spafe.utils.filters

spafe.utils.filters.gaussian_filter(M, std, sym=True)

Return a Gaussian window.

spafe.utils.filters.kalman(x, P, measurement, R, motion, Q, F, H)

Parameters:

• x – initial state
• P – initial uncertainty convariance matrix
• measurement – observed position (same shape as H*x)
• R – measurement noise (same shape as H)
• motion – external motion added to state vector x
• Q – motion noise (same shape as P)
• F – next state function: x_prime = F*x
• H – measurement function: position = H*x

Returns:

the updated and predicted new values for (x, P)

See also http://en.wikipedia.org/wiki/Kalman_filter

This version of kalman can be applied to many different situations by appropriately defining F and H.

spafe.utils.filters.kalman_xy(x, P, measurement, R, motion=matrix([[0.], [0.], [0.], [0.]]), Q=matrix([[1., 0., 0., 0.], [0., 1., 0., 0.], [0., 0., 1., 0.], [0., 0., 0., 1.]]))

Parameters:

• x – initial state 4-tuple of location and velocity: (x0, x1, x0_dot, x1_dot)
• P – initial uncertainty convariance matrix
• measurement – observed position
• R – measurement noise
• motion – external motion added to state vector x
• Q – motion noise (same shape as P)

spafe.utils.filters.rasta_filter(x)

% y = rastafilt(x) % % rows of x = critical bands, cols of x = frame % same for y but after filtering % % default filter is single pole at 0.94

spafe.utils.filters.sobel_filter(sig, axis=-1, mode='reflect', cval=0.0)

Calculate a Sobel filter.