old_contents: import os
  import glob
  import datetime
  
  print ("[INFO] program started on - {}".format(str(datetime.datetime.now)))
  
  # get the input and output path
  # C:\\Users\\prassha\\Desktop\\MachineLearning\\Python\\FlowerRecognition\\jpg
  input_path  = "C:\\Users\\prassha\\Desktop\\MachineLearning\\Python\\FlowerRecognition\\jpg"
  #input_path  = "G:\\Datasets\\flowers\\17flowers\\jpg"
  output_path = "C:\\Users\\prassha\\Desktop\\MachineLearning\\Python\\FlowerRecognition\\"
  #output_path = "G:\\Datasets\\flowers\\17flowers\\"
  
  # get the class label limit
  class_limit = 17
  
  # take all the images from the dataset
  image_paths = glob.glob(input_path + "\\*.jpg")
  
  # variables to keep track
  label = 0
  i = 0
  j = 80
  
  class_names = ["daffodil", "snowdrop", "lilyvalley", "bluebell", "crocus",
  			   "iris", "tigerlily", "tulip", "fritillary", "sunflower", 
  			   "daisy", "coltsfoot", "dandelion", "cowslip", "buttercup",
  			   "windflower", "pansy"]
  
  # change the current working directory
  os.chdir(output_path)
  
  # loop over the class labels
  for x in range(1, class_limit+1):
  	# create a folder for that class
  	os.system("mkdir " + class_names[label])
  	# get the current path
  	cur_path = output_path + "\\" + class_names[label] + "\\"
  	# loop over the images in the dataset
  	for image_path in image_paths[i:j]:
  		original_path = image_path
  		image_path = image_path.split("\\")
  		image_path = image_path[len(image_path)-1]
  		os.system("copy " + original_path + " " + cur_path + image_path)
  	i += 80
  	j += 80
  	label += 1
  
  print ("[INFO] program ended on - {}".format(str(datetime.datetime.now)))
new_contents: ### This file creates separates directories for each class of images.
  ### It considers first n ( here 80 ) imagesa are of one class, 2nd n images are of another class ans so on.
  
  import os
  import glob
  import datetime
  
  print ("[INFO] program started on - {}".format(str(datetime.datetime.now)))
  
  # get the input and output path
  # C:\\Users\\prassha\\Desktop\\MachineLearning\\Python\\FlowerRecognition\\jpg
  input_path  = "C:\\Users\\prassha\\Desktop\\MachineLearning\\Python\\FlowerRecognition\\jpg"
  #input_path  = "G:\\Datasets\\flowers\\17flowers\\jpg"
  output_path = "C:\\Users\\prassha\\Desktop\\MachineLearning\\Python\\FlowerRecognition\\"
  #output_path = "G:\\Datasets\\flowers\\17flowers\\"
  #output_path = "/home/paperspace/Prashanta/FlowerRecognition/flower17/"
  
  # get the class label limit
  class_limit = 17
  
  # take all the images from the dataset
  image_paths = glob.glob(input_path + "\\*.jpg")
  
  # variables to keep track
  label = 0
  i = 0
  j = 80
  
  class_names = ["daffodil", "snowdrop", "lilyvalley", "bluebell", "crocus",
  			   "iris", "tigerlily", "tulip", "fritillary", "sunflower", 
  			   "daisy", "coltsfoot", "dandelion", "cowslip", "buttercup",
  			   "windflower", "pansy"]
  
  # change the current working directory
  os.chdir(output_path)
  
  # loop over the class labels
  for x in range(1, class_limit+1):
  	# create a folder for that class
  	os.system("mkdir " + class_names[label])
  	# get the current path
  	cur_path = output_path + "\\" + class_names[label] + "\\"
  	# loop over the images in the dataset
  	for image_path in image_paths[i:j]:
  		original_path = image_path
  		image_path = image_path.split("\\")
  		image_path = image_path[len(image_path)-1]
  		os.system("copy " + original_path + " " + cur_path + image_path)
  	i += 80
  	j += 80
  	label += 1
  
  print ("[INFO] program ended on - {}".format(str(datetime.datetime.now)))
  
subject: Update organize_flowers17.py