PyVista `add_volume`: Garbled Output & Slower than ParaView
Questions:
Update 14-SEP-2021: Simplified problem even further to a smaller MRE. After some analysis, it doesn't seem Qt threading is the culprit, so corresponding Qt code was removed.
pyvistadoes not plot my volume along the correct axis and the output is garbled.ParaViewon the other hand plots things properly. How can I fix this?(NOTE: I cannot share the actual data because it is confidential. However, below you can see
pyvistaorients my data along thez-axis, when in fact it should be along thex-axis, and that it is garbled. I show the bounding box in ParaView.The results are the same regardless if I use the
fixed_pointvs.smartvolume mappers. I usefixed_pointsince I am on Windows.)
pyvista:
ParaView:
Plotting volumes in
pyvistais much slower than inParaView. Is there some way I can make this faster?The time for my code with
pyvistavs.ParaViewisMy Code: ~13 minutes, 9 seconds ParaView 5.9.1 (installed pre-built binary): ~24 secondsI've used
cProfileto help identify problem areas (please see below).
Setup:
Data
No. of DICOM Files: 1,172
DICOM File Size: 5.96 MB
Total Scan Size: 7GB
DICOM Image Dimensions: 2402 x 1301 pixels
System / Hardware
OS: Windows 10 Professional x64-bit, Build 1909
CPU: 2x Intel(R) Xeon(R) Gold 6248R
Disk: 2TB NVMe M.2 SSD
RAM: 192 GB DDR4
Compute GPUs: 2x NVIDIA Quadro RTX8000
Display GPU: 1x NVIDIA Quadro RTX4000
Software
Python: 3.8.10 x64-bit
pyvista: 0.32.1
VTK: 9.0.3
ParaView: 5.9.1
IDE: VSCode 1.59.0
Code:
import cProfile
import io
import os
import pstats
import numpy as np
import pyvista as pv
import SimpleITK as sitk
from SimpleITK import ImageSeriesReader
from trimesh import points
pv.rcParams["volume_mapper"] = "fixed_point" # Windows
folder = "C:\\path\\to\\DICOM\\stack\\folder"
def profile(fnc):
"""Wrapper for cProfile"""
def inner(*args, **kwargs):
pr = cProfile.Profile()
pr.enable()
retval = fnc(*args, **kwargs)
pr.disable()
s = io.StringIO()
sortby = "cumulative"
ps = pstats.Stats(pr, stream=s).sort_stats(sortby)
ps.print_stats()
print(s.getvalue())
return retval
return inner
@profile
def plot_volume(folder):
p = pv.Plotter()
dicom_reader = ImageSeriesReader()
dicom_files = dicom_reader.GetGDCMSeriesFileNames(folder)
dicom_reader.SetFileNames(dicom_files)
scan = dicom_reader.Execute()
origin = scan.GetOrigin()
spacing = scan.GetSpacing()
direction = scan.GetDirection()
data = sitk.GetArrayFromImage(scan)
data = (data // 256).astype(np.uint8) # Cast 16-bit to 8-bit
volume = pv.UniformGrid(data.shape)
volume.origin = origin
volume.spacing = spacing
volume.direction = direction
volume.point_data["Values"] = data.flatten(order="F")
volume.set_active_scalars("Values")
p.add_volume(
volume,
opacity="sigmoid",
reset_camera=True,
)
p.add_axes()
p.show()
if __name__ == "__main__":
plot_volume(folder)
Output:
WARNING: In d:\a\1\sitk-build\itk-prefix\include\itk-5.2\itkImageSeriesReader.hxx, line 480
ImageSeriesReader (0000021B082D3360): Non uniform sampling or missing slices detected, maximum nonuniformity:7.39539e-07
Ordered by: cumulative time
ncalls tottime percall cumtime percall filename:lineno(function)
1 11.220 11.220 772.300 772.300 gui\main.py:61(plot_volume)
1 86.881 86.881 648.445 648.445 .venv\lib\site-packages\pyvista\plotting\plotting.py:2271(add_volume)
1 0.000 0.000 373.896 373.896 .venv\lib\site-packages\pyvista\core\filters\data_set.py:2022(cell_data_to_point_data)
1 0.001 0.001 371.802 371.802 .venv\lib\site-packages\pyvista\core\filters\__init__.py:30(_update_alg)
2 371.802 185.901 371.802 185.901 {method 'Update' of 'vtkmodules.vtkCommonExecutionModel.vtkAlgorithm' objects}
606/273 8.916 0.015 134.346 0.492 {built-in method numpy.core._multiarray_umath.implement_array_function}
3 17.923 5.974 101.495 33.832 .venv\lib\site-packages\numpy\lib\nanfunctions.py:68(_replace_nan)
693 85.541 0.123 85.541 0.123 {built-in method numpy.array}
2 0.001 0.000 74.715 37.358 <__array_function__ internals>:2(nanmin)
2 0.718 0.359 69.822 34.911 .venv\lib\site-packages\numpy\lib\nanfunctions.py:228(nanmin)
57 46.992 0.824 46.992 0.824 {method 'astype' of 'numpy.ndarray' objects}
