I have a 3D geometry (vessels) in vit format which I do not have any information about, but I just visualize it with paraview. I do not know how to read and then convert this vti file to a 3D numpy array in python. Does anyone know how can I do this conversion?
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I need to extract all planes information(position, size, rotation) of 3d model in my own defined coordinate.
The model maybe given as .STEP, or any other CAD supported file.
I'm pretty familiar with 3d graphics, but it's my first time to deal with CAD file.
Is there any 3d file format having an advantage for handling all planes? If so, I would like to convert .STEP file to it.
Any helps?
how to convert the image into object file like as .obj or .ply . I need some code written in visualization toolkit and c++.
Thanks
Image data is pixel data and .obj/ .ply or for that matter .stl is 3D geometry data with Point and Cell (for .obj Cell is Triangle) information.
Your question is not clear, but to give you some steps -
First, you need to identify how would you convert the pixels into points? vtkImageDataGeometryFilter might be of help here. Although it might not be sufficient as you will also need triangles data.
Once you get vtkPolyData from image data, you can write this data to STL or OBJ or PLY format. You can use following VTK classes for that
vtkSTLWriter, vtkOBJWriter and vtkPLYWriter.
I have to generate a 2d mesh in a format compatible with optimesh, in order to refine it with the algorithms included in that library, (in particular Centroidal Voronoi tesselation smoothing). I'm starting from a set of unordered points, so I'm trying to understand which is the easiest chain of tools to do the job.I have no familiarity at all with geometry processing, so forgive me if my questions are stupid.
I found a lot of libraries to process a mesh from a file in a huge variety of format, but I'm missing how to generate it from points.
I've seen that with scipy I can get a triangulation, but the object returning from scipy, can't be fed directly to optimesh.
So, my problem now is basically something like this:
import numpy as np
from scipy.spatial import Delaunay,delaunay_plot_2d
points = np.random.random((100,2))
delaun = Delaunay(points)
#Magic code that I wish
delaun.to_meshfile('meshfile.xxx')
#
with a file format that i can process later with optimesh
optimesh author here. Your delaun object has delaun.points and delaun.simplices. Those can be fed into optimesh:
import numpy as np
from scipy.spatial import Delaunay, delaunay_plot_2d
import optimesh
points = np.random.random((100, 2))
delaun = Delaunay(points)
points, cells = optimesh.cvt.quasi_newton_uniform_blocks(
delaun.points, delaun.simplices, tol=1.0e-5, max_num_steps=100
)
If you really want to store them in a file, check out meshio.
Is there a way in SciPy to import 3D images of .nrrd format and rotate the image to a specific view(to some given specific coordinates) and then display the image?
Im new to medical image processing. how can i convert 3D DICOM medical images to numerical matrix format using either python or c++?
Another option, if you really want "3D" dicom image support (ie CT/MR/NM/PET 3d series - as opposed to purely 2D image handling) and you want do anything really 3d related and/or more complex, you might want to check out simple ITK.
That gives you very powerful true 3d handling and is fast (it's wrapped around complied C). It includes, for example, full 3D image registration and various filters/tools etc.
It can read an entire series at once and automatically create a fully spatially aware 3D numpy array for you (ie it takes care of processing all the dicom 3D spatial orientation/spacing etc tags for you)
However, because it's a lot more powerful than pydicom, it also has a much steeper learning curve - but does have many examples and online jupyter notebook tutorials.
...so, depending on your needs it might be good for you. However, if you only really want basic 2d image-at-a-time type processing, pydicom is the way to go.
You can use pydicom package in python. You can install it in python by:
pip install pydicom
Here is a simple example of reading DICOM images and converting to numpy array:
import os
import pydicom
import numpy as np
dicom_dir = your_dicom_folder_of_slices
file_names = os.listdir(dicom_dir)
file_names.sort()
dicom_data = []
for name in file_names:
path = os.path.join(dicom_dir, name)
dicom_data.append(pydicom.read_file(path))
array = [data.pixel_array for data in dicom_data]
array = np.stack(array, axis=-1) # or 0 if 'channel_first'
Here is a detailed example.
I prefer using SimpleElastix for medical image processing. it has many methods for segmentations and many other helpful methods. it is available in both python and C++. In my experience SimpleElastix handled DICOMS and niftis better than other Packages.