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vis_3d_scene.py
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import os
offscreen = False
if os.environ.get('DISP', 'f') == 'f':
from pyvirtualdisplay import Display
display = Display(visible=False, size=(2560, 1440))
display.start()
offscreen = True
from mayavi import mlab
import mayavi
mlab.options.offscreen = offscreen
print("Set mlab.options.offscreen={}".format(mlab.options.offscreen))
import os, time, argparse, math, os.path as osp, numpy as np
import torch
import torch.distributed as dist
import torch.nn.functional as F
import matplotlib.pyplot as plt
import matplotlib; matplotlib.use('agg')
import shutil
import mmcv
from mmengine import Config
from mmengine.logging import MMLogger
from mmseg.models import build_segmentor
import warnings
warnings.filterwarnings("ignore")
import model
from dataset import get_dataloader
from dataset.dataset_wrapper_temporal import custom_collate_fn_temporal
from utils.config_tools import modify_for_eval
from utils.metric_util import cityscapes2semantickitti, openseed2nuscenes
from vis_pics import create_label_colormap
from PIL import Image
def pass_print(*args, **kwargs):
pass
KITTI_ROOT = 'data/kitti'
import dataset.kitti.io_data as SemanticKittiIO
def read_semantic_kitti(metas, idx):
label_path = os.path.join(
KITTI_ROOT, "dataset/sequences", metas['sequence'], "voxels", "{}.label".format(metas['token']))
invalid_path = os.path.join(
KITTI_ROOT, "dataset/sequences", metas['sequence'], "voxels", "{}.invalid".format(metas['token']))
remap_lut = SemanticKittiIO.get_remap_lut("dataset/kitti/semantic-kitti.yaml")
LABEL = SemanticKittiIO._read_label_SemKITTI(label_path)
INVALID = SemanticKittiIO._read_invalid_SemKITTI(invalid_path)
LABEL = remap_lut[LABEL.astype(np.uint16)].astype(
np.float32
) # Remap 20 classes semanticKITTI SSC
LABEL[
np.isclose(INVALID, 1)
] = 255 # Setting to unknown all voxels marked on invalid mask...
LABEL = LABEL.reshape(256, 256, 32)
LABEL = np.flip(LABEL, 1)
save_info = f"{metas['sequence']}_{metas['token']}_{idx}.png"
return LABEL, save_info
def read_occ3d_label(metas, nusc, idx):
token = metas['token']
scene_token = nusc.get('sample', token)['scene_token']
scene_name = nusc.get('scene', scene_token)['name']
label_file = f'data/occ3d/gts/{scene_name}/{token}/labels.npz'
label = np.load(label_file)
save_info = f"{scene_name}_{token}_{idx}.png"
return label, save_info
def draw(
voxels, # semantic occupancy predictions
grid_coords,
voxel_size=0.2, # voxel size in the real world
save_dirs=None,
cam_positions=None,
focal_positions=None,
timestamp=None,
mode=0,
sem=False,
dataset='nuscenes',
):
if mode == 0:
grid_coords = np.vstack([grid_coords.T, voxels.reshape(-1)]).T
# draw a simple car at the middle
# car_vox_range = np.array([
# [w//2 - 2 - 4, w//2 - 2 + 4],
# [h//2 - 2 - 4, h//2 - 2 + 4],
# [z//2 - 2 - 3, z//2 - 2 + 3]
# ], dtype=np.int)
# car_x = np.arange(car_vox_range[0, 0], car_vox_range[0, 1])
# car_y = np.arange(car_vox_range[1, 0], car_vox_range[1, 1])
# car_z = np.arange(car_vox_range[2, 0], car_vox_range[2, 1])
# car_xx, car_yy, car_zz = np.meshgrid(car_x, car_y, car_z)
# car_label = np.zeros([8, 8, 6], dtype=np.int)
# car_label[:3, :, :2] = 