def test_mesmer_app(self):
with self.cached_session():
whole_cell_classes = [1, 3]
nuclear_classes = [1, 3]
num_semantic_classes = whole_cell_classes + nuclear_classes
num_semantic_heads = len(num_semantic_classes)
model = PanopticNet('resnet50',
input_shape=(256, 256, 2),
norm_method=None,
num_semantic_heads=num_semantic_heads,
num_semantic_classes=num_semantic_classes,
location=True,
include_top=True,
use_imagenet=False)
app = Mesmer(model)
# test output shape
shape = app.model.output_shape
self.assertIsInstance(shape, list)
self.assertEqual(len(shape), 4)
# test predict with default
x = np.random.rand(1, 500, 500, 2)
y = app.predict(x)
self.assertEqual(x.shape[:-1], y.shape[:-1])
# test predict with nuclear compartment only
x = np.random.rand(1, 500, 500, 2)
y = app.predict(x, compartment='nuclear')
self.assertEqual(x.shape[:-1], y.shape[:-1])
self.assertEqual(y.shape[-1], 1)
# test predict with cell compartment only
x = np.random.rand(1, 500, 500, 2)
y = app.predict(x, compartment='whole-cell')
self.assertEqual(x.shape[:-1], y.shape[:-1])
self.assertEqual(y.shape[-1], 1)
# test predict with both cell and nuclear compartments
x = np.random.rand(1, 500, 500, 2)
y = app.predict(x, compartment='both')
self.assertEqual(x.shape[:-1], y.shape[:-1])
self.assertEqual(y.shape[-1], 2)
# test legacy version
old_app = MultiplexSegmentation(model)
# test predict with default
x = np.random.rand(1, 500, 500, 2)
y = old_app.predict(x)
self.assertEqual(x.shape[:-1], y.shape[:-1])
def test_multiplex_app(self):
with self.cached_session():
app = MultiplexSegmentation(use_pretrained_weights=False)
# test output shape
shape = app.model.output_shape
self.assertIsInstance(shape, list)
self.assertEqual(len(shape), 4)
# test predict
x = np.random.rand(1, 500, 500, 2)
y = app.predict(x)
self.assertEqual(x.shape[:-1], y.shape[:-1])
def deepcell_segment(image: Array, compartment: str = None) -> Array:
from deepcell.applications import (
MultiplexSegmentation,
NuclearSegmentation,
CytoplasmSegmentation,
)
if compartment is None:
# If image has more than one channel i.e. CYX
# assume segmentation of both compartments, otherwise nuclear
compartment = "both" if image.shape[-1] > 1 else "nuclear"
if compartment == "nuclear":
app = NuclearSegmentation()
pred = app.predict(image).squeeze()
if compartment == "cytoplasm":
app = CytoplasmSegmentation()
pred = app.predict(image).squeeze()
if compartment == "both":
app = MultiplexSegmentation()
pred = app.predict(image, compartment=compartment).squeeze()
if len(pred.shape) == 4:
pred = resize(pred, image.shape[1:])
return pred
def test_multiplex_app(self):
with self.cached_session():
app = MultiplexSegmentation(use_pretrained_weights=False)
# test output shape
shape = app.model.output_shape
self.assertIsInstance(shape, list)
self.assertEqual(len(shape), 4)
# test predict with default
x = np.random.rand(1, 500, 500, 2)
y = app.predict(x)
self.assertEqual(x.shape[:-1], y.shape[:-1])
# test predict with nuclear compartment only
x = np.random.rand(1, 500, 500, 2)
y = app.predict(x, compartment='nuclear')
self.assertEqual(x.shape[:-1], y.shape[:-1])
self.assertEqual(y.shape[-1], 1)
# test predict with cell compartment only
x = np.random.rand(1, 500, 500, 2)
y = app.predict(x, compartment='whole-cell')
self.assertEqual(x.shape[:-1], y.shape[:-1])
self.assertEqual(y.shape[-1], 1)
