hog multiple

import os
from skimage.io import imread, imshow
from skimage.transform import resize
from skimage.feature import hog
from skimage import exposure
import matplotlib.pyplot as plt
#%matplotlib inline


# folder path
dir_path = r'C:\Users\Hamza\Desktop\dataset46k\0'
count = []
a="\"
# Iterate directory
for path in os.listdir(dir_path)[0:1]:
    # check if current path is a file
    if os.path.isfile(os.path.join(dir_path, path)):
        
        print(dir_path + a + path)


# for i in count[0:1]:    
#     print(i)
        
# # #reading the image
#     img = imread("G:\bowl dtst\DSB3\DS\dataset10k\0\" + i + ".jpg")
#     imshow(img)
#     print(img.shape)
    
#     #resizing image 
#     resized_img = resize(img, (128,64)) 
#     imshow(resized_img) 
#     print(resized_img.shape)
    
#     #creating hog features 
#     fd, hog_image = hog(resized_img, orientations=9, pixels_per_cell=(8, 8), 
#                         cells_per_block=(2, 2), visualize=True, multichannel=False)
#     fd.shape
    
#     fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(16, 8), sharex=True, sharey=True) 
    
#     ax1.imshow(resized_img, cmap=plt.cm.gray) 
#     ax1.set_title('Input image') 
    
#     # Rescale histogram for better display 
#     hog_image_rescaled = exposure.rescale_intensity(hog_image, in_range=(0, 10)) 
    
#     ax2.imshow(hog_image_rescaled, cmap=plt.cm.gray) 
#     ax2.set_title('Histogram of Oriented Gradients')
    
#     plt.show()