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 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('--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_model(args.config, args.checkpoint, device=args.device) # test a single image result, data = inference_segmentor(model, args.pcd) # show the results show_result_meshlab(data, result, args.out_dir, show=args.show, snapshot=args.snapshot, task='seg', palette=model.PALETTE)
def test_show_result_meshlab(): pcd = 'tests/data/nuscenes/samples/LIDAR_TOP/n015-2018-08-02-17-16-37+' \ '0800__LIDAR_TOP__1533201470948018.pcd.bin' box_3d = LiDARInstance3DBoxes( torch.tensor( [[8.7314, -1.8559, -1.5997, 0.4800, 1.2000, 1.8900, 0.0100]])) labels_3d = torch.tensor([0]) scores_3d = torch.tensor([0.5]) points = np.random.rand(100, 4) img_meta = dict( pts_filename=pcd, boxes_3d=box_3d, box_mode_3d=Box3DMode.LIDAR) data = dict(points=[[torch.tensor(points)]], img_metas=[[img_meta]]) result = [ dict( pts_bbox=dict( boxes_3d=box_3d, labels_3d=labels_3d, scores_3d=scores_3d)) ] temp_out_dir = tempfile.mkdtemp() out_dir, file_name = show_result_meshlab(data, result, temp_out_dir) expected_outfile_ply = file_name + '_pred.ply' expected_outfile_obj = file_name + '_points.obj' expected_outfile_ply_path = os.path.join(out_dir, file_name, expected_outfile_ply) expected_outfile_obj_path = os.path.join(out_dir, file_name, expected_outfile_obj) assert os.path.exists(expected_outfile_ply_path) assert os.path.exists(expected_outfile_obj_path) os.remove(expected_outfile_obj_path) os.remove(expected_outfile_ply_path) os.removedirs(os.path.join(temp_out_dir, file_name))
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) print("Selected pcd_file:", pcd_file) result, data = inference_detector(model, pcd_file) print("Number of detected bboxes:", len(result['labels_3d'])) # Convert to MeshLab os.makedirs(args.outdir, exist_ok=True) show_result_meshlab(data, result, args.outdir) print("Visualization result is saved in \'{}\'. " "Please use MeshLab to visualize it.".format( os.path.join(args.outdir, os.path.basename(pcd_file).split('.')[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=0.6, 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) # show the results show_result_meshlab(data, result, args.out_dir)
def test_show_result_meshlab(): pcd = 'tests/data/nuscenes/samples/LIDAR_TOP/n015-2018-08-02-17-16-37+' \ '0800__LIDAR_TOP__1533201470948018.pcd.bin' box_3d = LiDARInstance3DBoxes( torch.tensor( [[8.7314, -1.8559, -1.5997, 0.4800, 1.2000, 1.8900, 0.0100]])) labels_3d = torch.tensor([0]) scores_3d = torch.tensor([0.5]) points = np.random.rand(100, 4) img_meta = dict(pts_filename=pcd, boxes_3d=box_3d, box_mode_3d=Box3DMode.LIDAR) data = dict(points=[[torch.tensor(points)]], img_metas=[[img_meta]]) result = [ dict(pts_bbox=dict( boxes_3d=box_3d, labels_3d=labels_3d, scores_3d=scores_3d)) ] tmp_dir = tempfile.TemporaryDirectory() temp_out_dir = tmp_dir.name out_dir, file_name = show_result_meshlab(data, result, temp_out_dir) expected_outfile_pred = file_name + '_pred.obj' expected_outfile_pts = file_name + '_points.obj' expected_outfile_pred_path = os.path.join(out_dir, file_name, expected_outfile_pred) expected_outfile_pts_path = os.path.join(out_dir, file_name, expected_outfile_pts) assert os.path.exists(expected_outfile_pred_path) assert os.path.exists(expected_outfile_pts_path) tmp_dir.cleanup() # test