17
# car_label[3:6, :, :2] = 18
# car_label[6:, :, :2] = 19
# car_label[:3, :, 2:4] = 18
# car_label[3:6, :, 2:4] = 19
# car_label[6:, :, 2:4] = 17
# car_label[:3, :, 4:] = 19
# car_label[3:6, :, 4:] = 17
# car_label[6:, :, 4:] = 18
# car_grid = np.array([car_xx.flatten(), car_yy.flatten(), car_zz.flatten()]).T
# car_indexes = car_grid[:, 0] * h * z + car_grid[:, 1] * z + car_grid[:, 2]
# grid_coords[car_indexes, 3] = car_label.flatten()
fov_grid_coords = grid_coords
# Remove empty and unknown voxels
fov_voxels = fov_grid_coords[
(fov_grid_coords[:, 3] > 0) & (fov_grid_coords[:, 3] < 100)
]
print(len(fov_voxels))
figure = mlab.figure(size=(2560, 1440), bgcolor=(1, 1, 1))
# Draw occupied inside FOV voxels
voxel_size = sum(voxel_size) / 3
if not sem:
plt_plot_fov = mlab.points3d(
fov_voxels[:, 1],
fov_voxels[:, 0],
fov_voxels[:, 2],
fov_voxels[:, 3],
colormap="jet",
scale_factor=1.0 * voxel_size,
mode="cube",
opacity=1.0,
)
else:
plt_plot_fov = mlab.points3d(
fov_voxels[:, 1],
fov_voxels[:, 0],
fov_voxels[:, 2],
fov_voxels[:, 3],
scale_factor=1.0 * voxel_size,
mode="cube",
opacity=1.0,
vmin=1,
vmax=19 if dataset == 'kitti' else 16, # 16
)
plt_plot_fov.glyph.scale_mode = "scale_by_vector"
if sem:
if dataset == 'kitti':
colors = create_label_colormap()
colors = np.concatenate([colors, np.ones_like(colors[:, :1]) * 255], axis=-1)
else:
colors = np.array(
[
[255, 120, 50, 255], # barrier orange
[255, 192, 203, 255], # bicycle pink
[255, 255, 0, 255], # bus yellow
[ 0, 150, 245, 255], # car blue
[ 0, 255, 255, 255], # construction_vehicle cyan
[255, 127, 0, 255], # motorcycle dark orange
[255, 0, 0, 255], # pedestrian red
[255, 240, 150, 255], # traffic_cone light yellow
[135, 60, 0, 255], # trailer brown
[160, 32, 240, 255], # truck purple
[255, 0, 255, 255], # driveable_surface dark pink
# [175, 0, 75, 255], # other_flat dark red
[139, 137, 137, 255],
[ 75, 0, 75, 255], # sidewalk dard purple
[150, 240, 80, 255], # terrain light green
[230, 230, 250, 255], # manmade white
[ 0, 175, 0, 255], # vegetation green
# [ 0, 255, 127, 255], # ego car dark cyan
# [255, 99, 71, 255], # ego car
# [ 0, 191, 255, 255] # ego car
]
).astype(np.uint8)
plt_plot_fov.module_manager.scalar_lut_manager.lut.table = colors
scene = figure.scene
for i, save_dir in enumerate(save_dirs):
if i < 6:
scene.camera.position = cam_positions[i] - np.array([0.7, 1.3, -1.])
scene.camera.focal_point = focal_positions[i] - np.array([0.7, 1.3, -1.])
scene.camera.view_angle = 35 if i != 3 else 60
scene.camera.view_up = [0.0, 0.0, 1.0]
scene.camera.clipping_range = [0.01, 300.]
scene.camera.compute_view_plane_normal()
scene.render()
elif i == 6:
# scene.camera.position = [-4.69302904, -52.74874688, 19.16181492]
# scene.camera.focal_point = [-4.52985313, -51.8233303, 18.81979477]
# scene.camera.view_angle = 40.0
# scene.camera.view_up = [0.0, 0.0, 1.0]
# scene.camera.clipping_range = [0.01, 300.]
# scene.camera.compute_view_plane_normal()
# scene.render()
scene.camera.position = [ 0.75131739, -35.08337438, 16.71378558]
scene.camera.focal_point = [ 0.75131739, -34.21734897, 16.21378558]
scene.camera.view_angle = 40.0
scene.camera.view_up = [0.0, 0.0, 1.0]
scene.camera.clipping_range = [0.01, 300.]
