def test_sync_label_counts(self):
img_path = os.path.join(base_path, '../test_data/tiffconnector_1/im/')
label_path = os.path.join(base_path,
'../test_data/tiffconnector_1/labels/')
c = TiffConnector(img_path, label_path)
c1, c2 = c.split(1. / 3.)
d1 = Dataset(c1)
d2 = Dataset(c2)
d1.sync_label_counts(d2)
np.testing.assert_array_equal(set(d1.label_counts.keys()),
set(d2.label_counts.keys()))
assert_array_equal(d1.label_counts[1], np.array([4]))
assert_array_equal(d1.label_counts[2], np.array([3]))
assert_array_equal(d1.label_counts[3], np.array([3]))
assert_array_equal(d2.label_counts[1], np.array([0, 0]))
assert_array_equal(d2.label_counts[2], np.array([0, 11]))
assert_array_equal(d2.label_counts[3], np.array([0, 3]))
d1 = Dataset(c1)
d2 = Dataset(c2)
d2.sync_label_counts(d1)
np.testing.assert_array_equal(set(d1.label_counts.keys()),
set(d2.label_counts.keys()))
assert_array_equal(d1.label_counts[1], np.array([4]))
assert_array_equal(d1.label_counts[2], np.array([3]))
assert_array_equal(d1.label_counts[3], np.array([3]))
assert_array_equal(d2.label_counts[1], np.array([0, 0]))
assert_array_equal(d2.label_counts[2], np.array([0, 11]))
assert_array_equal(d2.label_counts[3], np.array([0, 3]))
def test_check_label_matrix_dimensions(self):
img_path = os.path.join(base_path,
'../test_data/tiffconnector_1/im/*.tif')
label_path = os.path.join(
base_path,
'../test_data/tiffconnector_1/labels_multichannel/*.tif')
c = TiffConnector(img_path, label_path)
c.check_label_matrix_dimensions()
def test_map_label_values_3(self):
img_path = os.path.join(base_path, '../test_data/tiffconnector_1/im/')
label_path = os.path.join(base_path,
'../test_data/tiffconnector_1/labels/')
c = TiffConnector(img_path, label_path)
original_labels = c.original_label_values_for_all_images()
res = c.calc_label_values_mapping(original_labels)
self.assertEqual(res, [{91: 1, 109: 2, 150: 3}])
def test_put_tile_multichannel(self):
img_path = os.path.join(base_path,
'../test_data/tiffconnector_1/im/*.tif')
label_path = os.path.join(base_path,
'../test_data/tiffconnector_1/labels/*.tif')
# savepath = os.path.join(
# base_path, '../test_data')
path = os.path.join(self.tmpdir, '6width4height3slices.tif')
c = TiffConnector(img_path, label_path, savepath=self.tmpdir)
pixels = np.array([[[.1, .2, .3], [.4, .5, .6]]], dtype=np.float32)
label_value = 3
c.put_tile(pixels,
pos_zxy=(0, 1, 1),
image_nr=2,
label_value=label_value,
multichannel=3)
slices = c._open_probability_map_file(2, 3, multichannel=3)
print(slices.shape)
probim = np.moveaxis(slices, (0, 1, 2, 3), (1, 3, 2, 0))
probim = probim[2:3, :, :, :]
val = \
np.array([[[[0., 0., 0., 0.],
[0., 0.1, 0.2, 0.3],
[0., 0.4, 0.5, 0.6],
[0., 0., 0., 0.],
[0., 0., 0., 0.],
[0., 0., 0., 0.]],
[[0., 0., 0., 0.],
[0., 0., 0., 0.],
[0., 0., 0., 0.],
[0., 0., 0., 0.],
[0., 0., 0., 0.],
[0., 0., 0., 0.]],
[[0., 0., 0., 0.],
[0., 0., 0., 0.],
[0., 0., 0., 0.],
[0., 0., 0., 0.],
[0., 0., 0., 0.],
[0., 0., 0., 0.]]]], dtype=np.float32)
np.testing.assert_array_equal(val, probim)
try:
os.remove(path)
except FileNotFoundError:
pass
def test_original_label_values(self):
img_path = os.path.join(base_path,
'../test_data/tiffconnector_1/im/*.tif')
