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import os, sys
currentdir = os.path.dirname(os.path.realpath(__file__))
parentdir = os.path.dirname(currentdir)
sys.path.append(parentdir) # PYTHON > 3.3 does not allow relative referencing
# import tensorflow as tf
# tf.enable_eager_execution()
# import neurite.py.utils as neurite_utils
from skimage.morphology import skeletonize_3d, ball
from skimage.morphology import binary_closing, binary_opening
from skimage.filters import median
from skimage.measure import regionprops, label
from skimage.transform import warp
from scipy.ndimage import zoom
from scipy.interpolate import LinearNDInterpolator, Rbf
import h5py
import numpy as np
from tqdm import tqdm
import re
import nibabel as nib
from nilearn.image import resample_img
from Centerline.graph_utils import graph_to_ndarray, deform_graph, get_bifurcation_nodes, subsample_graph, \
apply_displacement
from Centerline.skeleton_to_graph import get_graph_from_skeleton
from Centerline.visualization_utils import plot_skeleton, compare_graphs
from ddmr.utils.operators import min_max_norm
from ddmr.utils import constants as C
import cupy
from cupyx.scipy.ndimage import zoom as zoom_gpu
from cupyx.scipy.ndimage import map_coordinates
DATASET_LOCATION = '/mnt/EncryptedData1/Users/javier/vessel_registration/3Dirca/dataset/EVAL'
DATASET_NAMES = ['Affine', 'None', 'Translation']
DATASET_FILENAME = 'volume'
IMGS_FOLDER = '/home/jpdefrutos/workspace/ddmr/Centerline/centerlines'
DATASTE_RAW_FILES = '/mnt/EncryptedData1/Users/javier/vessel_registration/3Dirca/nifti3'
LITS_SEGMENTATION_FILE = 'segmentation'
LITS_CT_FILE = 'volume'
def warp_volume(volume, disp_map, indexing='ij'):
assert indexing is 'ij' or 'xy', 'Invalid indexing option. Only "ij" or "xy"'
grid_i = np.linspace(0, disp_map.shape[0], disp_map.shape[0], endpoint=False)
grid_j = np.linspace(0, disp_map.shape[1], disp_map.shape[1], endpoint=False)
grid_k = np.linspace(0, disp_map.shape[2], disp_map.shape[2], endpoint=False)
grid_i, grid_j, grid_k = np.meshgrid(grid_i, grid_j, grid_k, indexing=indexing)
grid_i = (grid_i.flatten() + disp_map[..., 0].flatten())[..., np.newaxis]
grid_j = (grid_j.flatten() + disp_map[..., 1].flatten())[..., np.newaxis]
grid_k = (grid_k.flatten() + disp_map[..., 2].flatten())[..., np.newaxis]
coords = np.hstack([grid_i, grid_j, grid_k]).reshape([*disp_map.shape[:-1], -1])
coords = coords.transpose((-1, 0, 1, 2))
# The returned volume has indexing xy
return warp(volume, coords)
def keep_largest_segmentation(img):
label_img = label(img)
rp = regionprops(label_img) # Regions labeled with 0 (bg) are ignored
biggest_area = (0, 0)
for l in range(0, label_img.max()):
if rp[l].area > biggest_area[1]:
biggest_area = (l + 1, rp[l].area)
img[label_img != biggest_area[0]] = 0.
