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data.py
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import os
import os.path
import cv2
import glob
import h5py
from PIL import Image
import skimage
import skimage.io
import numpy as np
import pandas as pd
import torch
from torchvision import transforms
import torchvision.transforms.functional as TF
import utils
DATASET_REGISTRY = {}
def build_dataset(name, *args, **kwargs):
return DATASET_REGISTRY[name](*args, **kwargs)
def register_dataset(name):
def register_dataset_fn(fn):
if name in DATASET_REGISTRY:
raise ValueError("Cannot register duplicate dataset ({})".format(name))
DATASET_REGISTRY[name] = fn
return fn
return register_dataset_fn
@register_dataset("DAVIS")
def load_DAVIS(data, batch_size=100, num_workers=0, image_size=None, stride=64, n_frames=5):
train_dataset = DAVIS(data, datatype="train", patch_size=image_size, stride=stride, n_frames=n_frames)
train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=batch_size, num_workers=8, shuffle=True)
valid_dataset = DAVIS(data, datatype="val", n_frames=n_frames)
valid_loader = torch.utils.data.DataLoader(valid_dataset, batch_size=1, num_workers=8, shuffle=False)
test_dataset = DAVIS(data, datatype="test", n_frames=n_frames)
test_loader = torch.utils.data.DataLoader(test_dataset, batch_size=1, num_workers=8, shuffle=False)
return train_loader, valid_loader, test_loader
@register_dataset("ImageDAVIS")
def load_ImageDAVIS(data, batch_size=100, num_workers=0, image_size=None, stride=64, n_frames=1):
train_dataset = ImageDAVIS(data, datatype="train", patch_size=image_size, stride=stride)
train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=batch_size, num_workers=4, shuffle=True)
valid_dataset = ImageDAVIS(data, datatype="val")
valid_loader = torch.utils.data.DataLoader(valid_dataset, batch_size=1, num_workers=4, shuffle=False)
test_dataset = ImageDAVIS(data, datatype="test")
test_loader = torch.utils.data.DataLoader(test_dataset, batch_size=1, num_workers=4, shuffle=False)
return train_loader, valid_loader, test_loader
@register_dataset("Set8")
def load_Set8(data, batch_size=100, num_workers=0, n_frames=5):
test_dataset = Set8(data, n_frames=n_frames)
test_loader = torch.utils.data.DataLoader(test_dataset, batch_size=1, num_workers=8, shuffle=False)
return test_loader
@register_dataset("CTC")
def load_CTC(data, batch_size=100, num_workers=0, image_size=None, stride=64, n_frames=5):
train_dataset = CTC(data, patch_size=image_size, stride=stride, n_frames=n_frames)
train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=batch_size, num_workers=4, shuffle=True)
valid_dataset = CTC(data, n_frames=n_frames)
valid_loader = torch.utils.data.DataLoader(valid_dataset, batch_size=1, num_workers=4, shuffle=False)
return train_loader, valid_loader
@register_dataset("SingleVideo")
def load_SingleVideo(data, batch_size=8, dataset="DAVIS", video="giant-slalom",image_size=None, stride=64, n_frames=5,
aug=0, dist="G", mode="S", noise_std=30, min_noise=0, max_noise=100, sample=False, heldout=False):
train_dataset = SingleVideo(data, dataset=dataset, video=video, patch_size=image_size, stride=stride, n_frames=n_frames,
