How to choose some specific images from a large dataset of images?

My dataset has 366 folders means folder for each day covering a duration of 1 year nd each folder containing 51-55 images, out of which I need only 36 images for building neural network. so can I assign some index to those images and select some based on their index..? Can someone suggest me code for doing so..?

Every element on list has own number/index - so first you could create lists with all filenames.

You can use os.listdir() to get all folders and later use os.listdir(folder) for every folder to get list with filenames in folder.

import os

base = '/home/furas/images/2021'

folders = os.listdir(base)

all_filenames = []

for folder_name in folders:

    # add base path to folder name to have full path
    full_path = os.path.join(base, folder_name)
    print(full_path)
    
    # get filenames (without path) in folder
    filenames = os.listdir(full_path)
    
    # add path to filenames
    filenames = [os.path.join(full_path, name) for name in filenames]
   
    all_filenames.append(filenames)
    
print(all_filenames)    

This way you have 2D list with all filenames (with full path) and you can select them.

first 36 images in some day

selected = all_filenames[day_index][:36]

first 36 images in every days

selected = []

for day in all_filenames:
    selected.append( day[:36] )

random 36 images in every day

import random

selected = []

for day in all_filenames:
    selected.append( random.choices(day, 36) )