#2.1

import cv2 as cv

dat = cv.imread( "sample.jpg" )
height, width, channel = dat.shape

for y in range(height):
    for x in range(width):
        b = dat[y][x][0]
        g = dat[y][x][1]
        r = dat[y][x][2]
        a = (int(r)+int(g)+int(b))/3
        if (a>=128):
            dat[y][x][0]=255
            dat[y][x][1]=255
            dat[y][x][2]=255
        else:
            dat[y][x][0]=0
            dat[y][x][1]=0
            dat[y][x][2]=0

cv.imwrite( "output2_1.jpg", dat )


#2.2


import cv2 as cv

dat = cv.imread( "sample.jpg" )
height, width, channel = dat.shape

for y in range(height):
    for x in range(width):
        b = dat[y][x][0]
        g = dat[y][x][1]
        r = dat[y][x][2]
        a = (int(r)+int(g)+int(b))/3
        dat[y][x][0]=a
        dat[y][x][1]=a
        dat[y][x][2]=a

cv.imwrite( "output2_2.jpg", dat )


#2.3


import cv2 as cv

dat = cv.imread( "sample.jpg" )
height, width, channel = dat.shape

total=0
stotal=0

for y in range(height):
    for x in range(width):
        b = dat[y][x][0]
        g = dat[y][x][1]
        r = dat[y][x][2]
        total += int(r)+int(g)+int(b)
        stotal += int(r)**2+int(g)**2+int(b)**2

ave=total/height/width/3;
var=stotal/height/width/3 - ave*ave
        
print("平均値: ", ave, "分散:", var)


#3.1

import cv2 as cv

dat = cv.imread( "sample.jpg" )
height, width, channel = dat.shape

for y in range(height):
    for x in range(width):
        b = dat[y][x][0]
        g = dat[y][x][1]
        r = dat[y][x][2]
        if (r==255 and g==255 and b==255) or (r==0 and g==0 and b==0):
            dat[y][x][2]=255
            dat[y][x][1]=0
            dat[y][x][0]=0

cv.imwrite( "output3_1.jpg", dat )


#3.2

import cv2 as cv

dat = cv.imread( "sample.jpg" )
height, width, channel = dat.shape

rtotal=0
gtotal=0
btotal=0
srtotal=0
sgtotal=0
sbtotal=0

for y in range(height):
    for x in range(width):
        b = dat[y][x][0]
        g = dat[y][x][1]
        r = dat[y][x][2]
        rtotal += int(r)
        gtotal += int(g)
        btotal += int(b)
        srtotal += int(r)**2
        sgtotal += int(g)**2
        sbtotal += int(b)**2

rave=rtotal/height/width;
gave=gtotal/height/width;
bave=btotal/height/width;
rvar=srtotal/height/width - rave*rave
gvar=sgtotal/height/width - gave*gave
bvar=sbtotal/height/width - bave*bave
        
print("R平均値: ", rave, "R分散:", rvar)
print("G平均値: ", gave, "G分散:", gvar)
print("B平均値: ", bave, "B分散:", bvar)


#3.4


import cv2 as cv
import math

dat = cv.imread( "sample.jpg" )
height, width, channel = dat.shape

#3.3の解答例の方法を実装する
#画像の平均値をave, 分散をvarとする
#明度値の分布が正規分布であると仮定し、正規分布表を
#用いれば、±127.5の範囲に90%の明度値が分布する場合
#の明度値の標準偏差は、127.5/1.64=77.74である。
#これを用いてa,bは以下のように決定できる

a=77.74/math.sqrt(var)
b=127.5-ave

for y in range(height):
    for x in range(width):
        for ch in range(channel):
            k=(int(dat[y][x][ch])-128)*a+128+b
            if (k>255):
                k=255
            elif (k<0):
                k=0
            dat[y][x][ch]=k
            
cv.imwrite( "output3_4.jpg", dat )


#3.5

import cv2 as cv
import numpy as np

dat = cv.imread( "sample.jpg" )
height, width, channel = dat.shape

# edge_x = dat.copy() などとしてもよい
edge_h = np.zeros((height, width, 3), dtype="uint8")
edge_v = np.zeros((height, width, 3), dtype="uint8")

total=0
stotal=0

#ここではRGBそれぞれについて出力の絶対値を可視化している

for y in range(1,height-1):
    for x in range(1,width-1):
        for ch in range(channel):
            edge_h[y][x][ch] = abs( int( dat[y][x+1][ch] )-int( dat[y][x-1][ch] ) ) 
            edge_v[y][x][ch] = abs( int( dat[y+1][x][ch] )-int( dat[y-1][x][ch] ) ) 
        
cv.imwrite( "edge_h.jpg", edge_h )
cv.imwrite( "edge_v.jpg", edge_v )

#3.7

import cv2 as cv

dat0 = cv.imread( "sample1.jpg" )
dat1 = cv.imread( "sample2.jpg" )
height, width, channel = dat1.shape

ssd=0

for y in range(height):
    for x in range(width):
        for ch in range(channel):
            ssd+=(int(dat0[y][x][ch])-int(dat1[y][x][ch]))**2

print( "SSD:", ssd )