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我正在尝试使用 GLCM 算法在卫星图像中进行纹理分析。scikit-image 文档对此非常有帮助,但对于 GLCM 计算,我们需要在图像上循环的窗口大小。这在 Python 中太慢了。我在 stackoverflow 上发现了很多关于滑动窗口的帖子,但计算需要永远。我有一个如下所示的示例,它可以工作但需要永远。我想这一定是一种天真的方式

image = np.pad(image, int(win/2), mode='reflect') 
row, cols = image.shape
feature_map = np.zeros((M, N))

for m in xrange(0, row):
    for n in xrange(0, cols):
        window = image[m:m+win, n:n+win]
        glcm = greycomatrix(window, d, theta, levels)
        contrast = greycoprops(glcm, 'contrast')
        feature_map[m,n] = contrast 

我遇到了skimage.util.view_as_windows对我来说可能是一个很好的解决方案的方法。我的问题是,当我尝试计算 GLCM 时,我收到一条错误消息:

ValueError:参数image必须是二维数组

这是因为 GLCM 图像的结果具有 4d 维度,而 scikit-imageview_as_windows方法只接受 2d 数组。这是我的尝试

win_w=40
win_h=40

features = np.zeros(image.shape, dtype='uint8')
target = features[win_h//2:-win_h//2+1, win_w//2:-win_w//2+1]
windowed = view_as_windows(image, (win_h, win_w))

GLCM = greycomatrix(windowed, [1], [0, np.pi/4, np.pi/2, 3*np.pi/4], symmetric=True, normed=True)
haralick = greycoprops(GLCM, 'ASM')

有谁知道我如何使用skimage.util.view_as_windows方法计算 GLCM?

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1 回答 1

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您尝试执行的特征提取是一项计算机密集型任务。我通过只为整个图像计算一次共现图来加快您的方法,而不是在滑动窗口的重叠位置上一遍又一遍地计算共现图。

共现图是与原始图像大小相同的图像堆栈,其中 - 对于每个像素 - 强度级别由整数替换,该整数编码两个强度的共现,即Ii在该像素处和Ij在偏移像素。共现图的层数与我们考虑的偏移量一样多(即所有可能的距离-角度对)。通过保留共现图,您不需要从头开始计算滑动窗口的每个位置的 GLCM,因为您可以重用先前计算的共现图来获得每个距离的邻接矩阵(GLCM) -角度对。这种方法为您提供了显着的速度增益。

我想出的解决方案依赖于以下功能:

import numpy as np
from skimage import io
from scipy import stats
from skimage.feature import greycoprops

def offset(length, angle):
    """Return the offset in pixels for a given length and angle"""
    dv = length * np.sign(-np.sin(angle)).astype(np.int32)
    dh = length * np.sign(np.cos(angle)).astype(np.int32)
    return dv, dh

def crop(img, center, win):
    """Return a square crop of img centered at center (side = 2*win + 1)"""
    row, col = center
    side = 2*win + 1
    first_row = row - win
    first_col = col - win
    last_row = first_row + side    
    last_col = first_col + side
    return img[first_row: last_row, first_col: last_col]

def cooc_maps(img, center, win, d=[1], theta=[0], levels=256):
    """
    Return a set of co-occurrence maps for different d and theta in a square 
    crop centered at center (side = 2*w + 1)
    """
    shape = (2*win + 1, 2*win + 1, len(d), len(theta))
    cooc = np.zeros(shape=shape, dtype=np.int32)
    row, col = center
    Ii = crop(img, (row, col), win)
    for d_index, length in enumerate(d):
        for a_index, angle in enumerate(theta):
            dv, dh = offset(length, angle)
            Ij = crop(img, center=(row + dv, col + dh), win=win)
            cooc[:, :, d_index, a_index] = encode_cooccurrence(Ii, Ij, levels)
    return cooc

def encode_cooccurrence(x, y, levels=256):
    """Return the code corresponding to co-occurrence of intensities x and y"""
    return x*levels + y

def decode_cooccurrence(code, levels=256):
    """Return the intensities x, y corresponding to code"""
    return code//levels, np.mod(code, levels)    

def compute_glcms(cooccurrence_maps, levels=256):
    """Compute the cooccurrence frequencies of the cooccurrence maps"""
    Nr, Na = cooccurrence_maps.shape[2:]
    glcms = np.zeros(shape=(levels, levels, Nr, Na), dtype=np.float64)
    for r in range(Nr):
        for a in range(Na):
            table = stats.itemfreq(cooccurrence_maps[:, :, r, a])
            codes = table[:, 0]
            freqs = table[:, 1]/float(table[:, 1].sum())
            i, j = decode_cooccurrence(codes, levels=levels)
            glcms[i, j, r, a] = freqs
    return glcms

def compute_props(glcms, props=('contrast',)):
    """Return a feature vector corresponding to a set of GLCM"""
    Nr, Na = glcms.shape[2:]
    features = np.zeros(shape=(Nr, Na, len(props)))
    for index, prop_name in enumerate(props):
        features[:, :, index] = greycoprops(glcms, prop_name)
    return features.ravel()

def haralick_features(img, win, d, theta, levels, props):
    """Return a map of Haralick features (one feature vector per pixel)"""
    rows, cols = img.shape
    margin = win + max(d)
    arr = np.pad(img, margin, mode='reflect')
    n_features = len(d) * len(theta) * len(props)
    feature_map = np.zeros(shape=(rows, cols, n_features), dtype=np.float64)
    for m in xrange(rows):
        for n in xrange(cols):
            coocs = cooc_maps(arr, (m + margin, n + margin), win, d, theta, levels)
            glcms = compute_glcms(coocs, levels)
            feature_map[m, n, :] = compute_props(glcms, props)
    return feature_map

演示

以下结果对应于(250, 200)来自 Landsat 图像的像素裁剪。我已经考虑了两个距离、四个角度和两个 GLCM 属性。这会为每个像素生成一个 16 维的特征向量。请注意,滑动窗口是正方形的,它的边是2*win + 1像素(在这个测试中win = 19,使用了 的值)。此示例运行大约需要 6 分钟,这比“永远”要短得多;-)

In [331]: img.shape
Out[331]: (250L, 200L)

In [332]: img.dtype
Out[332]: dtype('uint8')

In [333]: d = (1, 2)

In [334]: theta = (0, np.pi/4, np.pi/2, 3*np.pi/4)

In [335]: props = ('contrast', 'homogeneity')

In [336]: levels = 256

In [337]: win = 19

In [338]: %time feature_map = haralick_features(img, win, d, theta, levels, props)
Wall time: 5min 53s    

In [339]: feature_map.shape
Out[339]: (250L, 200L, 16L)

In [340]: feature_map[0, 0, :]    
Out[340]:  
array([ 10.3314,   0.3477,  25.1499,   0.2738,  25.1499,   0.2738,
        25.1499,   0.2738,  23.5043,   0.2755,  43.5523,   0.1882,
        43.5523,   0.1882,  43.5523,   0.1882])

In [341]: io.imshow(img)
Out[341]: <matplotlib.image.AxesImage at 0xce4d160>

卫星图像

于 2017-03-14T19:31:44.967 回答