Question

我有一个维度（224,224,3）的RGB图像。 l使用SLIC算法对其进行超像素分割。

如下：

img= skimageIO.imread("first_image.jpeg")
print('img shape', img.shape) # (224,224,3)
segments_slic = slic(img, n_segments=1000, compactness=0.01, sigma=1) # Up to 1000 segments
segments_slic.shape
(224,224)

返回的段数为：

np.max(segments_slic)
Out[49]: 595

从0到595.所以，我们有596个超像素（区域）。

让我们来看看segments_slic[0]

segments_slic[0]
Out[51]: 
array([ 0,  0,  0,  0,  0,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,
        1,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  3,  3,  3,  3,  3,
        3,  3,  3,  3,  3,  4,  4,  4,  4,  4,  4,  4,  5,  5,  5,  5,  5,
        5,  5,  5,  5,  6,  6,  6,  6,  6,  6,  6,  6,  6,  7,  7,  7,  7,
        8,  8,  8,  8,  8,  8,  8,  8,  9,  9,  9,  9,  9,  9,  9,  9,  9,
       10, 10, 10, 10, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 12, 12,
       12, 12, 12, 12, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 14, 14, 14,
       14, 14, 14, 14, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 16, 16, 16,
       16, 16, 16, 16, 17, 17, 17, 17, 17, 17, 17, 17, 17, 18, 18, 18, 18,
       18, 18, 18, 18, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 20, 20,
       20, 20, 20, 20, 20, 20, 20, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21,
       21, 21, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 23, 23, 23, 23, 23,
       23, 23, 23, 23, 23, 23, 24, 24, 24, 24, 24, 24, 24, 25, 25, 25, 25,
       25, 25, 25])

我想得到什么？

对于每个超像素区域，制作如下两个阵列：

1）数组：包含属于同一个超像素的像素的索引。

例如

superpixel_list[0]包含属于超像素0的像素的所有索引。

superpixel_list[400]包含属于超像素400的像素的所有索引

2）superpixel_pixel_values [0]：包含属于超像素0的像素的像素值（ RGB ）。

例如，让我们说像素0,24,29,53属于超像素0.然后我们得到

superpixel[0]= [[223,118,33],[245,222,198],[98,17,255],[255,255,0]]# RGB values of pixels belonging to superpixel 0

有效/优化的方法是什么？（因为我有l个图像数据集要循环）

修改-1

def sp_idx(s, index = True):
     u = np.unique(s)
         if index:
     return [np.where(s == i) for i in u]
         else:
     return [s[s == i] for i in u]
     #return [s[np.where(s == i)] for i in u] gives the same but is slower
superpixel_list = sp_idx(segments_slic)
superpixel      = sp_idx(segments_slic, index = False)

在superpixel_list中，我们应该得到一个包含属于同一个超像素的像素索引的列表。例如 superpixel_list[0]应该得到受影响的像素的所有像素索引到超像素0

然而，我得到以下内容：

superpixel_list[0]
Out[73]: 
(array([ 0,  0,  0,  0,  0,  1,  1,  1,  1,  1,  1,  2,  2,  2,  2,  2,  2,
         3,  3,  3,  3,  3,  3,  3,  4,  4,  4,  4,  4,  4,  4,  5,  5,  5,
         5,  5,  5,  5,  5,  6,  6,  6,  6,  6,  6,  6,  6,  7,  7,  7,  7,
         7,  7,  7,  8,  8,  8,  8,  8,  8,  8,  9,  9,  9,  9,  9,  9, 10,
        10, 10, 10, 10, 11, 11, 11, 11, 12, 12, 12, 12, 13, 13, 13]),
 array([0, 1, 2, 3, 4, 0, 1, 2, 3, 4, 5, 0, 1, 2, 3, 4, 5, 0, 1, 2, 3, 4, 5,
        6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 7, 0, 1, 2, 3, 4, 5, 6,
        7, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 0, 1,
        2, 3, 4, 0, 1, 2, 3, 0, 1, 2, 3, 0, 1, 2]))

为什么有两个阵列？

例如，在superpixel [0]中，我们应该得到受影响的每个像素的RGB像素值，如下所示：例如，像素0,24,29,53受到超像素0的影响：

superpixel[0]= [[223,118,33],[245,222,198],[98,17,255],[255,255,0]]

但是当我使用你的功能时，我得到以下内容：

superpixel[0]
Out[79]: 
array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0])

感谢您的帮助

Answer 1

可以使用np.where和结果索引来完成。

def sp_idx(s, index = True):
     u = np.unique(s)
     return [np.where(s == i) for i in u]

superpixel_list = sp_idx(segments_slic)
superpixel      = [img[idx] for idx in superpixel_list]

获取每个超像素

1 个答案: