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'''
This code is partially borrowed from IFRNet (https://github.com/ltkong218/IFRNet).
'''
import os
import cv2
import torch
import random
import numpy as np
from torch.utils.data import Dataset
from utils.utils import read
def random_resize(img0, imgt, img1, flow, p=0.1):
if random.uniform(0, 1) < p:
img0 = cv2.resize(img0, dsize=None, fx=2.0, fy=2.0, interpolation=cv2.INTER_LINEAR)
imgt = cv2.resize(imgt, dsize=None, fx=2.0, fy=2.0, interpolation=cv2.INTER_LINEAR)
img1 = cv2.resize(img1, dsize=None, fx=2.0, fy=2.0, interpolation=cv2.INTER_LINEAR)
flow = cv2.resize(flow, dsize=None, fx=2.0, fy=2.0, interpolation=cv2.INTER_LINEAR) * 2.0
return img0, imgt, img1, flow
def random_crop(img0, imgt, img1, flow, crop_size=(224, 224)):
h, w = crop_size[0], crop_size[1]
ih, iw, _ = img0.shape
x = np.random.randint(0, ih-h+1)
y = np.random.randint(0, iw-w+1)
img0 = img0[x:x+h, y:y+w, :]
imgt = imgt[x:x+h, y:y+w, :]
img1 = img1[x:x+h, y:y+w, :]
flow = flow[x:x+h, y:y+w, :]
return img0, imgt, img1, flow
def random_reverse_channel(img0, imgt, img1, flow, p=0.5):
if random.uniform(0, 1) < p:
img0 = img0[:, :, ::-1]
imgt = imgt[:, :, ::-1]
img1 = img1[:, :, ::-1]
return img0, imgt, img1, flow
def random_vertical_flip(img0, imgt, img1, flow, p=0.3):
if random.uniform(0, 1) < p:
img0 = img0[::-1]
imgt = imgt[::-1]
img1 = img1[::-1]
flow = flow[::-1]
flow = np.concatenate((flow[:, :, 0:1], -flow[:, :, 1:2], flow[:, :, 2:3], -flow[:, :, 3:4]), 2)
return img0, imgt, img1, flow
def random_horizontal_flip(img0, imgt, img1, flow, p=0.5):
if random.uniform(0, 1) < p:
img0 = img0[:, ::-1]
imgt = imgt[:, ::-1]
img1 = img1[:, ::-1]
flow = flow[:, ::-1]
flow = np.concatenate((-flow[:, :, 0:1], flow[:, :, 1:2], -flow[:, :, 2:3], flow[:, :, 3:4]), 2)
return img0, imgt, img1, flow
def random_rotate(img0, imgt, img1, flow, p=0.05):
if random.uniform(0, 1) < p:
img0 = img0.transpose((1, 0, 2))
imgt = imgt.transpose((1, 0, 2))
img1 = img1.transpose((1, 0, 2))
flow = flow.transpose((1, 0, 2))
flow = np.concatenate((flow[:, :, 1:2], flow[:, :, 0:1], flow[:, :, 3:4], flow[:, :, 2:3]), 2)
return img0, imgt, img1, flow
def random_reverse_time(img0, imgt, img1, flow, p=0.5):
if random.uniform(0, 1) < p:
tmp = img1
img1 = img0
img0 = tmp
flow = np.concatenate((flow[:, :, 2:4], flow[:, :, 0:2]), 2)
return img0, imgt, img1, flow
class Vimeo90K_Train_Dataset(Dataset):
def __init__(self,
dataset_dir='data/vimeo_triplet',
flow_dir=None,
augment=True,
crop_size=(224, 224)):
self.dataset_dir = dataset_dir
self.augment = augment
self.crop_size = crop_size
self.img0_list = []
self.imgt_list = []
self.img1_list = []
self.flow_t0_list = []
self.flow_t1_list = []
if flow_dir is None:
flow_dir = 'flow'
with open(os.path.join(dataset_dir, 'tri_trainlist.txt'), 'r') as f:
for i in f:
name = str(i).strip()
if(len(name) <= 1):
continue
self.img0_list.append(os.path.join(dataset_dir, 'sequences', name, 'im1.png'))
self.imgt_list.append(os.path.join(dataset_dir, 'sequences', name, 'im2.png'))
self.img1_list.append(os.path.join(dataset_dir, 'sequences', name, 'im3.png'))
