logger.info('Running on {}'.format(cohort))
for i in range(0, hdf5_file[cohort].shape[0]):
# cropping operation
logger.debug('{}:- Patient {}'.format(cohort, i + 1))
img_src = hdf5_file[cohort][i]
coords = config['cropping_coords']
if 'segmasks' in cohort:
# there are no channels for segmasks
img_dst = img_src[coords[0]:coords[1], coords[2]:coords[3], coords[4]:coords[5]]
else:
img_dst = img_src[:, coords[0]:coords[1], coords[2]:coords[3], coords[4]:coords[5]]
if cohort in list_std:
# save the image to this numpy array
im_np[i] = img_dst
print(img_src.max(), img_dst.max())
new_group_preprocessed[cohort][i] = img_dst
# find mean and standard deviation, and apply to data. Also write the mean/std values to disk
if cohort in list_std:
logger.info('The dataset {} needs standardization'.format(cohort))
_tmp, vals = standardize(im_np, findMeanVarOnly=True, saveDump=(config['pathd_hdf5files']/(cohort + '_mean_std.p')))
logging.info('Calculated normalization values for {}:\n{}'.format(cohort, vals))
del im_np
# ====================================================================================
hdf5_file_main.close()
new_hdf5.close()
logger.info('Running on {}'.format(run_on))
for i in range(0, hdf5_file[run_on].shape[0]):
# cropping operation
logger.debug('{}:- Patient {}'.format(run_on, i+1))
im = hdf5_file[run_on][i]
m = config['cropping_coords']
if 'segmasks' in run_on:
# there are no channels for segmasks
k = im[m[0]:m[1], m[2]:m[3], m[4]:m[5]]
else:
k = im[:, m[0]:m[1], m[2]:m[3], m[4]:m[5]]
if run_on in std_list:
# save the image to this numpy array
im_np[i] = k
new_group_preprocessed[run_on][i] = k
# find mean and standard deviation, and apply to data. Also write the mean/std values to disk
if run_on in std_list:
logger.info('The dataset {} needs standardization'.format(run_on))
_tmp, vals = standardize(im_np, findMeanVarOnly=True, saveDump=saveMeanVarFilename + run_on + '_mean_std.p')
logging.info('Calculated normalization values for {}:\n{}'.format(run_on, vals))
del im_np
# ====================================================================================
hdf5_file_main.close()
new_hdf5.close()