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from __future__ import print_function
from __future__ import division
import cv2
import matplotlib.colors
import numpy as np
import argparse
from matplotlib import pyplot as plt
import matplotlib.image as mpimg
from PIL import Image
from numpy import random

def rgb2yuv(rgb_arr):
    (row, col, _) = rgb_arr.shape
    arr_yuv = np.zeros([row,col,3], dtype=np.uint8)

    for i in range(0,row):
        for j in range(0,col):
            arr_yuv[i][j][0] = (rgb_arr[i][j][0]*299 + rgb_arr[i][j][1]*587 + rgb_arr[i][j][2]*114 + 500) / 1000
            arr_yuv[i][j][1]=(rgb_arr[i][j][0]*-0.16874 + rgb_arr[i][j][1]*-0.33126 + rgb_arr[i][j][2]*0.50000 + 500) / 1000
            arr_yuv[i][j][2] = (rgb_arr[i][j][0] * 0.50000 + rgb_arr[i][j][1] * -0.41869 + rgb_arr[i][j][2] * -0.08131 + 500) / 1000
    return arr_yuv



# parser = argparse.ArgumentParser(description='Code for Histogram Calculation tutorial.')
# parser.add_argument('--input', help='Path to input image.', default='.jpg')
# args = parser.parse_args()
#
# image = cv2.imread("obrazek.jpg",cv2.IMREAD_COLOR)
# histSize = 256
# histRange = (0, 256)
# accumulate = False
# hist_w = 512
# hist_h = 400
# bin_w = int(round( hist_w/histSize ))
# histImage = np.zeros((hist_h, hist_w, 3), np.uint8)
#
# # b- 0 g- 1 r -2
# red = image.copy()
# red[:,:,0]=red[:,:,1]=0
#
#
# green = image.copy()
# green[:,:,0]=green[:,:,2]=0
#
# blue = image.copy()
# blue[:,:,1]=blue[:,:,2]=0
# B, G, R = cv2.split(image)
#
# #RGB NA YUV
# image1 = Image.open("obrazek.jpg")
# (w,h)=image1.size
# arr_rgb = np.array(list(image1.getdata())).astype(np.uint8).reshape(h,w,3)
#
#
# arr_yuv_cal = rgb2yuv(arr_rgb)
# arr_ycal = np.zeros([h,w], dtype=np.uint8)
# arr_ycal[:,:]=arr_yuv_cal[:,:,0]
#
#
#
# #Grayscale
# img1 = mpimg.imread("obrazek.jpg")
# Re,Ge,Bl = img1[:,:,0],img1[:,:,1],img1[:,:,2]
# imgGray = 0.2898 * Re + 0.5870 * Ge + 0.1140* Bl
#
#
# #Grayscale from AVG
# image2=cv2.imread("obrazek.jpg",cv2.IMREAD_COLOR)
# h1,w1 = image2.shape[:2]
# for i in range(0,h1):
#     for j in range(0,w1):
#         b,g,r = image2[i,j]
#         image2[i,j]=((int(r)+int(g)+int(b))/3)
#
# #Grayscale lightness
# image3=cv2.imread("obrazek.jpg",cv2.IMREAD_COLOR)
# h2,w2,_ = image3.shape[:3]
# for i in range(0,h2):
#     for j in range(0,w2):
#          b1,g1,r1=image3[i,j]
#          image3[i,j]=(int(max(r1,g1,b1))+int(min(r1,g1,b1)))/2
#
# #Grayscale luminosity
# image4=cv2.imread("obrazek.jpg",cv2.IMREAD_COLOR)
# h3,w3,_=image3.shape[:3]
# for i in range(0,h3):
#     for j in range(0,w3):
#         b2,g2,r2=image4[i,j]
#         image4[i,j]=0.21*r2+0.72*g2+0.07*b2

#Y from YUV cv2
img = Image.open("obrazek.jpg")
(w,h) = img.size

arr_rgb = np.array(list(img.getdata())).astype(np.uint8).reshape(h, w, 3)
arr_y = np.zeros([h, w], dtype=np.uint8)
arr_yuv_cal = rgb2yuv(arr_rgb)
arr_ycal = np.zeros([h, w], dtype=np.uint8)
arr_ycal[:, :] = arr_yuv_cal[:, :, 0]


# W sumie to krawedzie znalazlo



###!!! coś nie pykło
#yuv=Image.fromarray((result*255).astype(np.uint8))



# b_hist = cv2.calcHist(B, [0], None, [256],[0,256]) #wyliczenie historgramu blue
# g_hist = cv2.calcHist(G, [1], None, [256],[0,256]) #wyliczenie histogramu green
# r_hist = cv2.calcHist(R, [2], None, [256],[0,256]) #wyliczenie histogramu red
#
# #!!!!!KIEDY UZYJE blue || green || red wyrzuca maly histogram
#
#
#
#
# cv2.normalize(b_hist,b_hist,0,hist_h,cv2.NORM_MINMAX) #eliminacja zlych charakterystyk, podniesienie jakosci pixela
# cv2.normalize(g_hist,g_hist,0,hist_h,cv2.NORM_MINMAX)#eliminacja zlych charakterystyk, podniesienie jakosci pixela
# cv2.normalize(r_hist,r_hist,0,hist_h,cv2.NORM_MINMAX)#eliminacja zlych charakterystyk, podniesienie jakosci pixela
#
# for i in range(1, histSize):
#     cv2.line(histImage, ( bin_w*(i-1), hist_h - int(b_hist[i-1]) ),
#             ( bin_w*(i), hist_h - int(b_hist[i]) ),
#             ( 255, 0, 0), 2)
#     cv2.line(histImage, ( bin_w*(i-1), hist_h - int(g_hist[i-1]) ),
#             ( bin_w*(i), hist_h - int(g_hist[i]) ),
#             ( 0, 255, 0), 2)
#     cv2.line(histImage, ( bin_w*(i-1), hist_h - int(r_hist[i-1]) ),
#             ( bin_w*(i), hist_h - int(r_hist[i]) ),
#             ( 0, 0, 255), 2)

# #Histogram
# cv2.imshow('Source image', image)
# cv2.imshow('calcHist Demo', histImage)
# #Pojedyncze kolory
# cv2.imshow("blue",blue)
# cv2.imshow("red",red)
# cv2.imshow("green",green)
# #Grayscale w okreslonej skali
# plt.imshow(imgGray,cmap='gray')
# #Grayscale AVG
# cv2.imshow("Grayscale AVG",image2)
# #Gryscale lightness
# cv2.imshow("Grayscale lighntess",image3)
# #Grayscale luminosity
# cv2.imshow("Grayscale luminosity",image4)

#Y

#img_yuv=cv2.imread("safdsaf.jpg",cv2.COLOR_RGB2YUV)
#cv2.imshow("yuv2",img_yuv)
#yuv.show()



#YUV w okreslonej skali
plt.imshow(arr_ycal)



#wyswietlenie plota
plt.show()


#Wyswietlenie pojedynczych kolorow
cv2.waitKey(0)
cv2.destroyAllWindows()