#!/usr/bin/env python
# -*- coding: utf-8 -*-
import numpy as np
from dataset import DatasetFactory, Config
from dataset.images import Image
Config().verbose = True
Config().dataset_type = 'wrapper_dataset'
ops = ('cropping', )
# val_ind
factory = DatasetFactory()
factory.add_image_type('image', 'label', 'mask', 'bounding_box')
factory.add_dataset(dataset_id='1', dirname='t_data_1', val_ind=[0, 2])
factory.add_dataset(dataset_id='2', dirname='t_data_2')
factory.add_dataset(dataset_id='3', dirname='t_data_3', val_ind=[0])
factory.add_training_operation(*ops)
t_dataset, v_dataset = factory.create()
t_keys = ['1/at1006', '2/at1025', '2/at1029', '3/at1034', '3/at1040']
v_keys = ['1/at1000', '1/at1007', '3/at1033']
assert list(t_dataset.images.keys()) == t_keys
assert list(v_dataset.images.keys()) == v_keys
for im in t_dataset[0]:
if hasattr(im, 'labels'):
print(im.labels)
assert isinstance(im, Image)
Config().dataset_type = 'dataset'
t_dataset, v_dataset = factory.create()
for im in t_dataset[0]:
#!/usr/bin/env python
# -*- coding: utf-8 -*-
import numpy as np
from dataset import DatasetFactory, Config
from dataset.trees import TensorTree
# Config().dataset_type = 'wrapper_dataset'
t_ops = ('cropping', 'label_normalization')
v_ops = ('cropping', 'label_normalization')
factory = DatasetFactory()
factory.add_image_type('image', 'hierachical_label', 'mask')
factory.add_dataset(dataset_id='tmc', dirname='data')
factory.add_dataset(dataset_id='kki', dirname='ped_data')
factory.add_training_operation(*t_ops)
t_dataset, v_dataset = factory.create()
indices = [0, 1, len(t_dataset) - 1]
tensor_trees = [t_dataset[ind][1] for ind in indices]
print(tensor_trees[-1])
tensor_tree = TensorTree.stack(tensor_trees)
print(tensor_tree)
