def main():
parser = ArgumentParser()
parser.add_argument('pcd', help='Point cloud file')
parser.add_argument('image', help='image file')
parser.add_argument('ann', help='ann file')
parser.add_argument('config', help='Config file')
parser.add_argument('checkpoint', help='Checkpoint file')
parser.add_argument(
'--device', default='cuda:0', help='Device used for inference')
parser.add_argument(
'--score-thr', type=float, default=0.0, help='bbox score threshold')
parser.add_argument(
'--out-dir', type=str, default='demo', help='dir to save results')
parser.add_argument(
'--show', action='store_true', help='show online visuliaztion results')
parser.add_argument(
'--snapshot',
action='store_true',
help='whether to save online visuliaztion results')
args = parser.parse_args()
# build the model from a config file and a checkpoint file
model = init_detector(args.config, args.checkpoint, device=args.device)
# test a single image
result, data = inference_multi_modality_detector(model, args.pcd,
args.image, args.ann)
# show the results
show_result_meshlab(
data,
result,
args.out_dir,
args.score_thr,
show=args.show,
snapshot=args.snapshot)
def test_inference_detector():
pcd = 'tests/data/kitti/training/velodyne_reduced/000000.bin'
detector_cfg = 'configs/pointpillars/hv_pointpillars_secfpn_' \
'6x8_160e_kitti-3d-3class.py'
detector = init_detector(detector_cfg, device='cpu')
results = inference_detector(detector, pcd)
bboxes_3d = results[0][0]['boxes_3d']
scores_3d = results[0][0]['scores_3d']
labels_3d = results[0][0]['labels_3d']
assert bboxes_3d.tensor.shape[0] >= 0
assert bboxes_3d.tensor.shape[1] == 7
assert scores_3d.shape[0] >= 0
assert labels_3d.shape[0] >= 0
def main():
parser = ArgumentParser()
parser.add_argument('pcd', help='Point cloud file')
parser.add_argument('config', help='Config file')
parser.add_argument('checkpoint', help='Checkpoint file')
parser.add_argument(
'--device', default='cuda:0', help='Device used for inference')
parser.add_argument(
'--score-thr', type=float, default=0.0, help='bbox score threshold')
parser.add_argument(
'--out-dir', type=str, default='demo', help='dir to save results')
args = parser.parse_args()
# build the model from a config file and a checkpoint file
model = init_detector(args.config, args.checkpoint, device=args.device)
# test a single image
result, data = inference_detector(model, args.pcd)
nargs='+',
default=0,
help="Sample index in the dataset")
parser.add_argument("--outdir",
type=str,
default='cache/',
help="Path to the output directory")
args = parser.parse_args()
# Build dataset and model
cfg = mmcv.Config.fromfile(args.cfg)
dataset = build_dataset(cfg.data.val)
model = init_detector(args.cfg, args.ckpt, device='cuda')
# Execution function
def execution(idx):
global dataset, model, cfg, args
print()
# Select and infer a sample
sample = dataset.__getitem__(idx)
if isinstance(sample['img_metas'], list):
filename = sample['img_metas'][0].data['pts_filename']
else:
filename = sample['img_metas'].data['pts_filename']
pcd_file = os.path.join(dataset.root_split, dataset.pts_prefix, filename)
from mmdet3d.apis import init_detector, inference_detector, show_result_meshlab #%% config_file = '../configs/3dssd/3dssd_kitti-3d-car.py' # download the checkpoint from model zoo and put it in `checkpoints/` checkpoint_file = '../checkpoints/3dssd_kitti-3d-car/latest.pth' #%% # build the model from a config file and a checkpoint file model = init_detector(config_file, checkpoint_file, device='cuda:0') #%% # test a single sample pcd = 'kitti_000008.bin' result, data = inference_detector(model, pcd) print(model)