2 45.969 22.985 45.969 22.985 {method 'flatten' of 'numpy.ndarray' objects}
1 0.000 0.000 45.027 45.027 <__array_function__ internals>:2(nanmax)
1 0.253 0.253 42.448 42.448 .venv\lib\site-packages\numpy\lib\nanfunctions.py:343(nanmax)
1 0.000 0.000 25.705 25.705 .venv\lib\site-packages\pyvista\plotting\plotting.py:4634(show)
3 0.000 0.000 20.822 6.941 .venv\lib\site-packages\pyvista\core\datasetattributes.py:539(set_array)
3 0.000 0.000 18.391 6.130 .venv\lib\site-packages\pyvista\core\datasetattributes.py:730(_prepare_array)
11 0.000 0.000 18.391 1.672 .venv\lib\site-packages\pyvista\utilities\helpers.py:132(convert_array)
4 0.001 0.000 18.391 4.598 .venv\lib\site-packages\vtkmodules\util\numpy_support.py:104(numpy_to_vtk)
1 17.685 17.685 17.685 17.685 {method 'DeepCopy' of 'vtkmodules.vtkCommonCore.vtkDataArray' objects}
1 0.000 0.000 16.113 16.113 .venv\lib\site-packages\SimpleITK\SimpleITK.py:7854(Execute)
1 16.113 16.113 16.113 16.113 {built-in method SimpleITK._SimpleITK.ImageSeriesReader_Execute}
1 0.000 0.000 15.542 15.542 .venv\lib\site-packages\pyvista\plotting\render_window_interactor.py:615(start)
1 15.542 15.542 15.542 15.542 {method 'Start' of 'vtkmodules.vtkRenderingCore.vtkRenderWindowInteractor' objects}
1 0.000 0.000 14.598 14.598 <__array_function__ internals>:2(percentile)
1 0.000 0.000 14.598 14.598 .venv\lib\site-packages\numpy\lib\function_base.py:3724(percentile)
1 0.000 0.000 14.598 14.598 .venv\lib\site-packages\numpy\lib\function_base.py:3983(_quantile_unchecked)
1 0.235 0.235 14.598 14.598 .venv\lib\site-packages\numpy\lib\function_base.py:3513(_ureduce)
1 0.000 0.000 14.362 14.362 .venv\lib\site-packages\numpy\lib\function_base.py:4018(_quantile_ureduce_func)
1 12.671 12.671 12.671 12.671 {method 'partition' of 'numpy.ndarray' objects}
2 0.000 0.000 10.132 5.066 .venv\lib\site-packages\pyvista\plotting\plotting.py:1185(render)
1 10.132 10.132 10.132 10.132 {method 'Render' of 'vtkmodules.vtkRenderingOpenGL2.vtkOpenGLRenderWindow' objects}
61 0.000 0.000 9.805 0.161 .venv\lib\site-packages\numpy\core\fromnumeric.py:69(_wrapreduction)
63 9.804 0.156 9.805 0.156 {method 'reduce' of 'numpy.ufunc' objects}
2 0.000 0.000 6.170 3.085 <__array_function__ internals>:2(amin)
2 0.000 0.000 6.170 3.085 .venv\lib\site-packages\numpy\core\fromnumeric.py:2763(amin)
2 0.000 0.000 6.170 3.085 {method 'min' of 'numpy.ndarray' objects}
2 0.000 0.000 6.170 3.085 .venv\lib\site-packages\numpy\core\_methods.py:42(_amin)
1 0.000 0.000 6.073 6.073 .venv\lib\site-packages\SimpleITK\SimpleITK.py:7828(GetGDCMSeriesFileNames)
1 6.073 6.073 6.073 6.073 {built-in method SimpleITK._SimpleITK.ImageSeriesReader_GetGDCMSeriesFileNames}
1 0.000 0.000 3.413 3.413 .venv\lib\site-packages\SimpleITK\extra.py:252(GetArrayFromImage)
1 0.000 0.000 3.358 3.358 <__array_function__ internals>:2(amax)
1 0.000 0.000 3.358 3.358 .venv\lib\site-packages\numpy\core\fromnumeric.py:2638(amax)
1 0.000 0.000 3.358 3.358 {method 'max' of 'numpy.ndarray' objects}
1 0.000 0.000 3.358 3.358 .venv\lib\site-packages\numpy\core\_methods.py:38(_amax)
2 0.000 0.000 2.807 1.403 .venv\lib\site-packages\pyvista\core\datasetattributes.py:212(__setitem__)
1 0.000 0.000 2.764 2.764 .venv\lib\site-packages\pyvista\core\dataset.py:1637(__setitem__)
3 2.430 0.810 2.430 0.810 {method 'AddArray' of 'vtkmodules.vtkCommonDataModel.vtkFieldData' objects}
2 2.290 1.145 2.290 1.145 .venv\lib\site-packages\pyvista\core\pyvista_ndarray.py:53(__setitem__)
1 0.000 0.000 2.093 2.093 .venv\lib\site-packages\pyvista\core\filters\__init__.py:39(_get_output)
2 0.000 0.000 2.093 1.046 .venv\lib\site-packages\pyvista\core\grid.py:291(__init__)
1 0.000 0.000 2.092 2.092 .venv\lib\site-packages\pyvista\utilities\helpers.py:797(wrap)
1 0.000 0.000 2.092 2.092 .venv\lib\site-packages\pyvista\core\dataobject.py:53(deep_copy)
1 2.092 2.092 2.092 2.092 {method 'DeepCopy' of 'vtkmodules.vtkCommonDataModel.vtkImageData' objects}
40 0.000 0.000 1.444 0.036 <__array_function__ internals>:2(copyto)
4 0.591 0.148 0.591 0.148 {method 'SetVoidArray' of 'vtkmodules.vtkCommonCore.vtkAbstractArray' objects}
