我想使用切片超像素分割图像,然后用所述超像素的平均颜色替换超像素的原始颜色。
import numpy as np
import matplotlib.pyplot as plt
from skimage import io
from skimage.segmentation import slic, mark_boundaries
from skimage.data import astronaut
from skimage.measure import regionprops
img = astronaut()
segments = slic(img, n_segments=512, compactness=10,
multichannel=True,
enforce_connectivity=True,
convert2lab=True)
regions = regionprops(segments, intensity_image=img)
我收到错误ValueError:标签和强度图像必须具有相同的形状。
分段形状为 (512,512),img 形状为 (512,512,3)。在我的例子中,regionprops
的正确用法是什么?
最佳答案
我遵循了已接受答案的第一个建议。我的代码的工作版本:
import matplotlib.pyplot as plt
from skimage.segmentation import slic
from skimage.data import astronaut
from skimage.measure import regionprops
def paint_region_with_avg_intensity(rp, mi, channel):
for i in range(rp.shape[0]):
img[rp[i][0]][rp[i][1]][channel] = mi
img = astronaut()
segments = slic(img, n_segments=512, compactness=10,
multichannel=True,
enforce_connectivity=True,
convert2lab=True)
for i in range(3):
regions = regionprops(segments, intensity_image=img[:,:,i])
for r in regions:
paint_region_with_avg_intensity(r.coords, int(r.mean_intensity), i)
plt.imshow(img)
plt.show()
关于python - 切片超像素的平均颜色,我们在Stack Overflow上找到一个类似的问题: https://stackoverflow.com/questions/53155771/