from torch.utils import data
from data.chunk_with_reference import ChunkWithReference
from data.image_corruptor import ImageCorruptor
import os
from bisect import bisect_left
import cv2
import torch
import numpy as np
import torchvision.transforms.functional as F
# Builds a dataset composed of a set of folders. Each folder represents a single high resolution image that has been
# chunked into patches of fixed size. A reference image is included as well as a list of center points for each patch.
class SingleImageDataset(data.Dataset):
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.for_eval = opt['eval'] if 'eval' in opt.keys() else False
self.scale = opt['scale'] if not self.for_eval else 1
self.paths = opt['paths']
if not isinstance(self.paths, list):
self.paths = [self.paths]
self.weights = [1]
else:
self.weights = opt['weights']
# See if there is a cached directory listing and use that rather than re-scanning everything. This will greatly
# reduce startup costs.
self.chunks = []
for path, weight in zip(self.paths, self.weights):
cache_path = os.path.join(path, 'cache.pth')
if os.path.exists(cache_path):
chunks = torch.load(cache_path)
# Update the options.
for c in chunks:
c.reload(opt)
else:
chunks = [ChunkWithReference(opt, d) for d in os.scandir(path) if d.is_dir()]
# Prune out chunks that have no images
res = []
for c in chunks:
if len(c) != 0:
res.append(c)
chunks = res
# Save to a cache.
torch.save(chunks, cache_path)
for w in range(weight):
self.chunks.extend(chunks)
# Indexing this dataset is tricky. Aid it by having a sorted list of starting indices for each chunk.
start = 0
self.starting_indices = []
for c in chunks:
self.starting_indices.append(start)
start += len(c)
self.len = start
def resize_point(self, point, orig_dim, new_dim):
oh, ow = orig_dim
nh, nw = new_dim
dh, dw = float(nh) / float(oh), float(nw) / float(ow)
point = int(dh * float(point[0])), int(dw * float(point[1]))
return point
def __getitem__(self, item):
chunk_ind = bisect_left(self.starting_indices, item)
chunk_ind = chunk_ind if chunk_ind < len(self.starting_indices) and self.starting_indices[chunk_ind] == item else chunk_ind-1
hq, hq_ref, hq_center, hq_mask, path = self.chunks[chunk_ind][item-self.starting_indices[chunk_ind]]
# Enforce size constraints
h, w, _ = hq.shape
if self.target_hq_size is not None and self.target_hq_size != h:
# It is assumed that the target size is a square.
target_size = (self.target_hq_size, self.target_hq_size)
hq = cv2.resize(hq, target_size, interpolation=cv2.INTER_LINEAR)
hq_ref = cv2.resize(hq_ref, target_size, interpolation=cv2.INTER_LINEAR)
hq_mask = cv2.resize(hq_mask, target_size, interpolation=cv2.INTER_LINEAR)
hq_center = self.resize_point(hq_center, (h, w), target_size)
h, w = self.target_hq_size, self.target_hq_size
hq_multiple = self.multiple * self.scale # Multiple must apply to LQ image.
if h % hq_multiple != 0 or w % hq_multiple != 0:
h, w = (h - h % hq_multiple), (w - w % hq_multiple)
hq_center = self.resize_point(hq_center, hq.shape[:1], (h, w))
hq = hq[:h, :w, :]
hq_ref = hq_ref[:h, :w, :]
hq_mask = hq_mask[:h, :w, :]
# Synthesize the LQ image
if self.for_eval:
lq, lq_ref = hq, hq_ref
else:
lq = cv2.resize(hq, (h // self.scale, w // self.scale), interpolation=cv2.INTER_LINEAR)
lq_ref = cv2.resize(hq_ref, (h // self.scale, w // self.scale), interpolation=cv2.INTER_LINEAR)
lq_mask = cv2.resize(hq_mask, (h // self.scale, w // self.scale), interpolation=cv2.INTER_LINEAR)
lq_center = self.resize_point(hq_center, (h, w), lq.shape[:2])
# Corrupt the LQ image
lq = self.corruptor.corrupt_images([lq])[0]
# Convert to torch tensor
hq = torch.from_numpy(np.ascontiguousarray(np.transpose(hq, (2, 0, 1)))).float()
hq_ref = torch.from_numpy(np.ascontiguousarray(np.transpose(hq_ref, (2, 0, 1)))).float()
hq_mask = torch.from_numpy(np.ascontiguousarray(hq_mask)).unsqueeze(dim=0)
hq_ref = torch.cat([hq_ref, hq_mask], dim=0)
lq = torch.from_numpy(np.ascontiguousarray(np.transpose(lq, (2, 0, 1)))).float()
lq_ref = torch.from_numpy(np.ascontiguousarray(np.transpose(lq_ref, (2, 0, 1)))).float()
lq_mask = torch.from_numpy(np.ascontiguousarray(lq_mask)).unsqueeze(dim=0)
lq_ref = torch.cat([lq_ref, lq_mask], dim=0)
return {'LQ': lq, 'GT': hq, 'gt_fullsize_ref': hq_ref, 'lq_fullsize_ref': lq_ref,
'lq_center': torch.tensor(lq_center, dtype=torch.long), 'gt_center': torch.tensor(hq_center, dtype=torch.long),
'LQ_path': path, 'GT_path': path}
def __len__(self):
return self.len
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.for_eval = opt['eval'] if 'eval' in opt.keys() else False
self.scale = opt['scale'] if not self.for_eval else 1
self.paths = opt['paths']
if not isinstance(self.paths, list):
self.paths = [self.paths]
self.weights = [1]
else:
self.weights = opt['weights']
for path, weight in zip(self.paths, self.weights):
chunks = [ChunkWithReference(opt, d) for d in os.scandir(path) if d.is_dir()]
for w in range(weight):
self.chunks.extend(chunks)
if __name__ == '__main__':
opt = {
'name': 'amalgam',
'paths': ['F:\\4k6k\\datasets\\images\\flickr\\testbed'],
'weights': [1],
'target_size': 128,
'force_multiple': 32,
'scale': 2,
'eval': False,
'fixed_corruptions': ['jpeg'],
'random_corruptions': ['color_quantization', 'gaussian_blur', 'motion_blur', 'smooth_blur', 'noise', 'saturation'],
'num_corrupts_per_image': 1
}
ds = SingleImageDataset(opt)
import os
os.makedirs("debug", exist_ok=True)
for i in range(0, len(ds)):
o = ds[i]
for k, v in o.items():
if '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))