from google.colab import drive
drive.mount('/content/drive')

import imageio as io
import os
from google.colab.patches import cv2_imshow
import numpy as np
import torch
import torchvision
import matplotlib.pyplot as plt
import cv2 as cv
import torch.nn as nn
from PIL import Image
import os
import albumentations as A
from albumentations.pytorch import ToTensor
from glob import glob
from tqdm  import tqdm
from torch.utils.data import Dataset
from genericpath import exists
import albumentations as A
from torch.utils.data import DataLoader
import matplotlib.image as mpimg
import imghdr
import mimetypes
import torchvision.transforms as tt
import torch.optim as optim
import numpy as np

np.random.seed(42)

train_image_dir           =  "/content/drive/MyDrive/retinal_blood_vessels/training/images"
train_mask_dir            =  "/content/drive/MyDrive/retinal_blood_vessels/training/1st_manual"
test_image_dir            =  "/content/drive/MyDrive/retinal_blood_vessels/test/images"
test_mask_dir             =  "/content/drive/MyDrive/retinal_blood_vessels/test/mask"
path1                     =  "/content/Data/Train/Images"
path2                     =  "/content/Data/Train/Masks"
path3                     =  "/content/Data/Test/Images"
path4                     =  "/content/Data/Test/Masks"
past_add = [path1,path2,path3,path4]
copy_add  = [train_image_dir,train_mask_dir,test_image_dir,test_mask_dir]

def create_dir(path):
  if not os.path.exists(path):
    os.makedirs(path)
for path in past_add:
  create_dir(path)

for x in copy_add:
  images  =os.listdir(x)
  print(len(images))
  for image in sorted(images):
    print(image)
    break
  break