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data.py
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data.py
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import os
import cv2
import glob
import torch
import random
import numpy as np
def get_dataset(config, type):
data = REDS_Dataset(config, type=type)
if type == 'train':
data_loader = torch.utils.data.DataLoader(data, batch_size=config.batch_size, drop_last=True, shuffle=True, num_workers=int(config.nThreads), pin_memory=True)
elif type == 'val':
data_loader = torch.utils.data.DataLoader(data, batch_size=1, drop_last=False, shuffle=False, num_workers=int(config.nThreads), pin_memory=True)
elif type == 'test':
data_loader = torch.utils.data.DataLoader(data, batch_size=1, drop_last=False, shuffle=False, num_workers=int(config.nThreads), pin_memory=True)
else:
raise NotImplementedError('not implemented for this mode: {}!'.format(type))
return data_loader
class REDS_Dataset:
def __init__(self, config, type):
self.config = config
self.type = type
self.num_seq = self.config.num_seq
bath_path = None
if type == 'train':
bath_path = os.path.join(config.dataset_path, 'train_blur_bicubic')
if type == 'val':
bath_path = os.path.join(config.dataset_path, 'val_blur_bicubic')
if type == 'test':
bath_path = os.path.join(config.dataset_path, 'val_blur_bicubic')
self.seq_path = self.get_seq_path(bath_path)
self.num_data = len(self.seq_path)
print(f'num {type} dataset: {self.num_data}')
def __getitem__(self, idx):
# input
lr_blur_path = self.seq_path[idx]
lr_blur_seq = [cv2.imread(path) for path in lr_blur_path]
lr_blur_seq = np.stack(lr_blur_seq, axis=0)
if self.type == 'train' or self.type == 'val':
# for TA loss
lr_sharp_path = [os.path.normpath(path.replace('blur', 'sharp')) for path in lr_blur_path]
lr_sharp_seq = [cv2.imread(path) for path in lr_sharp_path]
lr_sharp_seq = np.stack(lr_sharp_seq, axis=0)
# GT
hr_sharp_path = [os.path.normpath(path.replace('blur_bicubic', 'sharp').replace('X4', '')) for path in lr_blur_path]
hr_sharp_seq = [cv2.imread(path) for path in hr_sharp_path]
hr_sharp_seq = np.stack(hr_sharp_seq, axis=0)
# RAFT pseudo-GT optical flow
flow = []
img_c_name = os.path.basename(lr_blur_path[self.num_seq // 2]).replace('.png', '')
for i in range(self.num_seq):
if i == self.num_seq // 2:
flow.append(np.zeros_like(flow[0]))
continue
filename = os.path.normpath(lr_blur_path[i].replace("blur", "flow"))
img_name = os.path.basename(lr_blur_path[i].replace('.png', ''))
temp = np.load(f'{filename.replace(img_name + ".png", img_c_name + "_" + img_name)}.npy')
flow.append(temp)
flow = np.stack(flow, axis=0)
if self.type == 'train':
lr_blur_seq, hr_sharp_seq, lr_sharp_seq, flow = self.get_random_patch(lr_blur_seq, hr_sharp_seq, lr_sharp_seq, flow)
lr_blur_seq, hr_sharp_seq, lr_sharp_seq, flow = self.augment(lr_blur_seq, hr_sharp_seq, lr_sharp_seq, flow)
return self.np2tensor(lr_blur_seq), self.np2tensor(hr_sharp_seq), self.np2tensor(lr_sharp_seq), self.flow2tensor(flow)
if self.type == 'val':
return self.np2tensor(lr_blur_seq), self.np2tensor(hr_sharp_seq), self.np2tensor(lr_sharp_seq), self.flow2tensor(flow)
if self.type == 'test':
filename = lr_blur_path[self.num_seq // 2]
