在 python 上替换我的图像数组中的零元素

Question

I am training my model with several images.

When training my model I realized that I could increase my accuracy by replacing the zero elements in my image array with other values and so I replaced them with the median value of my image as shown with the following code.

import cv2
import imutils
import numpy as np

r_val_all = np.zeros((2000,112,112))
for r in range(len(r_val)): 
    #LOAD IMAGES
    r_image_v = cv2.imread(r_val[r])
    r_gray_v = cv2.cvtColor(r_image_v, cv2.COLOR_BGR2GRAY)
    r_gray_v = imutils.resize(r_gray_v, width=112, height=112)
    n = np.median(r_gray_v[r_gray_v > 0])
    r_gray_v[r_gray_v == 0] = n
    r_val_all[r,:,:] = r_gray_v

The accuracy did improve however it is not quite there yet.

What I actually require is something where the zero elements are replaced with a continuation of the pre-existent array values.

However I was not sure how to tackle such a problem are there any tools that perform the operation I require?

Answer 1

I used the second answer from the link, tell me if this is close to what you want, because it appeared to be what you wanted.

Creating one sample image and center it, so it's somewhat close to your first example image.

import numpy as np
import matplotlib.pyplot as plt
image = np.zeros((100, 100))
center_noise = np.random.normal(loc=10, size=(50, 50))
image[25:75, 25:75] = center_noise
plt.imshow(image, cmap='gray')

Inspired by rr_gray = np.where(rr_gray==0, np.nan, rr_gray) #convert zero elements to nan in your code, I'm replacing the zeros with NaN .

image_centered = np.where(image == 0, np.nan, image)
plt.imshow(image_centered, cmap='gray')

Now I used the function in the second answer of the link, fill .

test = fill(image_centered)
plt.imshow(test, cmap='gray')

This is the result

I'm sorry I can't help you more. I wish I could, I'm just not very well versed in image processing. I looked at your code and couldn't figure out why it's not working, sorry.