def load_vol_and_seg(
vol_file,
load_seg=True,
do_mask_vol=False,
load_contours=False,
keep_labels=None,
):
if 'train' in vol_file:
segs_dir = MRI_TRAIN_SEGS_DIR
elif 'valid' in vol_file:
segs_dir = MRI_VALIDATION_SEGS_DIR
else:
segs_dir = MRI_TEST_SEGS_DIR
# load volume and corresponding segmentation from file
vol_name = os.path.splitext(os.path.basename(vol_file))[0].split('_vol')[0]
img_data = np.load(vol_file)['vol_data'][..., np.newaxis]
if load_seg or do_mask_vol or load_contours:
seg_vol_file = os.path.join(segs_dir, '{}_seg.npz'.format(vol_name))
if not os.path.isfile(seg_vol_file):
print('Could not find corresponding seg file for {}!'.format(
vol_file))
return None
seg_data = np.load(seg_vol_file)['seg_data']
else:
seg_data = None
if do_mask_vol:
# background should always be 0 and other segs should be integers
img_data *= (seg_data > 0).astype(int)[..., np.newaxis]
if keep_labels is not None:
unique_seg_labels = np.unique(seg_data)
for l in unique_seg_labels:
if l not in keep_labels:
seg_data[seg_data == l] = 0
contours_file = os.path.join(
MRI_CONTOURS_DIR, '{}_{}labels.npz'.format(vol_name,
len(keep_labels)))
else:
contours_file = os.path.join(MRI_CONTOURS_DIR,
'{}_alllabels.npz'.format(vol_name))
contours = None
if load_contours:
if not os.path.isfile(contours_file):
contours = pynd_segutils.seg2contour(
seg_data, contour_type='both')[..., np.newaxis]
contours[contours > 0] = 1
np.savez(contours_file, contours_data=contours)
else:
contours = np.load(contours_file)['contours_data']
return img_data, seg_data, contours
def _load_vol_and_seg(vol_file,
load_seg=True, do_mask_vol=False,
load_contours=False,
keep_labels=None,
):
if 'train' in vol_file:
mode = 'train'
elif 'valid' in vol_file:
mode = 'validate'
else:
mode = 'test'
# load volume and corresponding segmentation from file
vol_name = os.path.splitext(os.path.basename(vol_file))[0]
if vol_name == 'atlas':
img_data, seg_data = _load_atlas_vol()
else:
if mri_vol_root in vol_file:
vol_base_name = '_'.join(vol_name.split('_')[:-1])
else:
vol_base_name = vol_name
img_data = np.load(vol_file)['vol_data'][..., np.newaxis]
if load_seg or do_mask_vol or load_contours:
if mri_vol_root in vol_file:
seg_vol_file = mri_vol_root + '/' + mode + '/asegs/' + vol_base_name + '_aseg.npz'
elif buckner_vol_root in vol_file:
seg_vol_file = os.path.join(buckner_vol_root, 'asegs', vol_base_name + '.npz')
else:
print('Could not find corresponding seg file for {}!'.format(vol_file))
sys.exit()
if not os.path.isfile(seg_vol_file):
print('Could not find corresponding seg file for {}!'.format(vol_file))
return None
seg_data = np.load(seg_vol_file)['vol_data']
else:
seg_data = None
if do_mask_vol:
# background should always be 0 and other segs should be integers
img_data *= (seg_data > 0).astype(int)[..., np.newaxis]
if keep_labels is not None:
unique_seg_labels = np.unique(seg_data)
for l in unique_seg_labels:
if l not in keep_labels:
seg_data[seg_data==l] = 0
contours_file = os.path.join(mri_contours_root, vol_name + '_{}labels.npz'.format(len(keep_labels)))
else:
contours_file = os.path.join(mri_contours_root, vol_name + '_alllabels.npz')
contours = None
if load_contours:
if not os.path.isfile(contours_file):
contours = segutils.seg2contour(seg_data, contour_type='both')[..., np.newaxis]
contours[contours > 0] = 1
np.savez(contours_file, contours_data=contours)
else:
contours = np.load(contours_file)['contours_data']
return img_data, seg_data, contours
def __compute_contour(seg_data):
contours = pynd_segutils.seg2contour(seg_data,
exclude_zero=True,
contour_type='both')[None]
contours[contours > 0] = 1
return torch.Tensor(contours).cuda()
def load_vol_and_seg(
vol_file,
load_seg=True,
do_mask_vol=False,
load_contours=False,
keep_labels=None,
):
if 'train' in vol_file:
segs_dir = MRI_TRAIN_SEGS_DIR
elif 'valid' in vol_file:
segs_dir = MRI_VALIDATION_SEGS_DIR
else:
segs_dir = MRI_TEST_SEGS_DIR
# load volume and corresponding segmentation from file #数据文件命名有强制要求'_vol'[wangxu]
vol_name = os.path.splitext(os.path.basename(vol_file))[0].split('_vol')[0]
# 到这里数据已加载进来 [wangxu]
img_data = np.load(vol_file)['vol_data'][
..., np.newaxis] # 文件内键值'vol_data'也写死了 [wangxu]
if load_seg or do_mask_vol or load_contours: # load_seg 和 load_contours都会加载分割标记，load_contours会将分割标记转换为轮廓 [wangxu]
seg_vol_file = os.path.join(segs_dir, '{}_seg.npz'.format(vol_name))
if not os.path.isfile(seg_vol_file):
print('Could not find corresponding seg file for {}!'.format(
vol_file))
return None
seg_data = np.load(seg_vol_file)[
'seg_data'] # 分割标记i文件内的键值为'seg_data' [wangxu]
else:
seg_data = None
if do_mask_vol: # 图像去背景 [wangxu]
# background should always be 0 and other segs should be integers
img_data *= (seg_data > 0).astype(int)[..., np.newaxis]
if keep_labels is not None:
unique_seg_labels = np.unique(seg_data)
for l in unique_seg_labels: # 去掉未包含在预设标签中的像素，标签置为0 [wangxu]
if l not in keep_labels:
seg_data[seg_data == l] = 0
contours_file = os.path.join(
MRI_CONTOURS_DIR, '{}_{}labels.npz'.format(
vol_name, len(keep_labels))) # 定义所要生成contours文件的路径 [wangxu]
else:
contours_file = os.path.join(MRI_CONTOURS_DIR,
'{}_alllabels.npz'.format(vol_name))
contours = None
if load_contours:
if not os.path.isfile(contours_file):
contours = pynd_segutils.seg2contour(
seg_data, contour_type='both')[...,
np.newaxis] # 这一步很耗时 [wangxu]
contours[contours > 0] = 1 # 轮廓进行了二值化 [wangxu]
np.savez(contours_file, contours_data=contours)
else:
contours = np.load(contours_file)['contours_data']
return img_data, seg_data, contours