farneback3d 0.0.1.1

CUDA-accelerated volumetric optical flow estimation using the Farneback algorithm

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farneback3d
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A CUDA implementation of the Farneback optical flow algorithm [1] for the calculation of dense volumetric flow fields. Since this algorithm is based on the approximation of the signal by polynomial expansion it is especial suited for the motion estimation in smooth signals without clear edges.

To know more about the implementation have a look on `this OpenCV class: <https://docs.opencv.org/3.3.0/de/d9e/classcv_1_1FarnebackOpticalFlow.html>`_ that was used as inspiration for this implementation.

Python interface
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The project uses `pycuda <https://github.com/inducer/pycuda`_ to provide a pure-python package available on PyPi::
pip install farneback3d

Usage::
import farneback3d

... # create some numpy volumes vol0 and vol1 (can also be pycuda GPUArrays)

# set parameters for optical flow
optflow = farneback3d.Farneback(
levels=5,
num_iterations=5,
poly_n=5
)
# calculate frame-to-frame flow between vol0 and vol1
flow = optflow.calc_flow(vol0, vol1)


C++ interface
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To be implemented...


Future plans
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The current implementation uses a naive approach to perform the necessary convolutions.
The algorithm could be sped up drastically by performing separable convolutions along each coordinate axis.


[1] Farnebäck, Gunnar. "Two-frame motion estimation based on polynomial expansion." Scandinavian conference on Image analysis. Springer, Berlin, Heidelberg, 2003.