def get_online_data(opt, spatial_transform, temporal_transform,
target_transform):
assert opt.dataset in ['egogesture', 'nv']
whole_path = opt.whole_path
if opt.dataset == 'egogesture':
online_data = EgoGestureOnline(opt.annotation_path,
opt.video_path,
opt.whole_path,
opt.n_val_samples,
spatial_transform,
temporal_transform,
target_transform,
modality=opt.modality,
stride_len=opt.stride_len,
sample_duration=opt.sample_duration)
if opt.dataset == 'nv':
online_data = NVOnline(opt.annotation_path,
opt.video_path,
opt.whole_path,
opt.n_val_samples,
spatial_transform,
temporal_transform,
target_transform,
modality=opt.modality,
stride_len=opt.stride_len,
sample_duration=opt.sample_duration)
return online_data
def get_online_data(opt, spatial_transform, temporal_transform, target_transform, modality="RGB-D"):
assert opt.dataset in [ 'egogesture', 'nv']
whole_path = opt.whole_path
if opt.dataset == 'egogesture':
if isinstance(spatial_transform, list):
online_data = EgoGestureOnlineMultiTransform(
opt.annotation_path,
opt.video_path,
opt.whole_path,
opt.n_val_samples,
spatial_transform,
temporal_transform,
target_transform,
modality=modality,
stride_len = opt.stride_len,
sample_duration=opt.sample_duration)
else:
online_data = EgoGestureOnline(
opt.annotation_path,
opt.video_path,
opt.whole_path,
opt.n_val_samples,
spatial_transform,
temporal_transform,
target_transform,
modality=modality,
stride_len = opt.stride_len,
sample_duration=opt.sample_duration)
if opt.dataset == 'nv':
online_data = NVOnline(
opt.annotation_path,
opt.video_path,
opt.whole_path,
opt.n_val_samples,
spatial_transform,
temporal_transform,
target_transform,
modality=modality,
stride_len = opt.stride_len,
sample_duration=opt.sample_duration)
return online_data
def get_online_data(opt, spatial_transform, temporal_transform,
target_transform):
assert opt.dataset in ['egogesture', 'nv', 'denso', 'AHG', 'ipn']
whole_path = opt.whole_path
if opt.dataset == 'egogesture':
online_data = EgoGestureOnline(opt.annotation_path,
opt.video_path,
opt.whole_path,
opt.n_val_samples,
spatial_transform,
temporal_transform,
target_transform,
modality=opt.modality,
stride_len=opt.stride_len,
sample_duration=opt.sample_duration)
if opt.dataset == 'nv':
online_data = NVOnline(opt.annotation_path,
opt.video_path,
opt.whole_path,
opt.n_val_samples,
spatial_transform,
temporal_transform,
target_transform,
modality=opt.modality,
stride_len=opt.stride_len,
sample_duration=opt.sample_duration)
if opt.dataset == 'ipn':
online_data = IPNOnline(opt.annotation_path,
opt.video_path,
opt.whole_path,
opt.n_val_samples,
spatial_transform,
temporal_transform,
target_transform,
modality=opt.modality,
stride_len=opt.stride_len,
sample_duration=opt.sample_duration)
if opt.dataset == 'AHG':
fill_ = True if opt.model_clf == 'c3d' else False
online_data = AHGOnline(opt.annotation_path,
opt.video_path,
opt.whole_path,
opt.n_val_samples,
spatial_transform,
temporal_transform,
target_transform,
modality=opt.modality,
stride_len=opt.stride_len,
fill=fill_,
sample_duration=opt.sample_duration)
if opt.dataset == 'denso':
fill_ = True if opt.model_clf == 'c3d' else False
online_data = densOnline(opt.annotation_path,
opt.video_path,
opt.whole_path,
opt.n_val_samples,
spatial_transform,
temporal_transform,
target_transform,
modality=opt.modality,
stride_len=opt.stride_len,
fill=fill_,
no_subject_crop=opt.no_scrop,
sample_duration=opt.sample_duration)
return online_data