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x = np.concatenate(256 * [x[None]], axis=0)
out_shape = 3 * (128,)
order = 0
out1 = vigra.sampling.resize(x, shape=out_shape, order=order)
out2 = ResizedVolume(x, shape=out_shape, order=order)
out3 = resize(x, out_shape, order=0, preserve_range=True, anti_aliasing=False)
assert out1.shape == out2.shape == out_shape
# bb = np.s_[:64, :, 64:]
bb = np.s_[:]
o1 = out1[bb]
o2 = out2[bb]
o3 = out3[bb]
import napari
with napari.gui_qt():
viewer = napari.Viewer()
viewer.add_image(o1, name='elf')
viewer.add_image(o2, name='vigra')
viewer.add_image(o3, name='skimage')
# viewer.add_labels(diff, name='pix-diff')
try:
import xarray as xr
except ImportError:
raise ImportError("""This example uses a xarray but xarray is not
installed. To install try 'pip install xarray'.""")
import numpy as np
import napari
data = np.random.random((20, 40, 50))
xdata = xr.DataArray(data, dims=['z', 'y', 'x'])
with napari.gui_qt():
# create an empty viewer
viewer = napari.Viewer()
# add the xarray
layer = viewer.add_image(xdata, name='xarray')
"""
import numpy as np
import vispy.color
from skimage import data
import napari
histo = data.astronaut() / 255
rch, gch, bch = np.transpose(histo, (2, 0, 1))
red = vispy.color.Colormap([[0.0, 0.0, 0.0], [1.0, 0.0, 0.0]])
green = vispy.color.Colormap([[0.0, 0.0, 0.0], [0.0, 1.0, 0.0]])
blue = vispy.color.Colormap([[0.0, 0.0, 0.0], [0.0, 0.0, 1.0]])
with napari.gui_qt():
v = napari.Viewer()
rlayer = v.add_image(rch, name='red channel')
rlayer.blending = 'additive'
rlayer.colormap = 'red', red
glayer = v.add_image(gch, name='green channel')
glayer.blending = 'additive'
glayer.colormap = green # this will appear as [unnamed colormap]
blayer = v.add_image(bch, name='blue channel')
blayer.blending = 'additive'
blayer.colormap = {'blue': blue}
def show_napari(array, metadata, verbose=True):
import napari
with napari.gui_qt():
# create scalefcator with all ones
scalefactors = [1] * len(array.shape)
# initialize the napari viewer
viewer = napari.Viewer()
if metadata['ImageType'] == 'ometiff':
# find position of dimensions
posZ = metadata['DimOrder BF Array'].find('Z')
posC = metadata['DimOrder BF Array'].find('C')
posT = metadata['DimOrder BF Array'].find('T')
# get the scalefactors from the metadata
scalef = get_scalefactor(metadata)
# modify the tuple for the scales for napari
scalefactors[posZ] = scalef['zx']
if verbose:
print('Dim PosT : ', posT)
print('Dim PosC : ', posC)
"""
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
"""
import napari
import numpy as np
with napari.gui_qt():
# create the viewer and window
viewer = napari.Viewer()
n = 20
m = 40
image = 0.2 * np.random.random((n, m)) + 0.5
layer = viewer.add_image(image, contrast_limits=[0, 1], name='background')
# 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)
# assign projections for each vector
pos[:, :, 0] = rand1.reshape((n, m))
def setup(self, n):
_ = QApplication.instance() or QApplication([])
np.random.seed(0)
self.data = np.random.random((n, n))
self.viewer = napari.Viewer()
large_values_are_attractive=True,
edge_direction=2)
else:
att1xy, rep1xy = visualise_attractive_and_repulsive_edges(rag, edge_feats,
ignore_edges=z_edges,
threshold=.5,
large_values_are_attractive=False,
edge_direction=0)
att1z, rep1z = visualise_attractive_and_repulsive_edges(rag, edge_feats,
ignore_edges=xy_edges,
threshold=.5,
large_values_are_attractive=False,
edge_direction=2)
with napari.gui_qt():
viewer = napari.Viewer()
viewer.add_image(raw, name='raw')
viewer.add_image(boundaries, name='boundaries')
viewer.add_image(att1xy, name='attractive-xy')
viewer.add_image(rep1xy, name='repuslive-xy')
viewer.add_image(att1z, name='attractive-z')
viewer.add_image(rep1z, name='repulsive-z')
affs = f['affinities'][:3, :]
boundaries = np.mean(affs, axis=0)
watershed, max_id = ws.stacked_watershed(boundaries, threshold=.5, sigma_seeds=2.)
# compute the region adjacency graph
rag = feats.compute_rag(watershed, n_labels=max_id + 1)
# compute the edge weights
edge_weights = feats.compute_boundary_features(rag, boundaries)[:, 0]
z_edges = feats.compute_z_edge_mask(rag, watershed)
xy_edges = ~z_edges
xy_vals = visualise_edges(rag, edge_weights, ignore_edges=z_edges, edge_direction=0)
z_vals = visualise_edges(rag, edge_weights, ignore_edges=xy_edges, edge_direction=2)
with napari.gui_qt():
viewer = napari.Viewer()
viewer.add_image(raw, name='raw')
viewer.add_image(boundaries, name='boundaries')
viewer.add_image(xy_vals, name='xy-edges')
viewer.add_image(z_vals, name='z-edges')
from skimage import data
import numpy as np
import napari
with napari.gui_qt():
blobs = np.asarray(
[
data.binary_blobs(length=64, volume_fraction=0.1, n_dim=3).astype(
float
)
for i in range(10)
]
)
viewer = napari.Viewer(ndisplay=3)
# add the volume
layer = viewer.add_image(blobs)
in_folder = '/home/pape/Work/data/data_science_bowl/dsb2018/test/images'
input_images = os.listdir(in_folder)
test_image = input_images[0]
test_name = os.path.splitext(test_image)[0]
im = np.asarray(imageio.imread(os.path.join(in_folder, test_image)))
pred_file = './predictions.h5'
with h5py.File(pred_file, 'r') as f:
pred = f[test_name][:]
pca = embed.embedding_pca(pred).transpose((1, 2, 0))
seg = embed.embedding_slic(pred)
with napari.gui_qt():
viewer = napari.Viewer()
viewer.add_image(im, name='image')
viewer.add_image(pca, rgb=True, name='pca')
viewer.add_labels(seg, name='segmentation')