160 lines
7.2 KiB
Python
160 lines
7.2 KiB
Python
import random
|
|
import numpy as np
|
|
import cv2
|
|
import lmdb
|
|
import torch
|
|
import torch.utils.data as data
|
|
import data.util as util
|
|
|
|
|
|
class LQGTDataset(data.Dataset):
|
|
"""
|
|
Read LQ (Low Quality, e.g. LR (Low Resolution), blurry, etc) and GT image pairs.
|
|
If only GT images are provided, generate LQ images on-the-fly.
|
|
"""
|
|
|
|
def get_lq_path(self, i):
|
|
which_lq = random.randint(0, len(self.paths_LQ)-1)
|
|
return self.paths_LQ[which_lq][i]
|
|
|
|
def __init__(self, opt):
|
|
super(LQGTDataset, self).__init__()
|
|
self.opt = opt
|
|
self.data_type = self.opt['data_type']
|
|
self.paths_LQ, self.paths_GT = None, None
|
|
self.sizes_LQ, self.sizes_GT = None, None
|
|
self.paths_PIX, self.sizes_PIX = None, None
|
|
self.LQ_env, self.GT_env, self.PIX_env = None, None, None # environments for lmdbs
|
|
|
|
self.paths_GT, self.sizes_GT = util.get_image_paths(self.data_type, opt['dataroot_GT'])
|
|
self.paths_LQ = []
|
|
if isinstance(opt['dataroot_LQ'], list):
|
|
# Multiple LQ data sources can be given, in case there are multiple ways of corrupting a source image and
|
|
# we want the model to learn them all.
|
|
for dr_lq in opt['dataroot_LQ']:
|
|
lq_path, self.sizes_LQ = util.get_image_paths(self.data_type, dr_lq)
|
|
self.paths_LQ.append(lq_path)
|
|
else:
|
|
lq_path, self.sizes_LQ = util.get_image_paths(self.data_type, opt['dataroot_LQ'])
|
|
self.paths_LQ.append(lq_path)
|
|
self.doCrop = opt['doCrop']
|
|
if 'dataroot_PIX' in opt.keys():
|
|
self.paths_PIX, self.sizes_PIX = util.get_image_paths(self.data_type, opt['dataroot_PIX'])
|
|
|
|
assert self.paths_GT, 'Error: GT path is empty.'
|
|
if self.paths_LQ and self.paths_GT:
|
|
assert len(self.paths_LQ[0]) == len(
|
|
self.paths_GT
|
|
), 'GT and LQ datasets have different number of images - {}, {}.'.format(
|
|
len(self.paths_LQ[0]), len(self.paths_GT))
|
|
self.random_scale_list = [1]
|
|
|
|
def _init_lmdb(self):
|
|
# https://github.com/chainer/chainermn/issues/129
|
|
self.GT_env = lmdb.open(self.opt['dataroot_GT'], readonly=True, lock=False, readahead=False,
|
|
meminit=False)
|
|
self.LQ_env = lmdb.open(self.opt['dataroot_LQ'], readonly=True, lock=False, readahead=False,
|
|
meminit=False)
|
|
if 'dataroot_PIX' in self.opt.keys():
|
|
self.PIX_env = lmdb.open(self.opt['dataroot_PIX'], readonly=True, lock=False, readahead=False,
|
|
meminit=False)
|
|
|
|
def __getitem__(self, index):
|
|
if self.data_type == 'lmdb' and (self.GT_env is None or self.LQ_env is None):
|
|
self._init_lmdb()
|
|
GT_path, LQ_path = None, None
|
|
scale = self.opt['scale']
|
|
GT_size = self.opt['target_size']
|
|
|
|
# get GT image
|
|
GT_path = self.paths_GT[index]
|
|
resolution = [int(s) for s in self.sizes_GT[index].split('_')
|
|
] if self.data_type == 'lmdb' else None
|
|
img_GT = util.read_img(self.GT_env, GT_path, resolution)
|
|
if self.opt['phase'] != 'train': # modcrop in the validation / test phase
|
|
img_GT = util.modcrop(img_GT, scale)
|
|
if self.opt['color']: # change color space if necessary
|
|
img_GT = util.channel_convert(img_GT.shape[2], self.opt['color'], [img_GT])[0]
|
|
|
|
# get the pix image
|
|
