l具有尺寸的RGB图像(224,224,3(。l使用SLIC算法在其上施加了超像素分割。
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)
返回段的数量为:
np.max(segments_slic)
Out[49]: 595
从0到595。因此,我们有596个超级像素(区域(。
让我们看一下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?
对于每个超级像素区域,都有两个数组,如下:
1(数组:包含属于同一超像素的像素的索引。
例如
superpixel_list[0]
包含属于Superpixel 0的像素的所有索引。
superpixel_list[400]
包含属于Superpixel 400
2(superpixel_pixel_values [0]:包含属于superpixel 0的像素的像素值(在 rgb 中(。
例如,假设像素0、24、29、53属于Superpixel0。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
这样做的高效/优化方法是什么?(因为L具有L图像数据集可以循环(
edit-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,我们应该获得一个包含属于同一超级像素的像素索引的列表。例如 superpixel_list[0]
应该将所有像素的像素索引影响到Superpixel 0
但是,请列出以下内容:
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]))
为什么要两个数组?
例如,在superpixel [0]中,我们应该将每个像素的RGB像素值对supepixel的影响到Supepixel 0如下:例如,像素0、24、29、53受到超级像素0的影响:
superpixel[0]= [[223,118,33],[245,222,198],[98,17,255],[255,255,0]]
但是,当l使用您的功能时,请列出以下内容:
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])
谢谢您的帮助
可以使用np.where
和结果索引完成。
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]