3 0.000 0.000 0.277 0.092 <__array_function__ internals>:2(all)
3 0.000 0.000 0.277 0.092 .venv\lib\site-packages\numpy\core\fromnumeric.py:2367(all)
3 0.000 0.000 0.277 0.092 {method 'all' of 'numpy.ndarray' objects}
3 0.000 0.000 0.277 0.092 .venv\lib\site-packages\numpy\core\_methods.py:60(_all)
80/4 0.001 0.000 0.219 0.055 <frozen importlib._bootstrap>:986(_find_and_load)
76/4 0.001 0.000 0.219 0.055 <frozen importlib._bootstrap>:956(_find_and_load_unlocked)
73/2 0.001 0.000 0.214 0.107 <frozen importlib._bootstrap>:650(_load_unlocked)
66/2 0.000 0.000 0.214 0.107 <frozen importlib._bootstrap_external>:842(exec_module)
78/11 0.027 0.000 0.213 0.019 {built-in method builtins.exec}
104/2 0.000 0.000 0.213 0.106 <frozen importlib._bootstrap>:211(_call_with_frames_removed)
2 0.000 0.000 0.193 0.096 .venv\lib\site-packages\pyvista\plotting\plotting.py:43(_has_matplotlib)
1 0.001 0.001 0.190 0.190 .venv\lib\site-packages\matplotlib\__init__.py:1(<module>)
104/27 0.000 0.000 0.146 0.005 <frozen importlib._bootstrap>:1017(_handle_fromlist)
32/9 0.000 0.000 0.145 0.016 {built-in method builtins.__import__}
1 0.001 0.001 0.119 0.119 .venv\lib\site-packages\matplotlib\rcsetup.py:1(<module>)
4 0.113 0.028 0.113 0.028 {method 'SetNumberOfTuples' of 'vtkmodules.vtkCommonCore.vtkAbstractArray' objects}
1 0.037 0.037 0.038 0.038 .venv\lib\site-packages\pyvista\plotting\mapper.py:4(make_mapper)
1 0.000 0.000 0.037 0.037 .venv\lib\site-packages\matplotlib\animation.py:19(<module>)
1 0.000 0.000 0.037 0.037 .venv\lib\site-packages\matplotlib\fontconfig_pattern.py:1(<module>)
2 0.005 0.002 0.036 0.018 .venv\lib\site-packages\matplotlib\__init__.py:709(_rc_params_in_file)
76 0.001 0.000 0.035 0.000 <frozen importlib._bootstrap>:890(_find_spec)
66 0.002 0.000 0.034 0.001 <frozen importlib._bootstrap_external>:914(get_code)
75 0.000 0.000 0.033 0.000 <frozen importlib._bootstrap_external>:1399(find_spec)
75 0.001 0.000 0.033 0.000 <frozen importlib._bootstrap_external>:1367(_get_spec)
612 0.002 0.000 0.033 0.000 .venv\lib\site-packages\matplotlib\__init__.py:574(__setitem__)
1 0.000 0.000 0.031 0.031 .venv\lib\site-packages\matplotlib\colors.py:1(<module>)
153 0.004 0.000 0.030 0.000 <frozen importlib._bootstrap_external>:1498(find_spec)
381/346 0.012 0.000 0.029 0.000 {built-in method builtins.__build_class__}
1 0.001 0.001 0.028 0.028 .venv\lib\site-packages\pyparsing.py:27(<module>)
1 0.000 0.000 0.027 0.027 .venv\lib\site-packages\pyvista\plotting\render_window_interactor.py:627(process_events)
1 0.027 0.027 0.027 0.027 {method 'ProcessEvents' of 'vtkmodules.vtkRenderingUI.vtkWin32RenderWindowInteractor' objects}
1 0.000 0.000 0.026 0.026 .venv\lib\site-packages\pyvista\plotting\colors.py:397(get_cmap_safe)
1 0.000 0.000 0.024 0.024 .venv\lib\site-packages\PIL\Image.py:27(<module>)
1 0.000 0.000 0.022 0.022 .venv\lib\site-packages\matplotlib\cm.py:1(<module>)
355 0.022 0.000 0.022 0.000 {built-in method nt.stat}
2 0.000 0.000 0.021 0.011 .venv\lib\site-packages\matplotlib\rcsetup.py:164(_validate_date_converter)
324 0.000 0.000 0.020 0.000 <frozen importlib._bootstrap_external>:135(_path_stat)
1 0.000 0.000 0.020 0.020 .venv\lib\site-packages\matplotlib\dates.py:1(<module>)
73 0.000 0.000 0.014 0.000 <frozen importlib._bootstrap>:549(module_from_spec)
66 0.002 0.000 0.014 0.000 <frozen importlib._bootstrap_external>:1034(get_data)
1 0.000 0.000 0.014 0.014 .venv\lib\site-packages\matplotlib\scale.py:1(<module>)
1 0.000 0.000 0.013 0.013 .venv\lib\site-packages\matplotlib\cm.py:32(_gen_cmap_registry)
1 0.000 0.000 0.012 0.012 .venv\lib\site-packages\dateutil\parser\__init__.py:2(<module>)
259 0.000 0.000 0.012 0.000 C:\Program Files\Python38\lib\re.py:289(_compile)
66 0.000 0.000 0.012 0.000 <frozen importlib._bootstrap_external>:638(_compile_bytecode)
66 0.011 0.000 0.011 0.000 {built-in method marshal.loads}
26 0.000 0.000 0.011 0.000 C:\Program Files\Python38\lib\sre_compile.py:759(compile)
1 0.000 0.000 0.011 0.011 .venv\lib\site-packages\pyparsing.py:6398(pyparsing_common)
1 0.000 0.000 0.010 0.010 .venv\lib\site-packages\matplotlib\ticker.py:1(<module>)
