'result_vn',
mat_dict={'normalization': np.asarray(norm)})
mriutils.saveAsMat(zf_volume,
'%s-zf-%s' % (output_name, patient_id),
'result_zf',
mat_dict={'normalization': np.asarray(norm)})
mriutils.saveAsMat(target_volume,
'%s-ref-%s' % (output_name, patient_id),
'reference',
mat_dict={'normalization': np.asarray(norm)})
# enhance image with same parameters for all images
v_min, v_max = mriutils.getContrastStretchingLimits(
np.abs(target_volume), saturated_pixel=0.002)
target_enhanced = mriutils.normalize(np.abs(target_volume),
v_min=v_min,
v_max=v_max)
u_i_enhanced = mriutils.normalize(np.abs(u_volume),
v_min=v_min,
v_max=v_max)
zf_enhanced = mriutils.normalize(np.abs(zf_volume),
v_min=v_min,
v_max=v_max)
# save pngs
for i in range(1, num_slices + 1):
mriutils.imsave(u_i_enhanced[i - 1],
'%s-vn-%s-sl%d.png' % (output_name, patient_id, i))
mriutils.imsave(
target_enhanced[i - 1],
'%s-ref-%s-sl%d.png' % (output_name, patient_id, i))
u_i = u_i * norm # renormalize
u_volume.append(u_i)
# postprocessing
u_volume = mriutils.postprocess(np.asarray(u_volume),
data_config['dataset']['name'])
time_reco = time.time() - eval_start_time
print('reconstruction of {} image took {:f}s'.format(
u_volume.shape, time_reco))
print('saving reconstructed image to "{}"'.format(output_name))
# save mat file
patient_id = '%s-p%d' % (data_config['dataset']['name'],
data_config['dataset']['patient'])
mriutils.saveAsMat(u_volume,
'%s-vn-%s' % (output_name, patient_id),
'result_vn',
mat_dict={'normalization': np.asarray(norm)})
# enhance volume
v_min, v_max = mriutils.getContrastStretchingLimits(
np.abs(u_volume), saturated_pixel=0.002)
volume_enhanced = mriutils.normalize(np.abs(u_volume),
v_min=v_min,
v_max=v_max)
# save pngs
for i in range(1, num_slices + 1):
mriutils.imsave(volume_enhanced[i - 1],
'%s-vn-%s-sl%d.png' % (output_name, patient_id, i))
print('reconstruction of {} image took {:f}s'.format(u_i.shape, time_reco))
print('saving reconstructed image to "{}"'.format(output_name))
# save mat files
patient_id = '%s-p%d-sl%d' % (data_config['dataset']['name'],
data_config['dataset']['patient'],
data_config['dataset']['slice'])
mriutils.saveAsMat(u_i, '%s-vn-%s' % (output_name, patient_id), 'result_vn',
mat_dict={'normalization': np.asarray(norm)})
mriutils.saveAsMat(zero_filling, '%s-zf-%s' % (output_name, patient_id), 'result_zf',
mat_dict={'normalization': np.asarray(norm)})
mriutils.saveAsMat(target, '%s-ref-%s' % (output_name, patient_id), 'reference',
mat_dict={'normalization': np.asarray(norm)})
# enhance image with same parameters for all images
v_min, v_max = mriutils.getContrastStretchingLimits(np.abs(target),
saturated_pixel=0.002)
target_enhanced = mriutils.normalize(np.abs(target), v_min=v_min, v_max=v_max)
u_i_enhanced = mriutils.normalize(np.abs(u_i), v_min=v_min, v_max=v_max)
zf_enhanced = mriutils.normalize(np.abs(zero_filling), v_min=v_min, v_max=v_max)
# save pngs
mriutils.imsave(u_i_enhanced,
'%s-vn-%s.png' % (output_name, patient_id))
mriutils.imsave(target_enhanced,
'%s-ref-%s.png' % (output_name, patient_id))
mriutils.imsave(zf_enhanced,
'%s-zf-%s.png' % (output_name, patient_id))