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():