scene.camera.compute_view_plane_normal()
scene.render()
else:
# scene.camera.position = [91.84365261779985, 87.2356528161641, 86.90232146965226]
# scene.camera.focal_point = [4.607997894287109, -1.9073486328125e-06, -0.33333325386047363]
# scene.camera.view_angle = 30.0
# scene.camera.view_up = [0.0, 0.0, 1.0]
# scene.camera.clipping_range = [33.458354318473965, 299.5433372220855]
# scene.camera.compute_view_plane_normal()
# scene.render()
scene.camera.position = [ 0.75131739, 0.78265103, 93.21378558]
scene.camera.focal_point = [ 0.75131739, 0.78265103, 92.21378558]
scene.camera.view_angle = 40.0
scene.camera.view_up = [0., 1., 0.]
scene.camera.clipping_range = [0.01, 400.]
scene.camera.compute_view_plane_normal()
scene.render()
mlab.savefig(os.path.join(save_dir, f'vis_{timestamp}.png'))
mlab.close()
def main(local_rank, args):
if args.dataset == 'nuscenes':
from nuscenes import NuScenes
nusc = NuScenes(version='v1.0-trainval', dataroot='data/nuscenes')
for scene_meta in nusc.scene:
if scene_meta['name'] == args.scene_name:
scene_token = scene_meta['token']
# global settings
torch.backends.cudnn.benchmark = True
# load config
cfg = Config.fromfile(args.py_config)
cfg = modify_for_eval(cfg, args.dataset)
cfg.work_dir = args.work_dir
# init DDP
if args.gpus > 1:
distributed = True
ip = os.environ.get("MASTER_ADDR", "127.0.0.1")
port = os.environ.get("MASTER_PORT", "20506")
hosts = int(os.environ.get("WORLD_SIZE", 1)) # number of nodes
rank = int(os.environ.get("RANK", 0)) # node id
gpus = torch.cuda.device_count() # gpus per node
print(f"tcp://{ip}:{port}")
dist.init_process_group(
backend="nccl", init_method=f"tcp://{ip}:{port}",
world_size=hosts * gpus, rank=rank * gpus + local_rank
)
world_size = dist.get_world_size()
cfg.gpu_ids = range(world_size)
torch.cuda.set_device(local_rank)
local_rank = dist.get_rank()
if dist.get_rank() != 0:
import builtins
builtins.print = pass_print
else:
distributed = False
local_rank = 0
# configure logger
if local_rank == 0:
os.makedirs(args.work_dir, exist_ok=True)
cfg.dump(osp.join(args.work_dir, 'eval' + osp.basename(args.py_config)))
timestamp = time.strftime('%Y%m%d_%H%M%S', time.localtime())
log_file = osp.join(args.work_dir, f'eval-{timestamp}.log')
logger = MMLogger(name='selfocc', log_file=log_file, log_level='INFO')
# logger.info(f'Config:\n{cfg.pretty_text}')
# build model
my_model = build_segmentor(cfg.model)
my_model.init_weights()
n_parameters = sum(p.numel() for p in my_model.parameters() if p.requires_grad)
logger.info(f'Number of params: {n_parameters}')
if distributed:
my_model = torch.nn.SyncBatchNorm.convert_sync_batchnorm(my_model)
print('converted sync bn.')
find_unused_parameters = cfg.get('find_unused_parameters', False)
ddp_model_module = torch.nn.parallel.DistributedDataParallel
my_model = ddp_model_module(
my_model.cuda(),
device_ids=[torch.cuda.current_device()],
broadcast_buffers=False,
find_unused_parameters=find_unused_parameters)
raw_model = my_model.module
else:
my_model = my_model.to(args.device)
raw_model = my_model
print('done ddp model')
cfg.train_wrapper_config.phase = 'val'
if args.scene_name:
cfg.val_dataset_config.update({
'type': 'nuScenes_One_Frame_Sweeps_Dist_Vis',
'imageset': 'data/nuscenes_infos_val_sweeps_lid.pkl',
'scene_name': args.scene_name,
'scene_token': scene_token})
cfg.val_wrapper_config.update({
'type': 'tpvformer_dataset_nuscenes_vis',
})
train_dataset_loader, val_dataset_loader = get_dataloader(
cfg.train_dataset_config,
cfg.val_dataset_config,
cfg.train_wrapper_config,
cfg.val_wrapper_config,
cfg.train_loader,
cfg.val_loader,
cfg.nusc,
dist=distributed,
iter_resume=False,
val_only=True)
if args.vis_train:
dataset = train_dataset_loader.dataset
else:
dataset = val_dataset_loader.dataset
cfg.resume_from = ''
if osp.exists(osp.join(args.work_dir, 'latest.pth')):
cfg.resume_from = osp.join(args.work_dir, 'latest.pth')
if args.resume_from:
cfg.resume_from = args.resume_from
print('resume from: ', cfg.resume_from)
print('work dir: ', args.work_dir)
epoch = 'last'
if cfg.resume_from and osp.exists(cfg.resume_from):
map_location = 'cpu'
ckpt = torch.load(cfg.resume_from, map_location=map_location)
if 'state_dict' in ckpt:
state_dict = ckpt['state_dict']
else:
state_dict = ckpt
if 'epoch' in ckpt:
epoch = ckpt['epoch']
print(raw_model.load_state_dict(state_dict, strict=False))
print(f'successfully resumed from {cfg.resume_from}')
elif cfg.load_from:
ckpt = torch.load(cfg.load_from, map_location='cpu')
if 'state_dict' in ckpt:
state_dict = ckpt['state_dict']
else:
state_dict = ckpt
print(raw_model.load_state_dict(state_dict, strict=False))
xx = torch.linspace(-40.0, 40.0, 200)
yy = torch.linspace(-40.0, 40.0, 200)
zz = torch.linspace(-1.0, 5.4, 16)
xyz = torch.stack([
xx[:, None, None].expand(-1, 200, 16),
yy[None, :, None].expand(200, -1, 16),
zz[None, None, :].expand(200, 200, -1),
torch.ones(200, 200, 16)
], dim=-1) # 200, 200, 16, 4
xyz = xyz.to(args.device)
# eval
my_model.eval()
vis_save_path = osp.join(args.work_dir, args.dir_name, f'epoch{epoch}')
if args.scene_name:
vis_save_path = osp.join(vis_save_path, args.scene_name)
clip_dirs = []
for video_clip in range(8):
clip_dir = os.path.join(vis_save_path, str(video_clip))
clip_dirs.append(clip_dir)
os.makedirs(clip_dir, exist_ok=True)
os.makedirs(vis_save_path, exist_ok=True)
# args.frame_idx = range(0, len(dataset), 10)
# args.frame_idx = range(6000, 3000, -10)
# args.frame_idx = [2400, 2420, 2490, 2530, 2670, 2680, 2780, 2820, 2950, 3210, 3450, 3510, 3560, 3610, 3730, 4150, 4320, 4330, 4450, 4530, 4580, 4640, 4890, 5040, 5120, 5220, 5370, 5550]
# args.frame_idx = [0,110,190,270,350,360,380,420,440,480,500,550,560,600,650,710,720,750,770,840,880,930,940,990,1090,1130,1200,1210,1300,1560,1600,1850,1930,1980,2000,2050,2040,2090,2120,2400,2490,2550,2670,2820,3200,3510,3560,3690,3800,4100,4150,4320,4530,4580,4890,5220,5370]
# args.frame_idx = [2000, 1300, 990, 1600, 1980]
#kitti
# args.frame_idx = [640, 0, 130, 260, 330, 470, 770, 730, 750, 570, 600, 400, 510, 100]
if args.scene_name:
args.frame_idx = list(range(args.start_idx, len(dataset)))
with torch.no_grad():
# num_iters = len(val_dataset_loader) if args.scene_name else len(args.frame_idx)
# if args.scene_name:
# loader_iter = iter(val_dataset_loader)
# for idx in range(num_iters):
# if args.scene_name:
# input_imgs, img_metas = next(loader_iter)
# real_idx = img_metas[0]['timestamp']
# else:
# real_idx = args.frame_idx[idx]
# one_batch = dataset[real_idx]
# input_imgs, img_metas = custom_collate_fn_temporal([one_batch])
for idx in args.frame_idx:
one_batch = dataset[idx]
input_imgs, img_metas = custom_collate_fn_temporal([one_batch])
real_idx = img_metas[0]['timestamp']
input_imgs = input_imgs.to(args.device)
