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import os
import cv2
import ast
import torch
import numpy as np
import random
from torch.utils.data import DataLoader, Dataset
cv2.setNumThreads(1)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
class VimeoDataset(Dataset):
def __init__(self, dataset_name, batch_size=32):
self.batch_size = batch_size
self.dataset_name = dataset_name
self.h = 256
self.w = 448
self.data_root = 'vimeo_triplet'
self.image_root = os.path.join(self.data_root, 'sequences')
train_fn = os.path.join(self.data_root, 'tri_trainlist.txt')
test_fn = os.path.join(self.data_root, 'tri_testlist.txt')
with open(train_fn, 'r') as f:
self.trainlist = f.read().splitlines()
with open(test_fn, 'r') as f:
self.testlist = f.read().splitlines()
self.load_data()
def __len__(self):
return len(self.meta_data)
def load_data(self):
cnt = int(len(self.trainlist) * 0.95)
if self.dataset_name == 'train':
self.meta_data = self.trainlist[:cnt]
elif self.dataset_name == 'test':
self.meta_data = self.testlist
else:
self.meta_data = self.trainlist[cnt:]
def crop(self, img0, gt, img1, h, w):
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, :]
img1 = img1[x:x+h, y:y+w, :]
gt = gt[x:x+h, y:y+w, :]
return img0, gt, img1
def getimg(self, index):
imgpath = os.path.join(self.image_root, self.meta_data[index])
imgpaths = [imgpath + '/im1.png', imgpath + '/im2.png', imgpath + '/im3.png']
# Load images
img0 = cv2.imread(imgpaths[0])
gt = cv2.imread(imgpaths[1])
img1 = cv2.imread(imgpaths[2])
timestep = 0.5
return img0, gt, img1, timestep
# RIFEm with Vimeo-Septuplet
# imgpaths = [imgpath + '/im1.png', imgpath + '/im2.png', imgpath + '/im3.png', imgpath + '/im4.png', imgpath + '/im5.png', imgpath + '/im6.png', imgpath + '/im7.png']
# ind = [0, 1, 2, 3, 4, 5, 6]
# random.shuffle(ind)
# ind = ind[:3]
# ind.sort()
# img0 = cv2.imread(imgpaths[ind[0]])
# gt = cv2.imread(imgpaths[ind[1]])
# img1 = cv2.imread(imgpaths[ind[2]])
# timestep = (ind[1] - ind[0]) * 1.0 / (ind[2] - ind[0] + 1e-6)
def __getitem__(self, index):
img0, gt, img1, timestep = self.getimg(index)
if self.dataset_name == 'train':
img0, gt, img1 = self.crop(img0, gt, img1, 224, 224)
if random.uniform(0, 1) < 0.5:
img0 = img0[:, :, ::-1]
img1 = img1[:, :, ::-1]
gt = gt[:, :, ::-1]
if random.uniform(0, 1) < 0.5:
img0 = img0[::-1]
img1 = img1[::-1]
gt = gt[::-1]
if random.uniform(0, 1) < 0.5:
img0 = img0[:, ::-1]
img1 = img1[:, ::-1]
gt = gt[:, ::-1]
if random.uniform(0, 1) < 0.5:
tmp = img1
img1 = img0
img0 = tmp
timestep = 1 - timestep
# random rotation
p = random.uniform(0, 1)
if p < 0.25:
img0 = cv2.rotate(img0, cv2.ROTATE_90_CLOCKWISE)
gt = cv2.rotate(gt, cv2.ROTATE_90_CLOCKWISE)
img1 = cv2.rotate(img1, cv2.ROTATE_90_CLOCKWISE)
elif p < 0.5:
img0 = cv2.rotate(img0, cv2.ROTATE_180)
gt = cv2.rotate(gt, cv2.ROTATE_180)
img1 = cv2.rotate(img1, cv2.ROTATE_180)
elif p < 0.75:
img0 = cv2.rotate(img0, cv2.ROTATE_90_COUNTERCLOCKWISE)
gt = cv2.rotate(gt, cv2.ROTATE_90_COUNTERCLOCKWISE)
img1 = cv2.rotate(img1, cv2.ROTATE_90_COUNTERCLOCKWISE)
img0 = torch.from_numpy(img0.copy()).permute(2, 0, 1)
img1 = torch.from_numpy(img1.copy()).permute(2, 0, 1)
gt = torch.from_numpy(gt.copy()).permute(2, 0, 1)
timestep = torch.tensor(timestep).reshape(1, 1, 1)
return torch.cat((img0, img1, gt), 0), timestep
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