koniec

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
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


def hist_plot(img):
    # empty list to store the count
    # of each intensity value
    count = []

    # empty list to store intensity
    # value
    r = []

    # loop to traverse each intensity
    # value
    for k in range(0, 256):
        r.append(k)
        count1 = 0

        # loops to traverse each pixel in
        # the image
        for i in range(m):
            for j in range(n):
                if img[i, j] == k:
                    count1 += 1
        count.append(count1)

    return (r, count)







# 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("name.png",cv2.IMREAD_COLOR)
histSize = 256
histRange = (0, 256)
accumulate = False
hist_h,hist_w =image.shape[:2]
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]


y,u,v=cv2.split(image);


###!!! 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

y_hist = cv2.calcHist(y, [0], None, [256],[0,256]) #wyliczenie historgramu y
u_hist = cv2.calcHist(u, [1], None, [256],[0,256]) #wyliczenie histogramu u
v_hist = cv2.calcHist(v, [2], None, [256],[0,256]) #wyliczenie histogramu v




# #!!!!!KIEDY UZYJE blue || green || red wyrzuca maly histogram
#
#
#
#
cv2.normalize(y_hist,y_hist,0,hist_h,cv2.NORM_MINMAX) #eliminacja zlych charakterystyk, podniesienie jakosci pixela
cv2.normalize(u_hist,u_hist,0,hist_h,cv2.NORM_MINMAX)#eliminacja zlych charakterystyk, podniesienie jakosci pixela
cv2.normalize(v_hist,v_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(y_hist[i-1]) ),
            ( bin_w*(i), hist_h - int(y_hist[i]) ),
            ( 255, 0, 0), 2)
    cv2.line(histImage, ( bin_w*(i-1), hist_h - int(u_hist[i-1]) ),
            ( bin_w*(i), hist_h - int(u_hist[i]) ),
            ( 0, 255, 0), 2)
    cv2.line(histImage, ( bin_w*(i-1), hist_h - int(v_hist[i-1]) ),
            ( bin_w*(i), hist_h - int(v_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()





# binaryzacja
## pobierz piksel z obrazu
image2=cv2.imread("name.png",cv2.IMREAD_GRAYSCALE)

h3,w3=image2.shape[:2]

img_thres=np.zeros((h3,w3))

for i in range(0,h3):
    for x in range(0,w3):
        pixel = image2[i,x]
        img_thres[i,x]=0 if pixel<128 else pixel

cv2.imshow("bin",img_thres)

















#YUV w okreslonej skali
plt.imshow(arr_ycal)

matplotlib.image.imsave('name.png',arr_ycal)

cv2.imshow('y',y)
cv2.imshow('u',u)
cv2.imshow('v',v)




#wyswietlenie plota
plt.show()


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