圖像分割算法：基於梯度分水嶺的分割算法

代碼實現：

import matplotlib.pyplot as plt
from scipy import ndimage as ndi
from skimage import morphology,color,data,filters
import cv2

# image =color.rgb2gray(data.camera())
image =cv2.imread('./Project/test/test.jpg',0)
denoised = filters.rank.median(image, morphology.disk(2)) #過濾噪聲

#將梯度值低於10的作爲開始標記點
markers = filters.rank.gradient(denoised, morphology.disk(5)) <10
markers = ndi.label(markers)[0]

gradient = filters.rank.gradient(denoised, morphology.disk(2)) #計算梯度
labels =morphology.watershed(gradient, markers, mask=image) #基於梯度的分水嶺算法

fig, axes = plt.subplots(nrows=2, ncols=2, figsize=(6, 6))
axes = axes.ravel()
ax0, ax1, ax2, ax3 = axes

ax0.imshow(image, cmap=plt.cm.gray, interpolation='nearest')
ax0.set_title("Original")
ax1.imshow(gradient, cmap=plt.cm.spectral, interpolation='nearest')
ax1.set_title("Gradient")
ax2.imshow(markers, cmap=plt.cm.spectral, interpolation='nearest')
ax2.set_title("Markers")
ax3.imshow(labels, cmap=plt.cm.spectral, interpolation='nearest')
ax3.set_title("Segmented")

for ax in axes:
    ax.axis('off')

fig.tight_layout()
plt.show()

參考博客：https://www.cnblogs.com/denny402/p/5167414.html