1 0.000 0.000 0.010 0.010 .venv\lib\site-packages\PIL\PngImagePlugin.py:34(<module>)
5 0.000 0.000 0.010 0.002 <frozen importlib._bootstrap_external>:1164(create_module)
5 0.010 0.002 0.010 0.002 {built-in method _imp.create_dynamic}
48 0.000 0.000 0.010 0.000 C:\Program Files\Python38\lib\re.py:250(compile)
1 0.000 0.000 0.009 0.009 .venv\lib\site-packages\matplotlib\__init__.py:138(_check_versions)
32 0.001 0.000 0.009 0.000 .venv\lib\site-packages\matplotlib\colors.py:915(from_list)
66 0.009 0.000 0.009 0.000 {built-in method io.open_code}
1 0.000 0.000 0.009 0.009 .venv\lib\site-packages\dateutil\parser\_parser.py:2(<module>)
754 0.006 0.000 0.008 0.000 <frozen importlib._bootstrap_external>:91(_path_join)
6 0.000 0.000 0.008 0.001 C:\Program Files\Python38\lib\importlib\__init__.py:109(imp
Update 14-SEP-2021 #2:
Interestingly, when trying to print out the shapes of data for debugging purposes as follows:
data_flattened = data.flatten(order="F")
volume.point_data["Values"] = data_flattened
volume.set_active_scalars("Values")
print(f"Points Shape: {volume.points.shape}")
print(f"Data Shape: {data.shape}")
print(f"Flattened Data Shape: {data_flattened.shape}")
I get the following error:
Error:
numpy.core._exceptions.MemoryError: Unable to allocate 81.9 GiB for an array with shape (3662502344, 3) and data type float64
Output:
Traceback (most recent call last):
File "C:\Program Files\Python38\lib\runpy.py", line 194, in _run_module_as_main
return _run_code(code, main_globals, None,
File "C:\Program Files\Python38\lib\runpy.py", line 87, in _run_code
exec(code, run_globals)
File "c:\Users\user\.vscode\extensions\ms-python.python-2021.9.1191016588\pythonFiles\lib\python\debugpy\__main__.py", line 45, in <module>
cli.main()
File "c:\Users\user\.vscode\extensions\ms-python.python-2021.9.1191016588\pythonFiles\lib\python\debugpy/..\debugpy\server\cli.py", line 444, in main
run()
File "c:\Users\user\.vscode\extensions\ms-python.python-2021.9.1191016588\pythonFiles\lib\python\debugpy/..\debugpy\server\cli.py", line 285, in run_file
runpy.run_path(target_as_str, run_name=compat.force_str("__main__"))
File "C:\Program Files\Python38\lib\runpy.py", line 265, in run_path
return _run_module_code(code, init_globals, run_name,
File "C:\Program Files\Python38\lib\runpy.py", line 97, in _run_module_code
_run_code(code, mod_globals, init_globals,
File "C:\Program Files\Python38\lib\runpy.py", line 87, in _run_code
exec(code, run_globals)
File "c:\Users\user\Code\gui\gui\main.py", line 81, in <module>
plot_volume(folder)
File "c:\Users\user\Code\gui\gui\main.py", line 22, in inner
retval = fnc(*args, **kwargs)
File "c:\Users\user\Code\gui\gui\main.py", line 65, in plot_volume
print(f"Points Shape: {volume.points.shape}")
File "c:\Users\user\Code\gui\.venv\lib\site-packages\pyvista\core\grid.py", line 368, in points
return np.c_[xx.ravel(order='F'), yy.ravel(order='F'), zz.ravel(order='F')]
File "c:\Users\user\Code\gui\.venv\lib\site-packages\numpy\lib\index_tricks.py", line 413, in __getitem__
res = self.concatenate(tuple(objs), axis=axis)
File "<__array_function__ internals>", line 5, in concatenate
numpy.core._exceptions.MemoryError: Unable to allocate 81.9 GiB for an array with shape (3662502344, 3) and data type float64
Solution
In pyvista version 0.32.1, the lines of code in pyvista/plotting/plotting.py function add_volume here are problematic:
scalars = scalars.astype(np.float_)
with np.errstate(invalid='ignore'):
idxs0 = scalars < clim[0]
idxs1 = scalars > clim[1]
scalars[idxs0] = clim[0]
scalars[idxs1] = clim[1]
scalars = ((scalars - np.nanmin(scalars)) / (np.nanmax(scalars) - np.nanmin(scalars))) * 255
# scalars = scalars.astype(np.uint8)
volume[title] = scalars
For a large dataset, such as mine
Shape: (1172, 2402, 1301)
dtype: 16-bit unsigneint (2 bytes)
Total Size = 1172 * 2402 * 1301 * 2 = 7.325 GB
the line
scalars = scalars.astype(np.float_)
quadruples the memory requirement by casting the data to floating point
Shape: (1172, 2402, 1301)
dtype: 16-bit int (8 bytes)
Total Size = 1172 * 2402 * 1301 * 8 = 58.6 GB!
