def slices_were_clicked(active_image, pos):
if WhereAmIPanel.slicer_state.mri is None:
return
modality = bpy.context.scene.slices_modality
images_names = [
'{}.{}'.format(persp,
_addon().get_figure_format())
for persp in ['sagital', 'coronal', 'axial']
]
# images_names = ['mri_sagital.png', 'mri_coronal.png', 'mri_axial.png']
WhereAmIPanel.slices_cursor_pos[active_image.name] = pos
print('Image {} was click in {}'.format(active_image.name, pos))
# print(active_image.name, pos)
image_ind = images_names.index(active_image.name.lower())
new_pos_vox = slicer.on_click(image_ind, pos, WhereAmIPanel.slicer_state,
modality)
if modality == 'ct':
new_pial_ras = apply_trans(_ct_trans().vox2ras,
np.array([new_pos_vox]))[0]
new_pos_vox = apply_trans(t1_trans().ras2vox,
np.array([new_pial_ras]))[0]
new_pos_pial = apply_trans(t1_trans().vox2ras_tkr,
np.array([new_pos_vox]))[0] #.astype(np.int)[0]
# Find the closest vertex on the pial brain, and convert it to the current inflation
new_pos = pos_to_current_inflation(new_pos_pial) / 10
bpy.context.scene.cursor_location = new_pos
_addon().save_cursor_position()
_addon().set_ct_intensity()
return new_pos
def slices_were_clicked():
if WhereAmIPanel.slicer_state is None:
return
screen = bpy.data.screens['Neuro']
images_names = ['mri_sagital.png', 'mri_coronal.png', 'mri_axial.png']
for area in screen.areas:
if area.type == 'IMAGE_EDITOR':
active_image = area.spaces.active.image
if active_image is not None:
pos = tuple(area.spaces.active.cursor_location)
if pos != WhereAmIPanel.slices_cursor_pos[active_image.name]:
print(active_image.name, pos)
image_ind = images_names.index(active_image.name)
new_pos = slicer.on_click(image_ind, pos,
WhereAmIPanel.slicer_state)
x, y, z = apply_trans(_trans().vox2ras_tkr,
np.array([new_pos])).astype(
np.int)[0] / 10
bpy.context.scene.cursor_location = (x, y, z)
_addon().save_cursor_position()
save_slices_cursor_pos()