def main():
if not os.path.exists(args.dump_root):
os.makedirs(args.dump_root)
global data_loader
if args.dataset_name == 'kitti_odom':
from kitti.kitti_odom_loader import kitti_odom_loader
data_loader = kitti_odom_loader(args.dataset_dir,
img_height=args.img_height,
img_width=args.img_width,
seq_length=args.seq_length)
if args.dataset_name == 'kitti_raw_eigen':
from kitti.kitti_raw_loader import kitti_raw_loader
data_loader = kitti_raw_loader(args.dataset_dir,
split='eigen',
img_height=args.img_height,
img_width=args.img_width,
seq_length=args.seq_length)
if args.dataset_name == 'kitti_raw_stereo':
from kitti.kitti_raw_loader import kitti_raw_loader
data_loader = kitti_raw_loader(args.dataset_dir,
split='stereo',
img_height=args.img_height,
img_width=args.img_width,
seq_length=args.seq_length)
if args.dataset_name == 'cityscapes':
from cityscapes.cityscapes_loader import cityscapes_loader
data_loader = cityscapes_loader(args.dataset_dir,
img_height=args.img_height,
img_width=args.img_width,
seq_length=args.seq_length)
if args.dataset_name == 'humanbody':
from humanbodys.humanbody_loader import humanbody_loader
data_loader = humanbody_loader(args.dataset_dir,
img_height=args.img_height,
img_width=args.img_width,
seq_length=args.seq_length)
Parallel(n_jobs=args.num_threads)(delayed(dump_example)(n) for n in range(data_loader.num_train))
# Split into train/val
np.random.seed(8964)
subfolders = os.listdir(args.dump_root)
with open(args.dump_root + 'train.txt', 'w') as tf:
with open(args.dump_root + 'val.txt', 'w') as vf:
for s in subfolders:
if not os.path.isdir(args.dump_root + '/%s' % s):
continue
imfiles = glob(os.path.join(args.dump_root, s, '*.jpg'))
frame_ids = [os.path.basename(fi).split('.')[0] for fi in imfiles]
for frame in frame_ids:
if np.random.random() < 0.1:
vf.write('%s %s\n' % (s, frame))
else:
tf.write('%s %s\n' % (s, frame))
def main():
if not os.path.exists(args.dump_root):
os.makedirs(args.dump_root)
global data_loader
if args.dataset_name == 'kitti_odom':
from kitti.kitti_odom_loader import kitti_odom_loader
data_loader = kitti_odom_loader(args.dataset_dir,
img_height=args.img_height,
img_width=args.img_width,
seq_length=args.seq_length)
if args.dataset_name == 'kitti_raw_eigen':
from kitti.kitti_raw_loader import kitti_raw_loader
data_loader = kitti_raw_loader(args.dataset_dir,
split='eigen',
img_height=args.img_height,
img_width=args.img_width,
seq_length=args.seq_length,
remove_static=args.remove_static)
if args.dataset_name == 'kitti_raw_stereo':
from kitti.kitti_raw_loader import kitti_raw_loader
data_loader = kitti_raw_loader(args.dataset_dir,
split='stereo',
img_height=args.img_height,
img_width=args.img_width,
seq_length=args.seq_length,
remove_static=args.remove_static)
if args.dataset_name == 'cityscapes':
from cityscapes.cityscapes_loader import cityscapes_loader
data_loader = cityscapes_loader(args.dataset_dir,
img_height=args.img_height,
img_width=args.img_width,
seq_length=args.seq_length)
Parallel(n_jobs=args.num_threads)(delayed(dump_example)(n) for n in range(data_loader.num_train))
# Split into train/val
np.random.seed(8964)
subfolders = os.listdir(args.dump_root)
with open(os.path.join(args.dump_root, 'train.txt'), 'w') as tf:
with open(os.path.join(args.dump_root, 'val.txt'), 'w') as vf:
for s in subfolders:
if not os.path.isdir(args.dump_root + '/%s' % s):
continue
imfiles = glob(os.path.join(args.dump_root, s, '*.jpg'))
frame_ids = [os.path.basename(fi).split('.')[0] for fi in imfiles]
for frame in frame_ids:
if np.random.random() < 0.1:
vf.write('%s %s\n' % (s, frame))
else:
tf.write('%s %s\n' % (s, frame))
def main():
if not os.path.exists(args.dump_root):
os.makedirs(args.dump_root)
global data_loader
if args.dataset_name == 'kitti_odom':
from kitti.kitti_odom_loader import kitti_odom_loader
data_loader = kitti_odom_loader(args.dataset_dir,
img_height=args.img_height,
img_width=args.img_width,
seq_length=args.seq_length)
if args.dataset_name == 'kitti_raw_eigen':
from kitti.kitti_raw_loader_stereo import kitti_raw_loader
data_loader = kitti_raw_loader(args.dataset_dir,
split='eigen',
img_height=args.img_height,
img_width=args.img_width,
seq_length=args.seq_length)
if args.dataset_name == 'kitti_raw_stereo':
from kitti.kitti_raw_loader_stereo import kitti_raw_loader
data_loader = kitti_raw_loader(args.dataset_dir,
split='stereo',
img_height=args.img_height,
img_width=args.img_width,
seq_length=args.seq_length)
print(data_loader.num_train)
Parallel(n_jobs=args.num_threads)(delayed(dump_example)(n) for n in range(data_loader.num_train-1))
# Split into train/val
np.random.seed(8964)
subfolders = os.listdir(args.dump_root)
with open(args.dump_root + 'train.txt', 'w') as tf:
with open(args.dump_root + 'val.txt', 'w') as vf:
for s in subfolders:
if not os.path.isdir(args.dump_root + '/%s' % s):
continue
os.chdir(args.dump_root)
left_imfiles = glob(s+'/image_02/*.jpg')
right_imfiles = glob(s+'/image_03/*.jpg')
for left,right in zip(left_imfiles,right_imfiles):
#print left
if np.random.random()<0.1:
vf.write(left+' '+right+'\n')
else:
tf.write(left+' '+right+'\n')
def main():
if not os.path.exists(args.dump_root):
os.makedirs(args.dump_root)
global data_loader
if args.dataset_name == 'kitti_odom':
from kitti.kitti_odom_loader import kitti_odom_loader
data_loader = kitti_odom_loader(args.dataset_dir,
img_height=args.img_height,
img_width=args.img_width,
seq_length=args.seq_length)
if args.dataset_name == 'kitti_raw_eigen':
from kitti.kitti_raw_loader import kitti_raw_loader
data_loader = kitti_raw_loader(args.dataset_dir,
split='eigen',
img_height=args.img_height,
img_width=args.img_width,
seq_length=args.seq_length)
if args.dataset_name == 'kitti_raw_stereo':
from kitti.kitti_raw_loader import kitti_raw_loader
data_loader = kitti_raw_loader(args.dataset_dir,
split='stereo',
img_height=args.img_height,
img_width=args.img_width,
seq_length=args.seq_length)
if args.dataset_name == 'cityscapes':
from cityscapes.cityscapes_loader import cityscapes_loader
data_loader = cityscapes_loader(args.dataset_dir,
split='train',
img_height=args.img_height,
img_width=args.img_width,
seq_length=args.seq_length)
is_training = not (args.generate_test)
print("is traing: ", is_training)
all_frames = range(data_loader.num_train) if is_training else range(
data_loader.num_test)
frame_chunks = np.array_split(all_frames,
NUM_CHUNKS) # len(frame_chunks)=NUM_CHUNKS
num_cores = multiprocessing.cpu_count()
num_threads = num_cores if args.num_threads is None else args.num_threads
pool = multiprocessing.Pool(num_threads)
if args.generate_test:
print("Generate Test")
logging.info('Generating test data...')