if 'kitti' in args.dataset:
point_cloud_range = [-25.6, 0.0, -2.0, 25.6, 51.2, 4.4]
resolution = 0.2
else:
if args.scene_size == 0:
point_cloud_range = [-51.2, -51.2, -4, 51.2, 51.2, 4]
resolution = 0.4
elif args.scene_size == 1:
point_cloud_range = [-40.0, -40.0, -2.8, 40.0, 40.0, 3.6]
resolution = 0.4
elif args.scene_size == 2:
point_cloud_range = [-40.0, -40.0, -3.1, 40.0, 40.0, 3.9]
resolution = 0.4
elif args.scene_size == 3:
point_cloud_range = [-40.0, -40.0, -3.2, 40.0, 40.0, 4.0]
resolution = 0.4
elif args.scene_size == 4:
point_cloud_range = [-40.0, -40.0, -1.0, 40.0, 40.0, 5.4]
resolution = 0.4
if args.model_pred:
result_dict = my_model(
imgs=input_imgs,
metas=img_metas,
occ_only=True,
aabb=point_cloud_range,
resolution=resolution)
if args.transform:
ego2lidar = img_metas[0]['ego2lidar']
ego2lidar = xyz.new_tensor(ego2lidar) # 4, 4
lidar_points = torch.matmul(ego2lidar.unsqueeze(0), xyz.reshape(-1, 4, 1))
lidar_points = lidar_points.squeeze(-1)[:, :3] # 200*200*16, 3
# ori_lidar_points = lidar_points.clone()
lidar_points[:, 0] = (lidar_points[:, 0] - point_cloud_range[0]) / (point_cloud_range[3] - point_cloud_range[0])
lidar_points[:, 1] = (lidar_points[:, 1] - point_cloud_range[1]) / (point_cloud_range[4] - point_cloud_range[1])
lidar_points[:, 2] = (lidar_points[:, 2] - point_cloud_range[2]) / (point_cloud_range[5] - point_cloud_range[2])
lidar_points = lidar_points.reshape(1, 200, 200, 16, 3)
sampled_sdf = F.grid_sample(
result_dict['sdf'][None, None, ...], # 1, 1, H, W, D
lidar_points[..., [2, 0, 1]] * 2 - 1,
mode='bilinear',
align_corners=True,
padding_mode='border') # 1, 1, 200, 200, 16
sdf = sampled_sdf.squeeze().permute(1, 0, 2).cpu()
else:
sdf = result_dict['sdf'].cpu()
grid_size = sdf.shape
voxel_density = sdf
predict_vox = (voxel_density <= args.thresh).to(torch.int)
predict_vox[:6, ...] = 0
predict_vox[-6:, ...] = 0
predict_vox[:, :6, :] = 0
predict_vox[:, -6:, :] = 0
print((voxel_density > 1.0).sum())
print((voxel_density > 2.0).sum())
print((voxel_density > 3.0).sum())
# if args.dataset == 'nuscenes':
# gt_occ, save_info = read_occ3d_label(img_metas[0], nusc, real_idx)
# camera_mask = torch.from_numpy(gt_occ['mask_camera']).bool()#.cuda()
# lidar_mask = torch.from_numpy(gt_occ['mask_lidar']).bool()#.cuda()
# gt_vox = torch.from_numpy(gt_occ['semantics'])#.cuda()
# gt_vox[gt_vox == 17] = 0
# gt_vox = gt_vox.permute(1, 0, 2)
# camera_mask = camera_mask.permute(1, 0, 2)
# lidar_mask = lidar_mask.permute(1, 0, 2)
# else:
# gt_vox, save_info = read_semantic_kitti(img_metas[0], real_idx)
# lidar_mask = gt_vox != 255
# if not args.model_pred:
# predict_vox = gt_vox
grid_size = predict_vox.shape
# if args.use_lidar_mask:
# predict_vox[~lidar_mask] = 0
# elif args.use_cam_mask and args.dataset == 'nuscenes':
# predict_vox[~camera_mask] = 0
predict_vox[..., (grid_size[2] - args.cap):] = 0
if args.sem:
if args.model_pred:
if args.transform:
sem = result_dict['logits'].permute(3, 0, 1, 2) # C, H, W, D
sampled_sem = F.grid_sample(
sem[None, ...], # 1, C, H, W, D
lidar_points[..., [2, 0, 1]] * 2 - 1,
mode='bilinear',
align_corners=True) # 1, C, 200, 200, 16
sampled_sem = torch.argmax(sampled_sem, dim=1).squeeze()
sem = sampled_sem.permute(1, 0, 2)
else:
sem = result_dict['sem']
if args.dataset == 'nuscenes':
sem = openseed2nuscenes(sem)
elif args.dataset == 'kitti':
sem = cityscapes2semantickitti(sem)
