def test_deepcell_transform_2d(self):
# test single edge class
maskstack = np.array([label(i) for i in _generate_test_masks()])
dc_maskstack = transform_utils.deepcell_transform(
maskstack, data_format=None, separate_edge_classes=False)
dc_maskstack_dil = transform_utils.deepcell_transform(
maskstack,
dilation_radius=1,
data_format='channels_last',
separate_edge_classes=False)
self.assertEqual(dc_maskstack.shape[-1], 3)
self.assertEqual(dc_maskstack_dil.shape[-1], 3)
self.assertGreater(
dc_maskstack_dil[..., 0].sum() + dc_maskstack_dil[..., 1].sum(),
dc_maskstack[..., 0].sum() + dc_maskstack[..., 1].sum())
# test separate edge classes
maskstack = np.array([label(i) for i in _generate_test_masks()])
dc_maskstack = transform_utils.deepcell_transform(
maskstack, data_format=None, separate_edge_classes=True)
dc_maskstack_dil = transform_utils.deepcell_transform(
maskstack,
dilation_radius=1,
data_format='channels_last',
separate_edge_classes=True)
self.assertEqual(dc_maskstack.shape[-1], 4)
self.assertEqual(dc_maskstack_dil.shape[-1], 4)
self.assertGreater(
dc_maskstack_dil[..., 0].sum() + dc_maskstack_dil[..., 1].sum(),
dc_maskstack[..., 0].sum() + dc_maskstack[..., 1].sum())
def test_deepcell_transform_2d(self):
maskstack = np.array([label(i) for i in _generate_test_masks()])
dc_maskstack = deepcell_transform(maskstack, data_format='channels_last')
dc_maskstack_dilated = deepcell_transform(
maskstack, dilation_radius=1, data_format='channels_last')
self.assertEqual(dc_maskstack.shape[-1], 4)
self.assertEqual(dc_maskstack_dilated.shape[-1], 4)
self.assertGreater(
dc_maskstack_dilated[:, :, :, 0].sum() + dc_maskstack_dilated[:, :, :, 1].sum(),
dc_maskstack[:, :, :, 0].sum() + dc_maskstack[:, :, :, 1].sum())
def test_deepcell_transform_3d(self):
frames = 10
img_list = []
for im in _generate_test_masks():
frame_list = []
for _ in range(frames):
frame_list.append(label(im))
img_stack = np.array(frame_list)
img_list.append(img_stack)
# test single edge class
maskstack = np.vstack(img_list)
batch_count = maskstack.shape[0] // frames
new_shape = (batch_count, frames, *maskstack.shape[1:])
maskstack = np.reshape(maskstack, new_shape)
dc_maskstack = transform_utils.deepcell_transform(
maskstack, data_format=None, separate_edge_classes=False)
dc_maskstack_dil = transform_utils.deepcell_transform(
maskstack,
dilation_radius=2,
data_format='channels_last',
separate_edge_classes=False)
self.assertEqual(dc_maskstack.shape[-1], 3)
self.assertEqual(dc_maskstack_dil.shape[-1], 3)
self.assertGreater(
dc_maskstack_dil[..., 0].sum() + dc_maskstack_dil[..., 1].sum(),
dc_maskstack[..., 0].sum() + dc_maskstack[..., 1].sum())
# test separate edge classes
maskstack = np.vstack(img_list)
batch_count = maskstack.shape[0] // frames
new_shape = (batch_count, frames, *maskstack.shape[1:])
maskstack = np.reshape(maskstack, new_shape)
dc_maskstack = transform_utils.deepcell_transform(
maskstack, data_format=None, separate_edge_classes=True)
dc_maskstack_dil = transform_utils.deepcell_transform(
maskstack,
dilation_radius=2,
data_format='channels_last',
separate_edge_classes=True)
self.assertEqual(dc_maskstack.shape[-1], 4)
self.assertEqual(dc_maskstack_dil.shape[-1], 4)
self.assertGreater(
dc_maskstack_dil[..., 0].sum() + dc_maskstack_dil[..., 1].sum(),
dc_maskstack[..., 0].sum() + dc_maskstack[..., 1].sum())
def test_deepcell_transform_3d(self):
frames = 10
img_list = []
for im in _generate_test_masks():
frame_list = []
for _ in range(frames):
frame_list.append(label(im))
img_stack = np.array(frame_list)
img_list.append(img_stack)
maskstack = np.vstack(img_list)
batch_count = maskstack.shape[0] // frames
maskstack = np.reshape(maskstack, (batch_count, frames, *maskstack.shape[1:]))
dc_maskstack = deepcell_transform(maskstack, data_format='channels_last')
dc_maskstack_dilated = deepcell_transform(
maskstack, dilation_radius=2, data_format='channels_last')
self.assertEqual(dc_maskstack.shape[-1], 4)
self.assertEqual(dc_maskstack_dilated.shape[-1], 4)
self.assertGreater(
dc_maskstack_dilated[:, :, :, :, 0].sum() + dc_maskstack_dilated[:, :, :, :, 1].sum(),
dc_maskstack[:, :, :, :, 0].sum() + dc_maskstack[:, :, :, :, 1].sum())