skimage.feature.greycomatrix仅产生对角线值

Question

我正在尝试在减少趋势的数字高程模型上生成glcm。我当前的问题是skimage.feature.greycomatrix（image）的输出仅包含矩阵对角线条目中的值。

glcm = greycomatrix(image,distances=[1],levels=100,angles=[0] ,symmetric=True,normed=True)

使用以下代码先对图像进行量化：

import numpy as np
from skimage.feature import greycomatrix
def quantize(raster):
    print("\n Quantizing \n")
    raster += (np.abs(np.min(raster)) + 1)
    mean = np.nanmean(raster.raster[raster.raster > 0])
    std = np.nanstd(raster.raster[raster.raster > 0])

    raster[raster == None] = 0 # set all None values to 0
    raster[np.isnan(raster)] = 0
    raster[raster > (mean + 1.5*std)] = 0
    raster[raster < (mean - 1.5*std)] = 0 # High pass filter
    raster[raster > 0] = raster[raster > 0] - (np.min(raster[raster > 0]) - 1)
    raster[raster>101] = 0
    raster = np.rint(raster)
    flat = np.ndarray.flatten(raster[raster > 0])
    range = np.max(flat) - np.min(flat)
    print("\n\nRaster Range: {}\n\n".format(range))
    raster = raster.astype(np.uint8)
    raster[raster > 101] = 0 

我将如何使glcm在对角矩阵之外计算值（即，仅是值本身的频率，而我的方法有根本上的错误吗？

Answer 1

import numpy as np
from skimage import data
import matplotlib.pyplot as plt
from skimage.feature import greycomatrix

x = data.brick()
y = data.gravel()

mx = greycomatrix(x, distances=[1], levels=256, angles=[0], normed=True)
my = greycomatrix(y, distances=[1], levels=256, angles=[0], normed=True)

fig, ax = plt.subplots(2, 2, figsize=(12, 8))
ax[0, 0].imshow(x, cmap='gray')
ax[0, 1].imshow(mx[:, :, 0, 0])
ax[1, 0].imshow(y, cmap='gray')
ax[1, 1].imshow(my[:, :, 0, 0])

尽管我没有看到您的光栅图像，但我猜想强度在quantize返回的图像上变化非常平滑，因此GLCM大部分是对角线的。