I'm currently going through issues in terms of acquiring multiple images at once to convert them to a vector for calculating the cosine distance to get similarity between say an image from the directory compared to another. From looking around, I can't use tensors/arrays to calculate these distances, but please correct me if I'm wrong.
Here is an example of my code:
import numpy as np
import os
import cv2
import glob
from skimage.transform import resize
ImageArray= [cv2.imread(file) for file in glob.glob("Image Directory/**/*.jpg",recursive = True)]
Image1 = ImageArray[0]
# I try to resize the images based on the first image in the dataset.
shape = Image1.shape
Other_Images_arrays = ImageArray[0:]
#I then tried to split these arrays according to the images in the dataset, but this is where I struggle and in where it makes calculating the cosine distance difficult.
Other_Images_arrays= np.array_split(Other_Images_arrays, 29)
Other_Images_arrays_resized = resize(np.array(Other_Images_array),shape)
#Vectorising the images
Image1_vector = Image1.ravel()
Other_Images_vector= Other_Images_arrays.ravel()
Then trying to use a specific cosine function that will work
But I know that the main issue is splitting up the rest of the arrays in a suitbale manner, according to the images in the dataset.
Can anyone assist on how I split these arrays up and still be able to use them for computation?
