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Zero
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"""NuScenes monocular dataset."""
from __future__ import annotations
import numpy as np
from tqdm import tqdm
from vis4d.common.imports import NUSCENES_AVAILABLE
from vis4d.common.logging import rank_zero_info
from vis4d.common.time import Timer
from vis4d.common.typing import ArgsType, DictStrAny
from vis4d.data.const import AxisMode
from vis4d.data.const import CommonKeys as K
from vis4d.data.typing import DictData
from .nuscenes import NuScenes, nuscenes_class_map
from .util import im_decode, print_class_histogram
if NUSCENES_AVAILABLE:
from nuscenes import NuScenes as NuScenesDevkit
from nuscenes.utils.splits import create_splits_scenes
else:
raise ImportError("nusenes-devkit is not available.")
class NuScenesMono(NuScenes):
"""NuScenes monocular dataset."""
def __init__(self, *args: ArgsType, **kwargs: ArgsType) -> None:
"""Initialize the dataset."""
super().__init__(*args, **kwargs)
# Needed for CBGS
def get_cat_ids(self, idx: int) -> list[int]:
"""Return the samples."""
return self.samples[idx]["CAM"]["annotations"]["boxes3d_classes"]
def _filter_data(self, data: list[DictStrAny]) -> list[DictStrAny]:
"""Remove empty samples."""
samples = []
frequencies = {cat: 0 for cat in nuscenes_class_map}
inv_nuscenes_class_map = {v: k for k, v in nuscenes_class_map.items()}
t = Timer()
for sample in data:
(
mask,
boxes3d,
boxes3d_classes,
boxes3d_attributes,
boxes3d_track_ids,
boxes3d_velocities,
) = self._filter_boxes(sample["CAM"]["annotations"])
sample["CAM"]["annotations"]["boxes3d"] = boxes3d
sample["CAM"]["annotations"]["boxes3d_classes"] = boxes3d_classes
sample["CAM"]["annotations"][
"boxes3d_attributes"
] = boxes3d_attributes
sample["CAM"]["annotations"][
"boxes3d_track_ids"
] = boxes3d_track_ids
sample["CAM"]["annotations"][
"boxes3d_velocities"
] = boxes3d_velocities
sample["CAM"]["annotations"]["boxes2d"] = sample["CAM"][
"annotations"
]["boxes2d"][mask]
for box3d_class in boxes3d_classes:
frequencies[inv_nuscenes_class_map[box3d_class]] += 1
if self.skip_empty_samples:
if len(sample["CAM"]["annotations"]["boxes3d"]) > 0:
samples.append(sample)
else:
samples.append(sample)
rank_zero_info(
f"Preprocessing {len(data)} frames takes {t.time():.2f}"
" seconds."
)
print_class_histogram(frequencies)
if self.skip_empty_samples:
rank_zero_info(
f"Filtered {len(data) - len(samples)} empty frames."
)
return samples
def __repr__(self) -> str:
"""Concise representation of the dataset."""
return f"NuScenes Monocular Dataset {self.version} {self.split}"
def _generate_data_mapping(self) -> list[DictStrAny]:
"""Generate data mapping.
Returns:
List[DictStrAny]: List of items required to load for a single
dataset sample.
