def test_screenshot(qtbot):
"Test taking a screenshot"
viewer = Viewer()
view = viewer.window.qt_viewer
qtbot.addWidget(view)
np.random.seed(0)
# Add image
data = np.random.random((10, 15))
viewer.add_image(data)
# Add labels
data = np.random.randint(20, size=(10, 15))
viewer.add_labels(data)
# Add points
data = 20 * np.random.random((10, 2))
viewer.add_points(data)
# Add vectors
data = 20 * np.random.random((10, 2, 2))
viewer.add_vectors(data)
# Add shapes
data = 20 * np.random.random((10, 4, 2))
viewer.add_shapes(data)
# Take screenshot
screenshot = viewer.screenshot()
assert screenshot.ndim == 3
# Close the viewer
viewer.window.close()
def test_nD_labels(qtbot):
"""Test adding nD labels image."""
viewer = Viewer()
view = viewer.window.qt_viewer
qtbot.addWidget(view)
np.random.seed(0)
data = np.random.randint(20, size=(6, 10, 15))
viewer.add_labels(data)
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 == 3
assert view.dims.nsliders == viewer.dims.ndim
assert np.sum(view.dims._displayed_sliders) == 1
# Flip dims order displayed
viewer.dims.order = [0, 2, 1]
assert viewer.dims.order == [0, 2, 1]
# Flip dims order including non-displayed
viewer.dims.order = [1, 0, 2]
assert viewer.dims.order == [1, 0, 2]
# Switch to 3D rendering
viewer.dims.ndisplay = 3
assert np.sum(view.dims._displayed_sliders) == 0
assert viewer.dims.ndisplay == 3
viewer.dims.ndisplay = 2
assert viewer.dims.ndisplay == 2
# Close the viewer
viewer.window.close()
def test_add_labels(qtbot):
"""Test adding labels image."""
viewer = Viewer()
view = viewer.window.qt_viewer
qtbot.addWidget(view)
np.random.seed(0)
data = np.random.randint(20, size=(10, 15))
viewer.add_labels(data)
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
# Switch to 3D rendering mode and back to 2D rendering mode
viewer.dims.ndisplay = 3
assert viewer.dims.ndisplay == 3
viewer.dims.ndisplay = 2
assert viewer.dims.ndisplay == 2
# Close the viewer
viewer.window.close()
def test_nD_labels(qtbot):
"""Test adding nD labels image."""
viewer = Viewer()
view = viewer.window.qt_viewer
qtbot.addWidget(view)
np.random.seed(0)
data = np.random.randint(20, size=(6, 10, 15))
viewer.add_labels(data)
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 == 3
assert view.dims.nsliders == 1
assert np.sum(view.dims._displayed) == 1
# Close the viewer
viewer.window.close()
def initialize_masks(
self,
viewer: Viewer,
im_shape: tuple,
cell_masks: list = [],
initial_state: Union[np.ndarray, str] = 'good',
accepted_layer_name: str = 'accepted_mask',
rejected_layer_name: str = 'rejected_mask',
):
self.masks = ContourManager(contours=cell_masks,
im_shape=im_shape,
initial_state=initial_state)
# put the masks in their respective labels layers
rejected_mask = self.masks.make_rejected_mask()
self.rejected_labels = viewer.add_labels(rejected_mask,
name=rejected_layer_name,
visible=False)
accepted_mask = self.masks.make_accepted_mask()
self.accepted_labels = viewer.add_labels(accepted_mask,
name=accepted_layer_name)
def test_labels_undo_redo(qtbot):
"""Test undoing/redoing on the labels layer."""
viewer = Viewer()
view = viewer.window.qt_viewer
qtbot.addWidget(view)
data = np.zeros((50, 50), dtype=np.uint8)
data[:5, :5] = 1
data[5:10, 5:10] = 2
data[25:, 25:] = 3
labels = viewer.add_labels(data)
l1 = labels.data.copy()
# fill
labels.fill((30, 30), 3, 42)
l2 = labels.data.copy()
assert not np.array_equal(l1, l2)
# undo
labels.undo()
assert np.array_equal(l1, labels.data)
# redo
labels.redo()
assert np.array_equal(l2, labels.data)
# history limit
labels._history_limit = 1
labels.fill((0, 0), 1, 3)
l3 = labels.data.copy()
assert not np.array_equal(l3, l2)
labels.undo()
assert np.array_equal(l2, labels.data)
# cannot undo as limit exceded
labels.undo()
assert np.array_equal(l2, labels.data)
# Close the viewer
viewer.window.close()
def test_data_change_ndisplay_labels(qtbot):
"""Test change data calls for labels layer with ndisplay change."""
