python实现暗通道去雾算法的示例

import cv2
import numpy as np
 
def zmMinFilterGray(src, r=7):
  '''最小值滤波，r是滤波器半径'''
  '''if r <= 0:
    return src
  h, w = src.shape[:2]
  I = src
  res = np.minimum(I , I[[0]+range(h-1) , :])
  res = np.minimum(res, I[range(1,h)+[h-1], :])
  I = res
  res = np.minimum(I , I[:, [0]+range(w-1)])
  res = np.minimum(res, I[:, range(1,w)+[w-1]])
  return zmMinFilterGray(res, r-1)'''
  return cv2.erode(src, np.ones((2*r+1, 2*r+1)))           #使用opencv的erode函数更高效
def guidedfilter(I, p, r, eps):
  '''引导滤波，直接参考网上的matlab代码'''
  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 getV1(m, r, eps, w, maxV1): #输入rgb图像，值范围[0,1]
  '''计算大气遮罩图像V1和光照值A, V1 = 1-t/A'''
  V1 = np.min(m,2)                     #得到暗通道图像
  V1 = guidedfilter(V1, zmMinFilterGray(V1,7), r, eps)   #使用引导滤波优化
  bins = 2000
  ht = np.histogram(V1, bins)               #计算大气光照A
  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)          #对值范围进行限制
   
  return V1,A
 
def deHaze(m, r=81, eps=0.001, w=0.95, maxV1=0.80, bGamma=False):
  Y = np.zeros(m.shape)
  V1,A = getV1(m, r, eps, w, maxV1)        #得到遮罩图像和大气光照
  for k in range(3):
    Y[:,:,k] = (m[:,:,k]-V1)/(1-V1/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,默认不进行该操作
  return Y
 
if __name__ == '__main__':
  m = deHaze(cv2.imread('land.jpg')/255.0)*255
  cv2.imwrite('defog.jpg', m)