multi-modality show # indoor scene pcd = 'tests/data/sunrgbd/points/000001.bin' filename = 'tests/data/sunrgbd/sunrgbd_trainval/image/000001.jpg' box_3d = DepthInstance3DBoxes( torch.tensor( [[-1.1580, 3.3041, -0.9961, 0.3829, 0.4647, 0.5574, 1.1213]])) img = np.random.randn(1, 3, 608, 832) k_mat = np.array([[529.5000, 0.0000, 365.0000], [0.0000, 529.5000, 265.0000], [0.0000, 0.0000, 1.0000]]) rt_mat = np.array([[0.9980, 0.0058, -0.0634], [0.0058, 0.9835, 0.1808], [0.0634, -0.1808, 0.9815]]) rt_mat = np.array([[1, 0, 0], [0, 0, -1], [0, 1, 0]]) @ rt_mat.transpose( 1, 0) depth2img = k_mat @ rt_mat img_meta = dict(filename=filename, depth2img=depth2img, pcd_horizontal_flip=False, pcd_vertical_flip=False, box_mode_3d=Box3DMode.DEPTH, box_type_3d=DepthInstance3DBoxes, pcd_trans=np.array([0., 0., 0.]), pcd_scale_factor=1.0, pts_filename=pcd, transformation_3d_flow=['R', 'S', 'T']) data = dict(points=[[torch.tensor(points)]], img_metas=[[img_meta]], img=[img]) result = [dict(boxes_3d=box_3d, labels_3d=labels_3d, scores_3d=scores_3d)] tmp_dir = tempfile.TemporaryDirectory() temp_out_dir = tmp_dir.name out_dir, file_name = show_result_meshlab(data, result, temp_out_dir, 0.3, task='multi_modality-det') expected_outfile_pred = file_name + '_pred.obj' expected_outfile_pts = file_name + '_points.obj' expected_outfile_png = file_name + '_img.png' expected_outfile_proj = file_name + '_pred.png' expected_outfile_pred_path = os.path.join(out_dir, file_name, expected_outfile_pred) expected_outfile_pts_path = os.path.join(out_dir, file_name, expected_outfile_pts) expected_outfile_png_path = os.path.join(out_dir, file_name, expected_outfile_png) expected_outfile_proj_path = os.path.join(out_dir, file_name, expected_outfile_proj) assert os.path.exists(expected_outfile_pred_path) assert os.path.exists(expected_outfile_pts_path) assert os.path.exists(expected_outfile_png_path) assert os.path.exists(expected_outfile_proj_path) tmp_dir.cleanup() # outdoor scene pcd = 'tests/data/kitti/training/velodyne_reduced/000000.bin' filename = 'tests/data/kitti/training/image_2/000000.png' box_3d = LiDARInstance3DBoxes( torch.tensor( [[6.4495, -3.9097, -1.7409, 1.5063, 3.1819, 1.4716, 1.8782]])) img = np.random.randn(1, 3, 384, 1280) lidar2img = np.array( [[6.09695435e+02, -7.21421631e+02, -1.25125790e+00, -1.23041824e+02], [1.80384201e+02, 7.64479828e+00, -7.19651550e+02, -1.01016693e+02], [9.99945343e-01, 1.24365499e-04, 1.04513029e-02, -2.69386917e-01], [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 1.00000000e+00]]) img_meta = dict(filename=filename, pcd_horizontal_flip=False, pcd_vertical_flip=False, box_mode_3d=Box3DMode.LIDAR, box_type_3d=LiDARInstance3DBoxes, pcd_trans=np.array([0., 0., 0.]), pcd_scale_factor=1.0, pts_filename=pcd, lidar2img=lidar2img) data = dict(points=[[torch.tensor(points)]], img_metas=[[img_meta]], img=[img]) result = [ dict(pts_bbox=dict( boxes_3d=box_3d, labels_3d=labels_3d, scores_3d=scores_3d)) ] out_dir, file_name = show_result_meshlab(data, result, temp_out_dir, 0.1, task='multi_modality-det') tmp_dir = tempfile.TemporaryDirectory() temp_out_dir = tmp_dir.name expected_outfile_pred = file_name + '_pred.obj' expected_outfile_pts = file_name + '_points.obj' expected_outfile_png = file_name + '_img.png' expected_outfile_proj = file_name + '_pred.png' expected_outfile_pred_path = os.path.join(out_dir, file_name, expected_outfile_pred) expected_outfile_pts_path = os.path.join(out_dir, file_name, expected_outfile_pts) expected_outfile_png_path = os.path.join(out_dir, file_name, expected_outfile_png) expected_outfile_proj_path = os.path.join(out_dir, file_name, expected_outfile_proj) assert os.path.exists(expected_outfile_pred_path) assert os.path.exists(expected_outfile_pts_path) assert os.path.exists(expected_outfile_png_path) assert os.path.exists(expected_outfile_proj_path) tmp_dir.cleanup() # test mono-3d show filename = 'tests/data/nuscenes/samples/CAM_BACK_LEFT/n015-2018-' \ '07-18-11-07-57+0800__CAM_BACK_LEFT__1531883530447423.jpg' box_3d = CameraInstance3DBoxes( torch.tensor( [[6.4495, -3.9097, -1.7409, 1.5063, 3.1819, 1.4716, 1.8782]])) img = np.random.randn(1, 3, 384, 1280) cam2img = np.array([[100.0, 0.0, 50.0], [0.0, 100.0, 50.0], [0.0, 0.0, 1.0]]) img_meta = dict(filename=filename, pcd_horizontal_flip=False, pcd_vertical_flip=False, box_mode_3d=Box3DMode.CAM, box_type_3d=CameraInstance3DBoxes, pcd_trans=np.array([0., 0., 0.]), pcd_scale_factor=1.0, cam2img=cam2img) data = dict(points=[[torch.tensor(points)]], img_metas=[[img_meta]], img=[img]) result = [ dict(img_bbox=dict( boxes_3d=box_3d, labels_3d=labels_3d, scores_3d=scores_3d)) ] out_dir, file_name = show_result_meshlab(data, result, temp_out_dir, 0.1, task='mono-det') tmp_dir = tempfile.TemporaryDirectory() temp_out_dir = tmp_dir.name expected_outfile_png = file_name + '_img.png' expected_outfile_proj = file_name + '_pred.png' expected_outfile_png_path = os.path.join(out_dir, file_name, expected_outfile_png) expected_outfile_proj_path = os.path.join(out_dir, file_name, expected_outfile_proj) assert os.path.exists(expected_outfile_png_path) assert os.path.exists(expected_outfile_proj_path) tmp_dir.cleanup() # test seg show pcd = 'tests/data/scannet/points/scene0000_00.bin' points = np.random.rand(100, 6) img_meta = dict(pts_filename=pcd) data = dict(points=[[torch.tensor(points)]], img_metas=[[img_meta]]) pred_seg = torch.randint(0, 20, (100, )) result = [dict(semantic_mask=pred_seg)] tmp_dir = tempfile.TemporaryDirectory() temp_out_dir = tmp_dir.name out_dir, file_name = show_result_meshlab(data, result, temp_out_dir, task='seg') expected_outfile_pred = file_name + '_pred.obj' expected_outfile_pts = file_name + '_points.obj' expected_outfile_pred_path = os.path.join(out_dir, file_name, expected_outfile_pred) expected_outfile_pts_path = os.path.join(out_dir, file_name, expected_outfile_pts) assert os.path.exists(expected_outfile_pred_path) assert os.path.exists(expected_outfile_pts_path) tmp_dir.cleanup()
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)
parser.add_argument("--outdir", type=str, default='cache/', help="Path to the output directory") args = parser.parse_args() # Build dataset cfg = mmcv.Config.fromfile(args.cfg) dataset = build_dataset(cfg.data.train) # Select a sample sample = dataset.__getitem__(args.idx) # Convert to MeshLab data = dict( img_metas=[[sample['img_metas'].data]], points=[[sample['points'].data]], ) gts = dict( boxes_3d=sample['gt_bboxes_3d'].data, scores_3d=torch.ones([len(sample['gt_labels_3d'].data)]), labels_3d=sample['gt_labels_3d'].data, ) os.makedirs(args.outdir, exist_ok=True) show_result_meshlab(data, gts, args.outdir) print( "\nVisualization result is saved in \'{}\'. Please use MeshLab to visualize it." .format(args.outdir))