"""
data = NuScenesDevkit(
version=self.version, dataroot=self.data_root, verbose=False
)
frames = []
instance_tokens: list[str] = []
scene_names_per_split = create_splits_scenes()
scenes = [
scene
for scene in data.scene
if scene["name"] in scene_names_per_split[self.split]
]
for scene in tqdm(scenes):
scene_name = scene["name"]
frame_ids = 0
sample_token = scene["first_sample_token"]
while sample_token:
sample = data.get("sample", sample_token)
# LIDAR data
lidar_token = sample["data"]["LIDAR_TOP"]
lidar_data = self._load_lidar_data(data, lidar_token)
lidar_data["annotations"] = self._load_annotations(
data,
lidar_data["extrinsics"],
sample["anns"],
instance_tokens,
)
# TODO add RADAR, Map data
# Get the sample data for each camera
for cam in self.CAMERAS:
frame: DictStrAny = {}
frame["scene_name"] = f"{scene_name}_{cam}"
frame["token"] = sample["token"]
frame["frame_ids"] = frame_ids
frame["LIDAR_TOP"] = lidar_data
cam_token = sample["data"][cam]
frame["CAM"] = self._load_cam_data(data, cam_token)
frame["CAM"]["annotations"] = self._load_annotations(
data,
frame["CAM"]["extrinsics"],
sample["anns"],
instance_tokens,
axis_mode=AxisMode.OPENCV,
export_2d_annotations=True,
intrinsics=frame["CAM"]["intrinsics"],
image_hw=frame["CAM"]["image_hw"],
)
frames.append(frame)
sample_token = sample["next"]
frame_ids += 1
return frames
def __getitem__(self, idx: int) -> DictData:
"""Get single sample.
Args:
idx (int): Index of sample.
Returns:
DictData: sample at index in Vis4D input format.
"""
sample = self.samples[idx]
data_dict: DictData = {}
if K.depth_maps in self.keys_to_load:
lidar_data = sample["LIDAR_TOP"]
points_bytes = self.data_backend.get(lidar_data["lidar_path"])
points = np.frombuffer(points_bytes, dtype=np.float32)
points = points.reshape(-1, 5)[:, :3]
if K.depth_maps in self.keys_to_load:
lidar_to_global = lidar_data["extrinsics"]
# load camera frame
data_dict = {
"token": sample["token"],
K.sequence_names: sample["scene_name"],
K.frame_ids: sample["frame_ids"],
K.timestamp: sample["CAM"]["timestamp"],
}
if K.images in self.keys_to_load:
im_bytes = self.data_backend.get(sample["CAM"]["image_path"])
image = np.ascontiguousarray(
im_decode(im_bytes), dtype=np.float32
)[None]
data_dict[K.images] = image
data_dict[K.input_hw] = sample["CAM"]["image_hw"]
data_dict[K.sample_names] = sample["CAM"]["sample_name"]
data_dict[K.intrinsics] = sample["CAM"]["intrinsics"]
if K.original_images in self.keys_to_load:
data_dict[K.original_images] = image
data_dict[K.original_hw] = sample["CAM"]["image_hw"]
if K.boxes3d in self.keys_to_load or K.boxes2d in self.keys_to_load:
if K.boxes3d in self.keys_to_load:
data_dict[K.boxes3d] = sample["CAM"]["annotations"]["boxes3d"]
data_dict[K.boxes3d_classes] = sample["CAM"]["annotations"][
"boxes3d_classes"
]
data_dict[K.boxes3d_track_ids] = sample["CAM"]["annotations"][
"boxes3d_track_ids"
]
data_dict[K.boxes3d_velocities] = sample["CAM"]["annotations"][
"boxes3d_velocities"
]
data_dict["attributes"] = sample["CAM"]["annotations"][
"boxes3d_attributes"
]
data_dict[K.extrinsics] = sample["CAM"]["extrinsics"]
data_dict[K.axis_mode] = AxisMode.OPENCV
if K.boxes2d in self.keys_to_load:
data_dict[K.boxes2d] = sample["CAM"]["annotations"]["boxes2d"]
data_dict[K.boxes2d_classes] = data_dict[K.boxes3d_classes]
data_dict[K.boxes2d_track_ids] = data_dict[K.boxes3d_track_ids]
if K.depth_maps in self.keys_to_load:
depth_maps = self._load_depth_map(
points,
lidar_to_global,
sample["CAM"]["extrinsics"],
sample["CAM"]["intrinsics"],
sample["CAM"]["image_hw"],
)
data_dict[K.depth_maps] = depth_maps
return data_dict
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