# Crop Labeled ImageData
# ======================

# Use `crop()` to crop labeled data such as segmented medical
# images.
import pyvista as pv
from pyvista import examples

# Load a dataset with a CT image and corresponding segmentation labels. Here we load
# `download_whole_body_ct_male()`.
dataset = examples.download_whole_body_ct_male()

# Get the `ImageData` for the CT data and one of the segmentation masks.
# For this example we choose a mask of the skull.
ct = dataset['ct']
skull = dataset['segmentations']['skull']

# Crop the CT image using the segmentation mask. Use `padding` to include additional
# data points around the masked region.
cropped_ct = ct.crop(mask=skull, padding=10)

# Use `points_to_cells()` to plot the cropped image
# as `VOXEL` cells.
cpos = pv.CameraPosition(
    position=(687.5, 763.6, 471.3),
    focal_point=(231.8, 296.3, 677.0),
    viewup=(0.107, 0.311, 0.944),
)

cropped_ct_voxels = cropped_ct.points_to_cells()
cropped_ct_voxels.plot(volume=True, cpos=cpos)

# Include a surface contour of the mask with the plot.
skull_surface = skull.contour_labels()

pl = pv.Plotter()
pl.add_mesh(skull_surface, color='white')
pl.add_volume(cropped_ct_voxels)
pl.camera_position = cpos
pl.show()

# After cropping, the CT image's dimensions are smaller than the mask's.
cropped_ct.dimensions == skull.dimensions

# To keep dimension the same, either
# crop the mask itself; the meshes will have smaller dimensions relative to the input
# pad the CT image as part of the initial crop; the meshes will have the same
# dimensions as the input
# To crop the mask itself, you can perform a similar crop as before using `mask=True`.
cropped_skull = skull.crop(mask=True, padding=10)
cropped_skull.dimensions

# However, computationally it is more efficient to crop using `extent` directly.
cropped_skull = skull.crop(extent=cropped_ct.extent)
cropped_skull.dimensions

# Alternatively, use `keep_dimensions` and `fill_value` when initially cropping the
# image so that the output dimensions match the input. A value of `-1000` is used, which
# may represent air in the scan.
cropped_ct = ct.crop(mask=skull, keep_dimensions=True, fill_value=-1000)
cropped_ct.dimensions

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