def main():
parser = ArgumentParser()
parser.add_argument(
'--pcd',
type=str,
default='./demo/kitti_000008.bin',
help='Point cloud file'
) #/data/cmpe249-fa21/4c_train5678/training/velodyne/008118.bin
parser.add_argument(
'--config',
type=str,
default=
'./configs/second/myhv_second_secfpn_sbn_2x16_2x_waymoD5-3d-4class.py',
help='Config file')
parser.add_argument(
'--checkpoint',
type=str,
default=
'../modelzoo_mmdetection3d/hv_second_secfpn_6x8_80e_kitti-3d-3class_20200620_230238-9208083a.pth',
help='Checkpoint file')
parser.add_argument('--device',
default='cuda:0',
help='Device used for inference')
parser.add_argument('--score-thr',
type=float,
default=0.6,
help='bbox score threshold')
parser.add_argument('--out-dir',
type=str,
default='demo',
help='dir to save results')
args = parser.parse_args()
# build the model from a config file and a checkpoint file
model = init_detector(args.config, args.checkpoint, device=args.device)
# test a single image
result, data = inference_detector(model, args.pcd)
print(result) # three keys: 'boxes_3d', 'scores_3d', 'labels_3d'
points = data['points'][0][0].cpu().numpy() # points number *4
pts_filename = data['img_metas'][0][0]['pts_filename']
print("pts_filename:", pts_filename)
file_name = osp.split(pts_filename)[-1].split('.')[0] #006767
print(data['img_metas'])
print(data['img_metas'][0][0]['box_mode_3d']) #Box3DMode.LIDAR
print("results len:", len(result[0])) # len=1,
for res in result[0].keys():
print(res)
#boxes_3d, scores_3d, labels_3d
pred_bboxes = result[0]['boxes_3d'].tensor.numpy()
print(
pred_bboxes
) # [11,7], Each row is (x, y, z, x_size, y_size, z_size, yaw) in Box3DMode.LIDAR
print(type(pred_bboxes)) #numpy.ndarray
print("box_mode_3d:",
data['img_metas'][0][0]['box_mode_3d']) #Box3DMode.LIDAR
# show the results (points save to xxx_points.obj file, pred3d box save to xxx_pred.ply, these two files can opend by meshlab)
show_result_meshlab(data, result, args.out_dir)
def __init__(self, checkpoint, config, calib_file, from_video=True):
self.model = init_detector(config, checkpoint)
self.calib = Calibration(calib_file, from_video=from_video)
sample_num = corners3d.shape[0]
corners3d_hom = np.concatenate((corners3d, np.ones((sample_num, 8, 1))), axis=2) # (N, 8, 4)
img_pts = np.matmul(corners3d_hom, P2.T) # (N, 8, 3), The transposed array.
x, y = img_pts[:, :, 0] / img_pts[:, :, 2], img_pts[:, :, 1] / img_pts[:, :, 2]
x1, y1 = np.min(x, axis=1), np.min(y, axis=1)
x2, y2 = np.max(x, axis=1), np.max(y, axis=1)
boxes = np.concatenate((x1.reshape(-1, 1), y1.reshape(-1, 1), x2.reshape(-1, 1), y2.reshape(-1, 1)), axis=1)
boxes_corner = np.concatenate((x.reshape(-1, 8, 1), y.reshape(-1, 8, 1)), axis=2)
return boxes, boxes_corner
# build the model from a config file and a checkpoint file
model = init_detector(base_folder+config_file, base_folder+checkpoint_file, device='cuda:0')
# test a single image and show the results
result, data = inference_detector(model, point_cloud)
points = data['points'][0][0].cpu().numpy()# points number *4
pts_filename = data['img_metas'][0][0]['pts_filename']
file_name = osp.split(pts_filename)[-1].split('.')[0] #006767
print(data['img_metas'])
print(data['img_metas'][0][0]['box_mode_3d']) #Box3DMode.LIDAR
print(len(result))
for res in result[0].keys():
print(res)
pred_bboxes = result[0]['boxes_3d'].tensor.numpy()
print(pred_bboxes)# [11,7], Each row is (x, y, z, x_size, y_size, z_size, yaw) in Box3DMode.LIDAR
