Python'da Görüntü İşleme - Sparse Matrix ve Dictionary Class Yapısı

import matplotlib.pyplot as plt

import numpy as np
import math
from scipy.misc import imsave
%matplotlib inline

def convert_RGB_to_monochrome_BW(image_1,threshold=100):
   img_1=plt.imread(image_1)
   img_2=np.zeros((img_1.shape[0],img_1.shape[1]))
   for i in range(img_2.shape[0]):
       for j in range(img_2.shape[1]):
           if(img_1[i,j,0]/3+img_1[i,j,1]/3+img_1[i,j,1]/3)>threshold:
               img_2[i,j]=1
           else:
               img_2[i,j]=0
   return img_2

image_file_1=r"test_t_one.jpg"
img_1=convert_RGB_to_monochrome_BW(image_file_1,threshold=100)
plt.imshow(img_1,cmap='gray')
plt.show()

img_1.ndim, img_1.shape
_______________________

(2, (372, 243))

____________________________

i2=img_1.reshape(1,img_1.shape[0]*img_1.shape[1])
i2.ndim, i2.shape

____________________________

(2, (1, 90396))

def get_Histogram(image): #image monochrome olmalı
   my_Histogram={}
   
   for i in range(image.shape[0]):
       for j in range(image.shape[1]):
           item=image[i,j]
           if item in my_Histogram.keys():
               my_Histogram[item]=my_Histogram[item]+1
           else:
               my_Histogram[item]=1
               
               
   return my_Histogram

my_Histogram=get_Histogram(img_1)
my_Histogram

______________________

{0.0: 3564, 1.0: 86832}

______________________

x=my_Histogram[0.0]
y=my_Histogram[1.0]
print("siyah beyaz oranı: ",x/y)

______________________

siyah beyaz oranı:  0.041044776119402986

______________________

class myMatrix():
   def __init__(self,x,y):
       self.D=x
       self.F=y

def create_D_F(img_1):
   d={(i,j) for i in range(img_1.shape[0])
            for j in range(img_1.shape[1])
            if img_1[i,j]==1
     }
   f={(i,j):1 for i,j in d}
   
   m=myMatrix(d,f)
   return m

my_sparce_Matrix_1=create_D_F(img_1)
my_sparce_Matrix_1.D, my_sparce_Matrix_1.F

____________________________

({(252, 36),
 (289, 22),
 (312, 241),
 (111, 227),
 (185, 101),
 (5, 178),
 (208, 66),
 (171, 86),
 (261, 216),
 (194, 237),
 (67, 137),
 (157, 23),
 (360, 157),
 (90, 42),
 (53, 166),
 (143, 36),
 (309, 204),
 (76, 201),
 (369, 81),
 (242, 161),
 (332, 35),
 (9, 0),
 (318, 26),
 (191, 232),

…