IFRNet
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Published
20 hours ago •
ltkong218
ltkong218
Adobe240 dataset
I would like to know which subset of Adobe dataset is used, I tested all of them and the values are very low, is it also cropped to 512*512? Thanks
For 8x frame interpolation, we follow the FLAVR project. It seems that FLAVR does not provide the Adobe240 evaluation dataset, so we select a subset of all Adobe240 video sequences and perform center crop with 512x512. Below is our Adobe240 dataset python script:
import os
import numpy as np
import torch
from torch.utils.data import Dataset, DataLoader
from torchvision import transforms
from PIL import Image
import random
import glob
from utils import read
def random_crop(img_0, img_1, img_2, img_3, img_4, img_5, img_6, img_7, img_8, crop_size):
h, w = crop_size[0], crop_size[1]
ih, iw, _ = img_0.shape
x = np.random.randint(0, ih-h+1)
y = np.random.randint(0, iw-w+1)
img_0 = img_0[x:x+h, y:y+w, :]
img_1 = img_1[x:x+h, y:y+w, :]
img_2 = img_2[x:x+h, y:y+w, :]
img_3 = img_3[x:x+h, y:y+w, :]
img_4 = img_4[x:x+h, y:y+w, :]
img_5 = img_5[x:x+h, y:y+w, :]
img_6 = img_6[x:x+h, y:y+w, :]
img_7 = img_7[x:x+h, y:y+w, :]
img_8 = img_8[x:x+h, y:y+w, :]
return img_0, img_1, img_2, img_3, img_4, img_5, img_6, img_7, img_8
def center_crop(img_0, img_1, img_2, img_3, img_4, img_5, img_6, img_7, img_8, crop_size):
h, w = crop_size[0], crop_size[1]
ih, iw, _ = img_0.shape
img_0 = img_0[(ih//2-h//2):(ih//2+h//2), (iw//2-w//2):(iw//2+w//2), :]
img_1 = img_1[(ih//2-h//2):(ih//2+h//2), (iw//2-w//2):(iw//2+w//2), :]
img_2 = img_2[(ih//2-h//2):(ih//2+h//2), (iw//2-w//2):(iw//2+w//2), :]
img_3 = img_3[(ih//2-h//2):(ih//2+h//2), (iw//2-w//2):(iw//2+w//2), :]
img_4 = img_4[(ih//2-h//2):(ih//2+h//2), (iw//2-w//2):(iw//2+w//2), :]
img_5 = img_5[(ih//2-h//2):(ih//2+h//2), (iw//2-w//2):(iw//2+w//2), :]
img_6 = img_6[(ih//2-h//2):(ih//2+h//2), (iw//2-w//2):(iw//2+w//2), :]
img_7 = img_7[(ih//2-h//2):(ih//2+h//2), (iw//2-w//2):(iw//2+w//2), :]
img_8 = img_8[(ih//2-h//2):(ih//2+h//2), (iw//2-w//2):(iw//2+w//2), :]
return img_0, img_1, img_2, img_3, img_4, img_5, img_6, img_7, img_8
def augment(img_0, img_1, img_2, img_3, img_4, img_5, img_6, img_7, img_8):
if random.uniform(0, 1) < 0.5:
img_0 = img_0[:, :, ::-1]
img_1 = img_1[:, :, ::-1]
img_2 = img_2[:, :, ::-1]
img_3 = img_3[:, :, ::-1]
img_4 = img_4[:, :, ::-1]
img_5 = img_5[:, :, ::-1]
img_6 = img_6[:, :, ::-1]
img_7 = img_7[:, :, ::-1]
img_8 = img_8[:, :, ::-1]
if random.uniform(0, 1) < 0.5:
img_0 = img_0[:, ::-1]
img_1 = img_1[:, ::-1]
img_2 = img_2[:, ::-1]
img_3 = img_3[:, ::-1]
img_4 = img_4[:, ::-1]
img_5 = img_5[:, ::-1]
img_6 = img_6[:, ::-1]
img_7 = img_7[:, ::-1]
img_8 = img_8[:, ::-1]
return img_0, img_1, img_2, img_3, img_4, img_5, img_6, img_7, img_8
class Adobe240(Dataset):
def __init__(self, data_root='/home/ltkong/Datasets/adobe240_frames', mode="train", interFrames=7, n_inputs=2, ext="png"):
super().__init__()
self.mode = mode
self.interFrames = interFrames
self.n_inputs = n_inputs
self.setLength = (n_inputs-1)*(interFrames+1)+1 ## We require these many frames in total for interpolating `interFrames` number of
