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predict.py
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predict.py
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import argparse
import os
import json
import re
import numpy as np
from tqdm import tqdm
import SimpleITK as sitk
from PIL import Image
import math
import scipy.misc
import xlwt
parser = argparse.ArgumentParser()
parser.add_argument('-c', '--checkpoint', type=str, default=None,
help='Specifies a previous checkpoint to load')
parser.add_argument('-r', '--rep', type=int, default=1,
help='Number of times of shared-weight cascading')
parser.add_argument('-g', '--gpu', type=str, default='0',
help='Specifies gpu device(s)')
parser.add_argument('-d', '--dataset', type=str, default=None,
help='Specifies a data config')
parser.add_argument('-v', '--val_subset', type=str, default=None)
parser.add_argument('--batch', type=int, default=4, help='Size of minibatch')
parser.add_argument('--fast_reconstruction', action='store_true')
parser.add_argument('--paired', action='store_true')
parser.add_argument('--data_args', type=str, default=None)
parser.add_argument('--net_args', type=str, default=None)
parser.add_argument('--name', type=str, default=None)
args = parser.parse_args()
os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu
import tensorflow as tf
import tflearn
import network
import data_util.liver
import data_util.brain
def main():
if args.checkpoint is None:
print('Checkpoint must be specified!')
return
if ':' in args.checkpoint:
args.checkpoint, steps = args.checkpoint.split(':')
steps = int(steps)
print(steps)
else:
steps = None
args.checkpoint = find_checkpoint_step(args.checkpoint, steps)
print(args.checkpoint)
model_dir = os.path.dirname(args.checkpoint)
try:
with open(os.path.join(model_dir, 'args.json'), 'r') as f:
model_args = json.load(f)
print(model_args)
except Exception as e:
print(e)
model_args = {}
if args.dataset is None:
args.dataset = model_args['dataset']
if args.data_args is None:
args.data_args = model_args['data_args']
Framework = network.FrameworkUnsupervised
Framework.net_args['base_network'] = model_args['base_network']
Framework.net_args['n_cascades'] = model_args['n_cascades']
Framework.net_args['rep'] = args.rep
Framework.net_args['augmentation'] = 'identity'
Framework.net_args.update(eval('dict({})'.format(model_args['net_args'])))
if args.net_args is not None:
Framework.net_args.update(eval('dict({})'.format(args.net_args)))
with open(os.path.join(args.dataset), 'r') as f:
cfg = json.load(f)
image_size = cfg.get('image_size', [160, 160, 160])
image_type = cfg.get('image_type')
gpus = 0 if args.gpu == '-1' else len(args.gpu.split(','))
framework = Framework(devices=gpus, image_size=image_size, segmentation_class_value=cfg.get(
'segmentation_class_value', None), fast_reconstruction=args.fast_reconstruction, validation=True)
print('Graph built')
Dataset = eval('data_util.{}.Dataset'.format(image_type))
ds = Dataset(args.dataset, batch_size=args.batch, paired=args.paired, **
eval('dict({})'.format(args.data_args)))
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
sess = tf.Session(config = config)
tf.global_variables_initializer().run(session=sess)
checkpoint = args.checkpoint
checkpoint = args.checkpoint
saver = tf.train.Saver(tf.get_collection(
tf.GraphKeys.GLOBAL_VARIABLES))
saver.restore(sess, checkpoint)
tflearn.is_training(False, session=sess)
val_subsets = [data_util.liver.Split.VALID]
if args.val_subset is not None:
val_subsets = args.val_subset.split(',')
tflearn.is_training(False, session=sess)
writebook = xlwt.Workbook()
testSheet1= writebook.add_sheet('dice')
keys = ['total_ncc','jaccs','landmark_dists','dices','det_A']
#'image_fixed','warped_moving','seg_fixed','warped_seg_moving','jaccs','landmark_dists','jacobian_det',
if not os.path.exists('evaluate'):
os.mkdir('evaluate')
path_prefix = os.path.join('evaluate', short_name(checkpoint))
if args.rep > 1:
path_prefix = path_prefix + '-rep' + str(args.rep)
if args.name is not None:
path_prefix = path_prefix + '-' + args.name
for val_subset in val_subsets:
if args.val_subset is not None:
output_fname = path_prefix + '-' + str(val_subset) + '.txt'
else:
output_fname = path_prefix + '.txt'
