b,g,r = cv2.split(im)
dc = cv2.min(cv2.min(r,g),b);
kernel = cv2.getStructuringElement(cv2.MORPH_RECT,(sz,sz))
dark = cv2.erode(dc,kernel)
return dark

[h,w] = im.shape[:2]
imsz = h*w
numpx = int(max(math.floor(imsz/1000),1))
darkvec = dark.reshape(imsz,1);
imvec = im.reshape(imsz,3);

indices = darkvec.argsort();
indices = indices[imsz-numpx::]

atmsum = np.zeros([1,3])
for ind in range(1,numpx):
   atmsum = atmsum + imvec[indices[ind]]

A = atmsum / numpx;
return A

omega = 0.95;
transmission = 1 - omega*DarkChannel(im,sz);
return transmission

mean_I = cv2.boxFilter(im,cv2.CV_64F,(r,r));
mean_p = cv2.boxFilter(p, cv2.CV_64F,(r,r));
mean_Ip = cv2.boxFilter(im*p,cv2.CV_64F,(r,r));
cov_Ip = mean_Ip - mean_I*mean_p;

mean_II = cv2.boxFilter(im*im,cv2.CV_64F,(r,r));
var_I   = mean_II - mean_I*mean_I;

a = cov_Ip/(var_I + eps);
b = mean_p - a*mean_I;

mean_a = cv2.boxFilter(a,cv2.CV_64F,(r,r));
mean_b = cv2.boxFilter(b,cv2.CV_64F,(r,r));

q = mean_a*im + mean_b;
return q;

gray = cv2.cvtColor(im,cv2.COLOR_BGR2GRAY);
gray = np.float64(gray)/255;
r = 60;
eps = 0.0001;
t = Guidedfilter(gray,et,r,eps);

return t;

J = np.empty(im.shape,im.dtype);
t = cv2.max(t,tx);

for ind in range(0,3):
    J[:,:,ind] = (im[:,:,ind]-A[0,ind])/t + A[0,ind]

return J

tx = 0.55

r_object = tx*abs(J - A)
return r_object

import sys

try:

    fn = sys.argv[1]

except:

    fn = 'pumpkins.jpg'

def nothing(*argv):

    pass

src = cv2.imread(fn);

I = src.astype('float64')/255;

dark = DarkChannel(I,15);
A = AtmLight(I,dark);
te = TransmissionEstimate(I,15);
t = TransmissionRefine(src,te);
J = Recover(I,t,A,0.55);
r_origin = r_origin(I, A)


cv2.imshow("Orginal", I)
cv2.imshow("r_object ", r_object)


cv2.waitKey(0);