label_path = os.path.join(
base_path,
'../test_data/tiffconnector_1/labels_multichannel/*.tif')
c = TiffConnector(img_path, label_path)
res = c.original_label_values_for_all_images()
self.assertEqual(res, [{91, 109, 150}, {91, 109, 150}])
def test_label_count_for_image(self):
img_path = os.path.join(base_path,
'../test_data/tiffconnector_1/im/*.tif')
label_path = os.path.join(base_path,
'../test_data/tiffconnector_1/labels/*.tif')
c = TiffConnector(img_path, label_path)
count = c.label_count_for_image(2)
print(c.labelvalue_mapping)
self.assertEqual(count, {2: 11, 3: 3})
def test_map_label_values_4(self):
img_path = os.path.join(
base_path,
'../test_data/tiffconnector_1/im/6width4height3slices_rgb.tif')
label_path = os.path.join(base_path,
'../test_data/tiffconnector_1/labels/*.tif')
c = TiffConnector(img_path, label_path)
original_labels = c.original_label_values_for_all_images()
c.calc_label_values_mapping(original_labels)
self.assertEqual(c.labelvalue_mapping, [{109: 1, 150: 2}])
def test_put_tile_1(self):
img_path = os.path.join(base_path,
'../test_data/tiffconnector_1/im/*.tif')
label_path = os.path.join(base_path,
'../test_data/tiffconnector_1/labels/*.tif')
savepath = tempfile.TemporaryDirectory()
c = TiffConnector(img_path, label_path, savepath=savepath.name)
pixels = np.array([[[.1, .2, .3], [.4, .5, .6]]], dtype=np.float32)
path = os.path.join(savepath.name,
'6width4height3slices_rgb_class_3.tif')
try:
os.remove(path)
except FileNotFoundError:
pass
c.put_tile(pixels, pos_zxy=(0, 1, 1), image_nr=2, label_value=3)
slices = c._open_probability_map_file(2, 3)
probim = np.array([[slices[0, 0, z].T for z in range(3)]])
val = \
np.array([[[[0., 0., 0., 0.],
[0., 0.1, 0.2, 0.3],
[0., 0.4, 0.5, 0.6],
[0., 0., 0., 0.],
[0., 0., 0., 0.],
[0., 0., 0., 0.]],
[[0., 0., 0., 0.],
[0., 0., 0., 0.],
[0., 0., 0., 0.],
[0., 0., 0., 0.],
[0., 0., 0., 0.],
[0., 0., 0., 0.]],
[[0., 0., 0., 0.],
[0., 0., 0., 0.],
[0., 0., 0., 0.],
[0., 0., 0., 0.],
[0., 0., 0., 0.],
[0., 0., 0., 0.]]]], dtype=np.float32)
np.testing.assert_array_equal(val, probim)
try:
os.remove(path)
except FileNotFoundError:
pass
def test_get_weight_tile_for_label_2(self):
img_path = os.path.join(base_path, '../test_data/tiffconnector_1/im/')
label_path = os.path.join(base_path,
'../test_data/tiffconnector_1/labels/')
c = TiffConnector(img_path, label_path)
d = Dataset(c)
d.label_weights[3] = 1.2
img_nr = 2
pos_czxy = np.array((0, 0, 0))
size_czxy = np.array((1, 6, 4))
label_value = 3
mat = d._get_weights_tile(img_nr, pos_czxy, size_czxy, label_value)
val = np.zeros(size_czxy)
print('valshae')
print(val.shape)
val[0, 0, 1] = 1.2
val[0, 4, 1] = 1.2
val[0, 5, 1] = 1.2
pprint('mat')
pprint(mat)
pprint(val)
self.assertTrue((val == mat).all())
def io_connector(image_path, label_path, *args, **kwds):
'''
Returns either a TiffConnector or an NapariConnector, depending on
input files.
Parameters
----------
image_path : str or list of str
Either wildcard or list of paths to pixel data in tiff format.
label_path : str of list of str
Either wildcard or list of paths to pixel data in tiff format
returns a TiffConnector. If a path to a single Ilastik ilp
file is given, an NapariConnector is returned.
