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# Copyright (c) OpenMMLab. All rights reserved.
import argparse
import glob
import json
import os.path as osp
import random
from mmengine.runner import set_random_seed
from tools.data.anno_txt2json import lines2dictlist
from tools.data.parse_file_list import (parse_directory, parse_diving48_splits,
parse_hmdb51_split,
parse_jester_splits,
parse_kinetics_splits,
parse_mit_splits, parse_mmit_splits,
parse_sthv1_splits, parse_sthv2_splits,
parse_ucf101_splits)
def parse_args():
parser = argparse.ArgumentParser(description='Build file list')
parser.add_argument(
'dataset',
type=str,
choices=[
'ucf101', 'kinetics400', 'kinetics600', 'kinetics700', 'thumos14',
'sthv1', 'sthv2', 'mit', 'mmit', 'activitynet', 'hmdb51', 'jester',
'diving48'
],
help='dataset to be built file list')
parser.add_argument(
'src_folder', type=str, help='root directory for the frames or videos')
parser.add_argument(
'--rgb-prefix', type=str, default='img_', help='prefix of rgb frames')
parser.add_argument(
'--flow-x-prefix',
type=str,
default='flow_x_',
help='prefix of flow x frames')
parser.add_argument(
'--flow-y-prefix',
type=str,
default='flow_y_',
help='prefix of flow y frames')
parser.add_argument(
'--num-split',
type=int,
default=3,
help='number of split to file list')
parser.add_argument(
'--subset',
type=str,
default='train',
choices=['train', 'val', 'test'],
help='subset to generate file list')
parser.add_argument(
'--level',
type=int,
default=2,
choices=[1, 2],
help='directory level of data')
parser.add_argument(
'--format',
type=str,
default='rawframes',
choices=['rawframes', 'videos'],
help='data format')
parser.add_argument(
'--out-root-path',
type=str,
default='data/',
help='root path for output')
parser.add_argument(
'--output-format',
type=str,
default='txt',
choices=['txt', 'json'],
help='built file list format')
parser.add_argument('--seed', type=int, default=None, help='random seed')
parser.add_argument(
'--shuffle',
action='store_true',
default=False,
help='whether to shuffle the file list')
args = parser.parse_args()
return args
def build_file_list(splits, frame_info, shuffle=False):
"""Build file list for a certain data split.
Args:
splits (tuple): Data split to generate file list.
frame_info (dict): Dict mapping from frames to path. e.g.,
'Skiing/v_Skiing_g18_c02': ('data/ucf101/rawframes/Skiing/v_Skiing_g18_c02', 0, 0). # noqa: E501
shuffle (bool): Whether to shuffle the file list.
Returns:
tuple: RGB file list for training and testing, together with
Flow file list for training and testing.
"""
def build_list(split):
"""Build RGB and Flow file list with a given split.
Args:
split (list): Split to be generate file list.
Returns:
tuple[list, list]: (rgb_list, flow_list), rgb_list is the
generated file list for rgb, flow_list is the generated
file list for flow.
"""
rgb_list, flow_list = list(), list()
for item in split:
if item[0] not in frame_info:
continue
if frame_info[item[0]][1] > 0:
# rawframes
rgb_cnt = frame_info[item[0]][1]
flow_cnt = frame_info[item[0]][2]
if isinstance(item[1], int):
rgb_list.append(f'{item[0]} {rgb_cnt} {item[1]}\n')
flow_list.append(f'{item[0]} {flow_cnt} {item[1]}\n')
elif isinstance(item[1], list):
# only for multi-label datasets like mmit
rgb_list.append(f'{item[0]} {rgb_cnt} ' +
' '.join([str(digit)
for digit in item[1]]) + '\n')
rgb_list.append(f'{item[0]} {flow_cnt} ' +
' '.join([str(digit)
for digit in item[1]]) + '\n')
else:
raise ValueError(
'frame_info should be ' +
'[`video`(str), `label`(int)|`labels(list[int])`')
else:
# videos
if isinstance(item[1], int):
rgb_list.append(f'{frame_info[item[0]][0]} {item[1]}\n')
flow_list.append(f'{frame_info[item[0]][0]} {item[1]}\n')
elif isinstance(item[1], list):
# only for multi-label datasets like mmit
rgb_list.append(f'{frame_info[item[0]][0]} ' +
' '.join([str(digit)
for digit in item[1]]) + '\n')
