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 develop

It 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