Returns
-------
Connector
Either NapariConnector or TiffConnector
See Also
--------
yapic_io.tiff_connector.TiffConnector
yapic_io.napari_connector.NapariConnector
'''
from yapic_io.tiff_connector import TiffConnector
from yapic_io.napari_connector import NapariConnector
if label_path.endswith('.h5'):
logger.info('Napari project file detected')
return NapariConnector(image_path, label_path)
else:
logger.info('Tiff files detected.')
return TiffConnector(image_path, label_path, *args, **kwds)
def test_multichannel_pixel_tile_3(self):
img_path = os.path.join(base_path, '../test_data/tiffconnector_1/im/')
label_path = os.path.join(base_path,
'../test_data/tiffconnector_1/labels/')
c = TiffConnector(img_path, label_path)
d = Dataset(c)
img = 2
pos_zxy = (0, 0, 0)
size_zxy = (1, 4, 3)
channels = [1]
pd = (0, 2, 3)
val = np.array([[[[73, 73, 43, 89, 89, 102, 81, 87, 78],
[82, 68, 78, 37, 102, 102, 102, 37, 68],
[161, 153, 78, 87, 81, 102, 89, 82, 78],
[180, 180, 107, 87, 87, 37, 82, 87, 87],
[143, 121, 121, 107, 78, 68, 78, 87, 87],
[111, 111, 121, 180, 125, 73, 73, 78, 82],
[121, 111, 143, 161, 106, 82, 73, 68, 43],
[121, 180, 147, 123, 106, 106, 125, 68, 78]]]])
tile = d.multichannel_pixel_tile(img,
pos_zxy,
size_zxy,
channels,
pixel_padding=pd,
augment_params={'rotation_angle': 45})
self.assertTrue((tile == val).all())
def test_n_images(self):
img_path = os.path.join(base_path, '../test_data/tiffconnector_1/im/')
c = TiffConnector(img_path, 'path/to/nowhere/')
d = Dataset(c)
np.testing.assert_array_equal(d.n_images, 3)
def test_random_pos_izxy(self):
img_path = os.path.join(base_path, '../test_data/tiffconnector_1/im/')
label_path = os.path.join(
base_path, '../test_data/tiffconnector_1/labels/')
c = TiffConnector(img_path, label_path)
d = Dataset(c)
np.random.seed(42)
img_nr, pos_zxy = d._random_pos_izxy(label_value=1,
tile_size_zxy=(1, 2, 2))
assert_array_equal(img_nr, 0)
assert_array_equal(pos_zxy, [2, 7, 20])
# this tile has same size as image, pos must always be 0
img_nr, pos_zxy = d._random_pos_izxy(label_value=1,
tile_size_zxy=(3, 40, 26))
assert_array_equal(img_nr, 0)
assert_array_equal(pos_zxy, [0, 0, 0])
# this tile is larger than the image
with self.assertRaises(AssertionError):
d._random_pos_izxy(label_value=1,
tile_size_zxy=(3, 40, 27))
np.random.seed(None)
def test_random_training_tile(self):
img_path = os.path.join(base_path, '../test_data/tiffconnector_1/im/')
label_path = os.path.join(base_path,
'../test_data/tiffconnector_1/labels/')
c = TiffConnector(img_path, label_path)
d = Dataset(c)
size = (1, 3, 4)
pad = (1, 2, 2)
channels = [0, 1, 2]
pixel_shape_val = (3, 3, 7, 8)
weight_shape_val = (3, 1, 3, 4)
# mapping [{91: 1, 109: 2, 150: 3}]
labels_val = [1, 2, 3]
tile = d.random_training_tile(size,
channels,
pixel_padding=pad,
augment_params={'rotation_angle': 45})
np.testing.assert_array_equal(tile.pixels.shape, pixel_shape_val)
np.testing.assert_array_equal(tile.channels, channels)
np.testing.assert_array_equal(tile.weights.shape, weight_shape_val)
np.testing.assert_array_equal(tile.labels, labels_val)
def test_getitem_multichannel_labels(self):
# define data loacations
pixel_image_dir = os.path.join(base_path,
'../test_data/tiffconnector_1/im/')
label_image_dir = os.path.join(
base_path, '../test_data/tiffconnector_1/labels_multichannel/')
savepath = tempfile.TemporaryDirectory()
tile_size = (1, 5, 4) # size of network output layer in zxy
