基于Python3.6和Opencv3的活动轮廓模型--CV和RSF
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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)
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