# Examples from pyvista.ImageDataFilters.contour_labels
# =====================================================

# Load labeled image data with a background region `0` and four foreground
# regions.
import pyvista as pv
import numpy as np
from pyvista import examples
image = examples.load_channels()
label_ids = np.unique(image.active_scalars)
label_ids
image.dimensions

# Crop the image to simplify the data.
image = image.extract_subset(voi=(75, 109, 75, 109, 85, 100))
image.dimensions

# Plot the cropped image for context. Use `color_labels()`
# to generate consistent coloring of the regions for all plots. Negative indexing
# is used for plotting internal boundaries.
def labels_plotter(mesh, zoom=None):
    colored_mesh = mesh.color_labels(negative_indexing=True)
    pl = pv.Plotter()
    pl.add_mesh(colored_mesh, show_edges=True)
    if zoom:
        pl.camera.zoom(zoom)
    return pl

labels_plotter(image).show()

# Generate surface contours of the foreground regions and plot it. Note that
# the `background_value` is `0` by default.
contours = image.contour_labels()
labels_plotter(contours, zoom=1.5).show()

# By default, only external boundary polygons are generated and the returned
# `'boundary_labels'` array is a single-component array. The output values
# match the input label values.
contours['boundary_labels'].ndim
np.unique(contours['boundary_labels'])

# Set `simplify_output` to `False` to generate a two-component
# array instead showing the two boundary regions associated with each polygon.
contours = image.contour_labels(simplify_output=False)
contours['boundary_labels'].ndim

# Show the unique values. Since only `'external'` boundaries are generated
# by default, the second component is always `0` (that is, the `background_value`).
# Note that all four foreground regions share a boundary with the background.
np.unique(contours['boundary_labels'], axis=0)

# Repeat the example but this time generate internal contours only. The generated
# array is 2D by default.
contours = image.contour_labels('internal')
contours['boundary_labels'].ndim

# Show the unique two-component boundary labels again. From these values we can
# determine that all foreground regions share an internal boundary with each
# other except  for regions `1` and `3` since the boundary value `[1, 3]`
# is missing.
np.unique(contours['boundary_labels'], axis=0)

# Simplify the output so that each internal multi-component boundary value is
# assigned a unique negative integer value instead. This makes it easier to
# visualize the result with `color_labels()` using
# the `negative_indexing` option.
contours = image.contour_labels('internal', simplify_output=True)
contours['boundary_labels'].ndim
np.unique(contours['boundary_labels'])

labels_plotter(contours, zoom=1.5).show()

# Generate contours for all boundaries, and use `select_outputs` to filter
# the output to only include polygons which share a boundary with region `3`.
region_3 = image.contour_labels(
    'all', select_outputs=3, simplify_output=True
)
labels_plotter(region_3, zoom=3).show()

# Note how using `select_outputs` preserves the sharp features and boundary
# labels for non-selected regions. If desired, use `select_inputs` instead to
# completely "ignore" non-selected regions.
region_3 = image.contour_labels(select_inputs=3)
labels_plotter(region_3, zoom=3).show()

# The sharp features are now smoothed and the internal boundaries are now labeled
# as external boundaries. Note that using `'all'` here is optional since
# using `select_inputs` converts previously internal boundaries into external
# ones.
# Do not pad the image with background values before contouring. Since the input image
# has foreground regions visible at the edges of the image (for example, the `+Z` bound),
# setting `pad_background=False` in this example causes the top and sides of
# the mesh to be "open".
surf = image.contour_labels(pad_background=False)
labels_plotter(surf, zoom=1.5).show()

# Disable smoothing to generate staircase-like surface. Without smoothing, the
# surface has quadrilateral cells by default.
surf = image.contour_labels(smoothing=False)
labels_plotter(surf, zoom=1.5).show()

# Keep smoothing enabled but reduce the smoothing scale. A smoothing scale
# less than one may help preserve sharp features (for example, corners).
surf = image.contour_labels(smoothing_scale=0.5)
labels_plotter(surf, zoom=1.5).show()

# Use the `'strict_external'` style to compute external contours quickly. Note
# that this produces jagged and non-smooth boundaries between regions, which may
# not be desirable. Also note how the top of the surface is perfectly flat compared
# to the default `'external'` style (see first example above) since the strict
# style ignores the smoothing effects of all internal boundaries.
surf = image.contour_labels('strict_external')
labels_plotter(surf, zoom=1.5).show()

# ----------------------------------------------------------------------
# Generated by sphinx-examples-as-code https://github.com/pyvista/sphinx-examples-as-code