if self.paths_PIX is not None:
|
|
PIX_path = self.paths_PIX[index]
|
|
img_PIX = util.read_img(self.PIX_env, PIX_path, resolution)
|
|
if self.opt['color']: # change color space if necessary
|
|
img_PIX = util.channel_convert(img_PIX.shape[2], self.opt['color'], [img_PIX])[0]
|
|
else:
|
|
img_PIX = img_GT
|
|
|
|
# get LQ image
|
|
if self.paths_LQ:
|
|
LQ_path = self.get_lq_path(index)
|
|
resolution = [int(s) for s in self.sizes_LQ[index].split('_')
|
|
] if self.data_type == 'lmdb' else None
|
|
img_LQ = util.read_img(self.LQ_env, LQ_path, resolution)
|
|
else: # down-sampling on-the-fly
|
|
# randomly scale during training
|
|
if self.opt['phase'] == 'train':
|
|
random_scale = random.choice(self.random_scale_list)
|
|
H_s, W_s, _ = img_GT.shape
|
|
|
|
def _mod(n, random_scale, scale, thres):
|
|
rlt = int(n * random_scale)
|
|
rlt = (rlt // scale) * scale
|
|
return thres if rlt < thres else rlt
|
|
|
|
H_s = _mod(H_s, random_scale, scale, GT_size)
|
|
W_s = _mod(W_s, random_scale, scale, GT_size)
|
|
img_GT = cv2.resize(img_GT, (W_s, H_s), interpolation=cv2.INTER_LINEAR)
|
|
if img_GT.ndim == 2:
|
|
img_GT = cv2.cvtColor(img_GT, cv2.COLOR_GRAY2BGR)
|
|
|
|
H, W, _ = img_GT.shape
|
|
# using matlab imresize
|
|
img_LQ = util.imresize_np(img_GT, 1 / scale, True)
|
|
if img_LQ.ndim == 2:
|
|
img_LQ = np.expand_dims(img_LQ, axis=2)
|
|
|
|
if self.opt['phase'] == 'train':
|
|
H, W, _ = img_GT.shape
|
|
assert H >= GT_size and W >= GT_size
|
|
|
|
H, W, C = img_LQ.shape
|
|
LQ_size = GT_size // scale
|
|
|
|
if self.doCrop:
|
|
# randomly crop
|
|
rnd_h = random.randint(0, max(0, H - LQ_size))
|
|
rnd_w = random.randint(0, max(0, W - LQ_size))
|
|
img_LQ = img_LQ[rnd_h:rnd_h + LQ_size, rnd_w:rnd_w + LQ_size, :]
|
|
rnd_h_GT, rnd_w_GT = int(rnd_h * scale), int(rnd_w * scale)
|
|
img_GT = img_GT[rnd_h_GT:rnd_h_GT + GT_size, rnd_w_GT:rnd_w_GT + GT_size, :]
|
|
img_PIX = img_PIX[rnd_h_GT:rnd_h_GT + GT_size, rnd_w_GT:rnd_w_GT + GT_size, :]
|
|
else:
|
|
img_LQ = cv2.resize(img_LQ, (LQ_size, LQ_size), interpolation=cv2.INTER_LINEAR)
|
|
img_GT = cv2.resize(img_GT, (GT_size, GT_size), interpolation=cv2.INTER_LINEAR)
|
|
img_PIX = cv2.resize(img_PIX, (GT_size, GT_size), interpolation=cv2.INTER_LINEAR)
|
|
|
|
# augmentation - flip, rotate
|
|
img_LQ, img_GT, img_PIX = util.augment([img_LQ, img_GT, img_PIX], self.opt['use_flip'],
|
|
self.opt['use_rot'])
|
|
|
|
if self.opt['color']: # change color space if necessary
|
|
img_LQ = util.channel_convert(C, self.opt['color'],
|
|
[img_LQ])[0] # TODO during val no definition
|
|
|
|
# BGR to RGB, HWC to CHW, numpy to tensor
|
|
if img_GT.shape[2] == 3:
|
|
img_GT = img_GT[:, :, [2, 1, 0]]
|
|
img_LQ = img_LQ[:, :, [2, 1, 0]]
|
|
img_PIX = img_PIX[:, :, [2, 1, 0]]
|
|
img_GT = torch.from_numpy(np.ascontiguousarray(np.transpose(img_GT, (2, 0, 1)))).float()
|
|
img_PIX = torch.from_numpy(np.ascontiguousarray(np.transpose(img_PIX, (2, 0, 1)))).float()
|
|
img_LQ = torch.from_numpy(np.ascontiguousarray(np.transpose(img_LQ, (2, 0, 1)))).float()
|
|
|
|
if LQ_path is None:
|
|
LQ_path = GT_path
|
|
return {'LQ': img_LQ, 'GT': img_GT, 'PIX': img_PIX, 'LQ_path': LQ_path, 'GT_path': GT_path}
|
|
|
|
def __len__(self):
|
|
return len(self.paths_GT)
|