def validate(self):
self.is_train = False
init_op = tf.global_variables_initializer()
self.saver = tf.train.Saver()
self.sess.run(init_op)
if self.load(self.args.checkpoint_dir):
print(" [*] Load SUCCESS")
else:
print(" [!] Load failed...")
genSample = AbdomenSampler(self.args, 'validate')
# self.generator_sample_targetwise(genSample, )
outputdir = self.args.sample_dir + "/atlas_target/"
ds_all = []
bf_ds_all = []
jt_all = []
for img_fix, lab_fix in zip(genSample.img_fix, genSample.lab_fix):
output_dir = outputdir + get_name_wo_suffix(img_fix)
mk_or_cleardir(output_dir)
print(output_dir)
for img_mv, lab_mv in zip(genSample.img_mv, genSample.lab_mv):
bfds, ds = self.gen_one_batch(genSample, img_fix, lab_fix,
img_mv, lab_mv, output_dir)
# print("sim= %f"%ds)
ds_all.append(ds)
bf_ds_all.append(bfds)
# print(ds_all)
self.logger.debug("%s total_sim %s->%s " % (Get_Name_By_Index(
self.args.component), self.args.Tatlas, self.args.Ttarget))
print_mean_and_std(bf_ds_all)
print_mean_and_std(ds_all)
def prepare_unsupervised_reg_data(args):
if not os.path.exists(args.dataset_dir):
mk_or_cleardir(args.dataset_dir)
types=parse_arg_list(args.mode)
for t in types:
imgs=sort_glob("../../dataset/%s/%s-image/*.nii.gz" % (args.task,t))
labs=sort_glob("../../dataset/%s/%s-label/*.nii.gz" % (args.task,t))
crop_ROI_data_by_label(imgs, labs, args.dataset_dir + "/%s/"%(t), args.component)
def generator_sample_atlaswise(self, genSample, outputdir):
for img_mv, lab_mv in zip(genSample.img_mv, genSample.lab_mv):
output_dir = outputdir + get_name_wo_suffix(img_mv)
mk_or_cleardir(output_dir)
print(output_dir)
for img_fix, lab_fix in zip(genSample.img_fix, genSample.lab_fix):
self.gen_test(genSample, img_fix, lab_fix, img_mv, lab_mv,
output_dir)
def prepare_crossvalidation_reg_data(args):
if not os.path.exists(args.dataset_dir):
mk_or_cleardir(args.dataset_dir)
imgs=sort_glob("../../dataset/%s/%s-image/*.nii.gz" % (args.task,args.Tatlas))
labs=sort_glob("../../dataset/%s/%s-label/*.nii.gz" % (args.task,args.Tatlas))
crop_ROI_data_by_label(imgs, labs, args.dataset_dir + "/atlas/", args.component)
#####
imgs=sort_glob("../../dataset/%s/%s-image/*.nii.gz" % (args.task,args.Ttarget))
labs=sort_glob("../../dataset/%s/%s-label/*.nii.gz" % (args.task,args.Ttarget))
crop_ROI_data_by_label(imgs, labs, args.dataset_dir + "/target/", args.component)
def split(test_imgs_mv, test_lab_mv, train_imgs_mv, train_lab_mv):
mk_or_cleardir(test_imgs_mv)
mk_or_cleardir(test_lab_mv)
file_img_mv = glob.glob(train_imgs_mv+"/*.nii.gz")
file_img_mv.sort()
file_lab_mv = glob.glob(train_lab_mv+"/*.nii.gz")
file_lab_mv.sort()
# L = random.sample(range(0, len(file_img_mv)), 8)
L=range(0,len(file_img_mv))
for i in L[:8]:
shutil.move(file_img_mv[i], test_imgs_mv)
shutil.move(file_lab_mv[i], test_lab_mv)
def sitk_write_lab(input_, parameter_img=None, dir=None, name=''):
if dir is not None:
if not os.path.exists(dir):
mk_or_cleardir(dir)
if not isinstance(input_, sitk.Image):
input_ = np.where(input_ > 0.5, 1, 0)
input_ = input_.astype(np.uint16)
img = sitk.GetImageFromArray(input_)
else:
img = input_
if parameter_img is not None:
img.CopyInformation(parameter_img)
sitk.WriteImage(img, os.path.join(dir, name + '.nii.gz'))
def generator_sample_targetwise(self, genSample, outputdir):
all_ds = []
all_hd = []
for img_fix, lab_fix in zip(genSample.img_fix, genSample.lab_fix):
output_dir = outputdir + get_name_wo_suffix(img_fix)
mk_or_cleardir(output_dir)
print(output_dir)
for img_mv, lab_mv in zip(genSample.img_mv, genSample.lab_mv):
ds, hd = self.gen_one_batch(genSample, img_fix, lab_fix,
img_mv, lab_mv, output_dir)
all_ds.append(ds)
all_hd.append(hd)
outpu2excel(self.args.res_excel, self.args.MOLD_ID + "_DS", all_ds)
outpu2excel(self.args.res_excel, self.args.MOLD_ID + "_HD", all_hd)
def sitk_write_images(input_, parameter_img=None, dir=None, name=''):
if dir is not None:
if not os.path.exists(dir):
mk_or_cleardir(dir)
batch_size = input_.shape[0]
for idx in range(batch_size):
if not isinstance(input_, sitk.Image):
img = sitk.GetImageFromArray(input_[idx, ...])
else:
img = input_[idx, ...]
