File size: 10,127 Bytes
34d1f8b |
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 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 |
# Copyright (c) OpenMMLab. All rights reserved.
import os.path as osp
from typing import Dict, List, Optional, Sequence, Union
import mmengine
import numpy as np
import torch
from mmengine.dataset import Compose
from mmengine.fileio import (get_file_backend, isdir, join_path,
list_dir_or_file)
from mmengine.infer.infer import ModelType
from mmengine.structures import InstanceData
from mmdet3d.registry import INFERENCERS
from mmdet3d.structures import (CameraInstance3DBoxes, DepthInstance3DBoxes,
Det3DDataSample, LiDARInstance3DBoxes)
from mmdet3d.utils import ConfigType
from .base_3d_inferencer import Base3DInferencer
InstanceList = List[InstanceData]
InputType = Union[str, np.ndarray]
InputsType = Union[InputType, Sequence[InputType]]
PredType = Union[InstanceData, InstanceList]
ImgType = Union[np.ndarray, Sequence[np.ndarray]]
ResType = Union[Dict, List[Dict], InstanceData, List[InstanceData]]
@INFERENCERS.register_module(name='det3d-lidar')
@INFERENCERS.register_module()
class LidarDet3DInferencer(Base3DInferencer):
"""The inferencer of LiDAR-based detection.
Args:
model (str, optional): Path to the config file or the model name
defined in metafile. For example, it could be
"pointpillars_kitti-3class" or
"configs/pointpillars/pointpillars_hv_secfpn_8xb6-160e_kitti-3d-3class.py". # noqa: E501
If model is not specified, user must provide the
`weights` saved by MMEngine which contains the config string.
Defaults to None.
weights (str, optional): Path to the checkpoint. If it is not specified
and model is a model name of metafile, the weights will be loaded
from metafile. Defaults to None.
device (str, optional): Device to run inference. If None, the available
device will be automatically used. Defaults to None.
scope (str): The scope of the model. Defaults to 'mmdet3d'.
palette (str): Color palette used for visualization. The order of
priority is palette -> config -> checkpoint. Defaults to 'none'.
"""
def __init__(self,
model: Union[ModelType, str, None] = None,
weights: Optional[str] = None,
device: Optional[str] = None,
scope: str = 'mmdet3d',
palette: str = 'none') -> None:
# A global counter tracking the number of frames processed, for
# naming of the output results
self.num_visualized_frames = 0
super(LidarDet3DInferencer, self).__init__(
model=model,
weights=weights,
device=device,
scope=scope,
palette=palette)
def _inputs_to_list(self, inputs: Union[dict, list], **kwargs) -> list:
"""Preprocess the inputs to a list.
Preprocess inputs to a list according to its type:
- list or tuple: return inputs
- dict: the value with key 'points' is
- Directory path: return all files in the directory
- other cases: return a list containing the string. The string
could be a path to file, a url or other types of string according
to the task.
Args:
inputs (Union[dict, list]): Inputs for the inferencer.
Returns:
list: List of input for the :meth:`preprocess`.
"""
if isinstance(inputs, dict) and isinstance(inputs['points'], str):
pcd = inputs['points']
backend = get_file_backend(pcd)
if hasattr(backend, 'isdir') and isdir(pcd):
# Backends like HttpsBackend do not implement `isdir`, so
# only those backends that implement `isdir` could accept
# the inputs as a directory
filename_list = list_dir_or_file(pcd, list_dir=False)
inputs = [{
'points': join_path(pcd, filename)
} for filename in filename_list]
if not isinstance(inputs, (list, tuple)):
inputs = [inputs]
return list(inputs)
def _init_pipeline(self, cfg: ConfigType) -> Compose:
"""Initialize the test pipeline."""
pipeline_cfg = cfg.test_dataloader.dataset.pipeline
load_point_idx = self._get_transform_idx(pipeline_cfg,
'LoadPointsFromFile')
if load_point_idx == -1:
raise ValueError(
'LoadPointsFromFile is not found in the test pipeline')
load_cfg = pipeline_cfg[load_point_idx]
self.coord_type, self.load_dim = load_cfg['coord_type'], load_cfg[
'load_dim']
self.use_dim = list(range(load_cfg['use_dim'])) if isinstance(
load_cfg['use_dim'], int) else load_cfg['use_dim']
pipeline_cfg[load_point_idx]['type'] = 'LidarDet3DInferencerLoader'
return Compose(pipeline_cfg)
def visualize(self,
inputs: InputsType,
preds: PredType,
return_vis: bool = False,
show: bool = False,
wait_time: int = -1,
draw_pred: bool = True,
pred_score_thr: float = 0.3,
no_save_vis: bool = False,
img_out_dir: str = '') -> Union[List[np.ndarray], None]:
"""Visualize predictions.
