以下是执行各向异性扩散的python代码，但是当我通过anaconda / ipython笔记本运行时没有发生任何事情，我假设需要输入图像，任何帮助将不胜感激。
import numpy as np
import warnings
def anisodiff(img,niter=1,kappa=50,gamma=0.1,step=(1.,1.),option=1,ploton=False):
"""
Anisotropic diffusion.
Usage:
imgout = anisodiff(im, niter, kappa, gamma, option)
Arguments:
img - input image
niter - number of iterations
kappa - conduction coefficient 20-100 ?
gamma - max value of .25 for stability
step - tuple, the distance between adjacent pixels in (y,x)
option - 1 Perona Malik diffusion equation No 1
2 Perona Malik diffusion equation No 2
ploton - if True, the image will be plotted on every iteration
Returns:
imgout - diffused image.
kappa controls conduction as a function of gradient. If kappa is low
small intensity gradients are able to block conduction and hence diffusion
across step edges. A large value reduces the influence of intensity
gradients on conduction.
gamma controls speed of diffusion (you usually want it at a maximum of
0.25)
step is used to scale the gradients in case the spacing between adjacent
pixels differs in the x and y axes
Diffusion equation 1 favours high contrast edges over low contrast ones.
Diffusion equation 2 favours wide regions over smaller ones.
Reference:
P. Perona and J. Malik.
Scale-space and edge detection using ansotropic diffusion.
IEEE Transactions on Pattern Analysis and Machine Intelligence,
12(7):629-639, July 1990.
Original MATLAB code by Peter Kovesi
School of Computer Science & Software Engineering
The University of Western Australia
pk @ csse uwa edu au
<http://www.csse.uwa.edu.au>
Translated to Python and optimised by Alistair Muldal
Department of Pharmacology
University of Oxford
<alistair.muldal@pharm.ox.ac.uk>
June 2000 original version.
March 2002 corrected diffusion eqn No 2.
July 2012 translated to Python
"""
# ...you could always diffuse each color channel independently if you
# really want
if img.ndim == 3:
warnings.warn("Only grayscale images allowed, converting to 2D matrix")
img = img.mean(2)
# initialize output array
img = img.astype('float32')
imgout = img.copy()
# initialize some internal variables
deltaS = np.zeros_like(imgout)
deltaE = deltaS.copy()
NS = deltaS.copy()
EW = deltaS.copy()
gS = np.ones_like(imgout)
gE = gS.copy()
# create the plot figure, if requested
if ploton:
import pylab as pl
from time import sleep
fig = pl.figure(figsize=(20,5.5),num="Anisotropic diffusion")
ax1,ax2 = fig.add_subplot(1,2,1),fig.add_subplot(1,2,2)
ax1.imshow(img,interpolation='nearest')
ih = ax2.imshow(imgout,interpolation='nearest',animated=True)
ax1.set_title("Original image")
ax2.set_title("Iteration 0")
fig.canvas.draw()
for ii in xrange(niter):
# calculate the diffs
deltaS[:-1,: ] = np.diff(imgout,axis=0)
deltaE[: ,:-1] = np.diff(imgout,axis=1)
# conduction gradients (only need to compute one per dim!)
if option == 1:
gS = np.exp(-(deltaS/kappa)**2.)/step[0]
gE = np.exp(-(deltaE/kappa)**2.)/step[1]
elif option == 2:
gS = 1./(1.+(deltaS/kappa)**2.)/step[0]
gE = 1./(1.+(deltaE/kappa)**2.)/step[1]
# update matrices
E = gE*deltaE
S = gS*deltaS
# subtract a copy that has been shifted 'North/West' by one
# pixel. don't as questions. just do it. trust me.
NS[:] = S
EW[:] = E
NS[1:,:] -= S[:-1,:]
EW[:,1:] -= E[:,:-1]
# update the image
imgout += gamma*(NS+EW)
if ploton:
iterstring = "Iteration %i" %(ii+1)
ih.set_data(imgout)
ax2.set_title(iterstring)
fig.canvas.draw()
# sleep(0.01)
return imgout
def anisodiff3(stack,niter=1,kappa=50,gamma=0.1,step=(1.,1.,1.),option=1,ploton=False):
"""
3D Anisotropic diffusion.
