See FluidImage for a working implementation of camera calibration and reconstruction.
Tomographic PIV code done as a part of a KTH course
To install in development mode.
$ python setup.py developIt may be a good idea to setup a virtual environment (virtualenv command for Linux and Anaconda for Windows), so that you don't clutter the root of your system. A quick tutorial can be found on that here.
To install dependencies (for eg: numpy, matplotlib, etc.) in Linux
$ pip search <package>
$ pip install <package>Anaconda users can find similar instructions here.
Images available as a zip archive from KTH. Do not upload the images or any binary files! They can increase the size of the repositories. Save the files under the following path & it will be ignored by git. (Maybe an unnecessary detail, but for the sake of consistency)
- examples/assignment/calibration_images/
- examples/assignment/particle_images/
- Image loading / jadelord
- Classes for storing calibration and particle datasets / jadelord
- Calibration / mubasharkhan
- Preprocessing: Threshold, sliding minima, Gaussian smoothing / jadelord
- Class for storing voxel and velocity vector datasets / jadelord
- Operators for tomo reconstruction: MLOS/MART/SMART
- Operators for 3D PIV: single pass cross-correlation
- Outlier or bad vector removal
- Statistics: Image density calculation, PDF of intensities ()
- Learn Python: Codecademy or tutorial in official Python documentation.
- OpenPIV: for Python and MATLAB
- LEGI UVmat: MATLAB code and tutorial
- Image handling: pillow, scikit-image, opencv
- Plotting: matplotlib, MayaVi
- Data structures: h5py
- Ships-project: TomoRecon, TomoSim etc. in Google Code
- Cernlib: Cylinder-Sphere intersection docs, code