Args:
inputs (InputsType): Inputs for the inferencer.
preds (PredType): Predictions of the model.
return_vis (bool): Whether to return the visualization result.
Defaults to False.
show (bool): Whether to display the image in a popup window.
Defaults to False.
wait_time (float): The interval of show (s). Defaults to -1.
draw_pred (bool): Whether to draw predicted bounding boxes.
Defaults to True.
pred_score_thr (float): Minimum score of bboxes to draw.
Defaults to 0.3.
no_save_vis (bool): Whether to force not to save prediction
vis results. Defaults to False.
img_out_dir (str): Output directory of visualization results.
If left as empty, no file will be saved. Defaults to ''.
Returns:
List[np.ndarray] or None: Returns visualization results only if
applicable.
"""
if no_save_vis is True:
img_out_dir = ''
if not show and img_out_dir == '' and not return_vis:
return None
if getattr(self, 'visualizer') is None:
raise ValueError('Visualization needs the "visualizer" term'
'defined in the config, but got None.')
results = []
for single_input, pred in zip(inputs, preds):
single_input = single_input['points']
if isinstance(single_input, str):
pts_bytes = mmengine.fileio.get(single_input)
points = np.frombuffer(pts_bytes, dtype=np.float32)
points = points.reshape(-1, self.load_dim)
points = points[:, self.use_dim]
pc_name = osp.basename(single_input).split('.bin')[0]
pc_name = f'{pc_name}.png'
elif isinstance(single_input, np.ndarray):
points = single_input.copy()
pc_num = str(self.num_visualized_frames).zfill(8)
pc_name = f'{pc_num}.png'
else:
raise ValueError('Unsupported input type: '
f'{type(single_input)}')
if img_out_dir != '' and show:
o3d_save_path = osp.join(img_out_dir, 'vis_lidar', pc_name)
mmengine.mkdir_or_exist(osp.dirname(o3d_save_path))
else:
o3d_save_path = None
data_input = dict(points=points)
self.visualizer.add_datasample(
pc_name,
data_input,
pred,
show=show,
wait_time=wait_time,
draw_gt=False,
draw_pred=draw_pred,
pred_score_thr=pred_score_thr,
o3d_save_path=o3d_save_path,
vis_task='lidar_det',
)
results.append(points)
self.num_visualized_frames += 1
return results
def visualize_preds_fromfile(self, inputs: InputsType, preds: PredType,
**kwargs) -> Union[List[np.ndarray], None]:
"""Visualize predictions from `*.json` files.
Args:
inputs (InputsType): Inputs for the inferencer.
preds (PredType): Predictions of the model.
Returns:
List[np.ndarray] or None: Returns visualization results only if
applicable.
"""
data_samples = []
for pred in preds:
pred = mmengine.load(pred)
data_sample = Det3DDataSample()
data_sample.pred_instances_3d = InstanceData()
data_sample.pred_instances_3d.labels_3d = torch.tensor(
pred['labels_3d'])
data_sample.pred_instances_3d.scores_3d = torch.tensor(
pred['scores_3d'])
if pred['box_type_3d'] == 'LiDAR':
data_sample.pred_instances_3d.bboxes_3d = \
LiDARInstance3DBoxes(pred['bboxes_3d'])
elif pred['box_type_3d'] == 'Camera':
data_sample.pred_instances_3d.bboxes_3d = \
CameraInstance3DBoxes(pred['bboxes_3d'])
elif pred['box_type_3d'] == 'Depth':
data_sample.pred_instances_3d.bboxes_3d = \
DepthInstance3DBoxes(pred['bboxes_3d'])
else:
raise ValueError('Unsupported box type: '
f'{pred["box_type_3d"]}')
data_samples.append(data_sample)
return self.visualize(inputs=inputs, preds=data_samples, **kwargs)
|