def main():
args.dump_root = Path(args.dump_root)
args.dump_root.mkdir_p()
global data_loader
if args.dataset_format == 'kitti':
from kitti_raw_loader import KittiRawLoader
data_loader = KittiRawLoader(args.dataset_dir,
static_frames_file=args.static_frames,
img_height=args.height,
img_width=args.width,
get_depth=args.with_depth,
get_pose=args.with_pose,
depth_size_ratio=args.depth_size_ratio)
if args.dataset_format == 'cityscapes':
from cityscapes_loader import cityscapes_loader
data_loader = cityscapes_loader(args.dataset_dir,
img_height=args.height,
img_width=args.width)
n_scenes = len(data_loader.scenes)
print('Found {} potential scenes'.format(n_scenes))
print('Retrieving frames')
if args.num_threads == 1:
for scene in tqdm(data_loader.scenes):
dump_example(args, scene)
else:
with ProcessPool(max_workers=args.num_threads) as pool:
tasks = pool.map(dump_example, [args]*n_scenes, data_loader.scenes)
try:
for _ in tqdm(tasks.result(), total=n_scenes):
pass
except KeyboardInterrupt as e:
tasks.cancel()
raise e
print('Generating train val lists')
np.random.seed(8964)
# to avoid data snooping, we will make two cameras of the same scene to fall in the same set, train or val
subdirs = args.dump_root.dirs()
canonic_prefixes = set([subdir.basename()[:-2] for subdir in subdirs])
with open(args.dump_root / 'train.txt', 'w') as tf:
with open(args.dump_root / 'val.txt', 'w') as vf:
for pr in tqdm(canonic_prefixes):
corresponding_dirs = args.dump_root.dirs('{}*'.format(pr))
if np.random.random() < 0.1:
for s in corresponding_dirs:
vf.write('{}\n'.format(s.name))
else:
for s in corresponding_dirs:
tf.write('{}\n'.format(s.name))
if args.with_depth and args.no_train_gt:
for gt_file in s.files('*.npy'):
gt_file.remove_p()
def main():
args.dump_root = Path(args.dump_root)
args.dump_root.mkdir_p()
global data_loader
if args.dataset_format == 'kitti':
from kitti_raw_loader import KittiRawLoader
data_loader = KittiRawLoader(args.dataset_dir,
static_frames_file=args.static_frames,
img_height=args.height,
img_width=args.width,
get_gt=args.with_gt)
if args.dataset_format == 'cityscapes':
from cityscapes_loader import cityscapes_loader
data_loader = cityscapes_loader(args.dataset_dir,
img_height=args.height,
img_width=args.width)
if args.dataset_format == 'sintel':
from sintel_loader import sintel_loader
data_loader = sintel_loader(args.dataset_dir,
img_height=args.height,
img_width=args.width)
print('Retrieving frames')
if args.dataset_format == 'sintel':
Parallel(n_jobs=args.num_threads)(
delayed(dump_sintel_example)(scene)
for scene in tqdm(data_loader.scenes))
else:
Parallel(n_jobs=args.num_threads)(
delayed(dump_example)(scene) for scene in tqdm(data_loader.scenes))
# Split into train/val
print('Generating train val lists')
np.random.seed(8964)
subfolders = args.dump_root.dirs()
with open(args.dump_root / 'train.txt', 'w') as tf:
with open(args.dump_root / 'val.txt', 'w') as vf:
for s in tqdm(subfolders):
if np.random.random() < 0.1:
vf.write('{}\n'.format(s.name))
else:
tf.write('{}\n'.format(s.name))
# remove useless groundtruth data for training comment if you don't want to erase it
for gt_file in s.files('*.npy'):
gt_file.remove_p()
def main():
args.dump_root = Path(args.dump_root)
args.dump_root.mkdir_p()
global data_loader
if args.dataset_format == 'kitti':
from kitti_raw_loader import KittiRawLoader
data_loader = KittiRawLoader(args.dataset_dir,
static_frames_file=args.static_frames,
img_height=args.height,
img_width=args.width,
get_depth=args.with_depth,
get_pose=args.with_pose,
depth_size_ratio=args.depth_size_ratio)
if args.dataset_format == 'cityscapes':
from cityscapes_loader import cityscapes_loader
data_loader = cityscapes_loader(args.dataset_dir,
img_height=args.height,
img_width=args.width)
print('Retrieving frames')
if args.num_threads == 1:
for scene in tqdm(data_loader.scenes):
dump_example(args, scene)
else:
Parallel(n_jobs=args.num_threads)(
delayed(dump_example)(args, scene)
for scene in tqdm(data_loader.scenes))
print('Generating train val lists')
np.random.seed(8964)
# to avoid data snooping, we will make two cameras of the same scene to fall in the same set, train or val
subdirs = args.dump_root.dirs()
canonic_prefixes = set([subdir.basename()[:-2] for subdir in subdirs])
with open(args.dump_root / 'train.txt', 'w') as tf:
with open(args.dump_root / 'val.txt', 'w') as vf:
for pr in tqdm(canonic_prefixes):
corresponding_dirs = args.dump_root.dirs('{}*'.format(pr))
if np.random.random() < 0.1:
for s in corresponding_dirs:
vf.write('{}\n'.format(s.name))
else:
for s in corresponding_dirs:
tf.write('{}\n'.format(s.name))
if args.with_depth and args.no_train_gt:
for gt_file in s.files('*.npy'):
gt_file.remove_p()
def main():
args.dump_root = Path(args.dump_root)
args.dump_root.mkdir_p()
global data_loader
if args.dataset_format == 'kitti':
from kitti_raw_loader import KittiRawLoader
data_loader = KittiRawLoader(args.dataset_dir,
static_frames_file=args.static_frames,
img_height=args.height,
img_width=args.width,
get_depth=args.with_depth,
get_pose=args.with_pose,
depth_size_ratio=args.depth_size_ratio)
if args.dataset_format == 'cityscapes':
from cityscapes_loader import cityscapes_loader
data_loader = cityscapes_loader(args.dataset_dir,
img_height=args.height,
img_width=args.width)
print('Retrieving frames')
if args.num_threads == 1:
for scene in tqdm(data_loader.scenes):
dump_example(args, scene)
else:
Parallel(n_jobs=args.num_threads)(
delayed(dump_example)(args, scene)
for scene in tqdm(data_loader.scenes))
print('Generating train val lists')
np.random.seed(8964)
subfolders = args.dump_root.dirs()
with open(args.dump_root / 'train.txt', 'w') as tf:
with open(args.dump_root / 'val.txt', 'w') as vf:
for s in tqdm(subfolders):
if np.random.random() < 0.1:
vf.write('{}\n'.format(s.name))
else:
tf.write('{}\n'.format(s.name))
if args.with_depth and args.no_train_gt:
for gt_file in s.files('*.npy'):
gt_file.remove_p()