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chan-vese模型

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Python--level set (水平集)和 chan-vese模型

2018年08月28日 10:51:54 GlassySky0816 阅读数:1604

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level set :https://www.zhihu.com/question/22608763?sort=created

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chan-vese模型(公式推导):<>

水平集(CV模型)代码:

import cv2

from pylab import*

Image = cv2.imread( '02.jpg', 1) # 读入原图

image = cv2.cvtColor(Image, cv2.COLOR_BGR2GRAY)

img = np.array(image, dtype=np.float64) # 读入到np的array中,并转化浮点类型

初始水平集函数

IniLSF = np.ones((img.shape[ 0], img.shape[1]), img.dtype)

IniLSF[ 300:320, 300:320] = -1

IniLSF = -IniLSF

画初始轮廓

Image = cv2.cvtColor(Image, cv2.COLOR_BGR2RGB)

创建图像窗口,并使用imshow函数显示图片Image;随后分别隐藏x轴和y轴的刻度标签

plt.contour(IniLSF, [ 0], color='b', linewidth=2) # 画LSF=0处的等高线

plt.draw(), plt.show(block= False)

def mat_math(intput, str):

output = intput

for i in range(img.shape[0]):

for j in range(img.shape[1]):

if str == "atan":

output[i, j] = math.atan(intput[i, j])

if str == "sqrt":

output[i, j] = math.sqrt(intput[i, j])

return output

CV函数

def CV(LSF, img, mu, nu, epison, step):

Drc = (epison / math.pi) / (episonepison + LSFLSF)

Hea = 0.5*(1 + (2 / math.pi)*mat_math(LSF/epison, "atan"))

Iy, Ix = np.gradient(LSF)

s = mat_math(IxIx+IyIy, "sqrt")

Nx = Ix / (s+ 0.000001)

Ny = Iy / (s+ 0.000001)

Mxx, Nxx = np.gradient(Nx)

Nyy, Myy = np.gradient(Ny)

cur = Nxx + Nyy

Length = nuDrccur

Lap = cv2.Laplacian(LSF, -1)

Penalty = mu*(Lap - cur)

s1 = Hea*img

s2 = ( 1-Hea)*img

s3 = 1-Hea

C1 = s1.sum() / Hea.sum()

C2 = s2.sum() / s3.sum()

CVterm = Drc*( -1 * (img - C1)(img - C1) + 1 * (img - C2)(img - C2))

LSF = LSF + step*(Length + Penalty + CVterm)

plt.imshow(s, cmap ='gray'),plt.show()

return LSF

模型参数

mu = 1

nu = 0.003 * 255

num = 20

epison = 1

step = 0.1

LSF = IniLSF

for i in range(1, num):

LSF = CV(LSF, img, mu, nu, epison, step) # 迭代

if i % 1 == 0: # 显示分割轮廓

plt.imshow(Image), plt.xticks([]), plt.yticks([])

plt.contour(LSF, [ 0], colors='r', linewidth=2)

plt.draw(), plt.show(block= False), plt.pause(0.01)

为什么上传图片这么麻烦。

一、文章参考

Chan T F and Vese L. The paper titled "Active contours without edges" was published in the IEEE Transactions on Image Processing in 2001.

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作者:jonson_zc
来源:
原文:
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转载于:https://www.cnblogs.com/shuimuqingyang/p/10860719.html

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