In addition, these lines
scalars[idxs0] = clim[0]
scalars[idxs1] = clim[1]
explode memory resources to over 100 GB!
Lastly, SimpleITK is too slow. pyvista.read() doesn't natively support reading image stack folders, but this can be overcome by the following:
from vtkmodules.vtkIOImage import vtkDICOMImageReader
reader = vtkDICOMImageReader()
reader.SetDirectoryName(folder)
reader.Update()
volume = pv.wrap(reader.GetOutputDataObject(0))
which casts it immediately to UniformGrid and is much faster. Also, setting the spacing to anything other than (1, 1, 1) is what causes the output to be garbled, so I removed setting the spacing. (At the time of this writing, this doesn't make sense to me since my actual dimensional spacing is not (1, 1, 1), but I won't fight it).
A fast and memory-efficient way of clipping and rescaling scalars data is as follows:
def load_data(folder):
reader = vtkDICOMImageReader()
reader.SetDirectoryName(folder)
reader.Update()
volume = pv.wrap(reader.GetOutputDataObject(0))
del reader # Why keep double memory?
clim_16bit = [10000, 20000] # 16-bit values; change to what you want to see
scalars = volume["DICOMImage"]
scalars.clip(clim_16bit[0], clim_16bit[1], out=scalars)
min_ = np.nanmin(scalars)
max_ = np.nanmax(scalars)
np.true_divide((scalars - min_), (max_ - min_) / 255, out=scalars, casting="unsafe")
volume["DICOMImage"] = np.array(scalars, dtype=np.uint8)
volume.spacing = (1, 1, 1) # Be sure to set; Otherwise, the DICOM stack spacing will be used and results will be garbled
return volume
Note that this change also requires modifying the line
2918: scalars = volume.active_scalars
to
2918: scalars = volume.active_scalars.copy()
because the former creates a reference, hence when we mutate scalars, we would also be mutating volume.active_scalars, which is not what we want. (see Trey Huner's talk Python Oddities Explained from 2022 and Ned Batchelder's talk Facts and Myths about Python names and values from 2015 for more details).
I have modified add_volume() to give the user the option to choose whether or not they would like to have add_volume() clip and rescale scalars and whether they would like to have it convert cell data to point data (which is also an expensive memory copy). Note however, that if you choose not to let add_volume() rescale that you will want to pass in clim explicitly because, by default, clim uses the range of volume.active_scalars to determine the appropriate scalar bar range (and these will have been scaled to 0 - 255):
def add_volume(
self,
volume,
scalars=None,
clim=None,
resolution=None,
opacity="linear",
n_colors=256,
cmap=None,
flip_scalars=False,
reset_camera=None,
name=None,
ambient=0.0,
categories=False,
culling=False,
multi_colors=False,
blending="composite",
mapper=None,
scalar_bar_args=None,
show_scalar_bar=None,
annotations=None,
pickable=True,
preference="point",
opacity_unit_distance=None,
shade=False,
diffuse=0.7,
specular=0.2,
specular_power=10.0,
render=True,
rescale_scalars=True,
copy_cell_to_point_data=True,
**kwargs,
):
"""Add a volume, rendered using a smart mapper by default.
Requires a 3D :class:`numpy.ndarray` or :class:`pyvista.UniformGrid`.
Parameters
----------
volume : 3D numpy.ndarray or pyvista.UniformGrid
The input volume to visualize. 3D numpy arrays are accepted.
scalars : str or numpy.ndarray, optional
Scalars used to "color" the mesh. Accepts a string name of an
array that is present on the mesh or an array equal
to the number of cells or the number of points in the
mesh. Array should be sized as a single vector. If ``scalars`` is
``None``, then the active scalars are used.
clim : 2 item list, optional
Color bar range for scalars. Defaults to minimum and
maximum of scalars array. Example: ``[-1, 2]``. ``rng``
is also an accepted alias for this.
resolution : list, optional
Block resolution.
opacity : str or numpy.ndarray, optional
Opacity mapping for the scalars array.
A string can also be specified to map the scalars range to a
predefined opacity transfer function (options include: 'linear',
'linear_r', 'geom', 'geom_r'). Or you can pass a custom made
transfer function that is an array either ``n_colors`` in length or
shorter.
n_colors : int, optional
Number of colors to use when displaying scalars. Defaults to 256.
The scalar bar will also have this many colors.
cmap : str, optional
Name of the Matplotlib colormap to us when mapping the ``scalars``.
See available Matplotlib colormaps. Only applicable for when
displaying ``scalars``. Requires Matplotlib to be installed.
