chan-vese模型
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 版权声明：本文为博主原创文章，未经博主允许不得转载。 https://blog.csdn.net/qq_38784098/article/details/82144106level set :https://www.zhihu.com/question/22608763?sort=created
https://blog.csdn.net/xiangyong58/article/details/11876019
SRE实战 互联网时代守护先锋，助力企业售后服务体系运筹帷幄！一键直达领取阿里云限量特价优惠。chan-vese模型(公式推导）：https://blog.csdn.net/zhangchen1003/article/details/48930377
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)
- plt.figure( 1), plt.imshow(Image), plt.xticks([]), plt.yticks([]) # to hide tick values on X and Y axis
- 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) / (epison*epison + LSF*LSF)
- Hea = 0.5*(1 + (2 / math.pi)*mat_math(LSF/epison, "atan"))
- Iy, Ix = np.gradient(LSF)
- s = mat_math(Ix*Ix+Iy*Iy, "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 = nu*Drc*cur
- 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 * 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, Vese L. Active contours without edges[J]. Image processing, IEEE transactions on, 2001, 10(2): 266-277.
1
二、公式推导过程
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作者：jonson_zc
来源：CSDN
原文：https://blog.csdn.net/zhangchen1003/article/details/48930377
版权声明：本文为博主原创文章，转载请附上博文链接！
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