导向滤波

import cv2
import numpy as np
def zmMinFilterGray(src,r=7):
    return cv2.erode(src,np.ones((2*r+1,2*r+1)))
def guidedfilter(I,p,r,eps):
    height,width=I.shape
    m_I=cv2.boxFilter(I,-1,(r,r))
    m_p=cv2.boxFilter(p,-1,(r,r))
    m_Ip=cv2.boxFilter(I*p,-1,(r,r))
    cov_Ip=m_Ip-m_I*m_p
    m_II=cv2.boxFilter(I*I,-1,(r,r))
    var_I=m_II-m_I*m_I
    a=cov_Ip/(var_I+eps)
    b=m_p-a*m_I
    m_a=cv2.boxFilter(a,-1,(r,r))
    m_b=cv2.boxFilter(b,-1,(r,r))
    return m_a*I+m_b
def Defog(m,r,eps,w,maxV1):                 
    V1=np.min(m,2) # 暗通道图像
    Dark_Channel=zmMinFilterGray(V1,7)
    cv2.imshow('result',Dark_Channel) 
    cv2.waitKey(0)
    cv2.destroyAllWindows()
    V1=guidedfilter(V1, Dark_Channel, r, eps)#引导滤波
    bins=2000
    ht=np.histogram(V1,bins)
    d=np.cumsum(ht[0])/float(V1.size)
    for lmax in range(bins-1,0,-1):
        if d[lmax]<=0.999:
            break
    A=np.mean(m,2)[V1>=ht[1][lmax]].max()
    V1=np.minimum(V1*w,maxV1)
    return V1,A
def deHaze(m,r=81,eps=0.001,w=0.95,maxV1=0.80,bGamma=False):
    Y=np.zeros(m.shape)
    Mask_img,A=Defog(m,r,eps,w,maxV1)
    for k in range(3):
        Y[:,:,k]=(m[:,:,k]-Mask_img)/(1-Mask_img/A)#颜色校正
    Y=np.clip(Y,0,1)
    if bGamma:
        Y=Y**(np.log(0.5)/np.log(Y.mean())) # gamma校正
    return Y
if __name__=='__main__':
    m=deHaze(cv2.imread('C:/Users/xpp/Desktop/Lena.png')/255.0)*255
    cv2.imwrite('C:/Users/xpp/Desktop/result.png',m)