visualization

Visualize and animate DRRs in 2D and 3D

2D Visualization

Uses matplotlib and imageio to plot DRRs and animate optimization over DRRs.

plot_drr

 plot_drr (img:torch.Tensor, title:str|None=None, ticks:bool|None=True,
           axs:matplotlib.axes._axes.Axes|None=None, cmap:str='gray',
           **imshow_kwargs)

Plot an image generated by a DRR module.

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plot_mask

 plot_mask (img:torch.Tensor, axs:matplotlib.axes._axes.Axes,
            colors=['rgb(102,194,165)', 'rgb(252,141,98)',
            'rgb(141,160,203)', 'rgb(231,138,195)', 'rgb(166,216,84)',
            'rgb(255,217,47)', 'rgb(229,196,148)'], alpha=0.5,
            return_masks=False)

Plot a 2D rendered segmentation mask. Meant to be called after plot_drr.

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animate

 animate (out:str|pathlib._local.Path, df:pandas.core.frame.DataFrame,
          drr:diffdrr.drr.DRR, parameterization:str, convention:str=None,
          ground_truth:torch.Tensor|None=None, verbose:bool=True,
          dtype=torch.float32, device='cpu', **kwargs)

Animate the optimization of a DRR.

	Type	Default	Details
out	str \| pathlib._local.Path		Savepath
df	DataFrame
drr	DRR
parameterization	str
convention	str	None
ground_truth	torch.Tensor \| None	None
verbose	bool	True
dtype	dtype	torch.float32
device	str	cpu
kwargs	VAR_KEYWORD

df is a pandas.DataFrame with columns ["alpha", "beta", "gamma", "bx", "by", "bz"]. Each row in df is an iteration of optimization with the updated values for that timestep.

3D Visualization

Uses pyvista and trame to interactively visualize DRR geometry in 3D.

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drr_to_mesh

 drr_to_mesh (subject:torchio.data.subject.Subject, method:str,
              threshold:float=150, extract_largest:bool=True,
              verbose:bool=True)

*Convert the CT in a DRR object into a mesh.

If using method=="surface_nets", ensure you have pyvista>=0.43 and vtk>=9.3 installed.

The mesh processing steps are:

Keep only largest connected components (optional)
Smooth
Decimate (if method=="marching_cubes")
Fill any holes
Clean (remove any redundant vertices/edges)*

	Type	Default	Details
subject	Subject		torchio.Subject with a `volume` attribute
method	str		Either `surface_nets` or `marching_cubes`
threshold	float	150	Min value for marching cubes (Hounsfield units)
extract_largest	bool	True	Extract the largest connected component from the mesh
verbose	bool	True	Display progress bars for mesh processing steps

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labelmap_to_mesh

 labelmap_to_mesh (subject:torchio.data.subject.Subject,
                   verbose:bool=True)

	Type	Default	Details
subject	Subject		torchio.Subject with a `mask` attribute
verbose	bool	True	Display progress bars for mesh processing steps

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img_to_mesh

 img_to_mesh (drr:diffdrr.drr.DRR, pose:diffdrr.pose.RigidTransform,
              calibration:diffdrr.pose.RigidTransform=None, **kwargs)

For a given pose (not batched), turn the camera and detector into a mesh. Additionally, render the DRR for the pose. Convert into a texture that can be applied to the detector mesh.

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add_image

 add_image (drr:diffdrr.drr.DRR, pose:diffdrr.pose.RigidTransform,
            pl:pyvista.plotting.plotter.Plotter)

Add a camera to an existing scene.

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visualize_scene

 visualize_scene (drr:diffdrr.drr.DRR, pose:diffdrr.pose.RigidTransform,
                  labelmap:bool=False, grid:bool=True, verbose:bool=False,
                  **kwargs)

*Given a DRR and a RigidTransform, render the 3D scene in PyVista. **kwargs are passed to drr_to_mesh.*