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test_stage3.py
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import os
from options.test_options import TestOptions
from data import CreateDataLoader
from models import create_model
from util.visualizer import Visualizer
from util import html
from data_custom.data_load import load_nifty_volume_as_array
from data_custom.data_load import save_array_as_nifty_volume
import numpy as np
import torch
import math
if __name__ == '__main__':
opt = TestOptions().parse()
opt.nThreads = 1 # test code only supports nThreads = 1
opt.batchSize = 1 # test code only supports batchSize = 1
opt.serial_batches = True # no shuffle
opt.no_flip = True # no flip
# data_loader = CreateDataLoader(opt)
# dataset = data_loader.load_data()
model = create_model(opt)
visualizer = Visualizer(opt)
# create website
# web_dir = os.path.join(opt.results_dir, opt.name, '%s_%s' % (opt.phase, opt.which_epoch))
# webpage = html.HTML(web_dir, 'Experiment = %s, Phase = %s, Epoch = %s' % (opt.name, opt.phase, opt.which_epoch))
# test
in_type1=opt.input1
in_type2=opt.input2
out_type=opt.out
for phase in ['train','val','test']:
target_path=opt.dataroot+'/'+phase
patients=os.listdir(target_path)
order_c=opt.order
for i in range(len(patients)):
# if i >= opt.how_many:
# break
target_subject=os.path.join(target_path,patients[i])+'/'
in_im1=np.float32(load_nifty_volume_as_array(filename=target_subject+in_type1+'.nii'))
in_im2=np.float32(load_nifty_volume_as_array(filename=target_subject+in_type2+'.nii'))
out_im1=np.float32(load_nifty_volume_as_array(filename=target_subject+out_type+'.nii'))
recovered_im=np.float32(load_nifty_volume_as_array(filename=target_subject+out_type+'_syn_3dpro_'+order_c+'_2.nii'))
out_im1[out_im1<0] = 0
in_im1[in_im1<0] = 0
in_im2[in_im2<0] = 0
#subject based normalization
out_im1=1.0*out_im1/out_im1.max()
in_im1=1.0*in_im1/in_im1.max()
in_im2=1.0*in_im2/in_im2.max()
shapes=out_im1.shape
s1=shapes[0]
s2=shapes[1]
s3=shapes[2]
if order_c[2]=='A':
slice_size=s1
elif order_c[2]=='C':
slice_size=s2
else:
slice_size=s3
fake_syn=np.zeros([s1,s2,s3])
for ind in range(slice_size):
if order_c[2]=='A':
data_x=np.array([in_im1[ind,:,:],in_im2[ind,:,:],recovered_im[:,ind,:]])
data_y=np.array([out_im1[ind,:,:]])
data_fake1=np.array([recovered_im[slice_ind,:,:]])
elif order_c[2]=='C':
data_x=np.array([in_im1[:,ind,:],in_im2[:,ind,:],recovered_im[:,ind,:]])
data_y=np.array([out_im1[:,ind,:]])
data_fake1=np.array([recovered_im[:,slice_ind,:]])
else:
data_x=np.array([in_im1[:,:,ind],in_im2[:,:,ind],recovered_im[:,ind,:]])
data_y=np.array([out_im1[:,:,ind]])
data_fake1=np.array([recovered_im[:,:,slice_ind]])
data_x=(data_x*1.0-0.5)*2
data_y=(data_y*1.0-0.5)*2
data_fake1=(data_fake1*1.0-0.5)*2
# data_x=np.float32(data_x)
# data_y=np.float32(data_y)
data_x=np.expand_dims(data_x,axis=0)
data_y=np.expand_dims(data_y,axis=0)
data_fake1=np.expand_dims(data_fake1,axis=0)
data={'A': torch.from_numpy(data_x), 'B':torch.from_numpy(data_y),'fake1':torch.from_numpy(data_fake1), 'A_paths':target_subject, 'B_paths':target_subject}
model.set_input(data)
model.test()
fake_im=model.fake_B.cpu().data.numpy()
fake_im=fake_im*0.5+0.5
if order_c[2]=='A':
fake_syn[ind,:,:]=fake_im
elif order_c[2]=='C':
fake_syn[:,ind,:]=fake_im
else:
fake_syn[:,:,ind]=fake_im
save_array_as_nifty_volume(fake_syn, filename=target_subject+out_type+'_syn_3dpro_'+order_c+'_3.nii')