__init__.py 文件源码

def HornSchunck(im1, im2, alpha=0.001, Niter=8, verbose=False):
    """
    im1: image at t=0
    im2: image at t=1
    alpha: regularization constant
    Niter: number of iteration
    """
    im1 = im1.astype(np.float32)
    im2 = im2.astype(np.float32)

    #set up initial velocities
    uInitial = np.zeros([im1.shape[0],im1.shape[1]])
    vInitial = np.zeros([im1.shape[0],im1.shape[1]])

    # Set initial value for the flow vectors
    U = uInitial
    V = vInitial

    # Estimate derivatives
    [fx, fy, ft] = computeDerivatives(im1, im2)

    if verbose:
        plotderiv(fx,fy,ft)

#    print(fx[100,100],fy[100,100],ft[100,100])

    # Iteration to reduce error
    for _ in range(Niter):
#%% Compute local averages of the flow vectors
        uAvg = filter2(U, HSKERN)
        vAvg = filter2(V, HSKERN)
#%% common part of update step
        der = (fx*uAvg + fy*vAvg + ft) / (alpha**2 + fx**2 + fy**2)
#%% iterative step
        U = uAvg - fx * der
        V = vAvg - fy * der

    return U,V