def test_vectorData_datatype1():
"""
test data of coord-like shape
:return:
"""
with app_context():
# create the viewer and window
viewer = ViewerApp()
N = 10
pos = np.zeros(shape=(N, 4), dtype=np.float32)
try:
viewer.add_vectors(pos)
except Exception as ex:
print("exception thrown when creating coord-like vector layer: "+str(ex))
def test_vectorData_image_assignment(self):
"""
test replacing vector data after layer construction
:return:
"""
with app_context():
# create the viewer and window
viewer = ViewerApp()
N = 10
pos = np.zeros(shape=(N, 4), dtype=np.float32)
pos2 = np.zeros(shape=(N, N, 2), dtype=np.float32)
try:
layer = viewer.add_vectors(pos)
layer.vectors = pos2
except Exception as ex:
print("exception thrown when : "+str(ex))
def test_vectorData_datatype_notimpl2(self):
"""
test data of vispy-coordinate shape (not allowed)
:return:
"""
with app_context():
# create the viewer and window
viewer = ViewerApp()
N = 10
pos = np.zeros(shape=(N, 2), dtype=np.float32)
try:
viewer.add_vectors(pos)
except InvalidDataFormatError:
print('invalid data format')
except Exception as ex:
print("exception thrown when creating not implemented vector layer: "+str(ex))
def test_vectorData_datatype_notimpl():
"""
test data of improper shape
:return:
"""
with app_context():
# create the viewer and window
viewer = ViewerApp()
N = 10
M = 5
pos = np.zeros(shape=(N, M, 4), dtype=np.float32)
try:
viewer.add_vectors(pos)
except InvalidDataFormatError:
print('invalid data format')
except Exception as ex:
print("exception thrown when creating not implemented vector layer: "+str(ex))
"""
Display one shapes layer ontop of one image layer using the add_shapes and
add_image APIs. When the window is closed it will print the coordinates of
your shapes.
"""
import numpy as np
from skimage import data
from skimage.color import rgb2gray
from napari import ViewerApp
from napari.util import app_context
with app_context():
# create the viewer and window
viewer = ViewerApp()
# add the image
layer = viewer.add_image(data.camera(), name='photographer')
layer.colormap = 'gray'
# create a list of polygons
polygons = [
np.array([[11, 13], [111, 113], [22, 246]]),
np.array([[505, 60], [402, 71], [383, 42], [251, 95], [212, 59],
[131, 137], [126, 187], [191, 204], [171, 248], [211, 260],
[273, 243], [264, 225], [430, 173], [512, 160]]),
np.array([[310, 382], [229, 381], [209, 401], [221, 411], [258, 411],
[300, 412], [306, 435], [268, 434], [265, 454], [298, 461],
[307, 461], [307, 507], [349, 510], [352, 369], [330, 366],
[330, 366]])
]
"""
Displays one image using the add_image API and then adjust some of its
properties
"""
from skimage import data
from skimage.color import rgb2gray
from napari import ViewerApp
from napari.util import app_context
with app_context():
# create the viewer with an image
viewer = ViewerApp(astronaut=rgb2gray(data.astronaut()))
# adjust some of the layer properties
layer = viewer.layers[0]
# change the layer name
layer.name = 'astronaut'
# change the layer visibility
layer.visible = False
layer.visible = True
# change the layer selection
layer.selected = False
layer.selected = True
# set the layer property widget to be expanded
layer._qt_properties.setExpanded(True)
"""
Display one markers layer ontop of one 4-D image layer using the
add_markers and add_image APIs, where the markes are visible as nD objects
accross the dimensions, specified by their size
"""
import numpy as np
from skimage import data
from napari import ViewerApp
from napari.util import app_context
with app_context():
# TODO: change axis back to 0 once dims displayed in sane order
blobs = np.stack([
data.binary_blobs(
length=128, blob_size_fraction=0.05, n_dim=3, volume_fraction=f)
for f in np.linspace(0.05, 0.5, 10)
],
axis=-1)
viewer = ViewerApp(blobs.astype(float))
# add the markers
markers = np.array([[100, 100, 0, 0], [50, 120, 0, 0], [100, 40, 1, 0],
[110, 20, 100, 2], [400, 100, 10, 8]])
viewer.add_markers(markers,
size=[10, 10, 6, 0],
face_color='blue',
n_dimensional=True)
"""
Displays an image and sets the theme to 'light'.
