基于Python3.6和Opencv3的活动轮廓模型--CV和RSF

目前网上能找到的活动轮廓代码大都是matlab版本的，我把它转化成了基于Python3.6和Opencv3版本的代码，仅供参考。注，代码的运行效率并不高，感觉没matlab快。

CV模型。源代码下载地址：http://download.csdn.net/download/dingkeyanlail/10141194

#coding:utf-8

# author Ding Keyan

import sys

import numpy as np

import cv2

import matplotlib.pyplot as plt

import math

from pylab import*

Image = cv2.imread( '1.bmp', 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[ 30: 80, 30: 80]= - 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)

RSF模型：源代码下载地址：http://download.csdn.net/download/dingkeyanlail/10141195

#coding:utf-8

# author Ding Keyan

import sys

import numpy as np

import cv2

import matplotlib.pyplot as plt

import math

def DrawContour(LSF,p1,p2):

plt.clf()

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

plt.contour(LSF,[ 0],color = p1,linewidth = p2)

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

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

def RSF (LSF, img, mu, nu, epison,step,lambda1,lambda2,kernel):

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)

KIH = cv2.filter2D(Hea*img,- 1,kernel)

KH = cv2.filter2D(Hea,- 1,kernel)

f1 = KIH / KH

KIH1 = cv2.filter2D(( 1-Hea)*img,- 1,kernel)

KH1 = cv2.filter2D( 1-Hea,- 1,kernel)

f2 = KIH1 / KH1

R1 = (lambda1- lambda2)*img*img

R2 = cv2.filter2D(lambda1*f1 - lambda2*f2,- 1,kernel)

R3 = cv2.filter2D(lambda1*f1*f1 - lambda2*f2*f2,- 1,kernel)

RSFterm = -Drc*(R1- 2*R2*img+R3)

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

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

return LSF

Image = cv2.imread( '1.bmp', 1)

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

img = np.float64(image)

#Kernel

sig= 3

kernel = np.ones((sig* 4+ 1,sig* 4+ 1),np.float64)/(sig* 4+ 1)** 2

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

IniLSF[ 30: 80, 30: 80]= - 1

IniLSF=-IniLSF

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

plt.figure( 1)

DrawContour(IniLSF, 'r', 2) # Draw contour

mu = 1

nu = 0.003 * 255 * 255

num = 50

epison = 1

step = 0.1

lambda1=lambda2= 1

LSF=IniLSF

for i in range( 1,num):

LSF = RSF(LSF, img, mu, nu, epison,step,lambda1,lambda2,kernel)

if i % 5 == 0:

DrawContour(LSF, 'r', 2)