區域隨着分水嶺變換而增長

Question

我正在嘗試使用 adfoucart 編寫的代碼，用於使用分水嶺變換進行區域增長，但在識別圖像標記時遇到了一些錯誤。

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

def get_markers(img2, indices=False):
    im_ = gaussian(img2, 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(img2, True)
plt.figure()
plt.imshow(grad, cmap=plt.cm.gray)
plt.plot(markers[:,1],markers[:,0],'b+')
plt.show()

我收到了這個錯誤。

IndexError                                Traceback (most recent call last)
~\AppData\Local\Temp/ipykernel_17316/2204442073.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(img2, True)
     15 plt.figure()
     16 plt.imshow(grad, cmap=plt.cm.gray)

~\AppData\Local\Temp/ipykernel_17316/2204442073.py in get_markers(img2, indices)
      5 def get_markers(img2, indices=False):
      6     im_ = gaussian(img2, 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！

Answer 1

您可能正在嘗試在只有 2 個維度（高度和寬度）的灰度圖像上運行代碼，而編寫代碼時期望 RGB 圖像具有 3 個維度（高度、寬度和顏色通道）。

在灰度圖像上，線條：

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

可以簡單地替換為：

grad = rank.gradient(im_, disk(5))