Visualizer2

visualizer2

Functions:

Name	Description
display_slices	Display orthogonal slices of a 3D array.

ImageVisualizer dataclass

ImageVisualizer(main_image: SimpleITK.Image)

Simple visualizer for sitk based images.

Methods:

Name	Description
from_image	Create an ImageVisualizer from a SimpleITK image.
from_image_and_mask	Create an ImageVisualizer with a mask overlay on the image.
view_grid	Visualize slices in a grid.

from_image classmethod

from_image(
    image: SimpleITK.Image,
) -> imgtools.vizualize.visualizer2.ImageVisualizer

Create an ImageVisualizer from a SimpleITK image.

Source code in src/imgtools/vizualize/visualizer2.py

@classmethod
def from_image(
    cls,
    image: sitk.Image,
) -> ImageVisualizer:
    """Create an ImageVisualizer from a SimpleITK image."""

    return cls(main_image=image)

from_image_and_mask classmethod

from_image_and_mask(
    image: SimpleITK.Image,
    mask: SimpleITK.Image,
    label: int = 1,
    as_contour: bool = False,
    mask_color: imgtools.vizualize.visualizer2.MaskColor = imgtools.vizualize.visualizer2.MaskColor.GREEN,
    opacity: float = 0.5,
    background_label: int = 0,
    crop_to_bbox: bool = True,
    croppad: int = 2,
) -> imgtools.vizualize.visualizer2.ImageVisualizer

Create an ImageVisualizer with a mask overlay on the image.

Parameters:

Name	Type	Description	Default
image	SimpleITK.Image	Base image for visualization	required
mask	SimpleITK.Image	Mask image to overlay on the base image	required
label	int	Label value in the mask to use for overlay	1
as_contour	bool	If True, convert the mask to a contour before overlay	False
mask_color	imgtools.vizualize.visualizer2.MaskColor	Color to use for the mask overlay	MaskColor.GREEN
opacity	float	Opacity of the mask overlay (0.0-1.0)	0.5
background_label	int	Label value in the mask to treat as background	0
crop_to_bbox	bool	If True, crop the image to the bounding box of the mask	True
croppad	int	Padding to add around the crop region	2

Returns:

Type	Description
imgtools.vizualize.visualizer2.ImageVisualizer	Instance with the mask overlaid on the image

Source code in src/imgtools/vizualize/visualizer2.py

@classmethod
def from_image_and_mask(
    cls,
    image: sitk.Image,
    mask: sitk.Image,
    label: int = 1,
    as_contour: bool = False,
    # overlay settings
    mask_color: MaskColor = MaskColor.GREEN,
    opacity: float = 0.5,
    background_label: int = 0,
    # preprocessing settings
    crop_to_bbox: bool = True,
    croppad: int = 2,
) -> ImageVisualizer:
    """Create an ImageVisualizer with a mask overlay on the image.

    Parameters
    ----------
    image : sitk.Image
        Base image for visualization
    mask : sitk.Image
        Mask image to overlay on the base image
    label : int, default=1
        Label value in the mask to use for overlay
    as_contour : bool, default=False
        If True, convert the mask to a contour before overlay
    mask_color : MaskColor, default=MaskColor.GREEN
        Color to use for the mask overlay
    opacity : float, default=0.5
        Opacity of the mask overlay (0.0-1.0)
    background_label : int, default=0
        Label value in the mask to treat as background
    crop_to_bbox : bool, default=True
        If True, crop the image to the bounding box of the mask
    croppad : int, default=2
        Padding to add around the crop region

    Returns
    -------
    ImageVisualizer
        Instance with the mask overlaid on the image
    """

    region = (
        RegionBox.from_mask_bbox(mask, label=label)
        .expand_to_cube()  # cube expanding will help for grid
        .pad(croppad)
    )

    if as_contour:
        f_mask = sitk.BinaryContour(mask, fullyConnected=True)
    else:
        f_mask = mask

    combined_image = sitk.LabelOverlay(
        sitk.Cast(image, pixelID=sitk.sitkUInt8),
        f_mask,
        opacity=opacity,
        backgroundValue=background_label,
        colormap=mask_color.value,
    )

    if crop_to_bbox:
        combined_image = region.crop_image(combined_image)

    return cls(main_image=combined_image)

view_grid

view_grid(
    every: int = 1,
    fig: matplotlib.pyplot.Figure | None = None,
) -> matplotlib.pyplot.Figure

Visualize slices in a grid.

