圖像/高斯濾波器陣列上的內核矩陣3x3

Question

我想實現一個高斯濾波器。爲此，我使用內核3x3和圖像數組。我遇到的問題是爲數組的每個[i，j]元素定義一個子矩陣3x3。我在代碼中寫下了細節。

import numpy as np 
import scipy 
from scipy import misc 
import matplotlib.pyplot as plt 


imagen = scipy.misc.imread("C:\\Users\\Reymi\\Downloads\\imagen.png") #importing image of original size (1929, 1280) 

imagen_real = scipy.pad(array=imagen, pad_width=[1, 1], mode='constant', constant_values=0) #add 1 column and 1 row of zeros to avoid the kernel of going outside the array. size is (1931, 1282) 

imagen_nueva = np.empty((1931, 1282)) #the new image. the same size as the image I will filter 

(dim_x,dim_y)=np.shape(imagen_real) 


ker1 = np.array([[1/16, 1/8, 1/16], #3x3 kernel 
       [1/8, 1/4, 1/8], 
       [1/16, 1/8, 1/16]]) 

def multiplicar_entero(): 

    global imagen_nueva 
    for i in range(1,dim_x): #the range starts from 1 to avoid the column and row of zeros, and ends before the last col and row of zeros 
     for j in range(1,dim_y): 
      imagen_entry = np.empty((3, 3))  #Main problem here: how do I define a 3x3 matrix for each entry? 
      valor = np.sum(imagen_entry*ker1) #Matrix 3x3 is filled with the elements around each [i, j] entry of the array 
      imagen_real[i, j] = valor 
      imagen_nueva = np.append(imagen_real[i, j], (1931, 1282)) #This is supposed to each new [i, j] entry to the new image 

print("La imagen con el blur effect es la siguiente:\n") 

multiplicar_entero() #calls function 


plt.imshow(imagen_nueva) #Show new image 
plt.gray() 
plt.show() 

很抱歉的長碼。並感謝您的幫助。

Answer 1

有你的代碼的幾個問題如：

• 您使用我們沒有獲得的圖像。在示例中始終使用免費提供的圖像，以便我們可以運行代碼。我在這裏使用了scipy.misc.ascent。
• 除非你真的需要，否則不要使用全局變量。
• 總是使用英文變量名和英文註釋代碼英文。它使這樣的評論更容易。

我對下面的代碼做了一些修復，我猜你可以看到我是如何解決你的問題的。具體要使用:索引，它允許你提取一個數組的子集，並使用array[i, j]分配結果：

import numpy as np 
import scipy 
from scipy import misc 
import matplotlib.pyplot as plt 

imagen = scipy.misc.ascent() # Freely available image 

(dim_x, dim_y) = np.shape(imagen) 

ker1 = np.array([[1/16, 1/8, 1/16], #3x3 kernel 
       [1/8, 1/4, 1/8], 
       [1/16, 1/8, 1/16]]) 

def multiplicar_entero(imagen): 
    imagen_nueva = np.zeros(imagen.shape) #the new image. the same size as the image I will filter 
    for i in range(1,dim_x-1): #the range starts from 1 to avoid the column and row of zeros, and ends before the last col and row of zeros 
     for j in range(1,dim_y-1): 
      imagen_entry = imagen[i-1:i+2, j-1:j+2] 
      valor = np.sum(imagen_entry*ker1) #Matrix 3x3 is filled with the elements around each [i, j] entry of the array 
      imagen_nueva[i, j] = valor 
    return imagen_nueva 

imagen_nueva = multiplicar_entero(imagen) 

plt.imshow(imagen_nueva) #Show new image 
plt.gray() 
plt.show()