How can I perform Gabor filter on 2d images?

Question

Everything I've read about extracting features with gabor filters calls for the use of images as input, while in my case, I have the matrix representation of such image in a shape of (145,145,10). So I'm confused on how can I apply gabor filter on my 10 2d image (145 * 145) and how can I get all gabor feature responses stored in a matrix (samples, gabor features).

Here is one interested code that I have found which applies gabor filter on an image and then stored all the gabor feature responses in a dataframe to be used then for machine learning purpose.

import numpy as np
import cv2
import pandas as pd
 
#img = cv2.imread('BSE_Image.jpg')
img = cv2.imread('images/synthetic.jpg')
img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)  
#Here, if you have multichannel image then extract the right channel instead of converting the image to grey. 
#For example, if DAPI contains nuclei information, extract the DAPI channel image first. 

#Multiple images can be used for training. For that, you need to concatenate the data

#Save original image pixels into a data frame. This is our Feature #1.
img2 = img.reshape(-1)
df = pd.DataFrame()
df['Original Image'] = img2

#Generate Gabor features
num = 1  #To count numbers up in order to give Gabor features a lable in the data frame
kernels = []  #Create empty list to hold all kernels that we will generate in a loop
for theta in range(8):   #Define number of thetas. Here only 2 theta values 0 and 1/4 . pi 
    theta = theta / 4. * np.pi
    for sigma in (1, 3, 5, 7):  #Sigma with values of 1 and 3
        for lamda in np.arange(0, np.pi, np.pi / 4):   #Range of wavelengths
            for gamma in (0.05, 0.5):   #Gamma values of 0.05 and 0.5
                           
                gabor_label = 'Gabor' + str(num)  #Label Gabor columns as Gabor1, Gabor2, etc.
#                print(gabor_label)
                ksize=5  #Try 15 for hidden image. Or 9 for others
                phi = 0  #0.8 for hidden image. Otherwise leave it to 0
                kernel = cv2.getGaborKernel((ksize, ksize), sigma, theta, lamda, gamma, phi, ktype=cv2.CV_32F)    
                kernels.append(kernel)
                #Now filter the image and add values to a new column 
                fimg = cv2.filter2D(img2, cv2.CV_8UC3, kernel)                
                filtered_img = fimg.reshape(-1)
                
                cv2.imwrite('images/gabor_filtered_images/'+gabor_label+'.jpg', filtered_img.reshape(img.shape))

                df[gabor_label] = filtered_img  #Labels columns as Gabor1, Gabor2, etc.
                print(gabor_label, ': theta=', theta, ': sigma=', sigma, ': lamda=', lamda, ': gamma=', gamma)
                
                num += 1  #Increment for gabor column label
                
print(df.head())