def testFrangi(self):
input_array = np.array([[1000, 1000, 1000, 2000, 3000],
[5000, 8000, 13000, 21000, 34000]])
result = np.zeros(input_array.shape)
ij.op().filter().frangiVesselness(imglyb.to_imglib(result),
imglyb.to_imglib(input_array),
[1, 1], 4)
correct_result = np.array([[0, 0, 0, 0.94282, 0.94283],
[0, 0, 0, 0.94283, 0.94283]])
result = np.ndarray.round(result, decimals=5)
self.assertTrue((result == correct_result).all())
def to_java(self, data):
"""
Converts the data into a java equivalent. For numpy arrays, the java image points to the python array
"""
if self._is_memoryarraylike(data):
return imglyb.to_imglib(data)
return to_java(data)
def to_java(self, data):
"""
Converts the data into a java equivalent. For numpy arrays, the java image points to the python array
"""
if type(data) == numpy.ndarray:
return imglyb.to_imglib(data)
return to_java(data)
def to_java(self, data):
"""
Converts the data into a java equivalent. For numpy arrays, the java image points to the python array.
In addition to the scyjava types, we allow ndarray-like and xarray-like variables
"""
if self._is_memoryarraylike(data):
return imglyb.to_imglib(data)
if self._is_xarraylike(data):
return self.to_dataset(data)
return to_java(data)
def add_labels(
self,
data,
name,
max_id,
resolution = [1.0] * 3,
offset = [0.0] * 3):
import imglyb
from .jfx import invoke_on_jfx_application_thread
img = imglyb.to_imglib(data)
invoke_on_jfx_application_thread(lambda: self._add_labels(img, resolution, offset, max_id, name))
def add_raw(
self,
data,
name,
resolution = [1.0] * 3,
offset = [0.0] * 3,
minv = 0.0,
maxv = 1.0):
import imglyb
from .jfx import invoke_on_jfx_application_thread
img = imglyb.to_imglib(data)
invoke_on_jfx_application_thread(lambda: self._add_raw(img, resolution, offset, minv, maxv, name))
def testGaussian(self):
input_array = np.array([[1000, 1000, 1000, 2000, 3000],
[5000, 8000, 13000, 21000, 34000]])
output_array = ij.op().filter().gauss(imglyb.to_imglib(input_array),
10)
result = []
correct_result = [8440, 8440, 8439, 8444]
ra = output_array.randomAccess()
for x in [0, 1]:
for y in [0, 1]:
ra.setPosition(x, y)
result.append(ra.get().get())
self.assertEqual(result, correct_result)
def testImageMath(self):
Views = autoclass('net.imglib2.view.Views')
input_array = np.array([[1, 1, 2], [3, 5, 8]])
result = []
correct_result = [192, 198, 205, 192, 198, 204]
java_in = Views.iterable(imglyb.to_imglib(input_array))
java_out = ij.op().image().equation(
java_in,
"64 * (Math.sin(0.1 * p[0]) + Math.cos(0.1 * p[1])) + 128")
itr = java_out.iterator()
while itr.hasNext():
result.append(itr.next().get())
self.assertEqual(result, correct_result)
def create_display(status):
status[1] = display.createDisplay(
title,
imglyb.to_imglib(img)
if isinstance(img, (np.ndarray, )) else img)
status[0] = True
def _numpy_to_dataset(self, data):
rai = imglyb.to_imglib(data)
return self._java_to_dataset(rai)
payntera.jfx.init_platform()
PainteraBaseView = autoclass(
'org.janelia.saalfeldlab.paintera.PainteraBaseView')
viewer = PainteraBaseView.defaultView()
pbv = viewer.baseView
scene, stage = payntera.jfx.start_stage(viewer.paneWithStatus.getPane())
shape = (80, 80, 50)
x, y, z = np.indices(shape)
fx, fy, fz = 2 * np.pi / np.array(shape) * np.array([10, 1, 3])
raw = (1 + np.sin(x * fx)) * (1 + np.sin(y * fy)) * (
1 + x * y / (shape[0] * shape[1]))**2 * (1 + np.cos(z * fz)) * (
(x + y + z) / np.sum(shape))
raw_img = imglyb.to_imglib(raw)
labels, nb = scipy.ndimage.label(raw > 0.5)
labels_img = imglyb.to_imglib(labels)
raw_state = pbv.addSingleScaleRawSource(raw_img,
[1.0, 1.0, 1.0], [0.0, 0.0, 0.0],
np.min(raw), 7, 'blub')
state = pbv.addSingleScaleLabelSource(labels_img, [1.0, 1.0, 1.0],
[0.0, 0.0, 0.0], nb + 1, 'bla')
viewer.keyTracker.installInto(scene)
scene.addEventFilter(
autoclass('javafx.scene.input.MouseEvent').ANY, viewer.mouseTracker)
while stage.isShowing():
time.sleep(0.1)
neuron_ids_dataset = f['volumes/labels/neuron_ids']
raw_res = [x for x in raw_dataset.attrs['resolution'][::-1]
] if 'resolution' in raw_dataset.attrs else [1.0, 1.0, 1.0]
raw_off = [x for x in raw_dataset.attrs['offset'][::-1]
] if 'offset' in raw_dataset.attrs else [1.0, 1.0, 1.0]
neuron_ids_res = [
x for x in neuron_ids_dataset.attrs['resolution'][::-1]
] if 'resolution' in neuron_ids_dataset.attrs else [1.0, 1.0, 1.0]
neuron_ids_off = [
x for x in neuron_ids_dataset.attrs['offset'][::-1]
] if 'offset' in neuron_ids_dataset.attrs else [1.0, 1.0, 1.0]
raw = raw_dataset.value
neuron_ids = neuron_ids_dataset.value
max_id = np.max(neuron_ids) + 1
img = imglyb.to_imglib(neuron_ids)
state = LabelSourceState.simpleSourceFromSingleRAI(
img, neuron_ids_res, neuron_ids_off, max_id, 'neuron ids',
viewer.baseView.viewer3D().meshesGroup(),
viewer.baseView.getMeshManagerExecutorService(),
viewer.baseView.getMeshWorkerExecutorService())
raw_img = imglyb.to_imglib(raw)
raw_state = RawSourceState.simpleSourceFromSingleRAI(raw_img, raw_res, raw_off,
0.0, 255.0, 'raw')
viewer.keyTracker.installInto(scene)
scene.addEventFilter(
autoclass('javafx.scene.input.MouseEvent').ANY, viewer.mouseTracker)
payntera.jfx.invoke_on_jfx_application_thread(