else:
raise NotImplementedError
sem = sem.cpu()
predict_vox = predict_vox * sem
else:
for z in range(grid_size[2]-args.cap):
mask = (predict_vox > 0)[..., z]
predict_vox[..., z][mask] = z + 1 # grid_size[2] - z
if args.dataset == 'nuscenes':
# ego2lidar = img_metas[0]['ego2lidar']
# ego2lidar = xyz.new_tensor(ego2lidar) # 4, 4
# lidar_points = torch.matmul(ego2lidar.unsqueeze(0), xyz.reshape(-1, 4, 1))
# lidar_points = lidar_points.squeeze(-1)[:, :3] # 200*200*16, 3
# lidar_points = lidar_points[..., [1, 0, 2]]
# gird_coords = lidar_points.cpu().numpy()
grid_coords = result_dict['xyz'].reshape(-1, 3).cpu().numpy()
voxel_origin = [-40.0, -40.0, -1.0]
voxel_max = [40.0, 40.0, 5.4]
else:
voxel_origin = point_cloud_range[:3]
voxel_max = point_cloud_range[3:]
# visualization
if args.save_rgb:
# frame_dir = os.path.join(vis_save_path, str(real_idx))
# os.makedirs(frame_dir, exist_ok=True)
# gt_imgs = input_imgs.squeeze(0).permute(0, 2, 3, 1).cpu().numpy() * 256
# gt_imgs = gt_imgs[..., [2, 1, 0]]
# gt_imgs = gt_imgs.astype(np.uint8)
for i in range(6):
# img = Image.fromarray(gt_imgs[i])
# img.save(osp.join(clip_dirs[i], 'img_'+str(real_idx)+'.jpg'))
# if video_clip < 6:
filename = img_metas[0]['input_imgs_path'][i]
shutil.copy(filename, os.path.join(clip_dirs[i], 'img_'+str(real_idx)+'.jpg'))
# save_path = os.path.join(vis_save_path, save_info)
draw(predict_vox,
grid_coords,
[resolution] * 3,
clip_dirs,
img_metas[0]['cam_positions'],
img_metas[0]['focal_positions'],
timestamp=real_idx,
mode=args.mode,
sem=args.sem,
dataset=args.dataset)
logger.info(f'done processing frame idx{idx}')
if __name__ == '__main__':
# Training settings
parser = argparse.ArgumentParser(description='')
parser.add_argument('--py-config', default='config/tpv_lidarseg.py')
parser.add_argument('--work-dir', type=str, default='./out/tpv_lidarseg')
parser.add_argument('--resume-from', type=str, default='')
parser.add_argument('--hfai', action='store_true', default=False)
parser.add_argument('--dir-name', type=str, default='vis_scene')
parser.add_argument('--frame-idx', type=int, nargs='+', default=[0])
parser.add_argument('--vis-train', action='store_true', default=False)
parser.add_argument('--thresh', type=float, default=0)
parser.add_argument('--mode', type=int, default=0, help='0: occupancy, 1: predicted point cloud, 2: gt point cloud')
parser.add_argument('--cap', type=int, default=0)
parser.add_argument('--dataset', type=str, default='nuscenes')
parser.add_argument('--device', type=str, default='cuda')
parser.add_argument('--model-pred', action='store_true', default=False)
parser.add_argument('--scene-size', type=int, default=0)
parser.add_argument('--sem', action='store_true', default=False)
parser.add_argument('--use-lidar-mask', action='store_true', default=False)
parser.add_argument('--use-cam-mask', action='store_true', default=False)
parser.add_argument('--transform', action='store_true', default=False)
parser.add_argument('--save-rgb', action='store_true', default=False)
parser.add_argument('--scene-name', type=str, default='')
parser.add_argument('--start-idx', type=int, default=0)
args = parser.parse_args()
ngpus = torch.cuda.device_count()
args.gpus = ngpus
print(args)
if args.hfai:
os.environ['HFAI'] = 'true'
if ngpus > 1:
torch.multiprocessing.spawn(main, args=(args,), nprocs=args.gpus)
else:
main(0, args)