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import os
from glob import glob
import numpy as np
import torch
import torchvision
from PIL import Image
from torch.utils.data import Dataset
from opensora.utils.dataset_utils import DecordInit, is_image_file
class ExtractVideo2Feature(Dataset):
def __init__(self, args, transform):
self.data_path = args.data_path
self.transform = transform
self.v_decoder = DecordInit()
self.samples = list(glob(f'{self.data_path}'))
def __len__(self):
return len(self.samples)
def __getitem__(self, idx):
video_path = self.samples[idx]
video = self.decord_read(video_path)
video = self.transform(video) # T C H W -> T C H W
return video, video_path
def tv_read(self, path):
vframes, aframes, info = torchvision.io.read_video(filename=path, pts_unit='sec', output_format='TCHW')
total_frames = len(vframes)
frame_indice = list(range(total_frames))
video = vframes[frame_indice]
return video
def decord_read(self, path):
decord_vr = self.v_decoder(path)
total_frames = len(decord_vr)
frame_indice = list(range(total_frames))
video_data = decord_vr.get_batch(frame_indice).asnumpy()
video_data = torch.from_numpy(video_data)
video_data = video_data.permute(0, 3, 1, 2) # (T, H, W, C) -> (T C H W)
return video_data
class ExtractImage2Feature(Dataset):
def __init__(self, args, transform):
self.data_path = args.data_path
self.transform = transform
self.data_all = list(glob(f'{self.data_path}'))
def __len__(self):
return len(self.data_all)
def __getitem__(self, index):
path = self.data_all[index]
video_frame = torch.as_tensor(np.array(Image.open(path), dtype=np.uint8, copy=True)).unsqueeze(0)
video_frame = video_frame.permute(0, 3, 1, 2)
video_frame = self.transform(video_frame) # T C H W
# video_frame = video_frame.transpose(0, 1) # T C H W -> C T H W
return video_frame, path
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