DL-Art-School/codes/data/image_folder_dataset.py
James Betker c963e5f2ce Add ImageFolderDataset
This one has been a long time coming.. How does torch not have something like this?
2020-12-01 17:45:37 -07:00

129 lines
4.9 KiB
Python

import glob
import itertools
import random
import cv2
import numpy as np
import torch
import os
from data import util
# Builds a dataset created from a simple folder containing a list of training/test/validation images.
from data.image_corruptor import ImageCorruptor
class ImageFolderDataset:
def __init__(self, opt):
self.opt = opt
self.corruptor = ImageCorruptor(opt)
self.target_hq_size = opt['target_size'] if 'target_size' in opt.keys() else None
self.multiple = opt['force_multiple'] if 'force_multiple' in opt.keys() else 1
self.scale = opt['scale']
self.paths = opt['paths']
self.corrupt_before_downsize = opt['corrupt_before_downsize'] if 'corrupt_before_downsize' in opt.keys() else False
assert (self.target_hq_size // self.scale) % self.multiple == 0 # If we dont throw here, we get some really obscure errors.
if not isinstance(self.paths, list):
self.paths = [self.paths]
self.weights = [1]
else:
self.weights = opt['weights']
# Just scan the given directory for images of standard types.
supported_types = ['jpg', 'JPG', 'jpeg', 'JPEG', 'png', 'PNG', 'gif', 'GIF']
self.image_paths = []
for path, weight in zip(self.paths, self.weights):
cache_path = os.path.join(path, 'cache.pth')
if os.path.exists(cache_path):
imgs = torch.load(cache_path)
else:
print("Building image folder cache, this can take some time for large datasets..")
imgs = []
for ext in supported_types:
imgs.extend(glob.glob(os.path.join(path, "*." + ext)))
torch.save(imgs, cache_path)
for w in range(weight):
self.image_paths.extend(imgs)
self.len = len(self.image_paths)
def get_paths(self):
return self.image_paths
# Given an HQ square of arbitrary size, resizes it to specifications from opt.
def resize_hq(self, imgs_hq):
# Enforce size constraints
h, w, _ = imgs_hq[0].shape
if self.target_hq_size is not None and self.target_hq_size != h:
hqs_adjusted = []
for hq in imgs_hq:
# It is assumed that the target size is a square.
target_size = (self.target_hq_size, self.target_hq_size)
hqs_adjusted.append(cv2.resize(hq, target_size, interpolation=cv2.INTER_AREA))
h, w = self.target_hq_size, self.target_hq_size
else:
hqs_adjusted = imgs_hq
hq_multiple = self.multiple * self.scale # Multiple must apply to LQ image.
if h % hq_multiple != 0 or w % hq_multiple != 0:
hqs_conformed = []
for hq in hqs_adjusted:
h, w = (h - h % hq_multiple), (w - w % hq_multiple)
hqs_conformed.append(hq[:h, :w, :])
return hqs_conformed
return hqs_adjusted
def synthesize_lq(self, hs):
h, w, _ = hs[0].shape
ls = []
if self.corrupt_before_downsize:
hs = self.corruptor.corrupt_images(hs)
for hq in hs:
ls.append(cv2.resize(hq, (h // self.scale, w // self.scale), interpolation=cv2.INTER_AREA))
# Corrupt the LQ image (only in eval mode)
if not self.corrupt_before_downsize:
ls = self.corruptor.corrupt_images(ls)
return ls
def __len__(self):
return self.len
def __getitem__(self, item):
hq = util.read_img(None, self.image_paths[item], rgb=True)
hs = self.resize_hq([hq])
ls = self.synthesize_lq(hs)
# Convert to torch tensor
hq = torch.from_numpy(np.ascontiguousarray(np.transpose(hs[0], (2, 0, 1)))).float()
lq = torch.from_numpy(np.ascontiguousarray(np.transpose(ls[0], (2, 0, 1)))).float()
return {'LQ': lq, 'GT': hq, 'LQ_path': self.image_paths[item], 'GT_path': self.image_paths[item]}
if __name__ == '__main__':
opt = {
'name': 'amalgam',
'paths': ['F:\\4k6k\\datasets\\ns_images\\imagesets\\random_100_1024px'],
'weights': [1],
'target_size': 128,
'force_multiple': 32,
'scale': 2,
'fixed_corruptions': ['jpeg-broad', 'gaussian_blur'],
'random_corruptions': ['noise-5', 'none'],
'num_corrupts_per_image': 1,
'corrupt_before_downsize': True,
}
ds = ImageFolderDataset(opt)
import os
os.makedirs("debug", exist_ok=True)
for i in range(0, len(ds)):
o = ds[random.randint(0, len(ds))]
#for k, v in o.items():
k = 'LQ'
v = o[k]
#if 'LQ' in k and 'path' not in k and 'center' not in k:
#if 'full' in k:
#masked = v[:3, :, :] * v[3]
#torchvision.utils.save_image(masked.unsqueeze(0), "debug/%i_%s_masked.png" % (i, k))
#v = v[:3, :, :]
import torchvision
torchvision.utils.save_image(v.unsqueeze(0), "debug/%i_%s.png" % (i, k))