``colormap`` is also an accepted alias for this. If ``colorcet`` or
``cmocean`` are installed, their colormaps can be specified by name.
flip_scalars : bool, optional
Flip direction of cmap. Most colormaps allow ``*_r`` suffix to do
this as well.
reset_camera : bool, optional
Reset the camera after adding this mesh to the scene.
name : str, optional
The name for the added actor so that it can be easily
updated. If an actor of this name already exists in the
rendering window, it will be replaced by the new actor.
ambient : float, optional
When lighting is enabled, this is the amount of light from
0 to 1 that reaches the actor when not directed at the
light source emitted from the viewer. Default 0.0.
categories : bool, optional
If set to ``True``, then the number of unique values in the scalar
array will be used as the ``n_colors`` argument.
culling : str, optional
Does not render faces that are culled. Options are ``'front'`` or
``'back'``. This can be helpful for dense surface meshes,
especially when edges are visible, but can cause flat
meshes to be partially displayed. Defaults ``False``.
multi_colors : bool, optional
Whether or not to use multiple colors when plotting MultiBlock
object. Blocks will be colored sequentially as 'Reds', 'Greens',
'Blues', and 'Grays'.
blending : str, optional
Blending mode for visualisation of the input object(s). Can be
one of 'additive', 'maximum', 'minimum', 'composite', or
'average'. Defaults to 'additive'.
mapper : str, optional
Volume mapper to use given by name. Options include:
``'fixed_point'``, ``'gpu'``, ``'open_gl'``, and
``'smart'``. If ``None`` the ``"volume_mapper"`` in the
``self._theme`` is used.
scalar_bar_args : dict, optional
Dictionary of keyword arguments to pass when adding the
scalar bar to the scene. For options, see
:func:`pyvista.BasePlotter.add_scalar_bar`.
show_scalar_bar : bool
If ``False``, a scalar bar will not be added to the
scene. Defaults to ``True``.
annotations : dict, optional
Pass a dictionary of annotations. Keys are the float
values in the scalars range to annotate on the scalar bar
and the values are the the string annotations.
pickable : bool, optional
Set whether this mesh is pickable.
preference : str, optional
When ``mesh.n_points == mesh.n_cells`` and setting
scalars, this parameter sets how the scalars will be
mapped to the mesh. Default ``'points'``, causes the
scalars will be associated with the mesh points. Can be
either ``'points'`` or ``'cells'``.
opacity_unit_distance : float
Set/Get the unit distance on which the scalar opacity
transfer function is defined. Meaning that over that
distance, a given opacity (from the transfer function) is
accumulated. This is adjusted for the actual sampling
distance during rendering. By default, this is the length
of the diagonal of the bounding box of the volume divided
by the dimensions.
shade : bool
Default off. If shading is turned on, the mapper may
perform shading calculations - in some cases shading does
not apply (for example, in a maximum intensity projection)
and therefore shading will not be performed even if this
flag is on.
diffuse : float, optional
The diffuse lighting coefficient. Default ``1.0``.
specular : float, optional
The specular lighting coefficient. Default ``0.0``.
specular_power : float, optional
The specular power. Between ``0.0`` and ``128.0``.
render : bool, optional
Force a render when True. Default ``True``.
rescale_scalars : bool, optional
Rescale scalar data. This is an expensive memory and time
operation, especially for large data. In that case, it is
best to set this to ``False``, clip and scale scalar data
of ``volume`` beforehand, and pass that to ``add_volume``.
Default ``True``.
copy_cell_to_point_data : bool, optional
Make a copy of the original ``volume``, passing cell data
to point data. This is an expensive memory and time
operation, especially for large data. In that case, it is
best to choose ``False``. However, this copy is a current
workaround to ensure original object data is not altered
and volume rendering on cells exhibits some issues. Use
with caution. Default ``True``.
**kwargs : dict, optional
Optional keyword arguments.
Returns
-------
vtk.vtkActor
VTK actor of the volume.
"""
# Handle default arguments
# Supported aliases
clim = kwargs.pop("rng", clim)
cmap = kwargs.pop("colormap", cmap)
culling = kwargs.pop("backface_culling", culling)
if "scalar" in kwargs:
raise TypeError(
"`scalar` is an invalid keyword argument for `add_mesh`. Perhaps you mean `scalars` with an s?"
)
assert_empty_kwargs(**kwargs)
# Avoid mutating input
if scalar_bar_args is None:
scalar_bar_args = {}
else:
scalar_bar_args = scalar_bar_args.copy()
# account for legacy behavior
if "stitle" in kwargs: # pragma: no cover
warnings.warn(USE_SCALAR_BAR_ARGS, PyvistaDeprecationWarning)
scalar_bar_args.setdefault("title", kwargs.pop("stitle"))
if show_scalar_bar is None:
show_scalar_bar = self._theme.show_scalar_bar
if culling is True:
culling = "backface"
if mapper is None:
mapper = self._theme.volume_mapper
# only render when the plotter has already been shown
if render is None:
render = not self._first_time
# Convert the VTK data object to a pyvista wrapped object if necessary
if not is_pyvista_dataset(volume):
if isinstance(volume, np.ndarray):
volume = wrap(volume)
if resolution is None:
resolution = [1, 1, 1]
elif len(resolution) != 3:
raise ValueError("Invalid resolution dimensions.")
volume.spacing = resolution
else:
volume = wrap(volume)
if not is_pyvista_dataset(volume):
raise TypeError(
f"Object type ({type(volume)}) not supported for plotting in PyVista."