aug=aug, dist=dist, mode=mode, noise_std=noise_std, min_noise=min_noise, max_noise=max_noise,
sample=sample, heldout=heldout
)
test_dataset = SingleVideo(data, dataset=dataset, video=video, patch_size=None, stride=stride, n_frames=n_frames,
aug=0, dist=dist, mode=mode, noise_std=noise_std, min_noise=min_noise, max_noise=max_noise,
sample=False, heldout=False
)
train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=batch_size, num_workers=2, shuffle=True)
test_loader = torch.utils.data.DataLoader(test_dataset, batch_size=1, num_workers=1, shuffle=False)
return train_loader, test_loader
@register_dataset("Nanoparticles")
def load_Nanoparticles(data, batch_size=8, image_size=None, stride=64, n_frames=5, aug=0):
train_dataset = Nanoparticles(data, datatype="train", patch_size=image_size, stride=stride, n_frames=n_frames, aug=aug)
test_dataset = Nanoparticles(data, datatype="test", patch_size=None, stride=200, n_frames=n_frames, aug=0)
train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=batch_size, num_workers=2, shuffle=True)
test_loader = torch.utils.data.DataLoader(test_dataset, batch_size=1, num_workers=1, shuffle=False)
return train_loader, test_loader
@register_dataset("RawVideo")
def load_RawVideo(data, batch_size=8, image_size=None, stride=64, n_frames=5, aug=0, scenes=[7, 8, 9, 10, 11], isos = [1600, 3200, 6400, 12800, 25600]):
train_dataset = RawVideo(data, datatype="train", patch_size=image_size, stride=stride, n_frames=n_frames, aug=aug, scenes=scenes, isos=isos)
valid_dataset = RawVideo(data, datatype="val", patch_size=1080, stride=1920-1080, n_frames=n_frames, aug=0, scenes=scenes, isos=isos)
test_dataset = RawVideo(data, datatype="test", patch_size=None, stride=6
5D32
4, n_frames=n_frames, aug=0, scenes=scenes, isos=isos)
train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=batch_size, num_workers=2, shuffle=True)
valid_loader = torch.utils.data.DataLoader(valid_dataset, batch_size=1, num_workers=1, shuffle=False)
test_loader = torch.utils.data.DataLoader(test_dataset, batch_size=1, num_workers=1, shuffle=False)
return train_loader, valid_loader, test_loader
class DAVIS(torch.utils.data.Dataset):
def __init__(self, data_path, datatype="train", patch_size=None, stride=64, n_frames=5):
super().__init__()
self.data_path = data_path
self.datatype = datatype
self.size = patch_size
self.stride = stride
self.n_frames = n_frames
if self.datatype == "train":
self.folders = pd.read_csv(os.path.join(data_path, "ImageSets", "2017", "train.txt"), header=None)
elif self.datatype == "val":
self.folders = pd.read_csv(os.path.join(data_path, "ImageSets", "2017", "val.txt"), header=None)
else:
self.folders = pd.read_csv(os.path.join(data_path, "ImageSets", "2017", "test-dev.txt"), header=None)
self.len = 0
self.bounds = []
for folder in self.folders.values:
files = sorted(glob.glob(os.path.join(data_path, "JPEGImages", "480p", folder[0], "*.jpg")))
self.len += len(files)
self.bounds.append(self.len)
if self.size is not None:
self.n_H = (int((480-self.size)/self.stride)+1)
self.n_W = (int((854-self.size)/self.stride)+1)
self.n_patches = self.n_H * self.n_W
self.len *= self.n_patches
self.transform = transforms.Compose([transforms.ToTensor()])