# padding of network input layer in zxy, in respect to output layer
padding = (0, 2, 2)
# make training_batch mb and prediction interface p with
# TiffConnector binding
c = TiffConnector(pixel_image_dir,
label_image_dir,
savepath=savepath.name)
m = TrainingBatch(Dataset(c), tile_size, padding_zxy=padding)
for counter, mini in enumerate(m):
# shape is (6, 6, 1, 5, 4):
# batchsize 6 , 6 label-classes, 1 z, 5 x, 4 y
weights = mini.weights()
# shape is (6, 3, 1, 9, 8):
# batchsize 6, 6 channels, 1 z, 9 x, 4 y (more xy due to padding)
pixels = mini.pixels()
self.assertEqual(weights.shape, (6, 6, 1, 5, 4))
self.assertEqual(pixels.shape, (6, 3, 1, 9, 8))
# apply training on mini.pixels and mini.weights goes here
if counter > 10: # m is infinite
break
def test_set_augmentation(self):
img_path = os.path.join(base_path, '../test_data/tiffconnector_1/im/')
label_path = os.path.join(base_path,
'../test_data/tiffconnector_1/labels/')
c = TiffConnector(img_path, label_path)
d = Dataset(c)
size = (1, 3, 4)
pad = (1, 2, 2)
m = TrainingBatch(d, size, padding_zxy=pad)
self.assertEqual(m.augmentation, {'flip'})
m.augment_by_rotation(True)
self.assertEqual(m.augmentation, {'flip', 'rotate'})
self.assertEqual(m.rotation_range, (-45, 45))
m.augment_by_shear(True)
self.assertEqual(m.augmentation, {'flip', 'rotate', 'shear'})
self.assertEqual(m.shear_range, (-5, 5))
m.augment_by_flipping(False)
self.assertEqual(m.augmentation, {'rotate', 'shear'})
def test_get_pixels_dimension_order(self):
img_path = os.path.join(base_path, '../test_data/tiffconnector_1/im/')
label_path = os.path.join(base_path,
'../test_data/tiffconnector_1/labels/')
c = TiffConnector(img_path, label_path)
d = Dataset(c)
size = (2, 5, 4)
pad = (0, 0, 0)
m = TrainingBatch(d, size, padding_zxy=pad)
b = next(m)
p = m.pixels()
w = m.weights()
self.assertEqual(p.shape, (3, 3, 2, 5, 4))
self.assertEqual(w.shape, (3, 3, 2, 5, 4))
m.set_pixel_dimension_order('bczxy')
p = m.pixels()
w = m.weights()
self.assertEqual(p.shape, (3, 3, 2, 5, 4))
self.assertEqual(w.shape, (3, 3, 2, 5, 4))
m.set_pixel_dimension_order('bzxyc')
p = m.pixels()
w = m.weights()
self.assertEqual(p.shape, (3, 2, 5, 4, 3))
self.assertEqual(w.shape, (3, 2, 5, 4, 3))
def test_get_tile(self):
img_path = os.path.join(
base_path, '../test_data/tiffconnector_1/together/img*.tif')
c = TiffConnector(img_path, 'path/to/nowhere/')
image_nr = 0
pos = (0, 0, 0, 0)
size = (1, 1, 1, 2)
tile = c.get_tile(image_nr=image_nr, pos=pos, size=size)
val = np.empty(shape=size)
val[0, 0, 0, 0] = 151
val[0, 0, 0, 1] = 151
val = val.astype(int)
print(val)
print(tile)
np.testing.assert_array_equal(tile, val)
def test_check_label_matrix_dimensions_2(self):
img_path = os.path.join(base_path, '../test_data/tiffconnector_1/im/')
label_path = os.path.join(
base_path,
'../test_data/tiffconnector_1/labels_multichannel_not_valid/')
self.assertRaises(AssertionError,
lambda: TiffConnector(img_path, label_path))
def test_random_training_tile_by_polling(self):
img_path = os.path.join(
base_path, '../test_data/tiffconnector_1/im/')
label_path = os.path.join(
base_path, '../test_data/tiffconnector_1/labels/')
size = (1, 4, 3)
channels = [0, 1, 2]
labels = set([1, 2, 3])
ensure_labelvalue = 2
c = TiffConnector(img_path, label_path)
d = Dataset(c)
# weights_val = np.array(
weights_val = np.array([[[[0., 0., 0.],
[0., 0., 0.],
[0., 0., 0.],
[0., 0., 0.]]],
[[[0., 0., 0.],
[0., 0., 0.],
[1., 1., 1.],
[1., 1., 1.]]],
[[[1., 0., 0.],
[0., 0., 0.],
[0., 0., 0.],
[0., 0., 0.]]]])
