results_filepath = net_dir + '/info.txt'
# Dbg?
isDbg = False
if isDbg:
dbg_step = 1 # step between images
min_dice_dbg = 0 #0.4
# params
orig_height, orig_width = (512, 512)
dc = utils.DataClass()
# Load data
_, val_filenames = utils.split_filenames_train_val(data_path, val_prec=0.15)
val_filenames_split = utils.split_to_patients(val_filenames)
val_masks_split = []
for i, val_filename in enumerate(val_filenames_split):
val_filenames_split[i] = [
os.path.join(data_path, filename) for filename in val_filename
]
val_masks_split.append([
os.path.join(masks_path, filename).replace('ct', 'seg')
for filename in val_filename
])
# Display data info
num_patients = len(val_filenames_split)
num_files = len(val_filenames)
# Dbg?
isDbg = False
if isDbg:
dbg_step = 10 # step between images
min_dice_dbg = 0 #0.5
# params
smooth = 1
orig_height, orig_width = (512, 512)
liver_crop_h, liver_crop_w = (320, 320)
dc = utils.DataClass()
# Load data
_, val_filenames = utils.split_filenames_train_val(data_path, is_sort=True)
val_filenames_split = utils.split_to_patients(val_filenames)
# Display data info
num_patients = len(val_filenames_split)
num_files = len(val_filenames)
# Load liver crops info
dict_file = liver_crops_dir.replace('ct', 'seg') + '/crop_list.p'
try:
import cPickle as pickle
except ImportError: # python 3.x
import pickle
with open(dict_file, 'rb') as fp:
crop_dict = pickle.load(fp)
masks_crop_dir = liver_crops_dir.replace('ct', 'seg')
# create output paths
data_path = src_dir + 'ct_test_p' # all test
dst_data_path = src_dir + 'ct_liver_crops_test'
dst_masks_path = src_dir + 'seg_liver_crops_test'
if not os.path.exists(dst_data_path):
os.mkdir(dst_data_path)
if not os.path.exists(dst_masks_path):
os.mkdir(dst_masks_path)
# Init liver crops dict
crop_dict = {}
# Load data
print('Running liver detection on data!!!!!\n')
filenames, _ = utils.split_filenames_train_val(data_path, val_prec=0)
all_filenames_split = utils.split_to_patients(filenames)
# Display data info
num_patients = len(all_filenames_split)
num_files = sum([len(x) for x in all_filenames_split])
print('number of samples: ', num_files)
print('num patients: ', num_patients)
# Load liver segmentation model + weights
print('\ngetting liver model...')
model_liver = get_model(Config, inference=True)
print('loading liver weight:', weights_path)
model_liver.load_weights(weights_path)
print('\nDone!\n')