)
else:
if copy_cell_to_point_data:
# HACK: Make a copy so the original object is not altered.
# Also, place all data on the nodes as issues arise when
# volume rendering on the cells.
volume = volume.cell_data_to_point_data()
if name is None:
name = f"{type(volume).__name__}({volume.memory_address})"
if isinstance(volume, pyvista.MultiBlock):
from itertools import cycle
cycler = cycle(["Reds", "Greens", "Blues", "Greys", "Oranges", "Purples"])
# Now iteratively plot each element of the multiblock dataset
actors = []
for idx in range(volume.GetNumberOfBlocks()):
if volume[idx] is None:
continue
# Get a good name to use
next_name = f"{name}-{idx}"
# Get the data object
block = wrap(volume.GetBlock(idx))
if resolution is None:
try:
block_resolution = block.GetSpacing()
except AttributeError:
block_resolution = resolution
else:
block_resolution = resolution
if multi_colors:
color = next(cycler)
else:
color = cmap
a = self.add_volume(
block,
resolution=block_resolution,
opacity=opacity,
n_colors=n_colors,
cmap=color,
flip_scalars=flip_scalars,
reset_camera=reset_camera,
name=next_name,
ambient=ambient,
categories=categories,
culling=culling,
clim=clim,
mapper=mapper,
pickable=pickable,
opacity_unit_distance=opacity_unit_distance,
shade=shade,
diffuse=diffuse,
specular=specular,
specular_power=specular_power,
render=render,
)
actors.append(a)
return actors
if not isinstance(volume, pyvista.UniformGrid):
raise TypeError(
f"Type {type(volume)} not supported for volume rendering at this time. Use `pyvista.UniformGrid`."
)
if opacity_unit_distance is None:
opacity_unit_distance = volume.length / (np.mean(volume.dimensions) - 1)
if scalars is None:
# Make sure scalars components are not vectors/tuples
scalars = volume.active_scalars.copy()
# Don't allow plotting of string arrays by default
if scalars is not None and np.issubdtype(scalars.dtype, np.number):
scalar_bar_args.setdefault("title", volume.active_scalars_info[1])
else:
raise ValueError("No scalars to use for volume rendering.")
# NOTE: AGH, 16-SEP-2021; Remove this as it is unnecessary
# elif isinstance(scalars, str):
# pass
# NOTE: AGH, 16-SEP-2021; Why this comment block
##############
title = "Data"
if isinstance(scalars, str):
title = scalars
scalars = get_array(volume, scalars, preference=preference, err=True)
scalar_bar_args.setdefault("title", title)
if not isinstance(scalars, np.ndarray):
scalars = np.asarray(scalars)
if not np.issubdtype(scalars.dtype, np.number):
raise TypeError(
"Non-numeric scalars are currently not supported for volume rendering."
)
if scalars.ndim != 1:
scalars = scalars.ravel()
# NOTE: AGH, 16-SEP-2021; An expensive unnecessary memory copy. Remove this.
# if scalars.dtype == np.bool_ or scalars.dtype == np.uint8:
# scalars = scalars.astype(np.float_)
# Define mapper, volume, and add the correct properties
mappers = {
"fixed_point": _vtk.vtkFixedPointVolumeRayCastMapper,
"gpu": _vtk.vtkGPUVolumeRayCastMapper,
"open_gl": _vtk.vtkOpenGLGPUVolumeRayCastMapper,
"smart": _vtk.vtkSmartVolumeMapper,
}
if not isinstance(mapper, str) or mapper not in mappers.keys():
raise TypeError(
f"Mapper ({mapper}) unknown. Available volume mappers include: {', '.join(mappers.keys())}"
)
self.mapper = make_mapper(mappers[mapper])
# Scalars interpolation approach
if scalars.shape[0] == volume.n_points:
volume.point_data.set_array(scalars, title, True)
self.mapper.SetScalarModeToUsePointData()
elif scalars.shape[0] == volume.n_cells:
volume.cell_data.set_array(scalars, title, True)
self.mapper.SetScalarModeToUseCellData()
else:
raise_not_matching(scalars, volume)
# Set scalars range
if clim is None:
clim = [np.nanmin(scalars), np.nanmax(scalars)]
elif isinstance(clim, float) or isinstance(clim, int):
clim = [-clim, clim]
# NOTE: AGH, 16-SEP-2021; Why this comment block
###############
# NOTE: AGH, 16-SEP-2021; Expensive and inneffecient code. Replace with below
# scalars = scalars.astype(np.float_)
# with np.errstate(invalid="ignore"):
# idxs0 = scalars < clim[0]
# idxs1 = scalars > clim[1]
# scalars[idxs0] = clim[0]
# scalars[idxs1] = clim[1]
# scalars = (
# (scalars - np.nanmin(scalars)) / (np.nanmax(scalars) - np.nanmin(scalars))
# ) * 255
# # scalars = scalars.astype(np.uint8)
# volume[title] = scalars
if rescale_scalars:
clim = np.asarray(clim, dtype=scalars.dtype)
scalars.clip(clim[0], clim[1], out=scalars)
min_ = np.nanmin(scalars)
max_ = np.nanmax(scalars)
np.true_divide((scalars - min_), (max_ - min_) / 255, out=scalars, casting="unsafe")
volume[title] = np.array(scalars, dtype=np.uint8)
self.mapper.scalar_range = clim
# Set colormap and build lookup table
table = _vtk.vtkLookupTable()
# table.SetNanColor(nan_color) # NaN's are chopped out with current implementation
# above/below colors not supported with volume rendering
if isinstance(annotations, dict):
for val, anno in annotations.items():
table.SetAnnotation(float(val), str(anno))
if cmap is None: # Set default map if matplotlib is available
if _has_matplotlib():
cmap = self._theme.cmap
if cmap is not None:
if not _has_matplotlib():
raise ImportError("Please install matplotlib for volume rendering.")