def __len__(self):
return self.len
def __getitem__(self, index):
if self.size is not None:
patch = index % self.n_patches
index = index // self.n_patches
ends = 0
x = (self.n_frames-1) // 2
for i, bound in enumerate(self.bounds):
if index < bound:
folder = self.folders.values[i][0]
if i>0:
index -= self.bounds[i-1]
newbound = bound - self.bounds[i-1]
else:
newbound = bound
if(index < x):
ends = x-index
elif(newbound-1-index < x):
ends = -(x-(newbound-1-index))
break
files = sorted(glob.glob(os.path.join(self.data_path, "JPEGImages", "480p", folder, "*.jpg")))
Img = Image.open(files[index])
Img = np.array(Img)
for i in range(1,x+1):
end = max(0, ends)
off = max(0,i-x+end)
img = Image.open(files[index-i+off])
img = np.array(img)
Img = np.concatenate((img, Img), axis=2)
for i in range(1,x+1):
end = -min(0,ends)
off = max(0,i-x+end)
img = Image.open(files[index+i-off])
img = np.array(img)
Img = np.concatenate((Img, img), axis=2)
AE96
if self.size is not None:
nh = (patch // self.n_W)*self.stride
nw = (patch % self.n_W)*self.stride
Img = Img[nh:(nh+self.size), nw:(nw+self.size), :]
return self.transform(np.array(Img)).type(torch.FloatTensor)
class ImageDAVIS(torch.utils.data.Dataset):
def __init__(self, data_path, datatype="train", patch_size=None, stride=40):
super().__init__()
self.data_path = data_path
self.datatype = datatype
self.size = patch_size
self.stride = stride
if self.datatype == "train":
self.folders = pd.read_csv(os.path.join(data_path, "ImageSets", "2017", "train.txt"), header=None)
elif self.datatype == "val":
self.folders = pd.read_csv(os.path.join(data_path, "ImageSets", "2017", "val.txt"), header=None)
else:
self.folders = pd.read_csv(os.path.join(data_path, "ImageSets", "2017", "test-dev.txt"), header=None)
self.len = 0
self.bounds = []
for folder in self.folders.values:
files = sorted(glob.glob(os.path.join(data_path, "JPEGImages", "480p", folder[0], "*.jpg")))
self.len += len(files)
self.bounds.append(self.len)
if self.size is not None:
self.n_H = (int((480-self.size)/self.stride)+1)
self.n_W = (int((854-self.size)/self.stride)+1)
self.n_patches = self.n_H * self.n_W
self.len *= self.n_patches
self.transform = transforms.Compose([transforms.ToTensor()])
def __len__(self):
return self.len
def __getitem__(self, index):
if self.size is not None:
patch = index % self.n_patches
index = index // self.n_patches
for i, bound in enumerate(self.bounds):
if index < bound:
folder = self.folders.values[i][0]
if i>0:
index -= self.bounds[i-1]
break
files = sorted(glob.glob(os.path.join(self.data_path, "JPEGImages", "480p", folder, "*.jpg")))
Img = np.array(Image.open(files[index]))
if self.size is not None:
nh = (patch // self.n_W)*self.stride
nw = (patch % self.n_W)*self.stride
Img = Img[nh:(nh+self.size), nw:(nw+self.size), :]
return self.transform(Img).type(torch.FloatTensor)
class Set8(torch.utils.data.Dataset):
def __init__(self, data_path, n_frames=5, hop=1):
super().__init__()
self.data_path = data_path
self.len = 0
self.bounds = []
self.hop = hop
self.n_frames = n_frames
self.folders = []
self.folders += sorted(glob.glob(os.path.join(data_path, "GoPro/snowboard")))
self.folders += sorted(glob.glob(os.path.join(data_path, "GoPro/hypersmooth")))