"""
from skimage import data
from napari import ViewerApp
from napari.util import app_context
with app_context():
# create the viewer with an image
viewer = ViewerApp(astronaut=data.astronaut(), title='napari light theme')
# set the theme to 'light'
viewer.theme = 'light'
Vector data is an array of shape (N, 4)
Each vector position is defined by an (x, y, x-proj, y-proj) element
where x and y are the center points
where x-proj and y-proj are the vector projections at each center
"""
from napari import ViewerApp
from napari.util import app_context
from skimage import data
import numpy as np
with app_context():
# create the viewer and window
viewer = ViewerApp()
layer = viewer.add_image(data.camera(), name='photographer')
layer.colormap = 'gray'
# sample vector coord-like data
n = 1000
pos = np.zeros((n, 4), dtype=np.float32)
phi_space = np.linspace(0, 4 * np.pi, n)
radius_space = np.linspace(0, 100, n)
# assign x-y position
pos[:, 0] = radius_space * np.cos(phi_space) + 350
pos[:, 1] = radius_space * np.sin(phi_space) + 256
# assign x-y projection
"""
This example generates an image of vectors
Vector data is an array of shape (N, M, 2)
Each vector position is defined by an (x-proj, y-proj) element
where x-proj and y-proj are the vector projections at each center
where each vector is centered on a pixel of the NxM grid
"""
from napari import ViewerApp
from napari.util import app_context
import numpy as np
with app_context():
# create the viewer and window
viewer = ViewerApp()
n = 100
m = 200
image = 0.2*np.random.random((n, m)) + 0.5
layer = viewer.add_image(image, clim_range=[0, 1], name='background')
layer.colormap = 'gray'
# sample vector image-like data
# n x m grid of slanted lines
# random data on the open interval (-1, 1)
pos = np.zeros(shape=(n, m, 2), dtype=np.float32)
rand1 = 2*(np.random.random_sample(n * m)-0.5)
rand2 = 2*(np.random.random_sample(n * m)-0.5)
"""
Display one markers layer ontop of one image layer using the add_markers and
add_image APIs
"""
import numpy as np
from skimage import data
from skimage.color import rgb2gray
from napari import ViewerApp
from napari.util import app_context
print("click to add markers; close the window when finished.")
with app_context():
viewer = ViewerApp(rgb2gray(data.astronaut()))
markers = viewer.add_markers(np.zeros((0, 2)))
markers.mode = 'add'
print("you clicked on:")
print(markers.coords)
"""
Display multiple image layers using the add_image API and then reorder them
using the layers swap method and remove one
"""
from skimage import data
from skimage.color import rgb2gray
from napari import ViewerApp
from napari.util import app_context
with app_context():
# create the viewer with several image layers
viewer = ViewerApp(astronaut=rgb2gray(data.astronaut()),
photographer=data.camera(),
coins=data.coins(),
moon=data.moon())
# remove the coins layer
viewer.layers.remove('coins')
# swap the order of astronaut and moon
viewer.layers['astronaut', 'moon'] = viewer.layers['moon', 'astronaut']
add_image APIs
"""
from skimage import data
from skimage.filters import threshold_otsu
from skimage.segmentation import clear_border
from skimage.measure import label
from skimage.morphology import closing, square, remove_small_objects
from napari import ViewerApp
from napari.util import app_context
with app_context():
image = data.coins()[50:-50, 50:-50]
# apply threshold
thresh = threshold_otsu(image)
bw = closing(image > thresh, square(4))
# remove artifacts connected to image border
cleared = remove_small_objects(clear_border(bw), 20)
# label image regions
label_image = label(cleared)
# initialise viewer with astro image
viewer = ViewerApp(coins=image, multichannel=False)
viewer.layers[0].colormap = 'gray'
# add the labels
label_layer = viewer.add_labels(label_image, name='segmentation')
"""
Display a labels layer above of an image layer using the add_labels and
add_image APIs
"""
from skimage import data
from scipy import ndimage as ndi
from napari import ViewerApp
from napari.util import app_context
with app_context():
blobs = data.binary_blobs(length=128, volume_fraction=0.1, n_dim=3)
v = ViewerApp(blobs=blobs)
v.layers[0].colormap = 'gray'
labeled = ndi.label(blobs)[0]
label_layer = v.add_labels(labeled, name='blob ID')
def set_index(index):
global curr_index
if index < 0:
index = 0
if index >= get_max_index():
index = get_max_index() - 1
curr_index = index
with app_context():
# create an empty viewer
viewer = ViewerApp()
def save(viewer, layer_name="annotations"):
"""Save the current annotations
"""
labels = viewer.layers[layer_name].image.astype(np.uint16)
if (not save_if_empty) and np.all(labels == 0):
msg = "{} layer is empty. save_if_empty set to False.".format(
viewer.layers[layer_name].name)
print(msg)
viewer.status = msg
return
save_path = annotation_paths[curr_index]
imsave(save_path, labels, plugin="tifffile", photometric="minisblack")
"""
Display one 4-D image layer using the add_image API
"""
import numpy as np
from skimage import data
from napari import ViewerApp
from napari.util import app_context
with app_context():
viewer = ViewerApp()
blobs = data.binary_blobs(length=128, blob_size_fraction=0.05,
n_dim=2, volume_fraction=.25).astype(float)
viewer.add_image(blobs, name='blobs')
def accept_image(viewer):
msg = 'this is a good image'
viewer.status = msg
print(msg)
next(viewer)
def reject_image(viewer):
msg = 'this is a bad image'
viewer.status = msg
print(msg)
next(viewer)
def next(viewer):
blobs = data.binary_blobs(length=128, blob_size_fraction=0.05,
"""
Display one shapes layer ontop of one image layer using the add_shapes and
add_image APIs. When the window is closed it will print the coordinates of
your shapes.
