Image convert to water color paint by python code

Water color paint

#---------------------------
#library

import numpy as np
import pandas as pd
from numpy import argmax
import matplotlib.pyplot as plt
import math
import statistics
import cv2
from matplotlib import pyplot as plt

#----------------------------
#colors clusters of original image

d = open("pixel.csv", "rb") #the image should be clustered by K-means before and prepared the color unique in an array
main_data= np.load(d)
np.array(main_data)
print(main_data)
twod = []

for i in range(len(main_data)):
    twod.append(sum(main_data[i])//3)
    
print(twod)#id color
print('--')

#----------------------------
#original 2D image:

img0 = cv2.imread('1.jpeg',0)

#----------------------------
#color clustered of original image

path = 'k.jpeg'

img = cv2.imread(path)
img = cv2.cvtColor(img,cv2.COLOR_RGB2BGR)
twodk = []

for i in range(len(img)):
    twodk2 = []
    for j in range(len(img[0])):
        twodk2.append(sum(img[i][j])//3)
    twodk.append(twodk2)    
twodk = np.array(twodk)    

#----------------------------
#smoothing

for i in range(len(img)):
    for j in range(len(img[0])):
        c = 0
        ls = []
        for l in range(len(main_data)):
            
            c = abs(twodk[i][j] - twod[l]) 
            ls.append(c)
        for m in range(len(ls)):
            if ls[m] == min(ls):
               twodk[i][j] = twod[m]
               
#----------------------------
#color box:
               #The image og color water 
yellow = cv2.imread('33.jpeg',0) 
red = cv2.imread('7.jpeg',0)
green = cv2.imread('5.jpeg',0)
blue = cv2.imread('6.jpeg',0)
pink = cv2.imread('4.jpeg',0)
perpul = cv2.imread('3.jpeg',0)
orange =  cv2.imread('2.jpeg',0)
color_box = [yellow, red, green, blue, pink, perpul, orange]
#-----------------------------

yellowc = cv2.imread('33.jpeg')
redc = cv2.imread('7.jpeg')
greenc = cv2.imread('5.jpeg')
bluec = cv2.imread('6.jpeg')
pinkc = cv2.imread('4.jpeg')
perpulc = cv2.imread('3.jpeg')
orangec =  cv2.imread('2.jpeg')
color_boxcc = [yellowc, redc, greenc, bluec, pinkc, perpulc, orangec]

intensity = []
for i in range(len(main_data)):
    s = 0
    m = len(color_box[i])
    n = len(color_box[i][0])
    for j in range(m):
        for k in range(n):
            s = s + color_box[i][j][k]
    intensity.append(s/(m*n)) #color id
   
#---------------------------
#matching:
    
team0 = np.array(twod)    
team0 = np.sort(team0) #main color sort

team = np.array(intensity)
team = np.sort(team) #color sort

#--------------------------
#normalization:

#image0

norim = []

for i in range(len(img0)):
    norimx = []
    for j in range(len(img0[0])):
        a = img0[i][j] /255
        norimx.append(a)
    norim.append(norimx)#norm original gray image
    
#-------------------------    
#norm water color
    
watercolornorm = []
for i in range(len(main_data)):
   
    m = len(color_box[i])
    n = len(color_box[i][0])
    c = []
    
    for j in range(m):
        r = []
        for k in range(n):
             a = color_box[i][j][k]/255
             
             r.append(a)     
        c.append(r)
    watercolornorm.append(c)

#--------------------------
#    

canva = []
forbiden = [[-1,-1]]
for i in range(len(main_data)):
    loop = True
    
    for j in range(len(norim)):
       
        canvasr = []
        for k in range(len(norim[0])):
            d = False
            
            for o in range(len(forbiden)):
                if forbiden[o][0]==j and forbiden[o][1]==k:
                    d = True
                    break
            if d == False:    
                if abs(twod[i]/255 - norim[j][k])<= 0.298:
                    if loop==True:
                        win = []
                        for l in range(len(intensity)):
                            a = abs(twod[i]-intensity[i])
                            win.append(a)
                        loop=False
                        print(i)
                        minimum = min(win)            
                    for l in range(len(intensity)):
                        
                        if win[l] == minimum:
                            candid = l
                            
                    win2 = []           
                    for m in range(len(watercolornorm[candid])):
                        for n in range(len(watercolornorm[candid][0])):
                            win2.append(abs(norim[j][k]-watercolornorm[candid][m][n]))
                    for m in range(len(win2)):        
                        if  min(win2) == win2[m]:
                            indexi = math.floor(m/len(watercolornorm[candid][0]))
                            indexj = m - (indexi*len(watercolornorm[candid][0]))
                    
                    img[j][k][0]=color_boxcc[candid][indexi][indexj][0]
                    img[j][k][1]=color_boxcc[candid][indexi][indexj][1]
                    img[j][k][2]=color_boxcc[candid][indexi][indexj][2]
                    f = []
                    f.append(j)
                    f.append(k)
                    forbiden.append(f)
                

plt.imshow(img , interpolation='nearest')
plt.show()