def setup(self):
_ = QApplication.instance() or QApplication([])
np.random.seed(0)
self.data = np.random.randint(10, size=(512, 512))
self.viewer = napari.view_labels(self.data)
self.layer = self.viewer.layers[0]
self.layer.brush_size = 10
self.layer.mode = 'paint'
self.layer.selected_label = 3
self.layer._last_cursor_coord = (511, 511)
Event = collections.namedtuple('Event', 'is_dragging')
self.event = Event(is_dragging=True)
def setup(self):
_ = QApplication.instance() or QApplication([])
np.random.seed(0)
self.data = np.random.randint(10, size=(512, 512))
self.viewer = napari.view_labels(self.data)
self.layer = self.viewer.layers[0]
self.layer.brush_size = 10
self.layer.mode = 'paint'
self.layer.selected_label = 3
self.layer._last_cursor_coord = (511, 511)
self.event = MouseEvent(
type='mouse_move',
is_dragging=True,
pos=(500, 500),
view_direction=None,
)
def test_view_labels(qtbot):
"""Test adding labels image."""
np.random.seed(0)
data = np.random.randint(20, size=(10, 15))
viewer = napari.view_labels(data)
view = viewer.window.qt_viewer
qtbot.addWidget(view)
assert np.all(viewer.layers[0].data == data)
assert len(viewer.layers) == 1
assert view.layers.vbox_layout.count() == 2 * len(viewer.layers) + 2
assert viewer.dims.ndim == 2
assert view.dims.nsliders == viewer.dims.ndim
assert np.sum(view.dims._displayed_sliders) == 0
# Close the viewer
viewer.window.close()
# Now we want to separate the two objects in image
# Generate the markers as local maxima of the distance to the background
distance = ndi.distance_transform_edt(image)
coords = peak_local_max(distance, footprint=np.ones((3, 3)), labels=image)
distance = 1 - (distance / distance.max())
out = watershed(distance, coords, image, 0, affinities=False)
from train_io import get_affinities
affs = get_affinities(out.copy())
from skimage.filters import gaussian
a_out = watershed(affs.copy(), coords, image, 0, affinities=True)
affs_g = np.stack(
[gaussian(affs[i], sigma=1) for i in range(affs.shape[0])])
ag_out = watershed(affs_g.copy(), coords, image, 0, affinities=True)
import napari
v = napari.view_labels(image,
name='mask',
blending='additive',
visible=False)
v.add_labels(out, name='watershed', blending='additive', visible=False)
v.add_image(affs[0],
name='y affinities',
blending='additive',
colormap='green',
visible=False)
v.add_image(affs[1],
name='x affinities',
blending='additive',
colormap='magenta',
visible=False)
v.add_labels(a_out,
name='affinity watershed',
blending='additive',
"""
Display a labels layer above of an image layer using the add_labels and
add_image APIs
"""
import numpy as np
from skimage import data
from scipy import ndimage as ndi
import napari
with napari.gui_qt():
blobs = data.binary_blobs(length=128, volume_fraction=0.1, n_dim=2)
labeled = ndi.label(blobs)[0]
viewer = napari.view_labels(labeled, name='blob IDs')
selection_layer = viewer.add_shapes([[[0, 0], [128, 128]]],
shape_type='rectangle',
face_color=[1, 1, 1, 0],
edge_color=[0, 0.6, 1, 1],
edge_width=1,
name='selection box')
@viewer.mouse_drag_callbacks.append
def get_unique_labels(viewer, event):
yield
# on move
while event.type == 'mouse_move':
labels_layer = viewer.layers[0]
shapes_layer = viewer.layers[1]
coords = np.round(shapes_layer.data[0]).astype(int)
top_left = np.min(coords, axis=0)
bottom_right = np.max(coords, axis=0)
"""
Test adding 4D followed by 5D image layers to the viewer
Intially only 2 sliders should be present, then a third slider should be
created.
"""
import numpy as np
from skimage import data
import napari
with napari.gui_qt():
viewer = napari.view_labels(np.random.randint(10, size=(1024, 1024)))