self.flow_t0_list.append(os.path.join(dataset_dir, flow_dir, name, 'flow_t0.flo'))
self.flow_t1_list.append(os.path.join(dataset_dir, flow_dir, name, 'flow_t1.flo'))
def __len__(self):
return len(self.imgt_list)
def __getitem__(self, idx):
img0 = read(self.img0_list[idx])
imgt = read(self.imgt_list[idx])
img1 = read(self.img1_list[idx])
flow_t0 = read(self.flow_t0_list[idx])
flow_t1 = read(self.flow_t1_list[idx])
flow = np.concatenate((flow_t0, flow_t1), 2).astype(np.float64)
if self.augment == True:
img0, imgt, img1, flow = random_resize(img0, imgt, img1, flow, p=0.1)
img0, imgt, img1, flow = random_crop(img0, imgt, img1, flow, crop_size=self.crop_size)
img0, imgt, img1, flow = random_reverse_channel(img0, imgt, img1, flow, p=0.5)
img0, imgt, img1, flow = random_vertical_flip(img0, imgt, img1, flow, p=0.3)
img0, imgt, img1, flow = random_horizontal_flip(img0, imgt, img1, flow, p=0.5)
img0, imgt, img1, flow = random_rotate(img0, imgt, img1, flow, p=0.05)
img0, imgt, img1, flow = random_reverse_time(img0, imgt, img1, flow, p=0.5)
img0 = torch.from_numpy(img0.transpose((2, 0, 1)).astype(np.float32) / 255.0)
imgt = torch.from_numpy(imgt.transpose((2, 0, 1)).astype(np.float32) / 255.0)
img1 = torch.from_numpy(img1.transpose((2, 0, 1)).astype(np.float32) / 255.0)
flow = torch.from_numpy(flow.transpose((2, 0, 1)).astype(np.float32))
embt = torch.from_numpy(np.array(1/2).reshape(1, 1, 1).astype(np.float32))
return {'img0': img0.float(), 'imgt': imgt.float(), 'img1': img1.float(), 'flow': flow.float(), 'embt': embt}
class Vimeo90K_Test_Dataset(Dataset):
def __init__(self, dataset_dir='data/vimeo_triplet'):
self.dataset_dir = dataset_dir
self.img0_list = []
self.imgt_list = []
self.img1_list = []
self.flow_t0_list = []
self.flow_t1_list = []
with open(os.path.join(dataset_dir, 'tri_testlist.txt'), 'r') as f:
for i in f:
name = str(i).strip()
if(len(name) <= 1):
continue
self.img0_list.append(os.path.join(dataset_dir, 'sequences', name, 'im1.png'))
self.imgt_list.append(os.path.join(dataset_dir, 'sequences', name, 'im2.png'))
self.img1_list.append(os.path.join(dataset_dir, 'sequences', name, 'im3.png'))
self.flow_t0_list.append(os.path.join(dataset_dir, 'flow', name, 'flow_t0.flo'))
self.flow_t1_list.append(os.path.join(dataset_dir, 'flow', name, 'flow_t1.flo'))
def __len__(self):
return len(self.imgt_list)
def __getitem__(self, idx):
img0 = read(self.img0_list[idx])
imgt = read(self.imgt_list[idx])
img1 = read(self.img1_list[idx])
flow_t0 = read(self.flow_t0_list[idx])
flow_t1 = read(self.flow_t1_list[idx])
flow = np.concatenate((flow_t0, flow_t1), 2)
img0 = torch.from_numpy(img0.transpose((2, 0, 1)).astype(np.float32) / 255.0)
imgt = torch.from_numpy(imgt.transpose((2, 0, 1)).astype(np.float32) / 255.0)
img1 = torch.from_numpy(img1.transpose((2, 0, 1)).astype(np.float32) / 255.0)
flow = torch.from_numpy(flow.transpose((2, 0, 1)).astype(np.float32))
embt = torch.from_numpy(np.array(1/2).reshape(1, 1, 1).astype(np.float32))
return {'img0': img0.float(),
'imgt': imgt.float(),
'img1': img1.float(),
'flow': flow.float(),
'embt': embt}
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