python数字图像处理之骨架提取与分水岭算法

from skimage import morphology,draw
import numpy as np
import matplotlib.pyplot as plt

#创建一个二值图像用于测试
image = np.zeros((400, 400))

#生成目标对象1(白色U型)
image[10:-10, 10:100] = 1
image[-100:-10, 10:-10] = 1
image[10:-10, -100:-10] = 1

#生成目标对象2（X型）
rs, cs = draw.line(250, 150, 10, 280)
for i in range(10):
 image[rs + i, cs] = 1
rs, cs = draw.line(10, 150, 250, 280)
for i in range(20):
 image[rs + i, cs] = 1

#生成目标对象3（O型）
ir, ic = np.indices(image.shape)
circle1 = (ic - 135)**2 + (ir - 150)**2 < 30**2
circle2 = (ic - 135)**2 + (ir - 150)**2 < 20**2
image[circle1] = 1
image[circle2] = 0

#实施骨架算法
skeleton =morphology.skeletonize(image)

#显示结果
fig, (ax1, ax2) = plt.subplots(nrows=1, ncols=2, figsize=(8, 4))

ax1.imshow(image, cmap=plt.cm.gray)
ax1.axis('off')
ax1.set_title('original', fontsize=20)

ax2.imshow(skeleton, cmap=plt.cm.gray)
ax2.axis('off')
ax2.set_title('skeleton', fontsize=20)

fig.tight_layout()
plt.show()

from skimage import morphology,data,color
import matplotlib.pyplot as plt

image=color.rgb2gray(data.horse())
image=1-image #反相
#实施骨架算法
skeleton =morphology.skeletonize(image)

#显示结果
fig, (ax1, ax2) = plt.subplots(nrows=1, ncols=2, figsize=(8, 4))

ax1.imshow(image, cmap=plt.cm.gray)
ax1.axis('off')
ax1.set_title('original', fontsize=20)

ax2.imshow(skeleton, cmap=plt.cm.gray)
ax2.axis('off')
ax2.set_title('skeleton', fontsize=20)

fig.tight_layout()
plt.show()

import numpy as np
import scipy.ndimage as ndi
from skimage import morphology
import matplotlib.pyplot as plt

#编写一个函数，生成测试图像
def microstructure(l=256):
 n = 5
 x, y = np.ogrid[0:l, 0:l]
 mask = np.zeros((l, l))
 generator = np.random.RandomState(1)
 points = l * generator.rand(2, n**2)
 mask[(points[0]).astype(np.int), (points[1]).astype(np.int)] = 1
 mask = ndi.gaussian_filter(mask, sigma=l/(4.*n))
 return mask > mask.mean()

data = microstructure(l=64) #生成测试图像

#计算中轴和距离变换值
skel, distance =morphology.medial_axis(data, return_distance=True)

#中轴上的点到背景像素点的距离
dist_on_skel = distance * skel

fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(8, 4))
ax1.imshow(data, cmap=plt.cm.gray, interpolation='nearest')
#用光谱色显示中轴
ax2.imshow(dist_on_skel, cmap=plt.cm.spectral, interpolation='nearest')
ax2.contour(data, [0.5], colors='w') #显示轮廓线

fig.tight_layout()
plt.show()

import numpy as np
import matplotlib.pyplot as plt
from scipy import ndimage as ndi
from skimage import morphology,feature

#创建两个带有重叠圆的图像
x, y = np.indices((80, 80))
x1, y1, x2, y2 = 28, 28, 44, 52
r1, r2 = 16, 20
mask_circle1 = (x - x1)**2 + (y - y1)**2 < r1**2
mask_circle2 = (x - x2)**2 + (y - y2)**2 < r2**2
image = np.logical_or(mask_circle1, mask_circle2)

#现在我们用分水岭算法分离两个圆
distance = ndi.distance_transform_edt(image) #距离变换
local_maxi =feature.peak_local_max(distance, indices=False, footprint=np.ones((3, 3)),
       labels=image) #寻找峰值
markers = ndi.label(local_maxi)[0] #初始标记点
labels =morphology.watershed(-distance, markers, mask=image) #基于距离变换的分水岭算法

fig, axes = plt.subplots(nrows=2, ncols=2, figsize=(8, 8))
axes = axes.ravel()
ax0, ax1, ax2, ax3 = axes

ax0.imshow(image, cmap=plt.cm.gray, interpolation='nearest')
ax0.set_title("Original")
ax1.imshow(-distance, cmap=plt.cm.jet, interpolation='nearest')
ax1.set_title("Distance")
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()

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

image =color.rgb2gray(data.camera())
denoised = filter.rank.median(image, morphology.disk(2)) #过滤噪声

#将梯度值低于10的作为开始标记点
markers = filter.rank.gradient(denoised, morphology.disk(5)) <10
markers = ndi.label(markers)[0]

gradient = filter.rank.gradient(denoised, morphology.disk(2)) #计算梯度
labels =morphology.watershed(gradient, markers, mask=image) #基于梯度的分水岭算法

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()