Python and C++ code for Hydra optical flow, behavior and neural analysis.

lansdell 2016

Dependencies:

• Vispy*
• Numpy
• PyCuda**
• DistMesh
• OpenCV2
• cvtools (https://github.com/benlansdell/cvtools)
• matplotlib

Notes:

• *Uses OpenGL rendering. If using remotely, you'll need to set up a VirtualGL server
• **If have a CUDA compatible graphics card

A state space model using an extended Kalman filter to track Hydra in video.

How to use:

run_kalmanfilter.py <input_video> <optic_flow_path> <output_video> [...]  
run_kalmanfilter.py -h for more information

Optic flow must be precomputed and in .mat binary files created using writeMatToFile(). Files must be named:

[optic_flow_path]_%03d_x.mat
[optic_flow_path]_%03d_y.mat

for the X,Y components of the optic flow, respectively. The easiest way to do this is to run bin/optical_flow_ext, which uses a GPU implementation of Brox optic flow [1].

Example: Run with mesh length 15

./run_kalmanfilter.py ./video/johntest_brightcontrast_short.tif ./video/johntest_brightcontrast_short/flow ./video/output.avi -s 15

Implements Kalman filter in which underlying states are
Similar to [2]. (Write-up coming soon)

[1] Thomas Brox, Andres Bruhn, Nils Papenberg, and Joachim Weickert (2004). "High Accuracy Optical Flow Estimation Based on a Theory for Warping". Proc. 8th European Conference on Computer Vision
[2] Dellaert, F., Thrun, S., Thorpe, C. (1998). "Jacobian Images of Super-Resolved TextureMaps forModel-BasedMotion Estimation and Tracking". IEEE Workshop on Applications of Computer Vision (WACV'98).