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dataset.py
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dataset.py
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import torch.utils.data as data
import torch
import numpy as np
import os
from os import listdir
from os.path import join
from PIL import Image, ImageOps
import random
import pyflow
from skimage import img_as_float
from random import randrange
import os.path
def is_image_file(filename):
return any(filename.endswith(extension) for extension in [".png", ".jpg", ".jpeg"])
def load_img(filepath, nFrames, scale, other_dataset, upscale_only):
seq = [i for i in range(1, nFrames)]
#random.shuffle(seq) #if random sequence
if other_dataset:
if upscale_only:
target = Image.open(filepath).convert('RGB')
input=target
else:
target = modcrop(Image.open(filepath).convert('RGB'),scale)
input=target.resize((int(target.size[0]/scale),int(target.size[1]/scale)), Image.BICUBIC)
char_len = len(filepath)
neigbor=[]
for i in seq:
index = int(filepath[char_len-7:char_len-4])-i
file_name=filepath[0:char_len-7]+'{0:03d}'.format(index)+'.png'
if os.path.exists(file_name):
if upscale_only:
temp = Image.open(filepath[0:char_len-7]+'{0:03d}'.format(index)+'.png').convert('RGB')
else:
temp = modcrop(Image.open(filepath[0:char_len-7]+'{0:03d}'.format(index)+'.png').convert('RGB'),scale).resize((int(target.size[0]/scale),int(target.size[1]/scale)), Image.BICUBIC)
neigbor.append(temp)
else:
# print('neigbor frame is not exist')
temp = input
neigbor.append(temp)
else:
if upscale_only:
target = Image.open(join(filepath,'im'+str(nFrames)+'.png')).convert('RGB')
input = target
neigbor = [Image.open(filepath+'/im'+str(j)+'.png').convert('RGB') for j in reversed(seq)]
else:
target = modcrop(Image.open(join(filepath,'im'+str(nFrames)+'.png')).convert('RGB'), scale)
input = target.resize((int(target.size[0]/scale), int(target.size[1]/scale)), Image.BICUBIC)
neigbor = [modcrop(Image.open(filepath+'/im'+str(j)+'.png').convert('RGB'), scale).resize((int(target.size[0]/scale),int(target.size[1]/scale)), Image.BICUBIC) for j in reversed(seq)]
return target, input, neigbor
def load_img_future(filepath, nFrames, scale, other_dataset, upscale_only):
tt = int(nFrames/2)
if other_dataset:
if upscale_only:
target = Image.open(filepath).convert('RGB')
input = target
else:
target = modcrop(Image.open(filepath).convert('RGB'),scale)
input = target.resize((int(target.size[0]/scale),int(target.size[1]/scale)), Image.BICUBIC)
char_len = len(filepath)
neigbor=[]
if nFrames%2 == 0:
seq = [x for x in range(-tt,tt) if x!=0] # or seq = [x for x in range(-tt+1,tt+1) if x!=0]
else:
seq = [x for x in range(-tt,tt+1) if x!=0]
#random.shuffle(seq) #if random sequence
for i in seq:
index1 = int(filepath[char_len-7:char_len-4])+i
file_name1=filepath[0:char_len-7]+'{0:03d}'.format(index1)+'.png'
if os.path.exists(file_name1):
if upscale_only:
temp = Image.open(file_name1).convert('RGB')
else:
temp = modcrop(Image.open(file_name1).convert('RGB'), scale).resize((int(target.size[0]/scale),int(target.size[1]/scale)), Image.BICUBIC)
neigbor.append(temp)
else:
# print('neigbor frame- is not exist')
temp=input
neigbor.append(temp)
else:
if upscale_only:
target = Image.open(join(filepath,'im4.png')).convert('RGB')
input = target
else:
target = modcrop(Image.open(join(filepath,'im4.png')).convert('RGB'),scale)
input = target.resize((int(target.size[0]/scale),int(target.size[1]/scale)), Image.BICUBIC)
neigbor = []
seq = [x for x in range(4-tt,5+tt) if x!=4]
#random.shuffle(seq) #if random sequence
for j in seq:
neigbor.append(modcrop(Image.open(filepath+'/im'+str(j)+'.png').convert('RGB'), scale).resize((int(target.size[0]/scale),int(target.size[1]/scale)), Image.BICUBIC))
return target, input, neigbor
def get_flow(im1, im2):
im1 = np.array(im1)
im2 = np.array(im2)
im1 = im1.astype(float) / 255.
im2 = im2.astype(float) / 255.