self.folders += sorted(glob.glob(os.path.join(data_path, "GoPro/rafting")))
self.folders += sorted(glob.glob(os.path.join(data_path, "GoPro/motorbike")))
self.folders += sorted(glob.glob(os.path.join(data_path, "Derfs/tractor")))
self.folders += sorted(glob.glob(os.path.join(data_path, "Derfs/sunflower")))
self.folders += sorted(glob.glob(os.path.join(data_path, "Derfs/touchdown")))
self.folders += sorted(glob.glob(os.path.join(data_path, "Derfs/park_joy")))
for folder in self.folders:
files = sorted(glob.glob(os.path.join(folder, "*.png")))
self.len += len(files)
self.bounds.append(self.len)
self.transform = transforms.Compose([transforms.ToTensor()])
def __len__(self):
return self.len
def __getitem__(self, index):
ends = 0
x = ((self.n_frames-1) // 2)*self.hop
for i, bound in enumerate(self.bounds):
if index < bound:
folder = self.folders[i]
if i>0:
index -= self.bounds[i-1]
newbound = bound - self.bounds[i-1]
else:
newbound = bound
if(index < x):
ends = x-index
elif(newbound-1-index < x):
ends = -(x-(newbound-1-index))
break
files = sorted(glob.glob(os.path.join(folder, "*.png")))
Img = Image.open(files[index])
Img = np.array(Img)
for i in range(self.hop, x+1, self.hop):
end = max(0, ends)
off = max(0,i-x+end)
img = Image.open(files[index-i+off])
img = np.array(img)
Img = np.concatenate((img, Img), axis=2)
for i in range(self.hop, x+1, self.hop):
end = -min(0,ends)
off = max(0,i-x+end)
img = Image.open(files[index+i-off])
img = np.array(img)
Img = np.concatenate((Img, img), axis=2)
return self.transform(Img).type(torch.FloatTensor)
class CTC(torch.utils.data.Dataset):
def __init__(self, data_path, patch_size=None, stride=64, n_frames=5):
super().__init__()
self.data_path = data_path
self.size = patch_size
self.stride = stride
self.len = 0
self.bounds = [0]
self.nHs = []
self.nWs = []
self.n_frames = n_frames
parent_folders = sorted([x for x in glob.glob(os.path.join(data_path, "*/*")) if os.path.isdir(x)])
self.folders = []
for folder in parent_folders:
self.folders.append(os.path.join(folder, "01"))
self.folders.append(os.path.join(folder, "02"))
for folder in self.folders:
files = sorted(glob.glob(os.path.join(folder, "*.tif")))
if self.size is not None:
(h, w) = np.array(cv2.imread(files[0], cv2.IMREAD_GRAYSCALE)).shape
nH = (int((h-self.size)/self.stride)+1)
nW = (int((w-self.size)/self.stride)+1)
self.len += len(files)*nH*nW
self.nHs.append(nH)
self.nWs.append(nW)
else:
self.len += len(files)
self.bounds.append(self.len)
self.transform = transforms.Compose([transforms.ToTensor()])
def __len__(self):
return self.len
def __getitem__(self, index):
ends = 0
x = (self.n_frames-1) // 2
for i, bound in enumerate(self.bounds):
if index < bound:
folder = self.folders[i-1]
index -= self.bounds[i-1]
newbound = bound - self.bounds[i-1]
if self.size is not None:
nH = self.nHs[i-1]
nW = self.nWs[i-1]
patch = index % (nH*nW)
index = index // (nH*nW)
newbound = newbound // (nH*nW)
if(index < x):
ends = x-index
elif(newbound-1-index < x):
ends = -(x-(newbound-1-index))
break
files = sorted(glob.glob(os.path.join(folder, "*.tif")))
img = cv2.imread(files[index], cv2.IMREAD_GRAYSCALE)
(h, w) = np.array(img).shape