"""
import numpy as np
from skimage import data
from skimage.color import rgb2gray
from napari import ViewerApp
from napari.util import app_context
from vispy.color import Colormap
with app_context():
# create the viewer and window
viewer = ViewerApp()
# add the image
layer = viewer.add_image(data.camera(), name='photographer')
layer.colormap = 'gray'
# create a list of polygons
polygons = [
np.array([[11, 13], [111, 113], [22, 246]]),
np.array([[505, 60], [402, 71], [383, 42], [251, 95], [212, 59],
[131, 137], [126, 187], [191, 204], [171, 248], [211, 260],
[273, 243], [264, 225], [430, 173], [512, 160]]),
np.array([[310, 382], [229, 381], [209, 401], [221, 411], [258, 411],
[300, 412], [306, 435], [268, 434], [265, 454], [298, 461],
[307, 461], [307, 507], [349, 510], [352, 369], [330, 366],
[330, 366]])
"""
Displays one image using the add_image API and then adjust some of its
properties
"""
from skimage import data
from skimage.color import rgb2gray
from napari import ViewerApp
from napari.util import app_context
with app_context():
# create the viewer with an image
viewer = ViewerApp(astronaut=rgb2gray(data.astronaut()),
title='napari example')
# adjust some of the layer properties
layer = viewer.layers[0]
# change the layer name
layer.name = 'astronaut'
# change the layer visibility
layer.visible = False
layer.visible = True
# change the layer selection
layer.selected = False
layer.selected = True
# set the layer property widget to be expanded
layer._qt_properties.setExpanded(True)
from recOrder.visualization.GUI.RecorderWindowControl import RecorderWindowControl
from recOrder.program.SignalController.SignalController import SignalController
from recOrder.analysis.Processing.ReconOrder import ReconOrder
from py4j.java_gateway import JavaGateway
from recOrder.MicroscopeController.mm2python_controller import py4j_monitor_LC
from napari import ViewerApp
from napari.util import app_context
if __name__ == '__main__':
with app_context():
gateway = JavaGateway()
#create Viewer, Windows
viewer = ViewerApp()
viewer_window = NapariWindow(viewer)
recorder_window = QtWidgets.QDialog()
recorder = RecorderWindowControl(recorder_window, gateway=gateway)
recorder_window.show()
#initialize file loaders
loader = PipeFromFiles(type="Test", sample_type="Sample1")
loader_bg = PipeFromFiles(type="Test", sample_type='BG')
#initialize processors
processor = ReconOrder()
#initialize SignalController
signals = SignalController()
"""
Display one 4-D image layer using the add_image API
"""
import numpy as np
from skimage import data
from napari import ViewerApp
from napari.util import app_context
with app_context():
blobs = np.stack([
data.binary_blobs(
length=128, blob_size_fraction=0.05, n_dim=3, volume_fraction=f)
for f in np.linspace(0.05, 0.5, 10)
],
axis=0)
viewer = ViewerApp(blobs.astype(float))
"""
Display one markers layer ontop of one image layer using the add_markers and
add_image APIs
"""
import numpy as np
from skimage import data
from skimage.color import rgb2gray
from napari import ViewerApp
from napari.util import app_context
with app_context():
# create the viewer and window
viewer = ViewerApp()
# add the image
viewer.add_image(rgb2gray(data.astronaut()))
# add the markers
markers = np.array([[100, 100], [200, 200], [333, 111]])
size = np.array([10, 20, 20])
viewer.add_markers(markers, size=size)
# unselect the image layer
viewer.layers[0].selected = False
# adjust some of the marker layer properties
layer = viewer.layers[1]
# change the layer name
layer.name = 'spots'
"""
Display one 4-D image layer using the add_image API
"""
import dask.array as da
import zarr
import numpy as np
from skimage import data
from napari import ViewerApp
from napari.util import app_context
with app_context():
data = zarr.zeros((102_000, 200, 210), chunks=(100, 200, 210))
data[53_000:53_100, 100:110, 110:120] = 1
array = da.from_zarr(data)
print(array.shape)
viewer = ViewerApp(array, clim_range=[0, 1], multichannel=False)
"""
Display a labels layer above of an image layer using the add_labels and
add_image APIs
"""
from skimage import data
from skimage.color import rgb2gray
from skimage.segmentation import slic
from napari import ViewerApp
from napari.util import app_context
with app_context():
astro = data.astronaut()
# initialise viewer with astro image
viewer = ViewerApp(astronaut=rgb2gray(astro), multichannel=False)
viewer.layers[0].colormap = 'gray'
# add the labels
# we add 1 because SLIC returns labels from 0, which we consider background
labels = slic(astro, multichannel=True, compactness=20) + 1
label_layer = viewer.add_labels(labels, name='segmentation')
print(f'The color of label 5 is {label_layer.label_color(5)}')