diffuse = mlab.pipeline.user_defined(median, filter=diffuse_filter) # Extract brain surface contour = mlab.pipeline.contour(diffuse, ) contour.filter.contours = [brain_thr, ] # Apply mesh filter to clean up the mesh (decimation and smoothing) dec = mlab.pipeline.decimate_pro(contour) dec.filter.feature_angle = 60. dec.filter.target_reduction = 0.7 smooth_ = tvtk.SmoothPolyDataFilter( number_of_iterations=10, relaxation_factor=0.1, feature_angle=60, feature_edge_smoothing=False, boundary_smoothing=False, convergence=0., ) smooth = mlab.pipeline.user_defined(dec, filter=smooth_) # Get the largest connected region connect_ = tvtk.PolyDataConnectivityFilter(extraction_mode=4) connect = mlab.pipeline.user_defined(smooth, filter=connect_) # Compute normals for shading the surface compute_normals = mlab.pipeline.poly_data_normals(connect) compute_normals.filter.feature_angle = 80 surf = mlab.pipeline.surface(compute_normals, color=(0.9, 0.72, 0.62))
vPD=vImage.GetPointData() vSC=vPD.GetScalars() data=numpy_support.vtk_to_numpy(vSC) indxs=np.unique(data) for ind in indxs[1:]: print ind tmp=data.copy() tmp[tmp!=ind]=0 tmp[tmp==ind]=1 vPD.SetScalars(numpy_support.numpy_to_vtk(tmp)) image=tvtk.to_tvtk(vImage) iso=tvtk.MarchingCubes() iso.set_input_data(image) iso.set_value(0,0.5) iso.update() smoother = tvtk.SmoothPolyDataFilter() smoother.convergence=0 smoother.number_of_iterations=30 smoother.relaxation_factor=0.1 smoother.feature_angle=60 smoother.feature_edge_smoothing=True smoother.boundary_smoothing=True smoother.set_input_data_object(iso.get_output_data_object(0)) smoother.update() write_data(smoother.get_output_data_object(0) ,'out_'+str(ind)+'.vtk')
def isosurfacing(data): """data should be a 3d array with channel last.""" # Heuristic for finding the threshold for the brain # Exctract the percentile 20 and 80 (without using # scipy.stats.scoreatpercentile) # sorted_data = np.sort(data.ravel()) # l = len(sorted_data) # lower_thr = sorted_data[int(0.2 * l)] # upper_thr = sorted_data[int(0.8 * l)] # The white matter boundary: find the densest part of the upper half # of histogram, and take a value 10% higher, to cut _in_ the white matter # hist, bins = np.histogram(data[data > np.mean(data)], bins=50) # brain_thr_idx = np.argmax(hist) # brain_thr = bins[brain_thr_idx + 4] # del hist, bins, brain_thr_idx # Display the data ############################################################# fig = mlab.figure(bgcolor=(0, 0, 0), size=(400, 500)) # to speed things up fig.scene.disable_render = True src = mlab.pipeline.scalar_field(data) # Our data is not equally spaced in all directions: src.spacing = [1, 1, 20] src.update_image_data = True #---------------------------------------------------------------------- # Brain extraction pipeline # In the following, we create a Mayavi pipeline that strongly # relies on VTK filters. For this, we make heavy use of the # mlab.pipeline.user_defined function, to include VTK filters in # the Mayavi pipeline. # Apply image-based filters to clean up noise # thresh_filter = tvtk.ImageThreshold() # thresh_filter.threshold_between(lower_thr, upper_thr) # thresh = mlab.pipeline.user_defined(src, filter=thresh_filter) median_filter = tvtk.ImageMedian3D() median_filter.kernel_size = [3, 3, 3] median = mlab.pipeline.user_defined(src, filter=median_filter) diffuse_filter = tvtk.ImageAnisotropicDiffusion3D( diffusion_factor=1.0, diffusion_threshold=100.0, number_of_iterations=5, ) diffuse = mlab.pipeline.user_defined(median, filter=diffuse_filter) # Extract brain surface contour = mlab.pipeline.contour(diffuse, ) contour.filter.contours = [0.5, ] # Apply mesh filter to clean up the mesh (decimation and smoothing) dec = mlab.pipeline.decimate_pro(mlab.pipeline.triangle_filter(contour)) dec.filter.feature_angle = 60. dec.filter.target_reduction = 0.5 smooth_ = tvtk.SmoothPolyDataFilter( number_of_iterations=10, relaxation_factor=0.1, feature_angle=60, feature_edge_smoothing=False, boundary_smoothing=False, convergence=0., ) smooth = mlab.pipeline.user_defined(dec, filter=smooth_) # Get the largest connected region connect_ = tvtk.PolyDataConnectivityFilter(extraction_mode=4) connect = mlab.pipeline.user_defined(smooth, filter=connect_) # Compute normals for shading the surface compute_normals = mlab.pipeline.poly_data_normals(connect) compute_normals.filter.feature_angle = 80 surf = mlab.pipeline.surface(compute_normals, color=(1, 1, 1)) #---------------------------------------------------------------------- # Display a cut plane of the raw data ipw = mlab.pipeline.image_plane_widget(src, colormap='bone', plane_orientation='z_axes', slice_index=55) # mlab.view(-165, 32, 350, [143, 133, 73]) # mlab.roll(180) fig.scene.disable_render = False #---------------------------------------------------------------------- # To make the link between the Mayavi pipeline and the much more # complex VTK pipeline, we display both: mlab.show_pipeline(rich_view=False) from tvtk.pipeline.browser import PipelineBrowser browser = PipelineBrowser(fig.scene) browser.show() mlab.show()
def reconstrucion(self, data, spacing): ''' resample, threshold, filter, display :param data: a 3d array with channel last :param spacing: PixelSpacing :return: none ''' mlab.clf() self.scene.disable_render = True # 以加快渲染速度 src = mlab.pipeline.scalar_field(data) # 重采样 src.spacing = spacing src.update_image_data = True # 提取心脏 # thresh_filter = tvtk.ImageThreshold() # thresh_filter.threshold_between(lower_thr, upper_thr) # thresh = mlab.pipeline.user_defined(src, filter=thresh_filter) # 中值滤波 median_filter = tvtk.ImageMedian3D() # median_filter.SetKernelSize(3, 3, 3) median = mlab.pipeline.user_defined(src, filter=median_filter) # 各项异性扩散滤波 diffuse_filter = tvtk.ImageAnisotropicDiffusion3D( diffusion_factor=1.0, diffusion_threshold=25.0, number_of_iterations=3, ) diffuse = mlab.pipeline.user_defined(median, filter=diffuse_filter) # 提取表面 contour = mlab.pipeline.contour(diffuse, ) contour.filter.contours = [ 110, ] # 网格抽取 dec = mlab.pipeline.decimate_pro(contour) dec.filter.feature_angle = 60. dec.filter.target_reduction = 0.7 # 网格平滑 smooth_ = tvtk.SmoothPolyDataFilter( number_of_iterations=10, relaxation_factor=0.1, feature_angle=60, feature_edge_smoothing=False, boundary_smoothing=False, convergence=0., ) smooth = mlab.pipeline.user_defined(dec, filter=smooth_) # 获取最大连通区 connect_ = tvtk.PolyDataConnectivityFilter(extraction_mode=4) connect = mlab.pipeline.user_defined(smooth, filter=connect_) # 计算法线 compute_normals = mlab.pipeline.poly_data_normals(connect) compute_normals.filter.feature_angle = 80 self.surf = mlab.pipeline.surface(compute_normals, color=(0.9, 0.72, 0.62), opacity=0.9) # Display a cut plane of the raw data self.ipw = mlab.pipeline.image_plane_widget(src, colormap='bone', plane_orientation='x_axes', slice_index=5) self.ipw.ipw.margin_size_x = 0 self.ipw.ipw.margin_size_y = 0 self.scene.disable_render = False