import numpy as np
import cv2
from PIL import Image,ExifTags
 
def calcMeanAndVariance(img):
 row=img.shape[0]
 col=img.shape[1]
 #channel=img.shape[2]
 total=row*col
 print (row,col,total)
 mean=np.zeros((3))
 variance=np.zeros((3))
 sum=np.zeros((3))
 
 for i in range(row):
  for j in range(col):
   sum[0]+=img[i][j][0]
   sum[1]+=img[i][j][1]
   sum[2]+=img[i][j][2]
 
 mean[0]=sum[0]/total
 mean[1]=sum[1]/total
 mean[2]=sum[2]/total
 sum=np.zeros((3))
 for i in range(row):
  for j in range(col):
   sum[0]=np.square(img[i][j][0]-mean[0])
   sum[1]=np.square(img[i][j][1]-mean[1])
   sum[2]=np.square(img[i][j][2]-mean[2])
 
 variance[0]=np.sqrt(sum[0]/total)
 variance[1]=np.sqrt(sum[1]/total)
 variance[2]=np.sqrt(sum[2]/total)
 print (mean,variance)
 return mean,variance
 
def cololTransit(img1,img2):
 image1 = cv2.cvtColor(img1, cv2.COLOR_BGR2LAB)
 image2=cv2.cvtColor(img2, cv2.COLOR_BGR2LAB)
 mean1,variance1=calcMeanAndVariance(image1)
 mean2,variance2=calcMeanAndVariance(image2)
 #print (mean1,variance1)
 radio=np.zeros((3))
 
 radio[0]=variance2[0]/variance1[0]
 radio[1]=variance2[1]/variance1[1]
 radio[2]=variance2[2]/variance1[2]
 
 print('test', radio)
 
 row=image1.shape[0]
 col=image1.shape[1]
 for i in range(row):
  for j in range(col):
   image1[i][j][0]=min(255,max(0,radio[0]*(image1[i][j][0]-mean1[0])+mean2[0]))
   image1[i][j][1]=min(255,max(0,radio[1]*(image1[i][j][1]-mean1[1])+mean2[1]))
   image1[i][j][2]=min(255,max(0,radio[2]*(image1[i][j][2]-mean1[2])+mean2[2]))
 image = cv2.cvtColor(image1, cv2.COLOR_BGR2LAB)
 return image
 
if __name__=='__main__':
 img1=cv2.imread('1.jpg')
 img2=cv2.imread('2.jpg')
 cv2.namedWindow('src')
 cv2.namedWindow('dst')
 #cv2.resizeWindow('src',500,500)
 #cv2.resizeWindow('dst',500,500)
 cv2.imshow('src',img1)
 cv2.imshow('dst',img2)
 cv2.waitKey()
 cv2.destroyAllWindows()
 
 img=cololTransit(img1,img2)
 cv2.namedWindow('result')
 cv2.imshow('result',img)
 cv2.waitKey()
 cv2.destroyAllWindows()
 #print (img)