File size: 14,016 Bytes
d40a0ff | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 | import cv2
import torch
import argparse
import os
import glob
import numpy as np
import mmcv
import matplotlib
import matplotlib.pyplot as plt
from nuscenes import NuScenes
from nuscenes.prediction import PredictHelper, convert_local_coords_to_global
from nuscenes.utils.geometry_utils import view_points, box_in_image, BoxVisibility, transform_matrix
from nuscenes.utils.data_classes import LidarPointCloud, Box
from nuscenes.utils import splits
from pyquaternion import Quaternion
from projects.mmdet3d_plugin.datasets.nuscenes_e2e_dataset import obtain_map_info
from projects.mmdet3d_plugin.datasets.eval_utils.map_api import NuScenesMap
from PIL import Image
from tools.analysis_tools.visualize.utils import color_mapping, AgentPredictionData
from tools.analysis_tools.visualize.render.bev_render import BEVRender
from tools.analysis_tools.visualize.render.cam_render import CameraRender
class Visualizer:
"""
BaseRender class
"""
def __init__(
self,
dataroot='/mnt/petrelfs/yangjiazhi/e2e_proj/data/nus_mini',
version='v1.0-mini',
predroot=None,
with_occ_map=False,
with_map=False,
with_planning=False,
with_pred_box=True,
with_pred_traj=False,
show_gt_boxes=False,
show_lidar=False,
show_command=False,
show_hd_map=False,
show_sdc_car=False,
show_sdc_traj=False,
show_legend=False):
self.nusc = NuScenes(version=version, dataroot=dataroot, verbose=True)
self.predict_helper = PredictHelper(self.nusc)
self.with_occ_map = with_occ_map
self.with_map = with_map
self.with_planning = with_planning
self.show_lidar = show_lidar
self.show_command = show_command
self.show_hd_map = show_hd_map
self.show_sdc_car = show_sdc_car
self.show_sdc_traj = show_sdc_traj
self.show_legend = show_legend
self.with_pred_traj = with_pred_traj
self.with_pred_box = with_pred_box
self.veh_id_list = [0, 1, 2, 3, 4, 6, 7]
self.use_json = '.json' in predroot
self.token_set = set()
self.predictions = self._parse_predictions_multitask_pkl(predroot)
self.bev_render = BEVRender(show_gt_boxes=show_gt_boxes)
self.cam_render = CameraRender(show_gt_boxes=show_gt_boxes)
if self.show_hd_map:
self.nusc_maps = {
'boston-seaport': NuScenesMap(dataroot=dataroot, map_name='boston-seaport'),
'singapore-hollandvillage': NuScenesMap(dataroot=dataroot, map_name='singapore-hollandvillage'),
'singapore-onenorth': NuScenesMap(dataroot=dataroot, map_name='singapore-onenorth'),
'singapore-queenstown': NuScenesMap(dataroot=dataroot, map_name='singapore-queenstown'),
}
def _parse_predictions_multitask_pkl(self, predroot):
outputs = mmcv.load(predroot)
outputs = outputs['bbox_results']
prediction_dict = dict()
for k in range(len(outputs)):
token = outputs[k]['token']
self.token_set.add(token)
if self.show_sdc_traj:
outputs[k]['boxes_3d'].tensor = torch.cat(
[outputs[k]['boxes_3d'].tensor, outputs[k]['sdc_boxes_3d'].tensor], dim=0)
outputs[k]['scores_3d'] = torch.cat(
[outputs[k]['scores_3d'], outputs[k]['sdc_scores_3d']], dim=0)
outputs[k]['labels_3d'] = torch.cat([outputs[k]['labels_3d'], torch.zeros(
(1,), device=outputs[k]['labels_3d'].device)], dim=0)
# detection
bboxes = outputs[k]['boxes_3d']
scores = outputs[k]['scores_3d']
labels = outputs[k]['labels_3d']
track_scores = scores.cpu().detach().numpy()
track_labels = labels.cpu().detach().numpy()
track_boxes = bboxes.tensor.cpu().detach().numpy()
track_centers = bboxes.gravity_center.cpu().detach().numpy()
track_dims = bboxes.dims.cpu().detach().numpy()