flow_list.append(
f'{frame_info[item[0]][0]} ' +
' '.join([str(digit) for digit in item[1]]) + '\n')
else:
raise ValueError(
'frame_info should be ' +
'[`video`(str), `label`(int)|`labels(list[int])`')
if shuffle:
random.shuffle(rgb_list)
random.shuffle(flow_list)
return rgb_list, flow_list
train_rgb_list, train_flow_list = build_list(splits[0])
test_rgb_list, test_flow_list = build_list(splits[1])
return (train_rgb_list, test_rgb_list), (train_flow_list, test_flow_list)
def main():
args = parse_args()
if args.seed is not None:
print(f'Set random seed to {args.seed}')
set_random_seed(args.seed)
if args.format == 'rawframes':
frame_info = parse_directory(
args.src_folder,
rgb_prefix=args.rgb_prefix,
flow_x_prefix=args.flow_x_prefix,
flow_y_prefix=args.flow_y_prefix,
level=args.level)
elif args.format == 'videos':
if args.level == 1:
# search for one-level directory
video_list = glob.glob(osp.join(args.src_folder, '*'))
elif args.level == 2:
# search for two-level directory
video_list = glob.glob(osp.join(args.src_folder, '*', '*'))
else:
raise ValueError(f'level must be 1 or 2, but got {args.level}')
frame_info = {}
for video in video_list:
video_path = osp.relpath(video, args.src_folder)
# video_id: (video_relative_path, -1, -1)
frame_info[osp.splitext(video_path)[0]] = (video_path, -1, -1)
else:
raise NotImplementedError('only rawframes and videos are supported')
if args.dataset == 'ucf101':
splits = parse_ucf101_splits(args.level)
elif args.dataset == 'sthv1':
splits = parse_sthv1_splits(args.level)
elif args.dataset == 'sthv2':
splits = parse_sthv2_splits(args.level)
elif args.dataset == 'mit':
splits = parse_mit_splits()
elif args.dataset == 'mmit':
splits = parse_mmit_splits()
elif args.dataset in ['kinetics400', 'kinetics600', 'kinetics700']:
splits = parse_kinetics_splits(args.level, args.dataset)
elif args.dataset == 'hmdb51':
splits = parse_hmdb51_split(args.level)
elif args.dataset == 'jester':
splits = parse_jester_splits(args.level)
elif args.dataset == 'diving48':
splits = parse_diving48_splits()
else:
raise ValueError(
f"Supported datasets are 'ucf101, sthv1, sthv2', 'jester', "
f"'mmit', 'mit', 'kinetics400', 'kinetics600', 'kinetics700', but "
f'got {args.dataset}')
assert len(splits) == args.num_split
out_path = args.out_root_path + args.dataset
if len(splits) > 1:
for i, split in enumerate(splits):
file_lists = build_file_list(
split, frame_info, shuffle=args.shuffle)
train_name = f'{args.dataset}_train_split_{i+1}_{args.format}.txt'
val_name = f'{args.dataset}_val_split_{i+1}_{args.format}.txt'
if args.output_format == 'txt':
with open(osp.join(out_path, train_name), 'w') as f:
f.writelines(file_lists[0][0])
with open(osp.join(out_path, val_name), 'w') as f:
f.writelines(file_lists[0][1])
elif args.output_format == 'json':
train_list = lines2dictlist(file_lists[0][0], args.format)
val_list = lines2dictlist(file_lists[0][1], args.format)
train_name = train_name.replace('.txt', '.json')
val_name = val_name.replace('.txt', '.json')
with open(osp.join(out_path, train_name), 'w') as f:
json.dump(train_list, f)
with open(osp.join(out_path, val_name), 'w') as f:
json.dump(val_list, f)
else:
lists = build_file_list(splits[0], frame_info, shuffle=args.shuffle)
if args.subset == 'train':
ind = 0
elif args.subset == 'val':
ind = 1
elif args.subset == 'test':
ind = 2
else:
raise ValueError(f"subset must be in ['train', 'val', 'test'], "
f'but got {args.subset}.')
filename = f'{args.dataset}_{args.subset}_list_{args.format}.txt'
if args.output_format == 'txt':
with open(osp.join(out_path, filename), 'w') as f:
f.writelines(lists[0][ind])
elif args.output_format == 'json':
data_list = lines2dictlist(lists[0][ind], args.format)
filename = filename.replace('.txt', '.json')
with open(osp.join(out_path, filename), 'w') as f:
json.dump(data_list, f)
if __name__ == '__main__':
main()
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