cmap = get_cmap_safe(cmap)
if categories:
if categories is True:
n_colors = len(np.unique(scalars))
elif isinstance(categories, int):
n_colors = categories
if flip_scalars:
cmap = cmap.reversed()
color_tf = _vtk.vtkColorTransferFunction()
for ii in range(n_colors):
color_tf.AddRGBPoint(ii, *cmap(ii)[:-1])
# Set opacities
if isinstance(opacity, (float, int)):
opacity_values = [opacity] * n_colors
elif isinstance(opacity, str):
opacity_values = pyvista.opacity_transfer_function(opacity, n_colors)
elif isinstance(opacity, (np.ndarray, list, tuple)):
opacity = np.array(opacity)
opacity_values = opacity_transfer_function(opacity, n_colors)
opacity_tf = _vtk.vtkPiecewiseFunction()
for ii in range(n_colors):
opacity_tf.AddPoint(ii, opacity_values[ii] / n_colors)
# Now put color tf and opacity tf into a lookup table for the scalar bar
table.SetNumberOfTableValues(n_colors)
lut = cmap(np.array(range(n_colors))) * 255
lut[:, 3] = opacity_values
lut = lut.astype(np.uint8)
table.SetTable(_vtk.numpy_to_vtk(lut))
table.SetRange(*clim)
self.mapper.lookup_table = table
self.mapper.SetInputData(volume)
blending = blending.lower()
if blending in ["additive", "add", "sum"]:
self.mapper.SetBlendModeToAdditive()
elif blending in ["average", "avg", "average_intensity"]:
self.mapper.SetBlendModeToAverageIntensity()
elif blending in ["composite", "comp"]:
self.mapper.SetBlendModeToComposite()
elif blending in ["maximum", "max", "maximum_intensity"]:
self.mapper.SetBlendModeToMaximumIntensity()
elif blending in ["minimum", "min", "minimum_intensity"]:
self.mapper.SetBlendModeToMinimumIntensity()
else:
raise ValueError(
f"Blending mode '{blending}' invalid. "
+ "Please choose one "
+ "of 'additive', "
"'composite', 'minimum' or " + "'maximum'."
)
self.mapper.Update()
self.volume = _vtk.vtkVolume()
self.volume.SetMapper(self.mapper)
prop = _vtk.vtkVolumeProperty()
prop.SetColor(color_tf)
prop.SetScalarOpacity(opacity_tf)
prop.SetAmbient(ambient)
prop.SetScalarOpacityUnitDistance(opacity_unit_distance)
prop.SetShade(shade)
prop.SetDiffuse(diffuse)
prop.SetSpecular(specular)
prop.SetSpecularPower(specular_power)
self.volume.SetProperty(prop)
actor, prop = self.add_actor(
self.volume,
reset_camera=reset_camera,
name=name,
culling=culling,
pickable=pickable,
render=render,
)
# Add scalar bar if scalars are available
if show_scalar_bar and scalars is not None:
self.add_scalar_bar(**scalar_bar_args)
self.renderer.Modified()
return actor
The final example code is as follows:
import numpy as np
import pyvista as pv
from vtkmodules.vtkIOImage import vtkDICOMImageReader
pv.rcParams["volume_mapper"] = "fixed_point" # Windows
folder = r"C:\path\to\DICOM\folder"
def load_data(folder):
reader = vtkDICOMImageReader()
reader.SetDirectoryName(folder)
reader.Update()
volume = pv.wrap(reader.GetOutputDataObject(0))
del reader # Why keep double memory?
clim_16bit = [10000, 20000] # 16-bit values; Change to what you want to see
clim_8bit = [int(clim_16bit[0] // 256), int(clim_16bit[1] // 256)] # Scaled 8-bit values; Here for example only
scalars = volume["DICOMImage"]
scalars.clip(clim_16bit[0], clim_16bit[1], out=scalars)
min_ = np.nanmin(scalars)
max_ = np.nanmax(scalars)
np.true_divide((scalars - min_), (max_ - min_) / 255, out=scalars, casting="unsafe")
volume["DICOMImage"] = np.array(scalars, dtype=np.uint8)
volume.spacing = (1, 1, 1) # Be sure to set; Otherwise, the DICOM stack spacing will be used and results will be garbled
return volume
if __name__ == "__main__":
print("Load Data Profile")
print("=================")
volume = load_data(folder)
print()
p = pv.Plotter()
print("Add Volume Profile")
print("==================")
p.add_volume(
volume,
blending="composite",
scalars="DICOMImage",
reset_camera=True,
rescale_scalars=False,
copy_cell_to_point_data=False,
)
print()
p.add_axes()
p.show()
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