyxd[:3])
voxel_coords = utils_lung.world2voxel(zyx, luna_origin, pixel_spacing)
zyxd_out = np.rint(np.append(voxel_coords, zyxd[-1]))
annotatations_out.append(zyxd_out)
annotatations_out = np.asarray(annotatations_out)
return lung_mask, lung_mask, lung_mask, annotatations_out, None
def test_luna3d():
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/1.3.6.1.4.1.14519.5.2.1.6279.6001.877026508860018521147620598474.mhd']
candidates = utils.load_pkl(
'/mnt/sda3/data/kaggle-lung/luna_test_patient/1.3.6.1.4.1.14519.5.2.1.6279.6001.877026508860018521147620598474.pkl')
candidates = candidates[:4]
print candidates
print '--------------'
print id2zyxd['1.3.6.1.4.1.14519.5.2.1.6279.6001.877026508860018521147620598474']
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_ira(img)
print np.min(lung_mask), np.max(lung_mask)
x, annotations_tf, tf_matrix, lung_mask_out = data_transforms.transform_scan3d(data=img,
pixel_spacing=pixel_spacing,
p_transform=p_transform,
luna_annotations=candidates,
p_transform_augment=None,
luna_origin=origin,
lung_mask=lung_mask,
world_coord_system=False)
print np.min(lung_mask_out), np.max(lung_mask_out)
plot_slice_3d_2(x, lung_mask_out, 0, id)
plot_slice_3d_2(x, lung_mask_out, 1, id)
plot_slice_3d_2(x, lung_mask_out, 2, id)
# for zyxd in annotations_tf:
# plot_slice_3d_2(x, lung_mask_out, 0, id, idx=zyxd)
# plot_slice_3d_2(x, lung_mask_out, 1, id, idx=zyxd)
# plot_slice_3d_2(x, lung_mask_out, 2, id, idx=zyxd)
for i in xrange(136, x.shape[1]):
plot_slice_3d_2(x, lung_mask_out, 1, str(id) + str(i), idx=np.array([200, i, 200]))
def test_dsb():
image_dir = utils.get_dir_path('analysis', pathfinder.METADATA_PATH)
image_dir = image_dir + '/test_1/'
utils.auto_make_dir(image_dir)
patient_data_paths = utils_lung.get_patient_data_paths(pathfinder.DATA_PATH)
print(len(patient_data_paths))
patient_data_paths = [pathfinder.DATA_PATH + '/01de8323fa065a8963533c4a86f2f6c1']
for k, p in enumerate(patient_data_paths):
pid = utils_lung.extract_pid_dir(p)
# sid2data, sid2metadata = utils_lung.get_patient_data(p)
# sids_sorted = utils_lung.sort_sids_by_position(sid2metadata)
# sids_sorted_jonas = utils_lung.sort_slices_jonas(sid2metadata)
# sid2position = utils_lung.slice_location_finder(sid2metadata)
#
# jonas_slicethick = []
# for i in range(len(sids_sorted_jonas) - 1):
# s = np.abs(sid2position[sids_sorted_jonas[i + 1]] - sid2position[sids_sorted_jonas[i]])
# jonas_slicethick.append(s)
#
# img = np.stack([data_transforms.ct2HU(sid2data[sid], sid2metadata[sid]) for sid in sids_sorted])
# xx = (jonas_slicethick[0],
# sid2metadata[sids_sorted[0]]['PixelSpacing'][0],
# sid2metadata[sids_sorted[0]]['PixelSpacing'][1])
# pixel_spacing = np.asarray(xx)
img, pixel_spacing = utils_lung.read_dicom_scan(p)
mask = lung_segmentation.segment_HU_scan_ira(img)
print(pid)
print(pixel_spacing)
print('====================================')
img_out, transform_matrix, mask_out = data_transforms.transform_scan3d(img,
pixel_spacing=pixel_spacing,
p_transform=config().p_transform,
p_transform_augment=None,
lung_mask=mask)
for i in range(100, img_out.shape[0], 5):
plot_slice_3d_2(img_out, mask_out, 0, str(pid) + str(i), idx=np.array([i, 200, 200]))
plot_slice_3d_2(img_out, mask_out, 0, pid, idx=np.array(img_out.shape) / 2)
plot_slice_3d_2(mask_out, img_out, 0, pid, idx=np.array(img_out.shape) / 4)
plot_slice_3d_2(mask_out, img_out, 0, pid, idx=np.array(img_out.shape) / 8)
def test_dsb():
image_dir = utils.get_dir_path('analysis', pathfinder.METADATA_PATH)
image_dir = image_dir + '/test_1/'
utils.auto_make_dir(image_dir)
patient_data_paths = utils_lung.get_patient_data_paths(pathfinder.DATA_PATH)
print len(patient_data_paths)
patient_data_paths = [pathfinder.DATA_PATH + '/01de8323fa065a8963533c4a86f2f6c1']
for k, p in enumerate(patient_data_paths):
pid = utils_lung.extract_pid_dir(p)
# sid2data, sid2metadata = utils_lung.get_patient_data(p)
# sids_sorted = utils_lung.sort_sids_by_position(sid2metadata)
# sids_sorted_jonas = utils_lung.sort_slices_jonas(sid2metadata)
# sid2position = utils_lung.slice_location_finder(sid2metadata)
#
# jonas_slicethick = []
# for i in xrange(len(sids_sorted_jonas) - 1):
# s = np.abs(sid2position[sids_sorted_jonas[i + 1]] - sid2position[sids_sorted_jonas[i]])
# jonas_slicethick.append(s)
#
# img = np.stack([data_transforms.ct2HU(sid2data[sid], sid2metadata[sid]) for sid in sids_sorted])
# xx = (jonas_slicethick[0],
# sid2metadata[sids_sorted[0]]['PixelSpacing'][0],
# sid2metadata[sids_sorted[0]]['PixelSpacing'][1])
# pixel_spacing = np.asarray(xx)
img, pixel_spacing = utils_lung.read_dicom_scan(p)
mask = lung_segmentation.segment_HU_scan_ira(img)
print pid
print pixel_spacing
print '===================================='
img_out, transform_matrix, mask_out = data_transforms.transform_scan3d(img,
pixel_spacing=pixel_spacing,
p_transform=config().p_transform,
p_transform_augment=None,
lung_mask=mask)
for i in xrange(100, img_out.shape[0], 5):
plot_slice_3d_2(img_out, mask_out, 0, str(pid) + str(i), idx=np.array([i, 200, 200]))
plot_slice_3d_2(img_out, mask_out, 0, pid, idx=np.array(img_out.shape) / 2)
plot_slice_3d_2(mask_out, img_out, 0, pid, idx=np.array(img_out.shape) / 4)
plot_slice_3d_2(mask_out, img_out, 0, pid, idx=np.array(img_out.shape) / 8)
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_ira(data)
annotatations_out = []
for zyxd in luna_annotations:
zyx = np.array(zyxd[:3])
voxel_coords = utils_lung.world2voxel(zyx, luna_origin, pixel_spacing)
zyxd_out = np.rint(np.append(voxel_coords, zyxd[-1]))
annotatations_out.append(zyxd_out)
annotatations_out = np.asarray(annotatations_out)
return lung_mask, lung_mask, lung_mask, annotatations_out, None