data.py

import tensorflow as tf
import os
import cv2



dataset_dir='./dataset'
image_source = sorted([os.path.join(dataset_dir, 'train', file) for file in os.listdir(dataset_dir + "/train") if file.endswith('.png')])
image_targrt = sorted([os.path.join(dataset_dir, "realfog", file) for file in os.listdir(dataset_dir + "/realfog") if file.endswith('.png')])



def decodefortrain(img):
    img = tf.io.read_file(img)
    img = tf.image.decode_png(img,channels = 3)
    img = tf.cast(img,dtype=tf.float32)
    scale = tf.random.uniform([1],minval = 0.25,maxval = 0.5,dtype = tf.float32)
    hi = tf.floor(scale*1024)
    wi = tf.floor(scale*2048)
    s = tf.concat([hi,wi],0)
    s = tf.cast(s,dtype=tf.int32)
    img = tf.compat.v1.image.resize_images(img,s,method = 0, align_corners = True)
    img = tf.image.random_crop(img,[256,512,3])
    img = tf.image.random_flip_left_right(img)
    #img = tf.image.convert_image_dtype(img,dtype = tf.float32,saturate=True)
    img = (img/255)*2-1    
    return img


def source_data(batchsize=1):
    train=tf.data.Dataset.from_tensor_slices(image_source).shuffle(50).map(decodefortrain).batch(batchsize)
    return train
	
def target_data(batchsize=1):
    train=tf.data.Dataset.from_tensor_slices(image_targrt).shuffle(50).map(decodefortrain).batch(batchsize)
    return train