Img = np.reshape(np.array(img), (h,w,1))
for i in range(1,x+1):
end = max(0, ends)
off = max(0,i-x+end)
img = cv2.imread(files[index-i+off], cv2.IMREAD_GRAYSCALE)
img = np.reshape(np.array(img), (h,w,1))
Img = np.concatenate((img, Img), axis=2)
for i in range(1,x+1):
end = -min(0,ends)
off = max(0,i-x+end)
img = cv2.imread(files[index+i-off], cv2.IMREAD_GRAYSCALE)
img = np.reshape(np.array(img), (h,w,1))
Img = np.concatenate((Img, img), axis=2)
if self.size is not None:
nh = (patch // nW)*self.stride
nw = (patch % nW)*self.stride
Img = Img[nh:(nh+self.size), nw:(nw+self.size), :]
return self.transform(Img).type(torch.FloatTensor)
class SingleVideo(torch.utils.data.Dataset):
def __init__(self, data_path, dataset="DAVIS", video="giant-slalom", patch_size=None, stride=64, n_frames=5,
aug=0, dist="G", mode="S", noise_std=30, min_noise=0, max_noise=100, sample=True, heldout=False):
super().__init__()
self.data_path = data_path
self.dataset = dataset
self.size = patch_size
self.stride = stride
self.n_frames = n_frames
self.aug = aug
self.heldout = heldout
if dataset == "DAVIS":
self.files = sorted(glob.glob(os.path.join(data_path, "JPEGImages", "480p", video, "*.jpg")))
elif dataset == "GoPro" or dataset == "Derfs":
self.files = sorted(glob.glob(os.path.join(data_path, video, "*.png")))
elif dataset == "Vid3oC":
self.files = sorted(glob.glob(os.path.join(data_path, "TrainingHR", video, "*.png")))
elif dataset == "Nanoparticles":
self.files = sorted(glob.glob(os.path.join(data_path, "*.png")))
self.noisy_files = sorted(glob.glob(os.path.join(data_path, "*.npy")))
self.len = self.bound = len(self.files)
if self.heldout:
self.len -= 5
self.transform = transforms.Compose([transforms.ToTensor()])
self.reverse = transforms.Compose([transforms.ToPILImage()])
Img = Image.open(self.files[0])
Img = np.array(Img)
if dataset == "Nanoparticles":
H, W = Img.shape
else:
H, W, C = Img.shape
if not dataset == "Nanoparticles":
os.makedirs(os.path.join(data_path, f"Noisy_Videos_{int(noise_std)}"), exist_ok=True)
os.makedirs(os.path.join(data_path, f"Noisy_Videos_{int(noise_std)}", video), exist_ok=True)
self.noisy_folder = os.path.join(data_path, f"Noisy_Videos_{int(noise_std)}", video)
if sample:
for i in range(self.len):
Img = Image.open(self.files[i])
Img = self.transform(Img)
self.C, self.H, self.W = Img.shape
Noise = utils.get_noise(Img, dist=dist, mode=mode, min_noise=min_noise, max_noise=max_noise, noise_std=noise_std).numpy()
Img = Img + Noise
np.save(os.path.join(self.noisy_folder, os.path.basename(self.files[i])[:-3]+".npy"), Img)
self.noisy_files = sorted(glob.glob(os.path.join(self.noisy_folder, "*.npy")))
if self.size is not None:
self.n_H = (int((H-self.size)/self.stride)+1)
self.n_W = (int((W-self.size)/self.stride)+1)
self.n_patches = self.n_H * self.n_W
self.len *= self.n_patches
self.hflip = transforms.Compose([transforms.RandomHorizontalFlip(p=1)])
self.vflip = transforms.Compose([transforms.RandomVerticalFlip(p=1)])
if aug >= 1: # Horizonatal and Vertical Flips
self.len *= 4
if aug >= 2: # Reverse the Video
self.len *= 2
if aug >= 3: # Variable Frame Rate
self.len *= 4
def __len__(self):
return self.len
def __getitem__(self, index):
hop = 1
reverse = 0
