區域隨着分水嶺變換誤差而增長

Question

大家好，我是圖像處理的新手，我一直在嘗試來自 adfoucart 的代碼，但我一直收到此錯誤

https://github.com/adfoucart/image-processing-notebooks/blob/main/V31%20-%20Region%20growth%20with%20the%20watershed%20transform.ipynb

image.shape
(240, 320)

from skimage.filters import rank,gaussian
from skimage.morphology import disk
from skimage.feature import peak_local_max

def get_markers(image, indices=False):
    im_ = gaussian(image, sigma=4)
    gradr = rank.gradient(im_[:,:,0],disk(5)).astype('int')
    gradg = rank.gradient(im_[:,:,1],disk(5)).astype('int')
    gradb = rank.gradient(im_[:,:,2],disk(5)).astype('int')
    grad = gradr+gradg+gradb
    
    return peak_local_max(grad.max()-grad,threshold_rel=0.5, min_distance=60,indices=indices),grad

markers,grad = get_markers(image, True)
plt.figure()
plt.imshow(grad, cmap=plt.cm.gray)
plt.plot(markers[:,1],markers[:,0],'b+')
plt.show()

---------------------------------------------------------------------------
IndexError                                Traceback (most recent call last)
/var/folders/f9/5vh23mb50wj5wq4vfl8wcgx40000gn/T/ipykernel_34090/3084023926.py in <module>
     12     return peak_local_max(grad.max()-grad,threshold_rel=0.5, min_distance=60,indices=indices),grad
     13 
---> 14 markers,grad = get_markers(image, True)
     15 plt.figure()
     16 plt.imshow(grad, cmap=plt.cm.gray)

/var/folders/f9/5vh23mb50wj5wq4vfl8wcgx40000gn/T/ipykernel_34090/3084023926.py in get_markers(image, indices)
      5 def get_markers(image, indices=False):
      6     im_ = gaussian(image, sigma=4)
----> 7     gradr = rank.gradient(im_[:,:,0],disk(5)).astype('int')
      8     gradg = rank.gradient(im_[:,:,1],disk(5)).astype('int')
      9     gradb = rank.gradient(im_[:,:,2],disk(5)).astype('int')

IndexError: too many indices for array: array is 2-dimensional, but 3 were indexed

任何幫助將不勝感激，非常感謝！

Answer 1

您的圖像是灰度的，即單通道。 我可以這么說，因為它的形狀是 (240,320) 而不是 (240,320,3)，如果是 3 通道 RGB。

這意味着您不需要跨 3 個通道進行平均，您應該只使用 2 個索引，即

grad = rank.gradient(im_[:,:],disk(5)).astype('int')

實際上，您可能只使用：

grad = rank.gradient(im_,disk(5)).astype('int')