def test_shift_and_crop_static(self):
shift_node = gp.ShiftAugment(sigma=1, shift_axis=0)
shift_node.ndim = 2
upstream_arr = np.arange(16).reshape(4, 4)
sub_shift_array = np.zeros(8, dtype=int).reshape(4, 2)
roi_shape = (4, 4)
voxel_size = gp.Coordinate((1, 1))
downstream_arr = np.arange(16).reshape(4, 4)
result = shift_node.shift_and_crop(upstream_arr, roi_shape,
sub_shift_array, voxel_size)
self.assertTrue(np.array_equal(result, downstream_arr))
def test_shift_and_crop3(self):
shift_node = gp.ShiftAugment(sigma=1, shift_axis=1)
shift_node.ndim = 2
upstream_arr = np.arange(16).reshape(4, 4)
sub_shift_array = np.zeros(8, dtype=int).reshape(4, 2)
sub_shift_array[:, 0] = np.array([0, 1, 0, 2], dtype=int)
roi_shape = (2, 4)
voxel_size = gp.Coordinate((1, 1))
downstream_arr = np.array([[8, 5, 10, 3], [12, 9, 14, 7]], dtype=int)
result = shift_node.shift_and_crop(upstream_arr, roi_shape,
sub_shift_array, voxel_size)
# print(result)
self.assertTrue(np.array_equal(result, downstream_arr))
def test_shift_and_crop2(self):
shift_node = gp.ShiftAugment(sigma=1, shift_axis=0)
shift_node.ndim = 2
upstream_arr = np.arange(16).reshape(4, 4)
sub_shift_array = np.zeros(8, dtype=int).reshape(4, 2)
sub_shift_array[:, 1] = np.array([0, -1, -2, 0], dtype=int)
roi_shape = (4, 2)
voxel_size = gp.Coordinate((1, 1))
downstream_arr = np.array([[0, 1], [5, 6], [10, 11], [12, 13]],
dtype=int)
result = shift_node.shift_and_crop(upstream_arr, roi_shape,
sub_shift_array, voxel_size)
self.assertTrue(np.array_equal(result, downstream_arr))
def test_pipeline3(self):
array_key = gp.ArrayKey("TEST_ARRAY")
points_key = gp.PointsKey("TEST_POINTS")
voxel_size = gp.Coordinate((1, 1))
spec = gp.ArraySpec(voxel_size=voxel_size, interpolatable=True)
hdf5_source = gp.Hdf5Source(self.fake_data_file,
{array_key: 'testdata'},
array_specs={array_key: spec})
csv_source = gp.CsvPointsSource(
self.fake_points_file, points_key,
gp.PointsSpec(
roi=gp.Roi(shape=gp.Coordinate((100, 100)), offset=(0, 0))))
request = gp.BatchRequest()
shape = gp.Coordinate((60, 60))
request.add(array_key, shape, voxel_size=gp.Coordinate((1, 1)))
request.add(points_key, shape)
shift_node = gp.ShiftAugment(prob_slip=0.2,
prob_shift=0.2,
sigma=5,
shift_axis=0)
pipeline = ((hdf5_source, csv_source) + gp.MergeProvider() +
gp.RandomLocation(ensure_nonempty=points_key) + shift_node)
with gp.build(pipeline) as b:
request = b.request_batch(request)
# print(request[points_key])
target_vals = [
self.fake_data[point[0]][point[1]] for point in self.fake_points
]
result_data = request[array_key].data
result_points = request[points_key].data
result_vals = [
result_data[int(point.location[0])][int(point.location[1])]
for point in result_points.values()
]
for result_val in result_vals:
self.assertTrue(
result_val in target_vals,
msg=
"result value {} at points {} not in target values {} at points {}"
.format(result_val, list(result_points.values()), target_vals,
self.fake_points))
def test_prepare1(self):
key = gp.ArrayKey("TEST_ARRAY")
spec = gp.ArraySpec(voxel_size=gp.Coordinate((1, 1)),
interpolatable=True)
hdf5_source = gp.Hdf5Source(self.fake_data_file, {key: 'testdata'},
array_specs={key: spec})
request = gp.BatchRequest()
shape = gp.Coordinate((3, 3))
request.add(key, shape, voxel_size=gp.Coordinate((1, 1)))
shift_node = gp.ShiftAugment(sigma=1, shift_axis=0)
with gp.build((hdf5_source + shift_node)):
shift_node.prepare(request)
self.assertTrue(shift_node.ndim == 2)
self.assertTrue(shift_node.shift_sigmas == tuple([0.0, 1.0]))
def test_pipeline2(self):
key = gp.ArrayKey("TEST_ARRAY")
spec = gp.ArraySpec(voxel_size=gp.Coordinate((3, 1)),
interpolatable=True)
hdf5_source = gp.Hdf5Source(self.fake_data_file, {key: 'testdata'},
array_specs={key: spec})
request = gp.BatchRequest()
shape = gp.Coordinate((3, 3))
request.add(key, shape, voxel_size=gp.Coordinate((3, 1)))
shift_node = gp.ShiftAugment(prob_slip=0.2,
prob_shift=0.2,
sigma=1,
shift_axis=0)
with gp.build((hdf5_source + shift_node)) as b:
b.request_batch(request)