from keras.callbacks import Callback
from keras.utils.np_utils import to_categorical
import numpy as np
import nibabel as nb
import pandas as pd
import pylab as plt
from PIL import Image
import scipy.ndimage.interpolation as itp
from scipy.ndimage.measurements import center_of_mass
# Internal
import dvpy as dv
import segcnn
import os
cg = segcnn.Experiment()
#fs = segcnn.FileSystem(cg.base_dir, cg.data_dir,cg.local_dir)
adapt_size = (int(os.environ['CG_CROP_X']), int(os.environ['CG_CROP_Y']),
int(os.environ['CG_CROP_Z']))
def in_adapt(x, target=adapt_size):
x = nb.load(x).get_data()
x = dv.crop_or_pad(x, target)
x = np.expand_dims(x, axis=-1)
return x
def relabel(x):
# flip the label of LAA and LVOT
# Internal
import dvpy as dv
import segcnn
# experiments = OrderedDict()
# for spacing in ['1-0', '1-5', '2-0']:
# with open('./.experiments/left-heart-all-spacing-{}.p'.format(spacing), 'rb') as f:
# experiments[spacing] = pickle.load(f)
# with open('./experiments/00-all-both-1-5-spacing.sh', 'rb') as f:
# experiments['1-5'] = pickle.load(f)
# FIGURE_PATH=os.path.expandvars('${HOME}/Dropbox/datasets/valve-plane-detection-figures/')
# STAT_PATH = ""
experiment = segcnn.Experiment()
class_labels = {
0: 'Background',
1: 'LV',
2: 'LA',
3: 'LAA',
4: 'LVOT',
5: 'Ascending Aorta',
6: 'Left Inferior Pulmonary Vein',
7: 'Right Inferior Pulmonary Vein',
8: 'Left Superior Pulmonary Vein',
9: 'Right Superior Pulmonary Vein',
}
def calculate_iou():