for index, frame_chunk in enumerate(frame_chunks):
pool.map(_dump_example_star,
zip(frame_chunk, itertools.repeat(is_training)))
logging.info('Chunk %d/%d: saving entries...', index + 1,
NUM_CHUNKS)
else:
print("Generate Train/Val")
# Split into train/val
np.random.seed(8964)
train_file_txt = os.path.join(args.dump_root, 'train.txt')
val_file_txt = os.path.join(args.dump_root, 'val.txt')
for _file in [train_file_txt, val_file_txt]:
if os.path.exists(_file):
file_t = os.path.getmtime(_file)
file_suffix = datetime.datetime.fromtimestamp(file_t).strftime(
"%Y%m%d")
os.rename(_file, _file + ".bak-" + file_suffix)
subfolders = ['%.2d' % s for s in data_loader.train_seqs]
with open(train_file_txt, 'w') as tf:
with open(val_file_txt, 'w') as vf:
logging.info('Generating train/val data...')
for index, frame_chunk in enumerate(frame_chunks):
pool.map(_dump_example_star,
zip(frame_chunk, itertools.repeat(is_training)))
logging.info('Chunk %d/%d: saving entries...', index + 1,
NUM_CHUNKS)
for s in subfolders:
if not os.path.isdir(args.dump_root + '/%s' % s):
continue
imfiles = glob(
os.path.join(args.dump_root, '%s' % s, '*.jpg'))
frame_ids = [
os.path.basename(fi).split('.')[0]
for fi in imfiles
]
for frame in frame_ids:
if np.random.random() < 0.1:
vf.write('%s %s\n' % (s, frame))
else:
tf.write('%s %s\n' % (s, frame))
logging.info('Generating train/val data...')
for index, frame_chunk in enumerate(frame_chunks):
pool.map(_dump_example_star,
zip(frame_chunk, itertools.repeat(is_training)))
logging.info('Chunk %d/%d: saving entries...', index + 1,
NUM_CHUNKS)
pool.close()
pool.join()
def main():
if not os.path.exists(args.dump_root):
os.makedirs(args.dump_root)
# initialize the data_loader for each task
global data_loader
# pose
if args.dataset_name == 'kitti_odom':
from kitti.kitti_odom_loader import kitti_odom_loader
data_loader = kitti_odom_loader(args.dataset_dir,
img_height=args.img_height,
img_width=args.img_width,
seq_length=args.seq_length)
print("hello")
print(data_loader.num_train)
print("hello babe")
# depth
if args.dataset_name == 'kitti_raw_eigen':
from kitti.kitti_raw_loader import kitti_raw_loader
data_loader = kitti_raw_loader(args.dataset_dir,
split='eigen',
img_height=args.img_height,
img_width=args.img_width,
seq_length=args.seq_length,
remove_static=args.remove_static)
# flow
if args.dataset_name == 'kitti_raw_stereo':
from kitti.kitti_raw_loader import kitti_raw_loader
data_loader = kitti_raw_loader(args.dataset_dir,
split='stereo',
img_height=args.img_height,
img_width=args.img_width,
seq_length=args.seq_length,
remove_static=args.remove_static)
if args.dataset_name == 'cityscapes':
from cityscapes.cityscapes_loader import cityscapes_loader
data_loader = cityscapes_loader(args.dataset_dir,
img_height=args.img_height,
img_width=args.img_width,
seq_length=args.seq_length)
# multi-threads for data preprocessing
# 45016 for depth, raw_data, kitti_raw_eigen
# 59552 for flow, raw_data, kitti_raw_stereo
# 20409 for pose, odometry, kitti_odom
Parallel(n_jobs=args.num_threads)(delayed(dump_example)(n, args)
for n in range(data_loader.num_train))
# Split into train/val, random seed to make it consistent
np.random.seed(8964)
subfolders = os.listdir(args.dump_root)
with open(os.path.join(args.dump_root, 'train.txt'), 'w') as tf:
with open(os.path.join(args.dump_root, 'val.txt'), 'w') as vf:
for s in subfolders:
if not os.path.isdir(args.dump_root + '/%s' % s):
continue
imfiles = glob(os.path.join(args.dump_root, s, '*.jpg'))
frame_ids = [
os.path.basename(fi).split('.')[0] for fi in imfiles
]
for frame in frame_ids:
# 10% for validation, 90% for training
if np.random.random() < 0.1:
vf.write('%s %s\n' % (s, frame))
else:
tf.write('%s %s\n' % (s, frame))
def main():
if not os.path.exists(args.dump_root):
os.makedirs(args.dump_root)
global data_loader
if args.dataset_name == 'kitti_odom':
from kitti.kitti_odom_loader import kitti_odom_loader
data_loader = kitti_odom_loader(args.dataset_dir,
img_height=args.img_height,
img_width=args.img_width,
seq_length=args.seq_length)
if args.dataset_name == 'kitti_raw_eigen':
from kitti.kitti_raw_loader import kitti_raw_loader
data_loader = kitti_raw_loader(args.dataset_dir,
split='eigen',
img_height=args.img_height,
img_width=args.img_width,
seq_length=args.seq_length,
remove_static=args.remove_static)
if args.dataset_name == 'kitti_raw_stereo':
from kitti.kitti_raw_loader import kitti_raw_loader
data_loader = kitti_raw_loader(args.dataset_dir,
split='stereo',
img_height=args.img_height,
img_width=args.img_width,
seq_length=args.seq_length,
remove_static=args.remove_static)
if args.dataset_name == 'cityscapes':
from cityscapes.cityscapes_loader import cityscapes_loader
data_loader = cityscapes_loader(args.dataset_dir,
img_height=args.img_height,
img_width=args.img_width,
seq_length=args.seq_length)
if args.dataset_name == 'hallway':
from hallway.hallway_loader import hallway_loader
data_loader = hallway_loader(args.dataset_dir,
split='mine',
img_height=args.img_height,
img_width=args.img_width,
seq_length=args.seq_length)
if args.dataset_name == 'rms_hallway':
from rms_hallway.rms_hallway_loader import rms_hallway_loader
if args.video_to_image == "True":
from rms_hallway.rms_hallway_video2image import rms_hallway_video2image
video_loader = rms_hallway_video2image(args.dataset_dir,
args.frame_height,
args.frame_width,
down_sample=5)
video_loader.collect_video_frames()
data_loader = rms_hallway_loader(args.dataset_dir,
split='mine',
img_height=args.img_height,
img_width=args.img_width,
seq_length=args.seq_length,
remove_static=args.remove_static)
if args.dataset_name == 'MobileRGBD':
from MobileRGBD.MobileRGBD_loader import MobileRGBD_loader
data_loader = MobileRGBD_loader(args.dataset_dir,
split='mine',
img_height=args.img_height,
img_width=args.img_width,
seq_length=args.seq_length,
remove_static=args.remove_static)
Parallel(n_jobs=args.num_threads)(delayed(dump_example)(n)
for n in range(data_loader.num_train))
# Split into train/val
np.random.seed(8964)
subfolders = os.listdir(args.dump_root)
with open(os.path.join(args.dump_root, 'train.txt'), 'w') as tf:
with open(os.path.join(args.dump_root, 'val.txt'), 'w') as vf:
for s in subfolders:
if not os.path.isdir(args.dump_root + '/%s' % s):
continue
imfiles = glob(os.path.join(args.dump_root, s, '*.jpg'))
frame_ids = [
os.path.basename(fi).split('.')[0] for fi in imfiles
]
for frame in frame_ids:
if np.random.random() < 0:
#vf.write('%s %s\n' % (s, frame))
vf.write('%s %s\n' % (s, frame))
else:
tf.write('%s %s\n' % (s, frame))