## intermediate frames with `n_input` input frames.
self.data_root = os.path.join(data_root)
video_list = os.listdir(self.data_root)[9::10]
self.frames_list = []
self.file_list = []
for video in video_list:
frames = sorted(os.listdir(os.path.join(self.data_root , video)))
n_sets = (len(frames) - self.setLength)//(interFrames+1) + 1
videoInputs = [frames[(interFrames+1)*i:(interFrames+1)*i+self.setLength ] for i in range(n_sets)]
videoInputs = [[os.path.join(video , f) for f in group] for group in videoInputs]
self.file_list.extend(videoInputs)
def __getitem__(self, idx):
imgpaths = [os.path.join(self.data_root , fp) for fp in self.file_list[idx]]
pick_idxs = list(range(0,self.setLength,self.interFrames+1))
rem = self.interFrames%2
gt_idx = list(range(self.setLength//2-self.interFrames//2 , self.setLength//2+self.interFrames//2+rem))
input_paths = [imgpaths[idx] for idx in pick_idxs]
gt_paths = [imgpaths[idx] for idx in gt_idx]
img_0 = np.array(read(input_paths[0]))
img_1 = np.array(read(gt_paths[0]))
img_2 = np.array(read(gt_paths[1]))
img_3 = np.array(read(gt_paths[2]))
img_4 = np.array(read(gt_paths[3]))
img_5 = np.array(read(gt_paths[4]))
img_6 = np.array(read(gt_paths[5]))
img_7 = np.array(read(gt_paths[6]))
img_8 = np.array(read(input_paths[1]))
if self.mode == 'train':
img_0, img_1, img_2, img_3, img_4, img_5, img_6, img_7, img_8 = random_crop(img_0, img_1, img_2, img_3, img_4, img_5, img_6, img_7, img_8, (256, 256))
img_0, img_1, img_2, img_3, img_4, img_5, img_6, img_7, img_8 = augment(img_0, img_1, img_2, img_3, img_4, img_5, img_6, img_7, img_8)
elif self.mode == 'test':
img_0, img_1, img_2, img_3, img_4, img_5, img_6, img_7, img_8 = center_crop(img_0, img_1, img_2, img_3, img_4, img_5, img_6, img_7, img_8, (512, 512))
img_0 = img_0.transpose((2, 0, 1)).astype(np.float32) / 255.0
img_1 = img_1.transpose((2, 0, 1)).astype(np.float32) / 255.0
img_2 = img_2.transpose((2, 0, 1)).astype(np.float32) / 255.0
img_3 = img_3.transpose((2, 0, 1)).astype(np.float32) / 255.0
img_4 = img_4.transpose((2, 0, 1)).astype(np.float32) / 255.0
img_5 = img_5.transpose((2, 0, 1)).astype(np.float32) / 255.0
img_6 = img_6.transpose((2, 0, 1)).astype(np.float32) / 255.0
img_7 = img_7.transpose((2, 0, 1)).astype(np.float32) / 255.0
img_8 = img_8.transpose((2, 0, 1)).astype(np.float32) / 255.0
emb_1 = np.array(1/8).reshape(1, 1, 1).astype(np.float32)
emb_2 = np.array(2/8).reshape(1, 1, 1).astype(np.float32)
emb_3 = np.array(3/8).reshape(1, 1, 1).astype(np.float32)
emb_4 = np.array(4/8).reshape(1, 1, 1).astype(np.float32)
emb_5 = np.array(5/8).reshape(1, 1, 1).astype(np.float32)
emb_6 = np.array(6/8).reshape(1, 1, 1).astype(np.float32)
emb_7 = np.array(7/8).reshape(1, 1, 1).astype(np.float32)
return img_0, img_1, img_2, img_3, img_4, img_5, img_6, img_7, img_8, emb_1, emb_2, emb_3, emb_4, emb_5, emb_6, emb_7
def __len__(self):
return len(self.file_list)
If you have found other Adobe240 test dataset selection, you can report IFRNet's results on your evaluation script. Thanks.