viewer = Viewer()
view = viewer.window.qt_viewer
qtbot.addWidget(view)
np.random.seed(0)
data = np.random.randint(20, size=(10, 15, 8))
layer = viewer.add_labels(data)
visual = view.layer_to_visual[layer]
@patch.object(visual, '_on_data_change', wraps=visual._on_data_change)
def test_ndisplay_change(mocked_method, ndisplay=3):
viewer.dims.ndisplay = ndisplay
mocked_method.assert_called_once()
# Switch to 3D rendering mode and back to 2D rendering mode
test_ndisplay_change(ndisplay=3)
test_ndisplay_change(ndisplay=2)
# Close the viewer
viewer.window.close()
# add the timeseries
movie_layer = viewer.add_image(movie,
name='timeseries',
contrast_limits=(0.0, 600.0),
colormap='gray')
mean_layer = viewer.add_image(mean,
name='mean',
contrast_limits=(0.0, 600.0),
colormap='gray',
visible=False)
#
# lc_layer = viewer.add_image(localcorr, name='localcorr', contrast_limits=(0.35, 1.0), colormap='gray', visible=False)
#
# centers_layer = viewer.add_points(centers, name='centers', edge_width=0, face_color='green', visible=False, opacity=0.5)
#
shapes_layer = viewer.add_shapes(polygons,
shape_type='polygon',
edge_width=0,
face_color='green',
opacity=0.5,
name='neurons',
visible=False)
#
labels = label(mask)
labels_layer = viewer.add_labels(labels, name='rois')
# polygons_edit = shapes_layer.data
# np.save('data/neurofinder/polygons_edit.npy', polygons_edit)
"""
Dynamically load irregularly shapes images of ants and bees
"""
import numpy as np
from dask_image.imread import imread
from dask.cache import Cache
from napari import Viewer, gui_qt
cache = Cache(2e9) # Leverage two gigabytes of memory
cache.register()
base_name = 'data/kaggle-nuclei/fixes/stage1_train/*'
images = imread(base_name + '/images/image_gray.tif')
labels = imread(base_name + '/labels/label.tif')
print(images.shape)
with gui_qt():
# create an empty viewer
viewer = Viewer()
# add the images
image_layer = viewer.add_image(images, name='nuceli', colormap='gray')
labels_layer = viewer.add_labels(labels, name='labels', opacity=0.5)
scale=[3, 1, 1])
ch3 = viewer.add_image(cells[:, 3, :, :],
name='yellow spots',
contrast_limits=(420.0, 500.0),
colormap='yellow',
blending='additive',
scale=[3, 1, 1])
ch4 = viewer.add_image(cells[:, 4, :, :],
name='DAPI',
colormap='blue',
contrast_limits=(500.0, 1300.0),
blending='additive',
scale=[3, 1, 1])
# add brightfield
brightfield = viewer.add_image(-cells[:, 0, :, :],
name='brightfield',
colormap='gray',
scale=[3, 1, 1])
#labels = np.zeros([cells.shape[i] for i in [0, 2, 3]])
labels_layer = viewer.add_labels(labels,
name='annotations',
scale=[3, 1, 1])
# labels = labels_layer.data
# imsave('data/allen_cell/labels.tif', labels.astype('uint32'), plugin='tifffile',
# photometric='minisblack')
"""
Displays the allen brain reference atlas at 10 um resolution
"""
from skimage.io import imread
from napari import Viewer, gui_qt
brain = imread('data/allen_brain/average_template_25.tif')
annotation = imread('data/allen_brain/annotation_25.tif')
with gui_qt():
# create an empty viewer
viewer = Viewer()
# add the brain
brain_layer = viewer.add_image(brain, name='brain')
# add the annotations
annotation_layer = viewer.add_labels(annotation, name='annotation')
"""Test converting an image to a pyramid.
"""
import numpy as np
from napari import Viewer, gui_qt
from skimage.transform import pyramid_gaussian
from skimage import data
from scipy import ndimage as ndi
blobs = data.binary_blobs(length=9_000, volume_fraction=0.1, n_dim=2)
labeled = ndi.label(blobs)[0]
print(labeled.shape)
with gui_qt():
viewer = Viewer()
viewer.add_labels(labeled)
@viewer.bind_key('r')
def rerun(viewer):
blobs = viewer.layers['input'].data
distance = viewer.layers['distance'].data
local_maxi = viewer.layers['markers'].data
print('Number of markers: ', len(local_maxi))
local_maxi_image = np.zeros(blobs.shape, dtype='bool')
for cord in local_maxi:
local_maxi_image[tuple(np.round(cord).astype(int))] = True
markers = ndi.label(local_maxi_image)[0]
labels = watershed(-distance, markers, mask=blobs)
viewer.layers['output'].data = labels
# add the raw image
viewer.add_image(image, name='raw', colormap='gray')
# add the input image
viewer.add_image(blobs.astype('float'), name='input', colormap='gray')
# add the distance image
viewer.add_image(distance, name='distance', colormap='gray')
# add the resulting labels image
viewer.add_labels(labels, name='output')
# add the ground truth as a labels image
viewer.add_labels(gt, name='gt')
# add the markers
viewer.add_points(local_maxi, face_color='blue', size=3, name='markers')