Python代码
def faster_LBP(img):
'''输入一张灰度图'''
dst = np.zeros(img.shape,dtype=img.dtype)
origin_array = cv2.merge([img for _ in range(8)])
nei_array = np.zeros_like(origin_array)
for i, (x_plus, y_plus) in enumerate(((-1, -1), (0, -1), (1, -1), (-1, 0), (1, 0), (-1, 1), (0, 1), (1, 1))):
if y_plus - 1 == 0 and x_plus - 1 == 0:
nei_array[1:-1, 1:-1, i] = img[y_plus + 1:, x_plus + 1:].copy()
elif y_plus - 1 == 0:
nei_array[1:-1, 1:-1, i] = img[y_plus + 1:, x_plus + 1:x_plus - 1].copy()
elif x_plus - 1 == 0:
nei_array[1:-1, 1:-1, i] = img[y_plus + 1:y_plus - 1, x_plus + 1:].copy()
else:
nei_array[1:-1, 1:-1, i] = img[y_plus + 1:y_plus - 1, x_plus + 1:x_plus - 1].copy()
sub_array = nei_array.astype(np.int) - origin_array.astype(np.int)
sub_array[sub_array >= 0] = 1
sub_array[sub_array < 0] = 0
sub_array = sub_array.astype(np.uint8)
for i in range(8):
dst += sub_array[:, :, i] * (2 ** (7 - i))
dst[0, :] = 0
dst[-1, :] = 0
dst[:, 0] = 0
dst[:, -1] = 0
hist = cv2.calcHist([dst], [0], None, [256], [0, 255])
return dst, hist