np.random.seed(43)
training_tile = d._random_training_tile_by_polling(
size, channels, labels,
ensure_labelvalue=ensure_labelvalue)
assert_array_equal(training_tile.weights, weights_val)
weights_val = np.array([[[[0., 0., 0.],
[0., 0., 0.],
[0., 0., 0.],
[0., 0., 0.]]],
[[[0., 0., 0.],
[0., 1., 1.],
[0., 1., 1.],
[0., 0., 0.]]],
[[[0., 0., 0.],
[0., 0., 0.],
[0., 0., 0.],
[0., 1., 0.]]]])
training_tile = d._random_training_tile_by_polling(
size, channels, labels,
ensure_labelvalue=None)
assert_array_equal(training_tile.weights, weights_val)
np.random.seed(None)
def test_split(self):
img_path = os.path.join(
base_path, '../test_data/tiffconnector_1/together/img*.tif')
lbl_path = os.path.join(
base_path, '../test_data/tiffconnector_1/together/lbl*.tif')
c = TiffConnector(img_path, lbl_path)
c1, c2 = c.split(0.5)
expected_names1 = [(Path('img_40width26height3slices_rgb.tif'),
Path('lbl_40width26height3slices_rgb.tif'))]
expected_names2 = [(Path('img_40width26height6slices_rgb.tif'), None),
(Path('img_6width4height3slices_rgb.tif'),
Path('lbl_6width4height3slices_rgb.tif'))]
self.assertEqual(set(c1.filenames), set(expected_names1))
self.assertEqual(set(c2.filenames), set(expected_names2))
# test for issue #1
self.assertEqual(c1.labelvalue_mapping, c.labelvalue_mapping)
self.assertEqual(c2.labelvalue_mapping, c.labelvalue_mapping)
def test_get_label_probs(self):
img_path = os.path.join(base_path, '../test_data/tiffconnector_1/im/')
label_path = os.path.join(base_path,
'../test_data/tiffconnector_1/labels/')
c = TiffConnector(img_path, label_path)
d = Dataset(c)
res = d._get_label_probs(label_value=None)
assert_array_almost_equal(res, [0.416667, 0., 0.583333])
res = d._get_label_probs(label_value=1)
assert_array_almost_equal(res, [1., 0., 0.])
def test_get_tile2(self):
img_path = os.path.join(base_path,
'../test_data/tiffconnector_1/c2z2y2x2.tif')
conn = TiffConnector(img_path, 'path/to/nowhere/')
image_nr = 0
pos = (0, 0, 0, 0)
size = (2, 2, 2, 2)
tile = conn.get_tile(image_nr=image_nr, pos=pos, size=size)
expected = [[[[
c * 2**3 + z * 2**2 + y * 2**1 + x * 2**0 for y in range(2)
] for x in range(2)] for z in range(2)] for c in range(2)]
np.testing.assert_array_equal(tile, expected)
size = (1, 1, 1, 1)
for c, z, x, y in itertools.product(range(2), repeat=4):
pos = (c, z, x, y)
tile = conn.get_tile(image_nr=image_nr, pos=pos, size=size)
v = c * 2**3 + z * 2**2 + y * 2**1 + x * 2**0
np.testing.assert_array_equal(tile, [[[[v]]]])
def test_put_tile_1(self):
img_path = os.path.join(base_path,
'../test_data/tiffconnector_1/im/*.tif')
label_path = os.path.join(base_path,
'../test_data/tiffconnector_1/labels/*.tif')
c = TiffConnector(img_path, label_path, savepath=self.tmpdir)
pixels = np.array([[[.1, .2, .3], [.4, .5, .6]]], dtype=np.float32)
path = os.path.join(self.tmpdir,
'6width4height3slices_rgb_class_3.tif')
c.put_tile(pixels, pos_zxy=(0, 1, 1), image_nr=2, label_value=3)
slices = c._open_probability_map_file(2, 3)
probim = np.moveaxis(slices, (0, 1, 2, 3), (1, 3, 2, 0))
val = \
np.array([[[[0., 0., 0., 0.],
[0., 0.1, 0.2, 0.3],
[0., 0.4, 0.5, 0.6],
[0., 0., 0., 0.],
[0., 0., 0., 0.],
[0., 0., 0., 0.]],
[[0., 0., 0., 0.],
[0., 0., 0., 0.],
[0., 0., 0., 0.],
[0., 0., 0., 0.],
[0., 0., 0., 0.],
[0., 0., 0., 0.]],
[[0., 0., 0., 0.],
[0., 0., 0., 0.],
[0., 0., 0., 0.],
