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get_data.py
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get_data.py
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import numpy as np
import pickle
def dataset(tp, grid_size):
with open('ground_truth_dataset_{}.pickle'.format(grid_size),'rb') as tf:
gt = pickle.load(tf)
with open('mask_dataset_{}.pickle'.format(grid_size),'rb') as rf:
masks = pickle.load(rf)
with open('image_dataset_{}.pickle'.format(grid_size),'rb') as f:
images = pickle.load(f)
d = []
if tp == 'train':
for i in range(len(gt['train'])):
image = images['train'][i]
mask = masks['train'][i]
ground_truth = gt['train'][i]
d.append([convert(image),convert(mask),convert(ground_truth)])
elif tp == 'val':
for i in range(len(gt['validation'])):
image = images['validation'][i]
mask = masks['validation'][i]
ground_truth = gt['validation'][i]
d.append([convert(image),convert(mask),convert(ground_truth)])
elif tp == 'test':
for i in range(len(gt['test'])):
image = images['test'][i]
mask = masks['test'][i]
ground_truth = gt['test'][i]
d.append([convert(image),convert(mask),convert(ground_truth)])
return d
def convert(image):
return np.array([image,image,image])