I am trying to create a h5 file for storing a dataset for training a super resolution GAN. Where each training pair would be a Low resolution and a High resolution image. The dataset will contain the data in the following manner:
[[LR1,HR1],[LR2,HR2],...[LRn,HRn]]. I have 256x256 RGB images for HR and 128x128 RGB for LR.
I am a bit skeptical about the best way to store this in a h5 file and shall I scale the images by 255 before storing them in the h5 file?
I have wrote the following code to do so. Any help/suggestions would be highly appreciated.
import h5py
import numpy as np
import os
import cv2
import globdef store_super_resolution_dataset_in_h5_file(path_to_LR,path_to_HR):'''This function takes the files with the same name from LR and HR folders and stores the new dataset in h5 format'''#create LR and HR image listsLR_images = glob.glob(path_to_LR+'*.jpg')HR_images = glob.glob(path_to_HR+'*.jpg')#sort the listsLR_images.sort()HR_images.sort()print('LR_images: ',LR_images)print('HR_images: ',HR_images)#create a h5 fileh5_file = h5py.File('super_resolution_dataset.h5','w')#create a dataset in the h5 filedataset = h5_file.create_dataset('super_resolution_dataset',(len(LR_images),2,256,256),dtype='f')#store the images in the datasetfor i in range(len(LR_images)):LR_image = cv2.imread(LR_images[i])HR_image = cv2.imread(HR_images[i])dataset[i,0,:,:] = LR_imagedataset[i,1,:,:] = HR_image#close the h5 fileh5_file.close()
There are 2 code segments below. The first code segment shows my recommended method: loading Hi-Res and Low-Res images to separate datasets to reduce the HDF5 file size. The second simply corrects errors in your code (modified to use the with/as: context manager). Both code segments begin after the #create a h5 file comment.
I ran a test with 43 images to compare resulting file sizes. Results are:
- 1 dataset size = 66.0 MB
- 2 dataset size = 41.3 MB (37% reduction)
Recommended method using 2 datasets:
# get image dtypes and create a h5 file
LR_dt = cv2.imread(LR_images[0]).dtype
HR_dt = cv2.imread(HR_images[0]).dtype
with h5py.File('low_hi_resolution_dataset.h5','w') as h5_file:#create 2 datasets for LR and HR images in the h5 filelr_ds = h5_file.create_dataset('low_res_dataset',(len(LR_images),128,128,3),dtype=LR_dt)hr_ds = h5_file.create_dataset('hi_res_dataset',(len(LR_images),256,256,3),dtype=HR_dt)#store the images in the datasetfor i in range(len(LR_images)):LR_image = cv2.imread(LR_images[i])HR_image = cv2.imread(HR_images[i])lr_ds[i] = LR_imagehr_ds[i] = HR_image
Modifications to your method:
# get LR image dtype and create a h5 file
LR_dt = cv2.imread(LR_images[0]).dtype
with h5py.File('super_resolution_dataset.h5','w') as h5_file:#create a dataset in the h5 filedataset = h5_file.create_dataset('super_resolution_dataset',(len(LR_images),2,256,256,3),dtype=LR_dt)#store the images in the datasetfor i in range(len(LR_images)):LR_image = cv2.imread(LR_images[i])HR_image = cv2.imread(HR_images[i])dataset[i,0,0:128,0:128,:] = LR_imagedataset[i,1,:,:,:] = HR_image