def run(self, sv_image, sv_path):
os.chdir(file_dir+'dl_template')
print os.getcwd()
nn_type = self.args_dict['nn_type']
spacing = float(self.args_dict['spacing'])
threshold = float(self.args_dict['threshold'])
global_config = io.load_yaml("./config/global.yaml")
ext = global_config["CROP_DIMS"]
if nn_type == "norm":
config = io.load_yaml("./config/case1_perturb15.yaml")
normalize = norm
if nn_type == "window":
config = io.load_yaml("./config/i2i_window.yaml")
center = int(self.args_dict['center'])
window = int(self.args_dict['window'])
normalize = build_window(center, window)
net = I2INet.Model(global_config, config)
net.load()
path_points = sv_path.get_path_points()
for i in tqdm(range(len(path_points))):
d = path_points[i]
fn = d['name']
p = d['p']
n = d['n']
x = d['x']
im = sv_image.get_reslice(ext,p,n,x,spacing)
im = normalize(im)
out = net.predict(im)[0,:,:,0]
out[out >= threshold] = 1
out[out < threshold] = 0
filename = self.output_directory+"{}{}".format(fn,self.output_file_type)
filename = call_dir + filename[1:]
# print filename
save_output(out,filename)
import os
import argparse
from modules import io
from modules import vascular_data as sv
import scipy
import numpy as np
from imageio import imsave
global_config_file = "./config/global.yaml"
case_config_file = "./config/case.yaml"
global_config = io.load_yaml(global_config_file)
case_config = io.load_yaml(case_config_file)
####################################
# Get necessary params
####################################
cases = os.listdir(global_config['CASES_DIR'])
cases = [global_config['CASES_DIR'] + '/' + f for f in cases if 'case.' in f]
spacing_vec = [case_config['SPACING']] * 2
dims_vec = [case_config['DIMS']] * 2
ext_vec = [case_config['DIMS'] - 1] * 2
path_start = case_config['PATH_START']
files = open(case_config['DATA_DIR'] + '/files.txt', 'w')
for i, case_fn in enumerate(cases):
case_dict = io.load_yaml(case_fn)
print(case_dict['NAME'])
from modules import io
config = io.load_yaml('./config/global.yaml')
cases_dir = config['CASES_DIR']
cases_prefix = config['CASES_PREFIX']
images = open(cases_dir + '/images.txt').readlines()
images = [f.replace('\n', '') for f in images]
truths = open(cases_dir + '/truths.txt')
truths = [f.replace('\n', '') for f in truths]
groups = open(cases_dir + '/groups.txt')
groups = [f.replace('\n', '') for f in groups]
paths = open(cases_dir + '/paths.txt')
paths = [f.replace('\n', '') for f in paths]
for i in range(len(images)):
d = {}
d['IMAGE'] = cases_prefix + images[i]
d['SEGMENTATION'] = cases_prefix + truths[i]
d['GROUPS'] = cases_prefix + groups[i]
d['PATHS'] = cases_prefix + paths[i]
name = images[i].split('/')[-1].replace('-cm.mha','')\
.replace('-image.mha','').replace('_contrast.mha','')
d['NAME'] = name
import subprocess
import os
from modules import io
dir_ = './data/cases'
cases = os.listdir(dir_)
config_file = './config/global.yaml'
config = io.load_yaml(config_file)
f = open(config['DATA_DIR'] + '/files.txt', 'w')
f.close()
for c in cases:
f = dir_ + '/' + c
print(f)
subprocess.check_call('python process_data.py {} {}'.format(
config_file, f),
shell=True)