Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

[Feature] Generate ann_file for flyingchairs #121

Merged
merged 7 commits into from
Apr 24, 2022
Merged
Changes from all commits
Commits
File filter

Filter by extension

Filter by extension

Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
78 changes: 78 additions & 0 deletions tools/prepare_datasets/prepare_flyingchairs.py
Original file line number Diff line number Diff line change
@@ -0,0 +1,78 @@
# Copyright (c) OpenMMLab. All rights reserved.
import argparse
import json
import os.path as osp

import mmcv
import numpy as np


def parse_args():
parser = argparse.ArgumentParser(
description='FlyingChairs dataset preparation')
parser.add_argument(
'--data-root',
type=str,
default='data/FlyingChairs_release',
help='Directory for dataset.')
parser.add_argument(
'--split-file',
type=str,
default='data/FlyingChairs_release/FlyingChairs_train_val.txt',
help='File name of '
'train-validation split file for FlyingChairs.')
args = parser.parse_args()

return args


def main():
args = parse_args()

split = np.loadtxt(args.split_file, dtype=np.int32).tolist()
# unpack FlyingChairs directly, will see `data` subdirctory.
img1_dir = osp.join(args.data_root, 'data')
img2_dir = osp.join(args.data_root, 'data')
flow_dir = osp.join(args.data_root, 'data')

# data in FlyingChairs dataset has specific suffix
img1_suffix = '_img1.ppm'
img2_suffix = '_img2.ppm'
flow_suffix = '_flow.flo'

img1_filenames = [f for f in mmcv.scandir(img1_dir, suffix=img1_suffix)]
img2_filenames = [f for f in mmcv.scandir(img2_dir, suffix=img2_suffix)]
flow_filenames = [f for f in mmcv.scandir(flow_dir, suffix=flow_suffix)]
img1_filenames.sort()
img2_filenames.sort()
flow_filenames.sort()

train_list = []
test_list = []
train_meta = dict(dataset='FlyingChairs', subset='train')
test_meta = dict(dataset='FlyingChairs', subset='test')

for i, flag in enumerate(split):

data_info = dict(
img1_dir='data',
img2_dir='data',
flow_dir='data',
img_info=dict(
filename1=img1_filenames[i], filename2=img2_filenames[i]),
ann_info=dict(filename_flow=flow_filenames[i]))

if flag == 1:
train_list.append(data_info)
else:
test_list.append(data_info)

with open('FlyingChairs_train.json', 'w') as jsonfile:
json.dump({'data_list': train_list, 'metainfo': train_meta}, jsonfile)

with open('FlyingChairs_test.json', 'w') as jsonfile:
json.dump({'data_list': test_list, 'metainfo': test_meta}, jsonfile)


if __name__ == '__main__':
main()