各向异性扩散二维图像

Question

我想在二维图像上使用各向异性扩散。

我想使用python，但不介意使用matlab或c。 他们有没有图书馆可以作为我的第一步？我在谷歌上搜索了一下，找到了Panda3D和OpenGl。
基本上我想给一组图像，让它应用过滤，然后将新图像输出到我想要的文件夹。

关于如何使用这些或者你认为更好的东西有什么建议吗？

编辑：意思是扩散对不起！

Answer 1

import math
try:
    from cv2 import cv2
except:
    import cv2
import numpy as np


class anisodiff2D(object):

    def __init__(self,num_iter=5,delta_t=1/7,kappa=30,option=2):

        super(anisodiff2D,self).__init__()

        self.num_iter = num_iter
        self.delta_t = delta_t
        self.kappa = kappa
        self.option = option

        self.hN = np.array([[0,1,0],[0,-1,0],[0,0,0]])
        self.hS = np.array([[0,0,0],[0,-1,0],[0,1,0]])
        self.hE = np.array([[0,0,0],[0,-1,1],[0,0,0]])
        self.hW = np.array([[0,0,0],[1,-1,0],[0,0,0]])
        self.hNE = np.array([[0,0,1],[0,-1,0],[0,0,0]])
        self.hSE = np.array([[0,0,0],[0,-1,0],[0,0,1]])
        self.hSW = np.array([[0,0,0],[0,-1,0],[1,0,0]])
        self.hNW = np.array([[1,0,0],[0,-1,0],[0,0,0]])

    def fit(self,img):

        diff_im = img.copy()

        dx=1; dy=1; dd = math.sqrt(2)

        for i in range(self.num_iter):

            nablaN = cv2.filter2D(diff_im,-1,self.hN)
            nablaS = cv2.filter2D(diff_im,-1,self.hS)
            nablaW = cv2.filter2D(diff_im,-1,self.hW)
            nablaE = cv2.filter2D(diff_im,-1,self.hE)
            nablaNE = cv2.filter2D(diff_im,-1,self.hNE)
            nablaSE = cv2.filter2D(diff_im,-1,self.hSE)
            nablaSW = cv2.filter2D(diff_im,-1,self.hSW)
            nablaNW = cv2.filter2D(diff_im,-1,self.hNW)

            cN = 0; cS = 0; cW = 0; cE = 0; cNE = 0; cSE = 0; cSW = 0; cNW = 0

            if self.option == 1:
                cN = np.exp(-(nablaN/self.kappa)**2)
                cS = np.exp(-(nablaS/self.kappa)**2)
                cW = np.exp(-(nablaW/self.kappa)**2)
                cE = np.exp(-(nablaE/self.kappa)**2)
                cNE = np.exp(-(nablaNE/self.kappa)**2)
                cSE = np.exp(-(nablaSE/self.kappa)**2)
                cSW = np.exp(-(nablaSW/self.kappa)**2)
                cNW = np.exp(-(nablaNW/self.kappa)**2)
            elif self.option == 2:
                cN = 1/(1+(nablaN/self.kappa)**2)
                cS = 1/(1+(nablaS/self.kappa)**2)
                cW = 1/(1+(nablaW/self.kappa)**2)
                cE = 1/(1+(nablaE/self.kappa)**2)
                cNE = 1/(1+(nablaNE/self.kappa)**2)
                cSE = 1/(1+(nablaSE/self.kappa)**2)
                cSW = 1/(1+(nablaSW/self.kappa)**2)
                cNW = 1/(1+(nablaNW/self.kappa)**2)

            diff_im = diff_im + self.delta_t * (

                (1/dy**2)*cN*nablaN +
                (1/dy**2)*cS*nablaS +
                (1/dx**2)*cW*nablaW +
                (1/dx**2)*cE*nablaE +

                (1/dd**2)*cNE*nablaNE +
                (1/dd**2)*cSE*nablaSE +
                (1/dd**2)*cSW*nablaSW +
                (1/dd**2)*cNW*nablaNW
            )

        return diff_im

Answer 2

Here's我的Python/numpy实现二维和三维各向异性（Perona-Malik）扩散。它不像C代码那么快，但对我来说它做得很好。

Answer 3

各向异性扩散自2013年起在^{} package中可用

import numpy as np
from medpy.filter.smoothing import anisotropic_diffusion

img = np.random.uniform(size=(32,32))
img_filtered = anisotropic_diffusion(img)