return img
def preprocess_image(img, keep_largest=False):
ret = binary_closing(img, ball(1))
ret = binary_opening(ret, ball(1))
#ret = median(ret, ball(1), mode='constant')
if keep_largest:
ret = keep_largest_segmentation(ret)
return ret.astype(np.float)
def build_displacement_map_interpolator(disp_map, backwards=False, indexing='ij'):
grid_i = np.linspace(0, disp_map.shape[0], disp_map.shape[0], endpoint=False)
grid_j = np.linspace(0, disp_map.shape[1], disp_map.shape[1], endpoint=False)
grid_k = np.linspace(0, disp_map.shape[2], disp_map.shape[2], endpoint=False)
grid_i, grid_j, grid_k = np.meshgrid(grid_i, grid_j, grid_k, indexing=indexing)
grid_i = grid_i.flatten()
grid_j = grid_j.flatten()
grid_k = grid_k.flatten()
# To generate the moving image, we used backwards mapping were the input was the fix image
# Now we are doing direct mapping from the fix graph coordinates to the moving coordinates
# The application points of the displacement map are thus the transformed "moving image"-grid
# and the displacement vectors are reversed
if backwards:
coords = np.hstack([grid_i[..., np.newaxis], grid_j[..., np.newaxis], grid_k[..., np.newaxis]])
return LinearNDInterpolator(coords, np.reshape(disp_map, [-1, 3]))
else:
grid_i = (grid_i + disp_map[..., 0].flatten())
grid_j = (grid_j + disp_map[..., 1].flatten())
grid_k = (grid_k + disp_map[..., 2].flatten())
coords = np.hstack([grid_i[..., np.newaxis], grid_j[..., np.newaxis], grid_k[..., np.newaxis]])
return LinearNDInterpolator(coords, -np.reshape(disp_map, [-1, 3]))
def resample_segmentation(img, output_shape, preserve_range, threshold=None, gpu=True):
# Preserve range can be a bool (keep or not the original dyn. range) or a list with a new dyn. range
zoom_f = np.divide(np.asarray(output_shape), np.asarray(img.shape))
if gpu:
out_img = zoom_gpu(cupy.asarray(img), zoom_f, order=1) # order = 0 or 1
else:
out_img = zoom(img, zoom_f)
if isinstance(preserve_range, bool):
if preserve_range:
range_min, range_max = np.amin(img), np.amax(img)
out_img = min_max_norm(out_img)
out_img = out_img * (range_max - range_min) + range_min
elif isinstance(preserve_range, list):
range_min, range_max = preserve_range
out_img = min_max_norm(out_img)
out_img = out_img * (range_max - range_min) + range_min
if threshold is not None and out_img.min() < threshold < out_img.max():
range_min, range_max = np.amin(out_img), np.amax(out_img)
out_img[out_img > threshold] = range_max
out_img[out_img < range_max] = range_min
return cupy.asnumpy(out_img) if gpu else out_img
if __name__ == '__main__':
for dataset_name in DATASET_NAMES:
dataset_loc = os.path.join(DATASET_LOCATION, dataset_name)
dataset_files = os.listdir(dataset_loc)
dataset_files.sort()
dataset_files = [os.path.join(dataset_loc, f) for f in dataset_files if DATASET_FILENAME in f]
iterator = tqdm(dataset_files)
for file_path in iterator:
file_num = int(re.findall('(\d+)', os.path.split(file_path)[-1])[0])
iterator.set_description('{} ({}): laoding data'.format(file_num, dataset_name))
vol_file = h5py.File(file_path, 'r')
# fix_vessels = vol_file[C.H5_FIX_VESSELS_MASK][..., 0]
disp_map = vol_file[C.H5_GT_DISP][:]
bbox = vol_file['parameters/bbox'][:]
bbox_min = bbox[:3]
bbox_max = bbox[3:] + bbox_min
# Load vessel segmentation mask and resize to 64^3
fix_labels = nib.load(os.path.join(DATASTE_RAW_FILES, 'segmentation-{:04d}.nii.gz'.format(file_num)))
fix_vessels = fix_labels.slicer[..., 1]
fix_vessels = resample_img(fix_vessels, np.eye(3))
fix_vessels = np.asarray(fix_vessels.dataobj)
fix_vessels = preprocess_image(fix_vessels)
fix_vessels = resample_segmentation(fix_vessels, vol_file['parameters/first_reshape'][:], [0, 1], 0.3,
gpu=True)
fix_vessels = fix_vessels[bbox_min[0]:bbox_max[0], bbox_min[1]:bbox_max[1], bbox_min[2]:bbox_max[2]]
fix_vessels = resample_segmentation(fix_vessels, [64] * 3, [0, 1], 0.3, gpu=True)
fix_vessels = preprocess_image(fix_vessels)
mov_vessels = preprocess_image(warp_volume(fix_vessels, disp_map))
mov_skel = skeletonize_3d(mov_vessels)