track_yaw = bboxes.yaw.cpu().detach().numpy()
if 'track_ids' in outputs[k]:
track_ids = outputs[k]['track_ids'].cpu().detach().numpy()
else:
track_ids = None
# speed
track_velocity = bboxes.tensor.cpu().detach().numpy()[:, -2:]
# trajectories
trajs = outputs[k][f'traj'].numpy()
traj_scores = outputs[k][f'traj_scores'].numpy()
predicted_agent_list = []
# occflow
if self.with_occ_map:
if 'topk_query_ins_segs' in outputs[k]['occ']:
occ_map = outputs[k]['occ']['topk_query_ins_segs'][0].cpu(
).numpy()
else:
occ_map = np.zeros((1, 5, 200, 200))
else:
occ_map = None
occ_idx = 0
for i in range(track_scores.shape[0]):
if track_scores[i] < 0.25:
continue
if occ_map is not None and track_labels[i] in self.veh_id_list:
occ_map_cur = occ_map[occ_idx, :, ::-1]
occ_idx += 1
else:
occ_map_cur = None
if track_ids is not None:
if i < len(track_ids):
track_id = track_ids[i]
else:
track_id = 0
else:
track_id = None
# if track_labels[i] not in [0, 1, 2, 3, 4, 6, 7]:
# continue
predicted_agent_list.append(
AgentPredictionData(
track_scores[i],
track_labels[i],
track_centers[i],
track_dims[i],
track_yaw[i],
track_velocity[i],
trajs[i],
traj_scores[i],
pred_track_id=track_id,
pred_occ_map=occ_map_cur,
past_pred_traj=None
)
)
if self.with_map:
map_thres = 0.7
score_list = outputs[k]['pts_bbox']['score_list'].cpu().numpy().transpose([
1, 2, 0])
predicted_map_seg = outputs[k]['pts_bbox']['lane_score'].cpu().numpy().transpose([
1, 2, 0]) # H, W, C
predicted_map_seg[..., -1] = score_list[..., -1]
predicted_map_seg = (predicted_map_seg > map_thres) * 1.0
predicted_map_seg = predicted_map_seg[::-1, :, :]
else:
predicted_map_seg = None
if self.with_planning:
# detection
bboxes = outputs[k]['sdc_boxes_3d']
scores = outputs[k]['sdc_scores_3d']
labels = 0
track_scores = scores.cpu().detach().numpy()
track_labels = labels
track_boxes = bboxes.tensor.cpu().detach().numpy()
track_centers = bboxes.gravity_center.cpu().detach().numpy()
track_dims = bboxes.dims.cpu().detach().numpy()
track_yaw = bboxes.yaw.cpu().detach().numpy()
track_velocity = bboxes.tensor.cpu().detach().numpy()[:, -2:]
if self.show_command:
command = outputs[k]['command'][0].cpu().detach().numpy()
else:
command = None
planning_agent = AgentPredictionData(
track_scores[0],
track_labels,
track_centers[0],
track_dims[0],
track_yaw[0],
track_velocity[0],
outputs[k]['planning_traj'][0].cpu().detach().numpy(),
1,
pred_track_id=-1,
pred_occ_map=None,
past_pred_traj=None,
is_sdc=True,
command=command,
)
predicted_agent_list.append(planning_agent)
else:
planning_agent = None
prediction_dict[token] = dict(predicted_agent_list=predicted_agent_list,
predicted_map_seg=predicted_map_seg,
predicted_planning=planning_agent)
return prediction_dict
def visualize_bev(self, sample_token, out_filename, t=None):
self.bev_render.reset_canvas(dx=1, dy=1)
self.bev_render.set_plot_cfg()
if self.show_lidar:
self.bev_render.show_lidar_data(sample_token, self.nusc)
if self.bev_render.show_gt_boxes:
self.bev_render.render_anno_data(
sample_token, self.nusc, self.predict_helper)
if self.with_pred_box:
self.bev_render.render_pred_box_data(
self.predictions[sample_token]['predicted_agent_list'])
if self.with_pred_traj:
self.bev_render.render_pred_traj(
self.predictions[sample_token]['predicted_agent_list'])
if self.with_map:
self.bev_render.render_pred_map_data(
self.predictions[sample_token]['predicted_map_seg'])
if self.with_occ_map:
self.bev_render.render_occ_map_data(
self.predictions[sample_token]['predicted_agent_list'])
if self.with_planning:
self.bev_render.render_pred_box_data(
[self.predictions[sample_token]['predicted_planning']])
self.bev_render.render_planning_data(
self.predictions[sample_token]['predicted_planning'], show_command=self.show_command)
if self.show_hd_map:
self.bev_render.render_hd_map(
self.nusc, self.nusc_maps, sample_token)
if self.show_sdc_car:
self.bev_render.render_sdc_car()
if self.show_legend:
self.bev_render.render_legend()
self.bev_render.save_fig(out_filename + '.jpg')
def visualize_cam(self, sample_token, out_filename):
self.cam_render.reset_canvas(dx=2, dy=3, tight_layout=True)
self.cam_render.render_image_data(sample_token, self.nusc)
self.cam_render.render_pred_track_bbox(
self.predictions[sample_token]['predicted_agent_list'], sample_token, self.nusc)
self.cam_render.render_pred_traj(
self.predictions[sample_token]['predicted_agent_list'], sample_token, self.nusc, render_sdc=self.with_planning)
self.cam_render.save_fig(out_filename + '_cam.jpg')
def combine(self, out_filename):
# pass
bev_image = cv2.imread(out_filename + '.jpg')
cam_image = cv2.imread(out_filename + '_cam.jpg')
merge_image = cv2.hconcat([cam_image, bev_image])
cv2.imwrite(out_filename + '.jpg', merge_image)
os.remove(out_filename + '_cam.jpg')
def to_video(self, folder_path, out_path, fps=4, downsample=1):
imgs_path = glob.glob(os.path.join(folder_path, '*.jpg'))
imgs_path = sorted(imgs_path)
img_array = []
for img_path in imgs_path:
img = cv2.imread(img_path)
height, width, channel = img.shape
img = cv2.resize(img, (width//downsample, height //
downsample), interpolation=cv2.INTER_AREA)
height, width, channel = img.shape
size = (width, height)
img_array.append(img)
out = cv2.VideoWriter(
out_path, cv2.VideoWriter_fourcc(*'DIVX'), fps, size)
for i in range(len(img_array)):
out.write(img_array[i])
out.release()
def main(args):
render_cfg = dict(
with_occ_map=False,
with_map=False,
with_planning=True,
with_pred_box=True,
with_pred_traj=True,
show_gt_boxes=False,
show_lidar=False,
show_command=True,
show_hd_map=False,
show_sdc_car=True,
show_legend=True,
show_sdc_traj=False
)
viser = Visualizer(version='v1.0-mini', predroot=args.predroot, dataroot='data/nuscenes', **render_cfg)
if not os.path.exists(args.out_folder):
os.makedirs(args.out_folder)
val_splits = splits.val
scene_token_to_name = dict()
for i in range(len(viser.nusc.scene)):
scene_token_to_name[viser.nusc.scene[i]['token']] = viser.nusc.scene[i]['name']
for i in range(len(viser.nusc.sample)):
sample_token = viser.nusc.sample[i]['token']
scene_token = viser.nusc.sample[i]['scene_token']
if scene_token_to_name[scene_token] not in val_splits:
continue
if sample_token not in viser.token_set:
print(i, sample_token, 'not in prediction pkl!')
continue
viser.visualize_bev(sample_token, os.path.join(args.out_folder, str(i).zfill(3)))
if args.project_to_cam:
viser.visualize_cam(sample_token, os.path.join(args.out_folder, str(i).zfill(3)))
viser.combine(os.path.join(args.out_folder, str(i).zfill(3)))
viser.to_video(args.out_folder, args.demo_video, fps=4, downsample=2)
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--predroot', default='/mnt/nas20/yihan01.hu/tmp/results.pkl', help='Path to results.pkl')
parser.add_argument('--out_folder', default='/mnt/nas20/yihan01.hu/tmp/viz/demo_test/', help='Output folder path')
parser.add_argument('--demo_video', default='mini_val_final.avi', help='Demo video name')
parser.add_argument('--project_to_cam', default=True, help='Project to cam (default: True)')
args = parser.parse_args()
main(args)
|