File size: 2,453 Bytes
af758d1 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 |
# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: Apache-2.0
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import torch
import os
from typing import Any, List, Optional
from src.models.data.radym import Radym
class RadymWrapper(Radym):
def __init__(self, is_static: bool = True, is_multi_view: bool = False, **kwargs):
super().__init__(**kwargs)
# For recon code base
self.is_static = is_static
self.sample_list = self.mp4_file_paths
self.num_cameras = len([camera_name for camera_name in os.listdir(self.root_path) if camera_name != 'flag']) if is_multi_view else 1
if is_multi_view:
self.n_views = self.num_cameras
def __len__(self):
return len(self.sample_list)
def count_frames(self, video_idx: int):
return self.num_frames(video_idx)
def count_cameras(self, video_idx: int):
return self.num_cameras
def get_data(
self,
idx,
data_fields: List[str],
frame_indices: Optional[List[int]] = None,
view_indices: Optional[List[int]] = None,
camera_convention: str = "opencv",
):
assert camera_convention == 'opencv', f"No support for camera convention {camera_convention}"
if view_indices is None or len(view_indices) == 0:
view_indices = list(range(self.count_cameras(idx)))
final_dict = None
for view_idx in view_indices:
output_dict = self._read_data(
idx, frame_indices, [view_idx], data_fields,
)
if final_dict is None:
final_dict = output_dict
else:
for k in final_dict:
if k == "__key__":
continue
final_dict[k] = torch.concatenate([final_dict[k], output_dict[k]])
return final_dict |