flip = 0
if self.aug >= 3: # Variable Frame Rate
hop = index % 4 + 1
index = index // 4
if self.aug >= 2: # Reverse the Video
reverse = index % 2
index = index // 2
if self.aug >= 1: # Horizonatal and Vertical Flips
flip = index % 4
index = index // 4
if self.size is not None:
patch = index % self.n_patches
index = index // self.n_patches
ends = 0
x = ((self.n_frames-1) // 2)*hop
if index < x:
ends = x - index
elif self.bound-1-index < x:
ends = -(x-(self.bound-1-index))
Img = Image.open(self.files[index])
Img = np.array(Img)
if self.dataset == "Nanoparticles":
H, W = Img.shape
else:
H, W, C = Img.shape
if self.dataset == "Nanoparticles":
Img = Img.reshape(H, W, 1)
noisy_Img = np.load(self.noisy_files[index])
for i in range(hop, x+1, hop):
end = max(0, ends)
off = max(0,i-x+end)
img = Image.open(self.files[index-i+off])
img = np.array(img)
if self.dataset == "Nanoparticles":
img = img.reshape(H, W, 1)
noisy_img = np.load(self.noisy_files[index-i+off])
if reverse == 0:
Img = np.concatenate((img, Img), axis=2)
noisy_Img = np.concatenate((noisy_img, noisy_Img), axis=0)
else:
Img = np.concatenate((Img, img), axis=2)
noisy_Img = np.concatenate((noisy_Img, noisy_img), axis=0)
for i in range(hop, x+1, hop):
end = -min(0,ends)
off = max(0,i-x+end)
img = Image.open(self.files[index+i-off])
img = np.array(img)
if self.dataset == "Nanoparticles":
img = img.reshape(H, W, 1)
noisy_img = np.load(self.noisy_files[index+i-off])
if reverse == 0:
Img = np.concatenate((Img, img), axis=2)
noisy_Img = np.concatenate((noisy_Img, noisy_img), axis=0)
else:
Img = np.concatenate((img, Img), axis=2)
noisy_Img = np.concatenate((noisy_img, noisy_Img), axis=0)
if self.size is not None:
nh = (patch // self.n_W)*self.stride
nw = (patch % self.n_W)*self.stride
Img = Img[nh:(nh+self.size), nw:(nw+self.size), :]
noisy_Img = noisy_Img[:, nh:(nh+self.size), nw:(nw+self.size)]
if flip == 1:
Img = np.flip(Img, 1)
noisy_Img = np.flip(noisy_Img, 2)
elif flip == 2:
Img = np.flip(Img, 0)
noisy_Img = np.flip(noisy_Img, 1)
elif flip == 3:
Img = np.fl
10000
ip(Img, (1,0))
noisy_Img = np.flip(noisy_Img, (2,1))
return self.transform(np.array(Img)).type(torch.FloatTensor), torch.from_numpy(noisy_Img.copy())
class Nanoparticles(torch.utils.data.Dataset):
def __init__(self, data_path, datatype="train", patch_size=None, stride=64, n_frames=5, aug=0):
super().__init__()
self.data_path = data_path
self.size = patch_size
self.stride = stride
self.n_frames = n_frames
self.datatype = datatype
self.aug = aug
self.files = sorted(glob.glob(os.path.join(data_path, "*.npy")))
if datatype == "train":
self.files = self.files[0:35]
elif datatype == "test":
self.files = self.files
self.len = self.bound = len(self.files)
self.transform = transforms.Compose([transforms.ToTensor()])
self.reverse = transforms.Compose([transforms.ToPILImage()])
Img = np.load(self.files[0])
C, H, W = Img.shape
if self.size is not None:
self.n_H = (int((H-self.size)/self.stride)+1)
self.n_W = (int((W-self.size)/self.stride)+1)
self.n_patches = self.n_H * self.n_W
self.len *= self.n_patches
self.hflip = transforms.Compose([transforms.RandomHorizontalFlip(p=1)])
self.vflip = transforms.Compose([transforms.RandomVerticalFlip(p=1)])
