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RynkunPokemon

koniec

May 21st, 2022
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  1. from __future__ import print_function
  2. from __future__ import division
  3. import cv2
  4. import matplotlib.colors
  5. import numpy as np
  6. import argparse
  7. from matplotlib import pyplot as plt
  8. import matplotlib
  9. from PIL import Image
  10. from numpy import random
  11.  
  12. def rgb2yuv(rgb_arr):
  13.     (row, col, _) = rgb_arr.shape
  14.     arr_yuv = np.zeros([row,col,3], dtype=np.uint8)
  15.  
  16.     for i in range(0,row):
  17.         for j in range(0,col):
  18.             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
  19.             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
  20.             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
  21.     return arr_yuv
  22.  
  23.  
  24. def hist_plot(img):
  25.     # empty list to store the count
  26.     # of each intensity value
  27.     count = []
  28.  
  29.     # empty list to store intensity
  30.     # value
  31.     r = []
  32.  
  33.     # loop to traverse each intensity
  34.     # value
  35.     for k in range(0, 256):
  36.         r.append(k)
  37.         count1 = 0
  38.  
  39.         # loops to traverse each pixel in
  40.         # the image
  41.         for i in range(m):
  42.             for j in range(n):
  43.                 if img[i, j] == k:
  44.                     count1 += 1
  45.         count.append(count1)
  46.  
  47.     return (r, count)
  48.  
  49.  
  50.  
  51.  
  52.  
  53.  
  54.  
  55. # parser = argparse.ArgumentParser(description='Code for Histogram Calculation tutorial.')
  56. # parser.add_argument('--input', help='Path to input image.', default='.jpg')
  57. # args = parser.parse_args()
  58. #
  59. image = cv2.imread("name.png",cv2.IMREAD_COLOR)
  60. histSize = 256
  61. histRange = (0, 256)
  62. accumulate = False
  63. hist_h,hist_w =image.shape[:2]
  64. bin_w = int(round( hist_w/histSize ))
  65. histImage = np.zeros((hist_h, hist_w, 3), np.uint8)
  66. #
  67. # # b- 0 g- 1 r -2
  68. # red = image.copy()
  69. # red[:,:,0]=red[:,:,1]=0
  70. #
  71. #
  72. # green = image.copy()
  73. # green[:,:,0]=green[:,:,2]=0
  74. #
  75. # blue = image.copy()
  76. # blue[:,:,1]=blue[:,:,2]=0
  77. # B, G, R = cv2.split(image)
  78. #
  79. # #RGB NA YUV
  80. # image1 = Image.open("obrazek.jpg")
  81. # (w,h)=image1.size
  82. # arr_rgb = np.array(list(image1.getdata())).astype(np.uint8).reshape(h,w,3)
  83. #
  84. #
  85. # arr_yuv_cal = rgb2yuv(arr_rgb)
  86. # arr_ycal = np.zeros([h,w], dtype=np.uint8)
  87. # arr_ycal[:,:]=arr_yuv_cal[:,:,0]
  88. #
  89. #
  90. #
  91. # #Grayscale
  92. # img1 = mpimg.imread("obrazek.jpg")
  93. # Re,Ge,Bl = img1[:,:,0],img1[:,:,1],img1[:,:,2]
  94. # imgGray = 0.2898 * Re + 0.5870 * Ge + 0.1140* Bl
  95. #
  96. #
  97. # #Grayscale from AVG
  98. # image2=cv2.imread("obrazek.jpg",cv2.IMREAD_COLOR)
  99. # h1,w1 = image2.shape[:2]
  100. # for i in range(0,h1):
  101. #     for j in range(0,w1):
  102. #         b,g,r = image2[i,j]
  103. #         image2[i,j]=((int(r)+int(g)+int(b))/3)
  104. #
  105. # #Grayscale lightness
  106. # image3=cv2.imread("obrazek.jpg",cv2.IMREAD_COLOR)
  107. # h2,w2,_ = image3.shape[:3]
  108. # for i in range(0,h2):
  109. #     for j in range(0,w2):
  110. #          b1,g1,r1=image3[i,j]
  111. #          image3[i,j]=(int(max(r1,g1,b1))+int(min(r1,g1,b1)))/2
  112. #
  113. # #Grayscale luminosity
  114. # image4=cv2.imread("obrazek.jpg",cv2.IMREAD_COLOR)
  115. # h3,w3,_=image3.shape[:3]
  116. # for i in range(0,h3):
  117. #     for j in range(0,w3):
  118. #         b2,g2,r2=image4[i,j]
  119. #         image4[i,j]=0.21*r2+0.72*g2+0.07*b2
  120.  
  121. #Y from YUV cv2
  122. img = Image.open("obrazek.jpg")
  123. (w,h) = img.size
  124.  
  125. arr_rgb = np.array(list(img.getdata())).astype(np.uint8).reshape(h, w, 3)
  126. arr_y = np.zeros([h, w], dtype=np.uint8)
  127. arr_yuv_cal = rgb2yuv(arr_rgb)
  128. arr_ycal = np.zeros([h, w], dtype=np.uint8)
  129. arr_ycal[:, :] = arr_yuv_cal[:, :, 0]
  130.  
