deepy/preprocessing/elastic_distortion.py
def create_2d_gaussian(dim, sigma):
    """
    This function creates a 2d gaussian kernel with the standard deviation
    denoted by sigma

    :param dim: integer denoting a side (1-d) of gaussian kernel
    :param sigma: floating point indicating the standard deviation

    :returns: a numpy 2d array
    """

    # check if the dimension is odd
    if dim % 2 == 0:
        raise ValueError("Kernel dimension should be odd")

    # initialize the kernel
    kernel = np.zeros((dim, dim), dtype=np.float16)

    # calculate the center point
    center = dim/2

    # calculate the variance
    variance = sigma ** 2

    # calculate the normalization coefficeint
    coeff = 1. / (2 * variance)

    # create the kernel
    for x in range(0, dim):
        for y in range(0, dim):
            x_val = abs(x - center)
            y_val = abs(y - center)
            numerator = x_val**2 + y_val**2
            denom = 2*variance

            kernel[x,y] = coeff * np.exp(-1. * numerator/denom)

    return kernel/sum(sum(kernel))
def create_2d_gaussian(dim, sigma):
    # check if the dimension is odd
    if dim % 2 == 0:
        raise ValueError("Kernel dimension should be odd")

    # initialize the kernel
    kernel = np.zeros((dim, dim), dtype=np.float16)

    # calculate the center point
    center = dim/2

    # calculate the variance
    variance = sigma ** 2

    # calculate the normalization coefficeint
    coeff = 1. / (2 * variance)

    # create the kernel
    for x in range(0, dim):
        for y in range(0, dim):
            x_val = abs(x - center)
            y_val = abs(y - center)
            numerator = x_val**2 + y_val**2
            denom = 2*variance

            kernel[x,y] = coeff * np.exp(-1. * numerator/denom)

    return kernel/sum(sum(kernel))
def create_2d_gaussian ( dim , sigma ) : # check if the dimension is odd if dim % 2 == 0 : raise ValueError ( "Kernel dimension should be odd" ) # initialize the kernel kernel = np . zeros ( ( dim , dim ) , dtype = np . float16 ) # calculate the center point center = dim / 2 # calculate the variance variance = sigma ** 2 # calculate the normalization coefficeint coeff = 1. / ( 2 * variance ) # create the kernel for x in range ( 0 , dim ) : for y in range ( 0 , dim ) : x_val = abs ( x - center ) y_val = abs ( y - center ) numerator = x_val ** 2 + y_val ** 2 denom = 2 * variance kernel [ x , y ] = coeff * np . exp ( - 1. * numerator / denom ) return kernel / sum ( sum ( kernel ) )
This function creates a 2d gaussian kernel with the standard deviation
    denoted by sigma

    :param dim: integer denoting a side (1-d) of gaussian kernel
    :param sigma: floating point indicating the standard deviation

    :returns: a numpy 2d array
This function creates a 2d gaussian kernel with the standard deviation denoted by sigma