# Flow Options:
alpha = 0.012
ratio = 0.75
minWidth = 20
nOuterFPIterations = 7
nInnerFPIterations = 1
nSORIterations = 30
colType = 0 # 0 or default:RGB, 1:GRAY (but pass gray image with shape (h,w,1))
u, v, im2W = pyflow.coarse2fine_flow(im1, im2, alpha, ratio, minWidth, nOuterFPIterations, nInnerFPIterations,nSORIterations, colType)
flow = np.concatenate((u[..., None], v[..., None]), axis=2)
#flow = rescale_flow(flow,0,1)
return flow
def rescale_flow(x,max_range,min_range):
max_val = np.max(x)
min_val = np.min(x)
return (max_range-min_range)/(max_val-min_val)*(x-max_val)+max_range
def modcrop(img, modulo):
(ih, iw) = img.size
ih = ih - (ih%modulo)
iw = iw - (iw%modulo)
img = img.crop((0, 0, ih, iw))
return img
def get_patch(img_in, img_tar, img_nn, patch_size, scale, nFrames, ix=-1, iy=-1):
(ih, iw) = img_in.size
(th, tw) = (scale * ih, scale * iw)
patch_mult = scale #if len(scale) > 1 else 1
tp = patch_mult * patch_size
ip = tp // scale
if ix == -1:
ix = random.randrange(0, iw - ip + 1)
if iy == -1:
iy = random.randrange(0, ih - ip + 1)
(tx, ty) = (scale * ix, scale * iy)
img_in = img_in.crop((iy,ix,iy + ip, ix + ip))#[:, iy:iy + ip, ix:ix + ip]
img_tar = img_tar.crop((ty,tx,ty + tp, tx + tp))#[:, ty:ty + tp, tx:tx + tp]
img_nn = [j.crop((iy,ix,iy + ip, ix + ip)) for j in img_nn] #[:, iy:iy + ip, ix:ix + ip]
info_patch = {
'ix': ix, 'iy': iy, 'ip': ip, 'tx': tx, 'ty': ty, 'tp': tp}
return img_in, img_tar, img_nn, info_patch
def augment(img_in, img_tar, img_nn, flip_h=True, rot=True):
info_aug = {'flip_h': False, 'flip_v': False, 'trans': False}
if random.random() < 0.5 and flip_h:
img_in = ImageOps.flip(img_in)
img_tar = ImageOps.flip(img_tar)
img_nn = [ImageOps.flip(j) for j in img_nn]
info_aug['flip_h'] = True
if rot:
if random.random() < 0.5:
img_in = ImageOps.mirror(img_in)
img_tar = ImageOps.mirror(img_tar)
img_nn = [ImageOps.mirror(j) for j in img_nn]
info_aug['flip_v'] = True
if random.random() < 0.5:
img_in = img_in.rotate(180)
img_tar = img_tar.rotate(180)
img_nn = [j.rotate(180) for j in img_nn]
info_aug['trans'] = True
return img_in, img_tar, img_nn, info_aug
def rescale_img(img_in, scale):
size_in = img_in.size
new_size_in = tuple([int(x * scale) for x in size_in])
img_in = img_in.resize(new_size_in, resample=Image.BICUBIC)
return img_in
class DatasetFromFolder(data.Dataset):
def __init__(self, image_dir,nFrames, upscale_factor, data_augmentation, file_list, other_dataset, patch_size, future_frame, transform=None):
super(DatasetFromFolder, self).__init__()
alist = [line.rstrip() for line in open(join(image_dir,file_list))]
self.image_filenames = [join(image_dir,x) for x in alist]
self.nFrames = nFrames
self.upscale_factor = upscale_factor
self.transform = transform
self.data_augmentation = data_augmentation
self.other_dataset = other_dataset
self.patch_size = patch_size
self.future_frame = future_frame
def __getitem__(self, index):
if self.future_frame:
target, input, neigbor = load_img_future(self.image_filenames[index], self.nFrames, self.upscale_factor, self.other_dataset)
else:
target, input, neigbor = load_img(self.image_filenames[index], self.nFrames, self.upscale_factor, self.other_dataset)
if self.patch_size != 0:
input, target, neigbor, _ = get_patch(input,target,neigbor,self.patch_size, self.upscale_factor, self.nFrames)
if self.data_augmentation:
input, target, neigbor, _ = augment(input, target, neigbor)
flow = [get_flow(input,j) for j in neigbor]
bicubic = rescale_img(input, self.upscale_factor)
if self.transform:
target = self.transform(target)
input = self.transform(input)
bicubic = self.transform(bicubic)
neigbor = [self.transform(j) for j in neigbor]
flow = [torch.from_numpy(j.transpose(2,0,1)) for j in flow]
return input, target, neigbor, flow, bicubic
def __len__(self):
return len(self.image_filenames)
class DatasetFromFolderTest(data.Dataset):
def __init__(self, image_dir, nFrames, upscale_factor, file_list, other_dataset, future_frame, transform=None, upscale_only=False):
super(DatasetFromFolderTest, self).__init__()
alist = [line.rstrip() for line in open(join(image_dir,file_list))]
self.image_filenames = [join(image_dir,x) for x in alist]
self.nFrames = nFrames
self.upscale_factor = upscale_factor
self.transform = transform
self.other_dataset = other_dataset
self.future_frame = future_frame
self.upscale_only = upscale_only
def __getitem__(self, index):
if self.future_frame:
target, input, neigbor = load_img_future(self.image_filenames[index], self.nFrames, self.upscale_factor, self.other_dataset, self.upscale_only)
else:
target, input, neigbor = load_img(self.image_filenames[index], self.nFrames, self.upscale_factor, self.other_dataset, self.upscale_only)
flow = [get_flow(input,j) for j in neigbor]
bicubic = rescale_img(input, self.upscale_factor)
if self.transform:
target = self.transform(target)
input = self.transform(input)
bicubic = self.transform(bicubic)
neigbor = [self.transform(j) for j in neigbor]
flow = [torch.from_numpy(j.transpose(2,0,1)) for j in flow]
return input, target, neigbor, flow, bicubic
def __len__(self):
return len(self.image_filenames)