epipolar-consistency 0.0.2

Python bindings for evaluating epipolar consistency conditions in transmission imaging.

Epipolar consistency for transmission imaging (Python bindings)

This repository provides python bindings for the computation of epipolar consistency conditions (ECC) as a metric in transmission imaging. The ECC functionality itself is not implemented in this repository. The source files are located in EpipolarConsistency and are written in C++ by André Aichert. This repository only provides bindings to the existing source code such that it can be used in Python. The bindings are based on pybind11 and this repository closely follows their provided cmake example.

Functionality

Only the core functionality of the original repository EpipolarConsistency is included in the Python module. This includes

• the initialization of images and projection matrices from numpy arrays,
• the computation of Radon intermediates,
• the evaluation of the ECC metric for all image pairs included in a given set of projection images and their corresponding projection matrices.

More advanced functionality of the original repository, that is not included in the bindings, includes

• everything related to optimization,
• graphical user interfaces,
• everything related to 3D reconstruction.

We think that these aspects can similarly be handled by native Python libraries.

Example code

import ecc
import numpy as np


# overview over all wrapped functions/ classes
print(help(ecc))

# set parameters for computation of Radon intermediates
size_alpha = 200
size_t = 100

# load example projection images
projection_images = np.load('example_data/sinogram_cone.npy').astype(np.float32)
# convert to list
projection_images = [projection_images[i, :, :] for i in range(projection_images.shape[0])]
# compute radon intermediates with 100 detector elements and 200 angular steps
radon_intermediates = []
for image in projection_images:
    image = ecc.ImageFloat2D(image.astype(np.float32))
    radon_intermediate = ecc.RadonIntermediate(image, size_alpha, size_t,
                                               ecc.RadonIntermediate.Filter.Derivative,
                                               ecc.RadonIntermediate.PostProcess.Identity)
    radon_intermediates.append(radon_intermediate)

# load example projection matrices
projection_matrices = np.load('example_data/projection_matrices.npy')
# convert to list
projection_matrices = [projection_matrices[i, :, :] for i in range(projection_matrices.shape[0])]

# create object to evaluate ECC metric on GPU
ecc_metric_gpu = ecc.MetricGPU(projection_matrices, radon_intermediates)

# set the angle between epipolar planes (otherwise default value is taken)
metric_gpu = ecc_metric_gpu.setdKappa(0.001)
# evaluate the metric
out_gpu = metric_gpu.evaluate()
print(f'Result: {out_gpu}')

Installation via pip

Prebuilt wheels are available via pip for Linux operating system and Python versions 3.8, 3.9, and 3.10. We strongly encourage to install the Python module directly from PyPI:

pip install epipolar-consistency

Installation from source

If you need to compile the code on Windows or want to build it yourself for another reason, you need to (1) build the necessary dependencies for the core ECC functionality (see EpipolarConsistency) (2) build and install the module in this repository. On Linux, you need a compiler with C++11 support and CMake >= 3.5. On Windows, Visual Studio 2015 or newer is required (Pybind11 requires a C++11 compliant compiler) as well as CMake >= 3.8 (3.8 was the first version to support VS 2015).

It is crucial to have all dependencies of the core ECC functionality written in C++ installed properly. Please also refer to the Readme in EpipolarConsistency. On Ubuntu 20.04, a working set of dependencies and their corresponding versions is:

• Eigen 3.3.9 (Even though Eigen is header only, in order to make sure that all files are located at the default locations, run cmake and make install as described below. I know that André Aichert suggests to use Eigen 3.3.0, but that did not work for me.)
• NLopt 2.6.2
• LibGetSet from André Aichert's fork
• Qt 5.12.11 (Any version with major version number 5 should work. It is helpful to set the CMAKE_PREFIX_PATH to Qt as described here.)
• Cuda 11.2

The standard way to install most of these packages (like NLopt, GetSet) on Ubuntu is

mkdir build
cd build
cmake ..
make
sudo make install

For Windows, you need Visual Studio to build the solutions provided by cmake.

To test whether all dependencies are installed and found correctly, it might make sense to first build the submodule EpipolarConsistency separately and then try to pip install it as a python package.

After having installed the necessary dependencies for the core ECC functionality, you can finally install the Python module. Clone this repository (with --recurse-submodules option) and run

cd ecc_python
pip install .

With the setup.py file included in this example, the pip install command will invoke CMake and build the pybind11 module as specified in CMakeLists.txt. This will both build the module and install it to your current Python environment.

Test call

To check if the Python module has been installed properly, you can run

import ecc
help(ecc)

Building a wheel

To build a wheel of the ecc package on Linux, you need to have ninja installed (sudo apt-get install ninja-build) and then run

python setup.py bdist_wheel --plat-name manylinux2014_x86_64

Create html documentation of ecc module

To create an overview of all functions and classes provided by the ecc module, run

pydoc -w ecc

from an environment with ecc installed. It creates a file ecc.html within the same folder.

Technical details

The functionality from EpipolarConsistency is compiled as a static library and linked into the shared python module. For more details on this see https://github.com/pybind/cmake_example/issues/11#issuecomment-405092832.

To take full advantage of this code, an NVIDIA GPU is needed for a parallelized computation of Radon intermediates and the ECC metric itself.

License

Pybind11 is provided under a BSD-style license that can be found in the LICENSE file. By using, distributing, or contributing to this project, you agree to the terms and conditions of this license.

Citation

If you use this code for your research, please cite the original work on ECC in transmission imaging by André Aichert:

@ARTICLE{7094279,
  author={Aichert, André and Berger, Martin and Wang, Jian and Maass, Nicole and Doerfler, Arnd and Hornegger, Joachim and Maier, Andreas K.},
  journal={IEEE Transactions on Medical Imaging}, 
  title={Epipolar Consistency in Transmission Imaging}, 
  year={2015},
  volume={34},
  number={11},
  pages={2205-2219},
  doi={10.1109/TMI.2015.2426417}}