  131.  
  132. y,u,v=cv2.split(image);
  133.  
  134.  
  135. ###!!! coś nie pykło
  136. #yuv=Image.fromarray((result*255).astype(np.uint8))
  137.  
  138.  
  139.  
  140. # b_hist = cv2.calcHist(B, [0], None, [256],[0,256]) #wyliczenie historgramu blue
  141. # g_hist = cv2.calcHist(G, [1], None, [256],[0,256]) #wyliczenie histogramu green
  142. # r_hist = cv2.calcHist(R, [2], None, [256],[0,256]) #wyliczenie histogramu red
  143.  
  144. y_hist = cv2.calcHist(y, [0], None, [256],[0,256]) #wyliczenie historgramu y
  145. u_hist = cv2.calcHist(u, [1], None, [256],[0,256]) #wyliczenie histogramu u
  146. v_hist = cv2.calcHist(v, [2], None, [256],[0,256]) #wyliczenie histogramu v
  147.  
  148.  
  149.  
  150.  
  151. # #!!!!!KIEDY UZYJE blue || green || red wyrzuca maly histogram
  152. #
  153. #
  154. #
  155. #
  156. cv2.normalize(y_hist,y_hist,0,hist_h,cv2.NORM_MINMAX) #eliminacja zlych charakterystyk, podniesienie jakosci pixela
  157. cv2.normalize(u_hist,u_hist,0,hist_h,cv2.NORM_MINMAX)#eliminacja zlych charakterystyk, podniesienie jakosci pixela
  158. cv2.normalize(v_hist,v_hist,0,hist_h,cv2.NORM_MINMAX)#eliminacja zlych charakterystyk, podniesienie jakosci pixela
  159.  
  160. for i in range(1, histSize):
  161.     cv2.line(histImage, ( bin_w*(i-1), hist_h - int(y_hist[i-1]) ),
  162.             ( bin_w*(i), hist_h - int(y_hist[i]) ),
  163.             ( 255, 0, 0), 2)
  164.     cv2.line(histImage, ( bin_w*(i-1), hist_h - int(u_hist[i-1]) ),
  165.             ( bin_w*(i), hist_h - int(u_hist[i]) ),
  166.             ( 0, 255, 0), 2)
  167.     cv2.line(histImage, ( bin_w*(i-1), hist_h - int(v_hist[i-1]) ),
  168.             ( bin_w*(i), hist_h - int(v_hist[i]) ),
  169.             ( 0, 0, 255), 2)
  170.  
  171. #Histogram
  172. cv2.imshow('Source image', image)
  173. cv2.imshow('calcHist Demo', histImage)
  174. #Pojedyncze kolory
  175. # cv2.imshow("blue",blue)
  176. # cv2.imshow("red",red)
  177. # cv2.imshow("green",green)
  178. #Grayscale w okreslonej skali
  179. # plt.imshow(imgGray,cmap='gray')
  180. #Grayscale AVG
  181. # cv2.imshow("Grayscale AVG",image2)
  182. # #Gryscale lightness
  183. # cv2.imshow("Grayscale lighntess",image3)
  184. # #Grayscale luminosity
  185. # cv2.imshow("Grayscale luminosity",image4)
  186.  
  187. #Y
  188.  
  189. #img_yuv=cv2.imread("safdsaf.jpg",cv2.COLOR_RGB2YUV)
  190. #cv2.imshow("yuv2",img_yuv)
  191. #yuv.show()
  192.  
  193.  
  194.  
  195.  
  196.  
  197. # binaryzacja
  198. ## pobierz piksel z obrazu
  199. image2=cv2.imread("name.png",cv2.IMREAD_GRAYSCALE)
  200.  
  201. h3,w3=image2.shape[:2]
  202.  
  203. img_thres=np.zeros((h3,w3))
  204.  
  205. for i in range(0,h3):
  206.     for x in range(0,w3):
  207.         pixel = image2[i,x]
  208.         img_thres[i,x]=0 if pixel<128 else pixel
  209.  
  210. cv2.imshow("bin",img_thres)
  211.  
  212.  
  213.  
  214.  
  215.  
  216.  
  217.  
  218.  
  219.  
  220.  
  221.  
  222.  
  223.  
  224.  
  225.  
  226.  
  227.  
  228. #YUV w okreslonej skali
  229. plt.imshow(arr_ycal)
  230.  
  231. matplotlib.image.imsave('name.png',arr_ycal)
  232.  
  233. cv2.imshow('y',y)
  234. cv2.imshow('u',u)
  235. cv2.imshow('v',v)
  236.  
  237.  
  238.  
  239.  
  240. #wyswietlenie plota
  241. plt.show()
  242.  
  243.  
  244. #Wyswietlenie pojedynczych kolorow
  245. cv2.waitKey(0)
  246. cv2.destroyAllWindows()
  247.  
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