if aug >= 1: # Horizonatal and Vertical Flips
self.len *= 4
if aug >= 2: # Reverse the Video
self.len *= 2
if aug >= 3: # Variable Frame Rate
self.len *= 4
def __len__(self):
return self.len
def __getitem__(self, index):
hop = 1
reverse = 0
flip = 0
if self.aug >= 3: # Variable Frame Rate
hop = index % 4 + 1
index = index // 4
if self.aug >= 2: # Reverse the Video
reverse = index % 2
index = index // 2
if self.aug >= 1: # Horizonatal and Vertical Flips
flip = index % 4
index = index // 4
if self.size is not None:
patch = index % self.n_patches
index = index // self.n_patches
ends = 0
x = ((self.n_frames-1) // 2)*hop
if index < x:
ends = x - index
Img = np.load(self.files[index])
C, H, W = Img.shape
for i in range(hop, x+1, hop):
end = max(0, ends)
off = max(0,i-x+end)
img = np.load(self.files[index-i+off])
if reverse == 0:
Img = np.concatenate((img, Img), axis=0)
else:
Img = np.concatenate((Img, img), axis=0)
if self.bound-1-index < x:
ends = -(x-(self.bound-1-index))
for i in range(hop, x+1, hop):
end = -min(0,ends)
off = max(0,i-x+end)
img = np.load(self.files[index+i-off])
if reverse == 0:
Img = np.concatenate((Img, img), axis=0)
else:
Img = np.concatenate((img, Img), axis=0)
if self.size is not None:
nh = (patch // self.n_W)*self.stride
nw = (patch % self.n_W)*self.stride
Img = Img[:, nh:(nh+self.size), nw:(nw+self.size)]
if flip == 1:
Img = np.flip(Img, 2)
elif flip == 2:
Img = np.flip(Img, 1)
elif flip == 3:
Img = np.flip(Img, (2,1))
return torch.from_numpy(Img.copy()).type(torch.FloatTensor)
class RawVideo(torch.utils.data.Dataset):
def __init__(self, data_path, datatype="train", patch_size=None, stride=64, n_frames=5, aug=0,
scenes=[7, 8, 9, 10, 11],
isos = [1600, 3200, 6400, 12800, 25600]):
super().__init__()
self.data_path = data_path
self.datatype = datatype
self.size = patch_size
self.stride = stride
self.n_frames = n_frames
self.aug = aug
self.noisy_path = os.path.join(self.data_path, "indoor_raw_noisy")
self.gt_path = os.path.join(self.data_path, "indoor_raw_gt")
self.scenes = scenes
self.isos = isos
if self.datatype == "train":
self.nr = 9 # noise_realisations
elif self.datatype == "val":
self.nr = 1 # only the 9th noise realisation used for heldout
elif self.datatype == "test":
self.nr = 10
self.fpv = self.bound = 7 # frames_per_video
self.len = self.fpv * self.nr * len(self.isos) * len(self.scenes)
self.transform = transforms.Compose([transforms.ToTensor()])
self.reverse = transforms.Compose([transforms.ToPILImage()])
Img = skimage.io.imread(os.path.join(self.noisy_path, f"scene{self.scenes[0]}",
f"ISO{self.isos[0]}", "frame1_noisy0.tiff"))
H, W = Img.shape
if self.size is not None:
self.n_H = (int((H-self.size)/self.stride)+1)
self.n_W = (int((W-self.size)/self.stride)+1)
self.n_patches = self.n_H * self.n_W
self.len *= self.n_patches
self.hflip = transforms.Compose([transforms.RandomHorizontalFlip(p=1)])
self.vflip = transforms.Compose([transforms.RandomVerticalFlip(p=1)])
if aug >= 1: # Horizonatal and Vertical Flips
self.len *= 4
if aug >= 2: # Reverse the Video
self.len *= 2
def __len__(self):
return self.len
def __getitem__(self, index):
hop = 1
reverse = 0
flip = 0
if self.aug >= 2: # Reverse the Video
