def test_doesnt_take_too_long_for_a_50_cube(self):
too_long_seconds = 0.1
width = 50
shape = (width,) * 3
size = reduce(mul, shape)
array = np.array([x % (size / 5) for x in range(size)], dtype=np.uint32)
whitelist = range(0, size / 5, 5)
start = time.time()
replace_array_except_whitelist(array, 0, whitelist)
execution_seconds = time.time() - start
self.assertLess(execution_seconds, too_long_seconds)
def test_doesnt_take_too_long_for_a_50_cube(self):
too_long_seconds = 0.1
width = 50
shape = (width, ) * 3
size = reduce(mul, shape)
array = np.array([x % (size / 5) for x in range(size)],
dtype=np.uint32)
whitelist = range(0, size / 5, 5)
start = time.time()
replace_array_except_whitelist(array, 0, whitelist)
execution_seconds = time.time() - start
self.assertLess(execution_seconds, too_long_seconds)
def test_works_with_ndarrays(self):
array = np.array([2, 0, 2, 3, 2, 3]).reshape((1, 3, 2))
whitelist = [3]
result = replace_array_except_whitelist(array, 0, whitelist)
expected = np.array([0, 0, 0, 3, 0, 3]).reshape((1, 3, 2))
assert result.shape == (1, 3, 2)
np.testing.assert_almost_equal(result, expected)
def test_works_with_ndarrays(self):
array = np.array([2, 0, 2, 3, 2, 3]).reshape((1, 3, 2))
whitelist = [3]
result = replace_array_except_whitelist(array, 0, whitelist)
expected = np.array([0, 0, 0, 3, 0, 3]).reshape((1, 3, 2))
assert result.shape == (1, 3, 2)
np.testing.assert_almost_equal(result, expected)
def get_datasets(dataset, origin):
# output_shape = (40, 30, 80)
# input_shape = (100, 110, 120)
output_shape = (2, 3, 4)
input_shape = tuple([x + 2 for x in output_shape])
borders = tuple([(in_ - out_) / 2
for (in_, out_) in zip(input_shape, output_shape)])
input_slices = tuple(
[slice(x, x + l) for x, l in zip(origin, input_shape)])
output_slices = tuple([
slice(x + b, x + b + l)
for x, b, l in zip(origin, borders, output_shape)
])
expected_dataset = dict()
data_slices = [slice(0, l) for l in dataset['data'].shape]
data_slices[-3:] = input_slices
data_slices = tuple(data_slices)
expected_data_array = np.array(dataset['data'][data_slices],
dtype=np.float32)
expected_data_array = expected_data_array.reshape((1, ) + input_shape)
expected_data_array /= (2.0**8)
expected_dataset['data'] = expected_data_array
components_slices = [slice(0, l) for l in dataset['components'].shape]
components_slices[-3:] = output_slices
components_slices = tuple(components_slices)
expected_components_array = np.array(
dataset['components'][components_slices]).reshape((1, ) +
output_shape)
if type(dataset['components']) is DVIDDataInstance:
print("Is DVIDDataInstance...")
print("uniques before:", np.unique(expected_components_array))
dvid_uuid = dataset['components'].uuid
body_names_to_exclude = dataset.get('body_names_to_exclude')
good_bodies = get_good_components(dvid_uuid, body_names_to_exclude)
expected_components_array = \
replace_array_except_whitelist(expected_components_array, 0, good_bodies)
print("uniques after:", np.unique(expected_components_array))
expected_dataset['components'] = expected_components_array
components_for_affinity_generation = expected_components_array.reshape(
output_shape)
expected_label = malis.seg_to_affgraph(
components_for_affinity_generation, malis.mknhood3d())
expected_dataset['label'] = expected_label
if type(dataset['components']) is DVIDDataInstance:
expected_mask = np.array(expected_components_array > 0).astype(
np.uint8)
else:
expected_mask = np.ones(shape=(1, ) + output_shape, dtype=np.uint8)
expected_dataset['mask'] = expected_mask
numpy_dataset = get_numpy_dataset(dataset, input_slices, output_slices,
True)
return numpy_dataset, expected_dataset
def get_datasets(dataset, origin):
# output_shape = (40, 30, 80)
# input_shape = (100, 110, 120)
output_shape = (2,3,4)
input_shape = tuple([x + 2 for x in output_shape])
borders = tuple([(in_ - out_) / 2 for (in_, out_) in zip(input_shape, output_shape)])
input_slices = tuple([slice(x, x + l) for x, l in zip(origin, input_shape)])
output_slices = tuple([slice(x + b, x + b + l) for x, b, l in zip(origin, borders, output_shape)])
expected_dataset = dict()
data_slices = [slice(0, l) for l in dataset['data'].shape]
data_slices[-3:] = input_slices
data_slices = tuple(data_slices)
expected_data_array = np.array(dataset['data'][data_slices], dtype=np.float32)
expected_data_array = expected_data_array.reshape((1,) + input_shape)
expected_data_array /= (2.0 ** 8)
expected_dataset['data'] = expected_data_array
components_slices = [slice(0, l) for l in dataset['components'].shape]
components_slices[-3:] = output_slices
components_slices = tuple(components_slices)
expected_components_array = np.array(dataset['components'][components_slices]).reshape((1,) + output_shape)
if type(dataset['components']) is DVIDDataInstance:
print("Is DVIDDataInstance...")
print("uniques before:", np.unique(expected_components_array))
dvid_uuid = dataset['components'].uuid
body_names_to_exclude = dataset.get('body_names_to_exclude')
good_bodies = get_good_components(dvid_uuid, body_names_to_exclude)
expected_components_array = \
replace_array_except_whitelist(expected_components_array, 0, good_bodies)
print("uniques after:", np.unique(expected_components_array))
expected_dataset['components'] = expected_components_array
components_for_affinity_generation = expected_components_array.reshape(output_shape)
expected_label = malis.seg_to_affgraph(components_for_affinity_generation, malis.mknhood3d())
expected_dataset['label'] = expected_label
if type(dataset['components']) is DVIDDataInstance:
expected_mask = np.array(expected_components_array > 0).astype(np.uint8)
else:
expected_mask = np.ones(shape=(1,) + output_shape, dtype=np.uint8)
expected_dataset['mask'] = expected_mask
numpy_dataset = get_numpy_dataset(dataset, input_slices, output_slices, True)
return numpy_dataset, expected_dataset
def test_replace_array_values(self):
array = np.array([2, 0, 2, 3, 2, 3])
whitelist = [3]
result = replace_array_except_whitelist(array, 0, whitelist)
expected = np.array([0, 0, 0, 3, 0, 3])
np.testing.assert_almost_equal(result, expected)
def test_replace_array_values(self):
array = np.array([2, 0, 2, 3, 2, 3])
whitelist = [3]
result = replace_array_except_whitelist(array, 0, whitelist)
expected = np.array([0, 0, 0, 3, 0, 3])
np.testing.assert_almost_equal(result, expected)
