函数

local_binary_pattern(image, P, R, method='default')

参数

image：（N，M）阵列Graylevel图像。

P：int圆对称邻居设置点的数量（角度空间的量化）。

R：float圆的半径（操作员的空间分辨率）。

method：{‘default’，‘ror’，‘uniform’，‘var’}确定模式的方法。

	'default'：原始的局部二值模式，它是灰度但不是旋转不变的。
	'ror'：扩展灰度和旋转不变的默认实现。
	'uniform'：改进的旋转不变性和均匀的模式以及角度空间的更精细的量化，灰度和旋转不变。
	'nri_uniform'：非旋转不变的均匀图案变体，它只是灰度不变的R199。
	'VAR'：局部对比度的旋转不变方差度量，图像纹理是旋转但不是灰度不变的。

返回值：

输出：（N，M）阵列LBP图像。

skimage函数库中local_binary_pattern函数定义：

def local_binary_pattern(image, P, R, method='default'):
    """Gray scale and rotation invariant LBP (Local Binary Patterns).

    LBP is an invariant descriptor that can be used for texture classification.

    Parameters
    ----------
    image : (N, M) array
        Graylevel image.
    P : int
        Number of circularly symmetric neighbour set points (quantization of
        the angular space).
    R : float
        Radius of circle (spatial resolution of the operator).
    method : {'default', 'ror', 'uniform', 'var'}
        Method to determine the pattern.

        * 'default': original local binary pattern which is gray scale but not
            rotation invariant.
        * 'ror': extension of default implementation which is gray scale and
            rotation invariant.
        * 'uniform': improved rotation invariance with uniform patterns and
            finer quantization of the angular space which is gray scale and
            rotation invariant.
        * 'nri_uniform': non rotation-invariant uniform patterns variant
            which is only gray scale invariant [2]_, [3]_.
        * 'var': rotation invariant variance measures of the contrast of local
            image texture which is rotation but not gray scale invariant.

    Returns
    -------
    output : (N, M) array
        LBP image.

    References
    ----------
    .. [1] T. Ojala, M. Pietikainen, T. Maenpaa, "Multiresolution gray-scale
           and rotation invariant texture classification with local binary
           patterns", IEEE Transactions on Pattern Analysis and Machine
           Intelligence, vol. 24, no. 7, pp. 971-987, July 2002
           :DOI:`10.1109/TPAMI.2002.1017623`
    .. [2] T. Ahonen, A. Hadid and M. Pietikainen. "Face recognition with
           local binary patterns", in Proc. Eighth European Conf. Computer
           Vision, Prague, Czech Republic, May 11-14, 2004, pp. 469-481, 2004.
           http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.214.6851
           :DOI:`10.1007/978-3-540-24670-1_36`
    .. [3] T. Ahonen, A. Hadid and M. Pietikainen, "Face Description with
           Local Binary Patterns: Application to Face Recognition",
           IEEE Transactions on Pattern Analysis and Machine Intelligence,
           vol. 28, no. 12, pp. 2037-2041, Dec. 2006
           :DOI:`10.1109/TPAMI.2006.244.`
    """
    check_nD(image, 2)

    methods = {
        'default': ord('D'),
        'ror': ord('R'),
        'uniform': ord('U'),
        'nri_uniform': ord('N'),
        'var': ord('V')
    }
    image = np.ascontiguousarray(image, dtype=np.double)
    output = _local_binary_pattern(image, P, R, methods[method.lower()])
    return output