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from pathlib import Path
import pandas as pd
from func_timeout import FunctionTimedOut, func_timeout
from torch.utils.data import DataLoader, Dataset
from utils.logger import logger
from utils.video_utils import get_video_path_list, extract_frames
ALL_VIDEO_EXT = set(["mp4", "webm", "mkv", "avi", "flv", "mov"])
VIDEO_READER_TIMEOUT = 10
def collate_fn(batch):
batch = list(filter(lambda x: x is not None, batch))
if len(batch) != 0:
return {k: [item[k] for item in batch] for k in batch[0].keys()}
return {}
class VideoDataset(Dataset):
def __init__(
self,
video_path_list=None,
video_folder=None,
video_metadata_path=None,
video_path_column=None,
sample_method="mid",
num_sampled_frames=1,
num_sample_stride=None,
):
self.video_path_column = video_path_column
self.video_folder = video_folder
self.sample_method = sample_method
self.num_sampled_frames = num_sampled_frames
self.num_sample_stride = num_sample_stride
if video_path_list is not None:
self.video_path_list = video_path_list
self.metadata_df = pd.DataFrame({video_path_column: self.video_path_list})
else:
self.video_path_list = get_video_path_list(
video_folder=video_folder,
video_metadata_path=video_metadata_path,
video_path_column=video_path_column
)
def __getitem__(self, index):
# video_path = os.path.join(self.video_folder, str(self.video_path_list[index]))
video_path = self.video_path_list[index]
try:
sample_args = (video_path, self.sample_method, self.num_sampled_frames, self.num_sample_stride)
sampled_frame_idx_list, sampled_frame_list = func_timeout(
VIDEO_READER_TIMEOUT, extract_frames, args=sample_args
)
except FunctionTimedOut:
logger.warning(f"Read {video_path} timeout.")
return None
except Exception as e:
logger.warning(f"Failed to extract frames from video {video_path}. Error is {e}.")
return None
item = {
"video_path": Path(video_path).name,
"sampled_frame_idx": sampled_frame_idx_list,
"sampled_frame": sampled_frame_list,
}
return item
def __len__(self):
return len(self.video_path_list)
if __name__ == "__main__":
video_folder = "your_video_folder"
video_dataset = VideoDataset(video_folder=video_folder)
video_dataloader = DataLoader(
video_dataset, batch_size=16, num_workers=16, collate_fn=collate_fn
)
for idx, batch in enumerate(video_dataloader):
if len(batch) != 0:
print(batch["video_path"], batch["sampled_frame_idx"], len(batch["video_path"])) |