if parameter_img is not None:
img.CopyInformation(parameter_img)
sitk.WriteImage(img, os.path.join(dir, name + '_%s.nii.gz' % idx))
def main(_):
globel_setup()
tfconfig = tf.ConfigProto(allow_soft_placement=True)
tfconfig.gpu_options.allow_growth = True
with tf.Session(config=tfconfig) as sess:
if args.phase == 'train':
mkdir_if_not_exist(args.log_dir)
# mk_or_cleardir(args.checkpoint_dir)
mk_or_cleardir(args.log_dir)
if args.task == 'CHAOS':
prepare_chaos_reg_working_data(args)
else:
prepare_mmwhs_reg_working_data(args)
model = LabAttentionReg(sess, args)
model.train()
elif args.phase == 'validate':
# mk_or_cleardir(args.sample_dir)
model = LabAttentionReg(sess, args)
# model.show_gated_info()
model.validate()
# cross_validate(args)
# elif args.phase=='test':
# mk_or_cleardir(args.test_dir)
# # prepare_data()
# model =LabAttentionReg (sess, args)
# model.test()
# post_process(args)
# elif args.phase=='post':
# post_process(args)
elif args.phase == 'gen':
#在../datasets/sim_ct_mr_**中生成数据
# mk_or_cleardir(args.fusion_dataset_dir)
# mk_or_cleardir(args.gen_dir)
mk_or_cleardir(args.sample_dir)
model = LabAttentionReg(sess, args)
# model.generate()
# model.validate()
#同时生成验证和训练的数据
model.generate_4_fusion()
# elif args.phase=='trainSim':
# mk_or_cleardir(args.sim_checkpoint_dir)
# mk_or_cleardir(args.sim_sample_dir)
# mk_or_cleardir(args.sim_log_dir)
# model=PatchEmbbeding(sess,args)
# model.train()
# elif args.phase=='testSim':
# mk_or_cleardir(args.sim_sample_dir)
# mk_or_cleardir(args.sim_test_dir)
# model = PatchEmbbeding(sess, args)
# model.test()
# elif args.phase == 'fusion':
# # 调用生成label
# # 进行融合
# ngf = NGFFusion(args)
# ngf.run()
# # mvfusion=MVFusion(args)
# # mvfusion.run()
# mvfusion = SitkSTAPLEFusion(args)
# mvfusion.run()
# mvfusion = SitkMVFusion(args)
# mvfusion.run()
elif args.phase == 'summary':
cross_validate(args)
else:
print("undefined phase")
def prepare_3dUnet_ROI_data(args):
if not os.path.exists(args.dataset_dir):
mk_or_cleardir(args.dataset_dir)
generator_ROI_data_for_3DUnet(args)
def prepare_mmwhs_reg_working_data(args):
if not os.path.exists(args.dataset_dir):
mk_or_cleardir(args.dataset_dir)
generator_ROI_data(args)
def gen_warp_atlas(self, dir, genSample, i, img_fix, lab_fix, is_aug=True):
output_dir = self.args.gen_dir + "/" + dir + "/target_" + str(
i) + "_" + get_name_wo_suffix(img_fix)
mk_or_cleardir(output_dir)
params = []
input_fix_imgs = []
input_fix_labels = []
warp_mv_imgs = []
warp_mv_labels = []
losses = []
sims = []
for img_mv, lab_mv in zip(genSample.img_mv, genSample.lab_mv):
fix_imgs, fix_labs, mv_imgs, mv_labs = genSample.get_batch_data_V2(
[img_mv], [img_fix], [lab_mv], [lab_fix])
trainFeed = self.create_feed_dict(fix_imgs, fix_labs, mv_imgs,
mv_labs, is_aug)
input_mv_label, \
input_fix_label, \
warp_mv_label, \
input_mv_img, \
input_fix_img, \
warp_mv_img = self.sess.run([self.input_MV_label,
self.input_FIX_label,
self.warped_MV_label,
self.input_MV_image,
self.input_FIX_image,
self.warped_MV_image], feed_dict=trainFeed)
input_fix_label = np.where(input_fix_label > 0.5, 1, 0)
warp_mv_label = np.where(warp_mv_label > 0.5, 1, 0)
sims.append(
calculate_binary_dice(input_fix_label[0, ...],
warp_mv_label[0, ...]))
param = sitk.ReadImage(img_fix)
params.append(param)
input_fix_imgs.append(input_fix_img[0, ...])
input_fix_labels.append(input_fix_label[0, ...])
warp_mv_imgs.append(warp_mv_img[0, ...])
warp_mv_labels.append(warp_mv_label[0, ...])
losses.append(
conditional_entropy_label_over_image(
np.squeeze(input_fix_img[0, ...]),
np.squeeze(warp_mv_label[0, ...])))
indexs = np.argsort(losses)
for ind in indexs:
sitk_write_image(warp_mv_imgs[ind],
params[ind],
dir=output_dir,
name=str(ind) + "_mv_img")
sitk_write_lab(warp_mv_labels[ind],
params[ind],
dir=output_dir,
name=str(ind) + "_mv_lab")
sitk_write_image(input_fix_imgs[0],
params[0],
dir=output_dir,
name=str(0) + "_fix_img")
sitk_write_lab(input_fix_labels[0],
params[0],
dir=output_dir,
name=str(0) + "_fix_lab")
self.logger.debug(sims)
self.logger.debug("%s %f -> %f" %
(output_dir, np.mean(sims),
np.mean([sims[ind] for ind in indexs[:5]])))