output_xls = path_prefix + '.xls'
with open(output_fname, 'w') as fo:
print("Validation subset {}".format(val_subset))
gen = ds.generator(val_subset, loop=False)
results = framework.validate(sess, gen, keys=keys, summary=False, predict=True, show_tqdm=True)
##################image save#########################
image_save_path = os.path.join('./test_images', short_name(checkpoint))#+'_onebyone'
if not os.path.isdir(image_save_path):
os.makedirs(image_save_path)
''' for i in range(len(results['image_fixed'])):
print(results['id2'][i])
writer = sitk.ImageFileWriter()
sitk.WriteImage(sitk.GetImageFromArray(np.squeeze(results['image_fixed'][i][:,:,:,0])), image_save_path+'/'+results['id2'][i]+'_fixed.mhd', True)
sitk.WriteImage(sitk.GetImageFromArray(np.squeeze(results['warped_moving'][i][:,:,:,0])), image_save_path+'/'+results['id2'][i]+'_moving.mhd', True)
sitk.WriteImage(sitk.GetImageFromArray(np.squeeze(results['moving_img'][i][:,:,:,0])), image_save_path+'/'+results['id2'][i]+'_moving_raw.mhd', True)
sitk.WriteImage(sitk.GetImageFromArray(np.squeeze(results['real_flow'][i][:,:,:,:])), image_save_path+'/'+results['id2'][i]+'_flow.mhd', True)
warped_seg = np.squeeze(np.zeros(results['warped_seg_moving'][i][:,:,:,0].shape))
seg_fixed = np.squeeze(np.zeros(results['warped_seg_moving'][i][:,:,:,0].shape))
for seg in range(results['warped_seg_moving'][i].shape[-1]):
sub_warp = np.squeeze((results['warped_seg_moving'][i])[:,:,:,seg])
sub_warp = np.where(sub_warp>127.5,seg+1,0)
sub_seg = np.squeeze((results['seg_fixed'][i])[:,:,:,seg])
sub_seg = np.where(sub_seg>127.5,seg+1,0)
warped_seg += sub_warp
seg_fixed += sub_seg
sitk.WriteImage(sitk.GetImageFromArray(warped_seg), image_save_path+'/'+results['id2'][i]+'_warped_seg.mhd', True)
sitk.WriteImage(sitk.GetImageFromArray(seg_fixed), image_save_path+'/'+results['id2'][i]+'_seg_fixed.mhd', True) '''
for i in range(len(results['dices'])):
print(results['id1'][i],results['id2'][i],np.mean(results['dices'][i]),np.mean(results['jaccs'][i]), np.mean(results['landmark_dists'][i]),results['det_A'][i], file=fo)#
writebook.save(output_xls)
print('Summary', file=fo)
jaccs, dices, landmarks,ncc = results['jaccs'], results['dices'], results['landmark_dists'],results['total_ncc']
print("Dice score: {} ({})".format(np.mean(dices), np.std(
np.mean(dices, axis=-1))), file=fo)
print("Jacc score: {} ({})".format(np.mean(jaccs), np.std(
np.mean(jaccs, axis=-1))), file=fo)
print("ncc score: {} ({})".format(np.mean(ncc), np.std(
np.mean(ncc, axis=-1))), file=fo)
print("Landmark distance: {} ({})".format(np.mean(landmarks), np.std(
np.mean(landmarks, axis=-1))), file=fo)
########################################
for seg in range(results['dices'].shape[1]):
print("dice score for seg {}: {}".format(seg, np.mean(
results['dices'][:,seg])), file=fo)
###########################################
def cbimage(img1, img2):
shape = img1.shape
num = 20
grid = np.zeros(shape)
bg_p_x = [int(shape[0]/num*i) for i in range(num)]
bg_p_y = [int(shape[1]/num*i) for i in range(num)]
for i in range(0, num, 2):
for j in range(0, num, 2):
grid[bg_p_x[i]:bg_p_x[i]+shape[0]//num,bg_p_y[j]:bg_p_y[j]+shape[1]//num] = 1
grid[bg_p_x[i+1]:bg_p_x[i+1]+shape[0]//num,bg_p_y[j+1]:bg_p_y[j+1]+shape[1]//num] = 1
img1_grid = img1*grid
img2_grid = img2*(1-grid)
cbimage = img1_grid+img2_grid
return cbimage
def short_name(checkpoint):
cpath, steps = os.path.split(checkpoint)
_, exp = os.path.split(cpath)
return exp + '-' + steps
def RenderFlow(flow, coef = 15, channel = (0, 1, 2), thresh = 1):
flow = flow[:, :, 64]
im_flow = np.stack([flow[:, :, c] for c in channel], axis = -1)
#im_flow = 0.5 + im_flow / coef
'''im_flow = np.abs(im_flow)
im_flow = np.exp(-im_flow / coef)
im_flow = im_flow * thresh
#im_flow = 1 - im_flow / 20
'''
return im_flow
def find_checkpoint_step(checkpoint_path, target_steps=None):
pattern = re.compile(r'model-(\d+).index')
checkpoints = []
for f in os.listdir(checkpoint_path):
m = pattern.match(f)
if m:
steps = int(m.group(1))
checkpoints.append((-steps if target_steps is None else abs(
target_steps - steps), os.path.join(checkpoint_path, f.replace('.index', ''))))
return min(checkpoints, key=lambda x: x[0])[1]
if __name__ == '__main__':
main()