Parameters:

Name	Type	Description	Default
every	int	Step size for slice selection	1
fig	matplotlib.pyplot.Figure	Existing figure to use for visualization If None, a new figure will be created	None

Returns:

Type	Description
matplotlib.pyplot.Figure	Figure containing the grid of slices

Source code in src/imgtools/vizualize/visualizer2.py

def view_grid(
    self,
    every: int = 1,
    fig: plt.Figure | None = None,
) -> plt.Figure:
    """Visualize slices in a grid.

    Parameters
    ----------
    every : int
        Step size for slice selection
    fig : plt.Figure, optional
        Existing figure to use for visualization
        If None, a new figure will be created

    Returns
    -------
    plt.Figure
        Figure containing the grid of slices
    """
    dimension = 2
    plot_size = 3

    image = self.main_image
    size = image.GetSize()

    slice_indices = list(range(0, size[dimension], every))

    # should be a square number of slices
    cols = math.ceil(np.sqrt(len(slice_indices)))
    rows = math.ceil(len(slice_indices) / cols)
    figsize = (plot_size * cols, plot_size * rows)

    if fig is None:
        fig, axes = plt.subplots(
            rows, cols, figsize=figsize, constrained_layout=True
        )
    else:
        axes = fig.subplots(rows, cols)

    axes = axes.ravel()

    for idx, slice_index in enumerate(slice_indices):
        slice_image = image[:, :, slice_index]
        array = sitk.GetArrayViewFromImage(slice_image)

        ax = axes[idx]
        ax.imshow(array, cmap="gray")
        ax.set_title(f"Slice {slice_index + 1}/{size[2]}", fontsize=8)
        ax.axis("off")

    # Hide any extra axes if the grid is larger than the number of slices
    for idx in range(len(slice_indices), rows * cols):
        axes[idx].axis("off")

    return fig

MaskColor

Bases: enum.Enum

Predefined color values for mask overlays.

Each color is represented as an RGB list with values from 0-255. The RGB value specifically contains concatenated R, G, B channels for sitk colormap.

display_slices

display_slices(
    array: numpy.ndarray,
    x_index: int,
    y_index: int,
    z_index: int,
) -> None

Display orthogonal slices of a 3D array.

Parameters:

Name	Type	Description	Default
array	numpy.ndarray	3D numpy array representing the image volume	required
x_index	int	Index for slice in the x dimension	required
y_index	int	Index for slice in the y dimension	required
z_index	int	Index for slice in the z dimension	required

Source code in src/imgtools/vizualize/visualizer2.py

def display_slices(
    array: np.ndarray, x_index: int, y_index: int, z_index: int
) -> None:
    """Display orthogonal slices of a 3D array.

    Parameters
    ----------
    array : np.ndarray
        3D numpy array representing the image volume
    x_index : int
        Index for slice in the x dimension
    y_index : int
        Index for slice in the y dimension
    z_index : int
        Index for slice in the z dimension
    """
    if not isinstance(array, np.ndarray):
        raise TypeError("Input must be a numpy array")

    fig, axes = plt.subplots(1, 3, figsize=(15, 5))
    axes[0].imshow(array[x_index, :, :], aspect="auto")
    axes[1].imshow(array[:, y_index, :], aspect="auto")
    axes[2].imshow(array[:, :, z_index], aspect="auto")
    # turn off axes
    for ax in axes:
        ax.axis("off")

    plt.show()
    for ax in axes:
        ax.axis("off")

    plt.show()