return self.np2tensor(lr_blur_seq), filename
def get_random_patch(self, lr_blur_seq, hr_sharp_seq, lr_sharp_seq, flow):
ih, iw, c = lr_blur_seq[0].shape
tp = self.config.patch_size
ip = tp // self.config.scale
ix = random.randrange(0, iw - ip + 1)
iy = random.randrange(0, ih - ip + 1)
(tx, ty) = (self.config.scale * ix, self.config.scale * iy)
lr_blur_seq = lr_blur_seq[:, iy:iy + ip, ix:ix + ip, :]
hr_sharp_seq = hr_sharp_seq[:, ty:ty + tp, tx:tx + tp, :]
lr_sharp_seq = lr_sharp_seq[:, iy:iy + ip, ix:ix + ip, :]
flow = flow[:, iy:iy + ip, ix:ix + ip, :]
return lr_blur_seq, hr_sharp_seq, lr_sharp_seq, flow
def augment(self, lr_blur_seq, hr_sharp_seq, lr_sharp_seq, flow):
# random horizontal flip
if random.random() < 0.5:
lr_blur_seq = lr_blur_seq[:, :, ::-1, :]
hr_sharp_seq = hr_sharp_seq[:, :, ::-1, :]
lr_sharp_seq = lr_sharp_seq[:, :, ::-1, :]
flow = flow[:, :, ::-1, :]
flow[:, :, :, 0] *= -1
# random vertical flip
if random.random() < 0.5:
lr_blur_seq = lr_blur_seq[:, ::-1, :, :]
hr_sharp_seq = hr_sharp_seq[:, ::-1, :, :]
lr_sharp_seq = lr_sharp_seq[:, ::-1, :, :]
flow = flow[:, ::-1, :, :]
flow[:, :, :, 1] *= -1
return lr_blur_seq, hr_sharp_seq, lr_sharp_seq, flow
def np2tensor(self, x):
# x shape: [T, H, W, C]
# reshape to [C, T, H, W]
ts = (3, 0, 1, 2)
x = torch.Tensor(x.transpose(ts).astype(float)).mul_(1.0)
# normalization [0,1]
x = x / 255.0
return x
def flow2tensor(self, flow):
# flow shape: [T, H, W, C]
# reshape to [C, T, H, W]
ts = (3, 0, 1, 2)
flow = torch.Tensor(flow.transpose(ts).astype(float)).mul_(1.0)
return flow
def get_seq_path(self, bath_path):
seq_list = []
dir_list = glob.glob(os.path.join(bath_path, '*/*/*/*'))
for dir in dir_list:
frame_list = sorted(glob.glob(os.path.join(dir, '*.png')))
start = (self.num_seq - 1) // 2
end = len(frame_list) - (self.num_seq - 1) // 2
for i in range(start, end):
frame_seq = []
for seq_num in range(self.num_seq):
frame_seq.append(frame_list[i + seq_num - start])
seq_list.append(frame_seq)
return seq_list
def __len__(self):
return self.num_data
class Custom_Dataset:
def __init__(self, config):
self.config = config
self.num_seq = self.config.num_seq
bath_path = os.path.join(config.custom_path)
self.seq_path = self.get_seq_path(bath_path)
self.num_data = len(self.seq_path)
print(f'num custom dataset: {self.num_data}')
def __getitem__(self, idx):
# input
lr_blur_path = self.seq_path[idx]
lr_blur_seq = [cv2.imread(path) for path in lr_blur_path]
lr_blur_seq = np.stack(lr_blur_seq, axis=0)
filename = lr_blur_path[self.num_seq // 2]
return self.np2tensor(lr_blur_seq), filename
def np2tensor(self, x):
# x shape: [T, H, W, C]
# reshape to [C, T, H, W]
ts = (3, 0, 1, 2)
x = torch.Tensor(x.transpose(ts).astype(float)).mul_(1.0)
# normalization [0,1]
x = x / 255.0
return x
def get_seq_path(self, bath_path):
seq_list = []
frame_list = sorted(glob.glob(os.path.join(bath_path, '*.png')))
start = (self.num_seq - 1) // 2
end = len(frame_list) - (self.num_seq - 1) // 2
for i in range(start, end):
frame_seq = []
for seq_num in range(self.num_seq):
frame_seq.append(frame_list[i + seq_num - start])
seq_list.append(frame_seq)
return seq_list
def __len__(self):
return self.num_data