reverse = index % 2
index = index // 2
if self.aug >= 1: # Horizonatal and Vertical Flips
flip = index % 4
index = index // 4
if self.size is not None:
patch = index % self.n_patches
index = index // self.n_patches
scene = index % len(self.scenes)
index = index // len(self.scenes)
iso = index % len(self.isos)
index = index // len(self.isos)
if self.datatype == "val":
nr = 9
else:
nr = index % self.nr
index = index // self.nr
ends = 0
x = ((self.n_frames-1) // 2)*hop
if index < x:
ends = x - index
elif self.bound-1-index < x:
ends = -(x-(self.bound-1-index))
Img = skimage.io.imread(os.path.join(self.gt_path,
f"scene{self.scenes[scene]}",
f"ISO{self.isos[iso]}",
f"frame{index+1}_clean_and_slightly_denoised.tiff"))
H, W = Img.shape
Img = Img.reshape(H, W, 1)
noisy_Img = skimage.io.imread(os.path.join(self.noisy_path,
f"scene{self.scenes[scene]}",
f"ISO{self.isos[iso]}",
f"frame{index+1}_noisy{nr}.tiff"))
noisy_Img = noisy_Img.reshape(H, W, 1)
for i in range(hop, x+1, hop):
end = max(0, ends)
off = max(0,i-x+end)
# img = Image.open(self.files[index-i+off])
img = skimage.io.imread(os.path.join(self.gt_path,
f"scene{self.scenes[scene]}",
f"ISO{self.isos[iso]}",
f"frame{index-i+off+1}_clean_and_slightly_denoised.tiff"))
img = img.reshape(H, W, 1)
# noisy_img = np.load(self.noisy_files[index-i+off])
noisy_img = skimage.io.imread(os.path.join(self.noisy_path,
f"scene{self.scenes[scene]}",
f"ISO{self.isos[iso]}",
f"frame{index-i+off+1}_noisy{nr}.tiff"))
noisy_img = noisy_img.reshape(H, W, 1)
if reverse == 0:
Img = np.concatenate((img, Img), axis=2)
noisy_Img = np.concatenate((noisy_img, noisy_Img), axis=2)
else:
Img = np.concatenate((Img, img), axis=2)
noisy_Img = np.concatenate((noisy_Img, noisy_img), axis=2)
for i in range(hop, x+1, hop):
end = -min(0,ends)
off = max(0,i-x+end)
# img = Image.open(self.files[index+i-off])
img = skimage.io.imread(os.path.join(self.gt_path,
f"scene{self.scenes[scene]}",
f"ISO{self.isos[iso]}",
f"frame{index+i-off+1}_clean_and_slightly_denoised.tiff"))
img = img.reshape(H, W, 1)
# noisy_img = np.load(self.noisy_files[index+i-off])
noisy_img = skimage.io.imread(os.path.join(self.noisy_path,
f"scene{self.scenes[scene]}",
f"ISO{self.isos[iso]}",
f"frame{index+i-off+1}_noisy{nr}.tiff"))
noisy_img = noisy_img.reshape(H, W, 1)
if reverse == 0:
Img = np.concatenate((Img, img), axis=2)
noisy_Img = np.concatenate((noisy_Img, noisy_img), axis=2)
else:
Img = np.concatenate((img, Img), axis=2)
noisy_Img = np.concatenate((noisy_img, noisy_Img), axis=2)
if self.size is not None:
nh = (patch // self.n_W)*self.stride
nw = (patch % self.n_W)*self.stride
Img = Img[nh:(nh+self.size), nw:(nw+self.size), :]
noisy_Img = noisy_Img[nh:(nh+self.size), nw:(nw+self.size), :]
if flip == 1:
Img = np.flip(Img, 1)
noisy_Img = np.flip(noisy_Img, 1)
elif flip == 2:
Img = np.flip(Img, 0)
noisy_Img = np.flip(noisy_Img, 0)
elif flip == 3:
Img = np.flip(Img, (1,0))
noisy_Img = np.flip(noisy_Img, (1,0))
Img = Img.astype(np.float32)
noisy_Img = noisy_Img.astype(np.float32)
Img = (Img-240)/(2**12-1-240)
noisy_Img = (noisy_Img-240)/(2**12-1-240)
return self.transform(np.array(Img)).type(torch.FloatTensor), self.transform(np.array(noisy_Img)).type(torch.FloatTensor)