Python API¶
This section includes information for using the pure Python API of
bob.ip.optflow.liu.
Root module functions¶
Conjugate-Gradient (CG) based Implementation¶
Ce Liu’s Optical Flow implementations using CG
-
bob.ip.optflow.liu.cg.flow(i1, i2, [alpha=0.02, [ratio=0.75, [min_width=30, [n_outer_fp_iterations=20, [n_inner_fp_iterations=1, [n_cg_iterations=50]]]]]]) -> (u, v, w2)¶ This method computes the dense optical flow field using a coarse-to-fine approach. C++ code running under this call is extracted from the old version (pre Aug 1, 2011) of Ce Liu’s homepage and should give the exact same output as the Matlab equivalent.
Note
This variant does not use the Successive Over-Relaxation (SOR) that was implemented on August 1st., 2011 by C. Liu, but the old version based on Conjugate-Gradient (CG).
Parameters:
- i1
First input frame (grayscale/double image)
- i2
Second input frame (same dimension and type of the first frame)
- alpha
[optional] Regularization weight
- ratio
[optional] Downsample ratio
- min_width
[optional] Width of the coarsest level
- n_outer_fp_iterations
[optional] The number of outer fixed point iterations
- n_inner_fp_iterations
[optional] The number of inner fixed point iterations
- n_cg_iterations
[optional] The number of conjugate-gradient (CG) iterations
Returns a tuple containing three 2D double arrays with the same dimensions as the input images:
- u
Output velocities in
x(horizontal axis).- v
Output velocities in
y(vertical axis).- warped_i2
i2 as estimated by the optical flow field from i1
Successive-Over-Relaxation (SOR) based Implementation¶
Ce Liu’s Optical Flow implementations using SOR
-
bob.ip.optflow.liu.sor.flow(i1, i2, [alpha=1.0, [ratio=0.5, [min_width=40, [n_outer_fp_iterations=4, [n_inner_fp_iterations=1, [n_sor_iterations=20]]]]]]) -> (u, v, w2)¶ This method computes the dense optical flow field using a coarse-to-fine approach. C++ code running under this call is extracted from the old version (pre Aug 1, 2011) of Ce Liu’s homepage and should give the exact same output as the Matlab equivalent.
Note
This variant uses the Successive Over-Relaxation (SOR) that was implemented on August 1st., 2011 by C. Liu.
Parameters:
- i1
First input frame (grayscale/double image)
- i2
Second input frame (same dimension and type of the first frame)
- alpha
[optional] Regularization weight
- ratio
[optional] Downsample ratio
- min_width
[optional] Width of the coarsest level
- n_outer_fp_iterations
[optional] The number of outer fixed point iterations
- n_inner_fp_iterations
[optional] The number of inner fixed point iterations
- n_sor_iterations
[optional] The number of successive-over-relaxation (SOR) iterations
Returns a tuple containing three 2D double arrays with the same dimensions as the input images:
- u
Output velocities in
x(horizontal axis).- v
Output velocities in
y(vertical axis).- warped_i2
i2 as estimated by the optical flow field from i1