import cv2
import numpy as npdef zmMinFilterGray(src, r=7):'''最小值滤波，r是滤波器半径''''''if r <= 0:return srch, w = src.shape[:2]I = srcres = np.minimum(I  , I[[0]+range(h-1)  , :])res = np.minimum(res, I[range(1,h)+[h-1], :])I = resres = 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.shapem_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_pm_II = cv2.boxFilter(I*I, -1, (r,r))var_I = m_II-m_I*m_Ia = cov_Ip/(var_I+eps)b = m_p-a*m_Im_a = cv2.boxFilter(a, -1, (r,r))m_b = cv2.boxFilter(b, -1, (r,r))return m_a*I+m_bdef 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 = 2000ht = np.histogram(V1, bins)                              #计算大气光照Ad = np.cumsum(ht[0])/float(V1.size)for lmax in range(bins-1, 0, -1):if d[lmax]<=0.999:breakA  = 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,Adef 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 Yif __name__ == '__main__':m = deHaze(cv2.imread('land.jpg')/255.0)*255cv2.imwrite('defog.jpg', m)