/
create_kernel_dataset.py
107 lines (90 loc) · 4.72 KB
/
create_kernel_dataset.py
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import argparse
import os
import torch.utils.data
import yaml
import glob
import utils
from PIL import Image
import torchvision.transforms.functional as TF
from tqdm import tqdm
from KernelGAN.imresize import imresize
from scipy.io import loadmat
import numpy as np
parser = argparse.ArgumentParser(description='Apply the trained model to create a dataset')
parser.add_argument('--kernel_path', default='./preprocess/KernelGAN/results', type=str, help='kernel path to use')
parser.add_argument('--artifacts', default='', type=str, help='selecting different artifacts type')
parser.add_argument('--name', default='', type=str, help='additional string added to folder path')
parser.add_argument('--dataset', default='df2k', type=str, help='selecting different datasets')
parser.add_argument('--track', default='train', type=str, help='selecting train or valid track')
parser.add_argument('--num_res_blocks', default=8, type=int, help='number of ResNet blocks')
parser.add_argument('--cleanup_factor', default=2, type=int, help='downscaling factor for image cleanup')
parser.add_argument('--upscale_factor', default=4, type=int, choices=[4], help='super resolution upscale factor')
opt = parser.parse_args()
# define input and target directories
with open('./preprocess/paths.yml', 'r') as stream:
PATHS = yaml.load(stream)
if opt.dataset == 'df2k':
path_sdsr = PATHS['datasets']['df2k'] + '/generated/sdsr/'
path_tdsr = PATHS['datasets']['df2k'] + '/generated/tdsr/'
input_source_dir = PATHS['df2k']['tdsr']['source']
input_target_dir = PATHS['df2k']['tdsr']['target']
source_files = [os.path.join(input_source_dir, x) for x in os.listdir(input_source_dir) if utils.is_image_file(x)]
target_files = [os.path.join(input_target_dir, x) for x in os.listdir(input_target_dir) if utils.is_image_file(x)]
else:
path_sdsr = PATHS['datasets'][opt.dataset] + '/generated/' + opt.artifacts + '/' + opt.track + opt.name + '_sdsr/'
path_tdsr = PATHS['datasets'][opt.dataset] + '/generated/' + opt.artifacts + '/' + opt.track + opt.name + '_tdsr/'
input_source_dir = PATHS[opt.dataset][opt.artifacts]['hr'][opt.track]
input_target_dir = None
source_files = [os.path.join(input_source_dir, x) for x in os.listdir(input_source_dir) if utils.is_image_file(x)]
target_files = []
tdsr_hr_dir = path_tdsr + 'HR'
tdsr_lr_dir = path_tdsr + 'LR'
assert not os.path.exists(PATHS['datasets'][opt.dataset])
if not os.path.exists(tdsr_hr_dir):
os.makedirs(tdsr_hr_dir)
if not os.path.exists(tdsr_lr_dir):
os.makedirs(tdsr_lr_dir)
kernel_paths = glob.glob(os.path.join(opt.kernel_path, '*/*_kernel_x4.mat'))
kernel_num = len(kernel_paths)
print('kernel_num: ', kernel_num)
# generate the noisy images
with torch.no_grad():
for file in tqdm(source_files, desc='Generating images from source'):
# load HR image
input_img = Image.open(file)
input_img = TF.to_tensor(input_img)
# Resize HR image to clean it up and make sure it can be resized again
resize2_img = utils.imresize(input_img, 1.0 / opt.cleanup_factor, True)
_, w, h = resize2_img.size()
w = w - w % opt.upscale_factor
h = h - h % opt.upscale_factor
resize2_cut_img = resize2_img[:, :w, :h]
# Save resize2_cut_img as HR image for TDSR
path = os.path.join(tdsr_hr_dir, os.path.basename(file))
resize2_cut_img = TF.to_pil_image(resize2_cut_img)
resize2_cut_img.save(path, 'PNG')
# Generate resize3_cut_img and apply model
kernel_path = kernel_paths[np.random.randint(0, kernel_num)]
mat = loadmat(kernel_path)
k = np.array([mat['Kernel']]).squeeze()
resize3_cut_img = imresize(np.array(resize2_cut_img), scale_factor=1.0 / opt.upscale_factor, kernel=k)
# Save resize3_cut_img as LR image for TDSR
path = os.path.join(tdsr_lr_dir, os.path.basename(file))
TF.to_pil_image(resize3_cut_img).save(path, 'PNG')
for file in tqdm(target_files, desc='Generating images from target'):
# load HR image
input_img = Image.open(file)
input_img = TF.to_tensor(input_img)
# Save input_img as HR image for TDSR
path = os.path.join(tdsr_hr_dir, os.path.basename(file))
HR_img = TF.to_pil_image(input_img)
HR_img.save(path, 'PNG')
# generate resized version of input_img
kernel_path = kernel_paths[np.random.randint(0, kernel_num)]
mat = loadmat(kernel_path)
# print(mat)
k = np.array([mat['Kernel']]).squeeze()
resize_img = imresize(np.array(HR_img), scale_factor=1.0 / opt.upscale_factor, kernel=k)
# Save resize_noisy_img as LR image for TDSR
path = os.path.join(tdsr_lr_dir, os.path.basename(file))
TF.to_pil_image(resize_img).save(path, 'PNG')