# test predict with both cell and nuclear compartments
x = np.random.rand(1, 500, 500, 2)
y = app.predict(x, compartment='both')
print("x shape is {}, y shape is {}".format(x.shape, y.shape))
self.assertEqual(x.shape[:-1], y.shape[:-1])
self.assertEqual(y.shape[-1], 2)
class TestMultiplexSegmentation(test.TestCase):
def setUp(self):
self.app = MultiplexSegmentation(use_pretrained_weights=False)
def test_multiplex_app(self):
# Check shape parameters
shape = self.app.model.output_shape
self.assertIsInstance(shape, list)
self.assertEqual(len(shape), 4)
def test_predict(self):
x = np.random.rand(1, 500, 500, 2)
y = self.app.predict(x)
self.assertEqual(x.shape[:-1], y.shape[:-1])
def setUp(self):
self.app = MultiplexSegmentation(use_pretrained_weights=False)
def test_mesmer_app(self):
with self.cached_session():
whole_cell_classes = [1, 3]
nuclear_classes = [1, 3]
num_semantic_classes = whole_cell_classes + nuclear_classes
num_semantic_heads = len(num_semantic_classes)
model = PanopticNet('resnet50',
input_shape=(256, 256, 2),
norm_method=None,
num_semantic_heads=num_semantic_heads,
num_semantic_classes=num_semantic_classes,
location=True,
include_top=True,
use_imagenet=False)
app = Mesmer(model)
# test output shape
shape = app.model.output_shape
self.assertIsInstance(shape, list)
self.assertEqual(len(shape), 4)
# test predict with default
x = np.random.rand(1, 500, 500, 2)
y = app.predict(x)
self.assertEqual(x.shape[:-1], y.shape[:-1])
# test predict with nuclear compartment only
x = np.random.rand(1, 500, 500, 2)
y = app.predict(x, compartment='nuclear')
self.assertEqual(x.shape[:-1], y.shape[:-1])
self.assertEqual(y.shape[-1], 1)
# test predict with cell compartment only
x = np.random.rand(1, 500, 500, 2)
y = app.predict(x, compartment='whole-cell')
self.assertEqual(x.shape[:-1], y.shape[:-1])
self.assertEqual(y.shape[-1], 1)
# test predict with both cell and nuclear compartments
x = np.random.rand(1, 500, 500, 2)
y = app.predict(x, compartment='both')
self.assertEqual(x.shape[:-1], y.shape[:-1])
self.assertEqual(y.shape[-1], 2)
# test that kwargs are passed through successfully
app._predict_segmentation = Mock()
# get defaults
_ = app.predict(x, compartment='whole-cell')
args = app._predict_segmentation.call_args[1]
default_cell_kwargs = args['postprocess_kwargs'][
'whole_cell_kwargs']
default_nuc_kwargs = args['postprocess_kwargs']['nuclear_kwargs']
# change one of the args for each compartment
maxima_threshold_cell = default_cell_kwargs[
'maxima_threshold'] + 0.1
radius_nuc = default_nuc_kwargs['radius'] + 2
_ = app.predict(x,
compartment='whole-cell',
postprocess_kwargs_whole_cell={
'maxima_threshold': maxima_threshold_cell
},
postprocess_kwargs_nuclear={'radius': radius_nuc})
args = app._predict_segmentation.call_args[1]
cell_kwargs = args['postprocess_kwargs']['whole_cell_kwargs']
assert cell_kwargs['maxima_threshold'] == maxima_threshold_cell
nuc_kwargs = args['postprocess_kwargs']['nuclear_kwargs']
assert nuc_kwargs['radius'] == radius_nuc
# check that rest are unchanged
cell_kwargs['maxima_threshold'] = default_cell_kwargs[
'maxima_threshold']
assert cell_kwargs == default_cell_kwargs
nuc_kwargs['radius'] = default_nuc_kwargs['radius']
assert nuc_kwargs == default_nuc_kwargs
# test legacy version
old_app = MultiplexSegmentation(model)
# test predict with default
x = np.random.rand(1, 500, 500, 2)
y = old_app.predict(x)
self.assertEqual(x.shape[:-1], y.shape[:-1])