[0., 0., 0., 0.],
[0., 0., 0., 0.],
[0., 0., 0., 0.]]]], dtype=np.float32)
np.testing.assert_array_equal(val, probim)
def test_normalize_zscore(self):
img_path = os.path.join(base_path, '../test_data/tiffconnector_1/im/')
label_path = os.path.join(base_path,
'../test_data/tiffconnector_1/labels/')
c = TiffConnector(img_path, label_path)
d = Dataset(c)
size = (1, 2, 2)
pad = (0, 0, 0)
batchsize = 2
nr_channels = 3
nz = 1
nx = 4
ny = 5
m = TrainingBatch(d,
size,
padding_zxy=pad,
batch_size=len(d.label_values()))
m.set_normalize_mode('local_z_score')
pixels = np.zeros((batchsize, nr_channels, nz, nx, ny))
pixels[:, 0, :, :, :] = 1
pixels[:, 1, :, :, :] = 2
pixels[:, 2, :, :, :] = 3
p_norm = m._normalize(pixels)
self.assertTrue((p_norm == 0).all())
# add variation
pixels[:, 0, 0, 0, 0] = 2
pixels[:, 0, 0, 0, 1] = 0
pixels[:, 1, 0, 0, 0] = 3
pixels[:, 1, 0, 0, 1] = 1
pixels[:, 2, 0, 0, 0] = 4
pixels[:, 2, 0, 0, 1] = 2
p_norm = m._normalize(pixels)
assert_array_equal(p_norm[:, 0, :, :, :], p_norm[:, 1, :, :, :])
assert_array_equal(p_norm[:, 0, :, :, :], p_norm[:, 2, :, :, :])
val = np.array([[[3.16227766, -3.16227766, 0., 0., 0.],
[0., 0., 0., 0., 0.], [0., 0., 0., 0., 0.],
[0., 0., 0., 0., 0.]]])
assert_array_almost_equal(val, p_norm[0, 0, :, :, :])
def test_load_label_filenames(self):
img_path = os.path.join(base_path,
'../test_data/tiffconnector_1/im/*.tif')
label_path = os.path.join(base_path,
'../test_data/tiffconnector_1/labels/*.tif')
c = TiffConnector(img_path, label_path)
self.assertEqual(c.filenames[0][1],
Path('40width26height3slices_rgb.tif'))
self.assertIsNone(c.filenames[1][1])
self.assertEqual(c.filenames[2][1],
Path('6width4height3slices_rgb.tif'))
def test_load_filenames(self):
img_path = os.path.join(base_path,
'../test_data/tiffconnector_1/im/*.tif')
c = TiffConnector(img_path, 'path/to/nowhere/')
img_filenames = [
Path('6width4height3slices_rgb.tif'),
Path('40width26height3slices_rgb.tif'),
Path('40width26height6slices_rgb.tif')
]
fnames = [e[0] for e in c.filenames]
self.assertEqual(set(img_filenames), set(fnames))
def test_set_label_weight(self):
img_path = os.path.join(base_path, '../test_data/tiffconnector_1/im/')
label_path = os.path.join(base_path,
'../test_data/tiffconnector_1/labels/')
c = TiffConnector(img_path, label_path)
d = Dataset(c)
d.label_weights[3] = 0.7
self.assertTrue(
(d.label_weights[3] == np.array([0.7, 0.7, 0.7, 0.7, 0.7,
0.7])).all())
self.assertTrue((d.label_weights[1] == np.array([1, 1, 1, 1])).all())
def test_random_tile(self):
img_path = os.path.join(base_path, '../test_data/tiffconnector_1/im/')
label_path = os.path.join(base_path,
'../test_data/tiffconnector_1/labels/')
c = TiffConnector(img_path, label_path)
d = Dataset(c)
size = (1, 3, 4)
pad = (1, 2, 2)
m = TrainingBatch(d, size, padding_zxy=pad)
m._random_tile(for_label=1)
def test_filter_labeled(self):
img_path = os.path.join(
base_path, '../test_data/tiffconnector_1/together/img*.tif')
lbl_path = os.path.join(
base_path, '../test_data/tiffconnector_1/together/lbl*.tif')
c = TiffConnector(img_path, lbl_path).filter_labeled()
expected_names = \
[(Path('img_40width26height3slices_rgb.tif'),
Path('lbl_40width26height3slices_rgb.tif')),
(Path('img_6width4height3slices_rgb.tif'),
Path('lbl_6width4height3slices_rgb.tif'))]
self.assertEqual(set(c.filenames), set(expected_names))