I have around 500 .ply files that need to be converted to vtk file. I usually use Paraview to convert them manually, but it takes forever. I wonder if there is a tool to convert ply files into vtk files in Python?
Thanks in advance.
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I currently have a PNG of a spectrogram such as:
I don't have the original audio file but am wondering if there is a way I can convert this into a SciPy spectrogram object. I was thinking I could try to convert the image to an audio file first, but it seems like there aren't many packages reconstructing spectrogram audio since there's already so much lost data.
Any ideas and suggestions would be appreciated!
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.
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.
I have around 200,000 images that need to be rotated correctly.
Also, 30 images with their corresponding rotated images, how will I train opencv to achieve what I want? Some tips would be appreciated.
I'm using this library for opencv
Thanks!
Open each file using the readImage method as per their examples and the Matrix your callback receives has a rotate function you can use.
Does ITK/SimpleITK automatically cater for the HU by using rescale intercept and slope from the metadata for Nifti files, as it does for dicom files (source)? If it doesn't how can I read the metadata in python 3.4? I went through this class however I can't seem to access the ReadImageInformation() function.
Judging by the source code, slope/intercept rescaling is done automatically.