[英]How to find superpixels' centroid using masked region?
Newbie here!新手来了! I'm working with python plus opencv and skimage packages.
我正在使用 python 加上 opencv 和 skimage 包。 I've segmented an image in superpixels using:
我使用以下方法对超像素中的图像进行了分割:
segments = slic(image, n_segments=numSegments, sigma=1, convert2lab=True)
I can access every superpixel with:我可以通过以下方式访问每个超像素:
#FOR-LOOP-1
for v in np.unique(segments):
#create a mask to access one region at the time
mask = np.ones(image.shape[:2])
mask[segments == v] = 0
#my function to calculate mean of A channel in LAB color space
A = mean_achannel(img, mask)
Now I'd like to get the coordinates associated with each superpixel's centroid, how can I do that?现在我想获得与每个超像素质心相关的坐标,我该怎么做? I tried using:
我尝试使用:
from skimage.measure import regionprops
#FOR-LOOP-2
regions = regionprops(segments)
for props in regions:
cx, cy = props.centroid # centroid coordinates
But I can't understand how to link each region in the "FOR-LOOP-2" with the right one in the "FOR-LOOP-1".但我无法理解如何将“FOR-LOOP-2”中的每个区域与“FOR-LOOP-1”中的正确区域联系起来。 How can I calculate each region centroid inside "FOR-LOOP-1"?
如何计算“FOR-LOOP-1”内的每个区域质心?
All the desired values can be found using regionprops in for-loop-2:可以使用 for-loop-2 中的 regionprops 找到所有所需的值:
from skimage.measure import regionprops
#FOR-LOOP-2
regions = regionprops(segments,
intensity_image=img[..., 1])
for props in regions:
cx, cy = props.centroid # centroid coordinates
v = props.label # value of label
mean_a = props.mean_intensity
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