def render_mouse_brain(self, view_matrix: np.ndarray,
annotation_volume: Volume, energy_volumes: dict,
image_size: int, image: np.ndarray):
"""
fucntion that implements the visualization of the mouse brain.
select below the function that you want in order to visualize the data.
"""
# create volumes for the gradient magnitude and mask
magnitude_volume = np.zeros(annotation_volume.data.shape)
for x in range(0, annotation_volume.dim_x):
for y in range(0, annotation_volume.dim_y):
for z in range(0, annotation_volume.dim_z):
magnitude_volume[
x, y,
z] = self.annotation_gradient_volume.get_gradient(
x, y, z).magnitude
magnitude_volume = Volume(magnitude_volume)
mask_volume = np.copy(self.annotation_gradient_volume.mask).astype(int)
mask_volume = Volume(mask_volume)
#################################################
# set of different functions
# self.visualize_annotations_only(view_matrix, mask_volume, annotation_volume, magnitude_volume, image_size, image, csv_colors = False, precision = 1)
self.visualize_energies_only(view_matrix,
mask_volume,
energy_volumes,
image_size,
image,
annotation_aware=True,
precision=1)
def handle_click(self, event):
"""Show the load file dialog"""
if self.load_dialog.ShowModal() == wx.ID_CANCEL:
return
pathname = self.load_dialog.GetPath()
try:
volume_io = VolumeIO(pathname)
volume_data = volume_io.data
volume = Volume(volume_data)
TFUNC.init(volume.get_minimum(), volume.get_maximum())
self.file_name_label.SetLabel(f"File name: {basename(pathname)}")
self.dimensions_label.SetLabel(
f"Dimensions: {volume_io.dim_x}x{volume_io.dim_y}x{volume_io.dim_z}"
)
self.value_range_label.SetLabel(
f"Voxel value range: {volume.get_minimum():.3g}-{volume.get_maximum():.3g}"
)
self.visualization.set_volume(volume)
self.on_data_loaded(volume)
except Exception:
exc_type, exc_value, exc_traceback = sys.exc_info()
traceback.print_exception(exc_type,
exc_value,
exc_traceback,
limit=2,
file=sys.stdout)
dialog = wx.MessageDialog(
None,
message="An error occurred while reading the file",
style=wx.ICON_ERROR | wx.OK)
dialog.ShowModal()
dialog.Destroy()
def render_mouse_brain(self, view_matrix: np.ndarray,
annotation_volume: Volume, energy_volumes: dict,
image_size: int, image: np.ndarray):
self.tfunc.init(
0,
math.ceil(
self.annotation_gradient_volume.get_max_gradient_magnitude()))
magnitudeVolume = Volume(
self.annotation_gradient_volume.magnitudeVolume)
# Chose the visulization mode
option = 1
if option == 0:
# Compositing rendering of the region specified in volume file
self.render_compositing(view_matrix, magnitudeVolume, image_size,
image)
elif option == 1:
# Compositing rendering of multiple energy in the whole brain
self.render_energy_compositing(
view_matrix, self.annotation_gradient_volume.volume,
image_size, image, energy_volumes)
elif option == 2:
# Compositing rendering of multiple energy in the region specified in volume file
self.render_energy_region_compositing(
view_matrix, self.annotation_gradient_volume.volume,
image_size, image, energy_volumes, magnitudeVolume)
pass
def handle_energy_selected(self, evt: wx.CommandEvent):
selection = evt.GetSelection()
file_name = self.available_energy_items[selection]
energy_number = int(file_name[:-11])
if energy_number in self.energy_selected:
self.energy_selected.remove(energy_number)
self.visualization.remove_energy_volume(energy_number)
else:
self.energy_selected.add(energy_number)
volumeio = VolumeIO(os.path.join(self.energies_path, file_name))
volume = Volume(volumeio.data, compute_histogram=False)
self.visualization.add_energy_volume(energy_number, volume)
self.on_challenge_data_changed(len(self.energy_selected) > 0)
def test_interpolate():
data = np.fromfile('tests/default_volume', dtype=np.int).reshape((256, 256, 163))
volume = Volume(data)
view_matrix = np.array([[1, 0, 0], [0, 1, 0], [0, 0, 1]])
inverse_view_matrix = np.linalg.inv(view_matrix)
points_raw = np.fromfile('tests/default_points', dtype=np.float).reshape(4,3)
interpolated_values = interpolate(volume, points_raw, inverse_view_matrix)
# assert (np.abs(interpolated_values - np.array([ 47.44855585, 71.62875015, 133.71909277, 59.86651823]))).all(), \
# "works with just raw points in input"
assert np.isclose(interpolated_values, np.array([ 47.44855585, 71.62875015, 133.71909277, 59.86651823])).all(), \
"works with just raw points in input"
inverse_view_matrix = [[0.1, -0.5, -0.8], [-0.3, 0.8, -0.5], [0.9, 0.3, 0]]
interpolated_values = interpolate(volume, points_raw, inverse_view_matrix)
# assert (np.abs(interpolated_values - np.array([ 47.44855585, 71.62875015, 133.71909277, 59.86651823]))).all(), \
# "works with just raw points in input"
assert np.isclose(interpolated_values, np.array([ 47.44855585, 71.62875015, 133.71909277, 59.86651823])).all(), \
"should work with wierd view_matrix"
def handle_annotation_selected(self, evt: wx.CommandEvent):
selection = evt.GetSelection() # type: int
if self.selection != selection:
self.selection = selection
item = self.annotations_items[selection]
mhd_energies = ANNOTATION_2_ENERGY[item][:10]
intersection = set(self.energy_items) & set(mhd_energies)
self.available_energy_items = [
file for file in mhd_energies if file in intersection
]
self.energy_list.Clear()
self.energy_list.AppendItems(self.available_energy_items)
self.energy_selected.clear()
self.visualization.clear_energy_volumes()
volumeio = VolumeIO(
os.path.join(self.annotations_path,
self.annotations_items[selection]))
self.visualization.set_annotation_volume(
Volume(volumeio.data, compute_histogram=False))
self.on_challenge_data_changed(False)