dataloader.py

import os
import sys

import torch
import torch.utils.data as data

import numpy as np
from PIL import Image
import glob
import random
import cv2

random.seed(1143)


def populate_train_list(lowlight_images_path):




	image_list_lowlight = glob.glob(lowlight_images_path + "*.jpg")

	train_list = image_list_lowlight

	random.shuffle(train_list)

	return train_list

	

class lowlight_loader(data.Dataset):

	def __init__(self, lowlight_images_path):

		self.train_list = populate_train_list(lowlight_images_path) 
		self.size = 256

		self.data_list = self.train_list
		print("Total training examples:", len(self.train_list))


		

	def __getitem__(self, index):

		data_lowlight_path = self.data_list[index]
		
		data_lowlight = Image.open(data_lowlight_path)
		
		# data_lowlight = cv2.imread(data_lowlight_path)
		
		data_lowlight = data_lowlight.resize((self.size,self.size), Image.ANTIALIAS)
		# data_lowlight = cv2.resize(data_lowlight, (self.size,self.size))


		data_lowlight = (np.asarray(data_lowlight)/255.0) 
		data_lowlight = torch.from_numpy(data_lowlight).float()

		return data_lowlight.permute(2,0,1)

	def __len__(self):
		return len(self.data_list)