Usage:
stackout = anisodiff(stack, niter, kappa, gamma, option)
Arguments:
stack - input stack
niter - number of iterations
kappa - conduction coefficient 20-100 ?
gamma - max value of .25 for stability
step - tuple, the distance between adjacent pixels in (z,y,x)
option - 1 Perona Malik diffusion equation No 1
2 Perona Malik diffusion equation No 2
ploton - if True, the middle z-plane will be plotted on every
iteration
Returns:
stackout - diffused stack.
kappa controls conduction as a function of gradient. If kappa is low
small intensity gradients are able to block conduction and hence diffusion
across step edges. A large value reduces the influence of intensity
gradients on conduction.
gamma controls speed of diffusion (you usually want it at a maximum of
0.25)
step is used to scale the gradients in case the spacing between adjacent
pixels differs in the x,y and/or z axes
Diffusion equation 1 favours high contrast edges over low contrast ones.
Diffusion equation 2 favours wide regions over smaller ones.
Reference:
P. Perona and J. Malik.
Scale-space and edge detection using ansotropic diffusion.
IEEE Transactions on Pattern Analysis and Machine Intelligence,
12(7):629-639, July 1990.
Original MATLAB code by Peter Kovesi
School of Computer Science & Software Engineering
The University of Western Australia
pk @ csse uwa edu au
<http://www.csse.uwa.edu.au>
Translated to Python and optimised by Alistair Muldal
Department of Pharmacology
University of Oxford
<alistair.muldal@pharm.ox.ac.uk>
June 2000 original version.
March 2002 corrected diffusion eqn No 2.
July 2012 translated to Python
"""
# ...you could always diffuse each color channel independently if you
# really want
if stack.ndim == 4:
warnings.warn("Only grayscale stacks allowed, converting to 3D matrix")
stack = stack.mean(3)
# initialize output array
stack = stack.astype('float32')
stackout = stack.copy()
# initialize some internal variables
deltaS = np.zeros_like(stackout)
deltaE = deltaS.copy()
deltaD = deltaS.copy()
NS = deltaS.copy()
EW = deltaS.copy()
UD = deltaS.copy()
gS = np.ones_like(stackout)
gE = gS.copy()
gD = gS.copy()
# create the plot figure, if requested
if ploton:
import pylab as pl
from time import sleep
showplane = stack.shape[0]//2
fig = pl.figure(figsize=(20,5.5),num="Anisotropic diffusion")
ax1,ax2 = fig.add_subplot(1,2,1),fig.add_subplot(1,2,2)
ax1.imshow(stack[showplane,...].squeeze(),interpolation='nearest')
ih = ax2.imshow(stackout[showplane,...].squeeze(),interpolation='nearest',animated=True)
ax1.set_title("Original stack (Z = %i)" %showplane)
ax2.set_title("Iteration 0")
fig.canvas.draw()
for ii in xrange(niter):
# calculate the diffs
deltaD[:-1,: ,: ] = np.diff(stackout,axis=0)
deltaS[: ,:-1,: ] = np.diff(stackout,axis=1)
deltaE[: ,: ,:-1] = np.diff(stackout,axis=2)
# conduction gradients (only need to compute one per dim!)
if option == 1:
gD = np.exp(-(deltaD/kappa)**2.)/step[0]
gS = np.exp(-(deltaS/kappa)**2.)/step[1]
gE = np.exp(-(deltaE/kappa)**2.)/step[2]
elif option == 2:
gD = 1./(1.+(deltaD/kappa)**2.)/step[0]
gS = 1./(1.+(deltaS/kappa)**2.)/step[1]
gE = 1./(1.+(deltaE/kappa)**2.)/step[2]
# update matrices
D = gD*deltaD
E = gE*deltaE
S = gS*deltaS
# subtract a copy that has been shifted 'Up/North/West' by one
# pixel. don't as questions. just do it. trust me.
UD[:] = D
NS[:] = S
EW[:] = E
UD[1:,: ,: ] -= D[:-1,: ,: ]
NS[: ,1:,: ] -= S[: ,:-1,: ]
EW[: ,: ,1:] -= E[: ,: ,:-1]
# update the image
stackout += gamma*(UD+NS+EW)
if ploton:
iterstring = "Iteration %i" %(ii+1)
ih.set_data(stackout[showplane,...].squeeze())
ax2.set_title(iterstring)
fig.canvas.draw()
# sleep(0.01)
return stackout
答案 0 :(得分：1)
这是一些示例使用代码。将上面的代码粘贴到iPython单元格中，然后按control + Enter。然后将下面的代码粘贴到下面的单元格中，按下Ctrl + Enter键，您会看到两个图像 - 原始图像和平滑结果。
您可以使用from scipy.misc import imread并执行im = imread("my_file_name.png")替换lena内容。
import matplotlib
%matplotlib inline
import matplotlib.pyplot as plt
from skimage.data import lena
im = lena()[..., 0]
result = anisodiff(im, niter=10, kappa=25)
plt.imshow(im, cmap="Greys_r")
plt.figure()
plt.imshow(result, cmap="Greys_r")