Function bivariate_Gaussian

utils/optical_flow_utils.py:197–219

Generate a bivariate isotropic or anisotropic Gaussian kernel. In the isotropic mode, only `sig_x` is used. `sig_y` and `theta` is ignored. Args: kernel_size (int): sig_x (float): sig_y (float): theta (float): Radian measurement. grid (ndarray, optiona

(kernel_size, sig_x, sig_y, theta, grid=None, isotropic=True)

    return kernel

def bivariate_Gaussian(kernel_size, sig_x, sig_y, theta, grid=None, isotropic=True):
    """Generate a bivariate isotropic or anisotropic Gaussian kernel.
    In the isotropic mode, only `sig_x` is used. `sig_y` and `theta` is ignored.
    Args:
        kernel_size (int):
        sig_x (float):
        sig_y (float):
        theta (float): Radian measurement.
        grid (ndarray, optional): generated by :func:`mesh_grid`,
            with the shape (K, K, 2), K is the kernel size. Default: None
        isotropic (bool):
    Returns:
        kernel (ndarray): normalized kernel.
    """
    if grid is None:
        grid, _, _ = mesh_grid(kernel_size)
    if isotropic:
        sigma_matrix = np.array([[sig_x**2, 0], [0, sig_x**2]])
    else:
        sigma_matrix = sigma_matrix2(sig_x, sig_y, theta)
    kernel = pdf2(sigma_matrix, grid)
    kernel = kernel / np.sum(kernel)
    return kernel