### Fix the incorrect scaling ###
disp_map *= 2
bbox_size = np.asarray(bbox[3:]) # Only load the bbox size
rescale_factors = 64 / bbox_size
disp_map[..., 0] = np.multiply(disp_map[..., 0], rescale_factors[0])
disp_map[..., 1] = np.multiply(disp_map[..., 1], rescale_factors[1])
disp_map[..., 2] = np.multiply(disp_map[..., 2], rescale_factors[2])
#################################
iterator.set_description('{} ({}): getting graphs'.format(file_num, dataset_name))
# Prepare displacement map
disp_map_interpolator = build_displacement_map_interpolator(disp_map, backwards=False)
# Get skeleton and graph
fix_skel = skeletonize_3d(fix_vessels)
fix_graph = get_graph_from_skeleton(fix_skel, subsample=True)
mov_graph = get_graph_from_skeleton(mov_skel, subsample=True) # deform_graph(fix_graph, disp_map_interpolator)
##### TODO: ERASE Check the mov graph ######
# check_mov_vessels = vol_file[C.H5_MOV_VESSELS_MASK][..., 0]
# check_mov_vessels = preprocess_image(check_mov_vessels)
# check_mov_skel = skeletonize_3d(check_mov_vessels)
# check_mov_graph = get_graph_from_skeleton(check_mov_skel, subsample=True)
###########
fix_pts, fix_nodes, fix_edges = graph_to_ndarray(fix_graph)
mov_pts, mov_nodes, mov_edges = graph_to_ndarray(mov_graph)
fix_bifur_loc, fix_bifur_id = get_bifurcation_nodes(fix_graph)
mov_bifur_loc, mov_bifur_id = get_bifurcation_nodes(mov_graph)
iterator.set_description('{} ({}): saving data'.format(file_num, dataset_name))
pts_file_path, pts_file_name = os.path.split(file_path)
pts_file_name = pts_file_name.replace(DATASET_FILENAME, 'points')
pts_file_path = os.path.join(pts_file_path, pts_file_name)
pts_file = h5py.File(pts_file_path, 'w')
pts_file.create_dataset('fix/points', data=fix_pts)
pts_file.create_dataset('fix/nodes', data=fix_nodes)
pts_file.create_dataset('fix/edges', data=fix_edges)
pts_file.create_dataset('fix/bifurcations', data=fix_bifur_loc)
pts_file.create_dataset('fix/graph', data=fix_graph)
pts_file.create_dataset('fix/img', data=fix_vessels)
pts_file.create_dataset('fix/skeleton', data=fix_skel)
pts_file.create_dataset('fix/centroid', data=vol_file[C.H5_FIX_CENTROID][:])
pts_file.create_dataset('mov/points', data=mov_pts)
pts_file.create_dataset('mov/nodes', data=mov_nodes)
pts_file.create_dataset('mov/edges', data=mov_edges)
pts_file.create_dataset('mov/bifurcations', data=mov_bifur_loc)
pts_file.create_dataset('mov/graph', data=mov_graph)
pts_file.create_dataset('mov/img', data=mov_vessels)
pts_file.create_dataset('mov/skeleton', data=mov_skel)
pts_file.create_dataset('mov/centroid', data=vol_file[C.H5_MOV_CENTROID][:])
pts_file.create_dataset('parameters/voxel_size', data=vol_file['parameters/voxel_size'][:])
pts_file.create_dataset('parameters/original_affine', data=vol_file['parameters/original_affine'][:])
pts_file.create_dataset('parameters/isotropic_affine', data=vol_file['parameters/isotropic_affine'][:])
pts_file.create_dataset('parameters/original_shape', data=vol_file['parameters/original_shape'][:])
pts_file.create_dataset('parameters/isotropic_shape', data=vol_file['parameters/isotropic_shape'][:])
pts_file.create_dataset('parameters/first_reshape', data=vol_file['parameters/first_reshape'][:])
pts_file.create_dataset('parameters/bbox', data=vol_file['parameters/bbox'][:])
pts_file.create_dataset('parameters/last_reshape', data=vol_file['parameters/last_reshape'][:])
pts_file.create_dataset('displacement_map', data=disp_map)
vol_file.close()
pts_file.close()
iterator.set_description('{} ({}): drawing plots'.format(file_num, dataset_name))
num = pts_file_name.split('-')[-1].split('.hd5')[0]
imgs_folder = os.path.join(IMGS_FOLDER, dataset_name, num)
os.makedirs(imgs_folder, exist_ok=True)
plot_skeleton(fix_vessels, fix_skel, fix_graph, os.path.join(imgs_folder, 'fix'), ['.pdf', '.png'])
plot_skeleton(mov_vessels, mov_skel, mov_graph, os.path.join(imgs_folder, 'mov'), ['.pdf', '.png'])
iterator.set_description('{} ({})'.format(file_num, dataset_name))
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