run pcd_demo.py

[PengboLi1998]

I want to use the trained model to test point cloud,but I meet following question:
Traceback (most recent call last):
File "/media/ketizu/086C03DE086C03DE/project1/fcaf3d-master/demo/pcd_demo.py", line 102, in
main()
File "/media/ketizu/086C03DE086C03DE/project1/fcaf3d-master/demo/pcd_demo.py", line 90, in main
show_result_meshlab(
File "/media/ketizu/086C03DE086C03DE/project1/fcaf3d-master/mmdet3d/apis/inference.py", line 481, in show_result_meshlab
file_name = show_det_result_meshlab(data, result, out_dir, score_thr,
File "/media/ketizu/086C03DE086C03DE/project1/fcaf3d-master/mmdet3d/apis/inference.py", line 324, in show_det_result_meshlab
show_result(
TypeError: show_result() missing 2 required positional arguments: 'out_dir' and 'filename'

Process finished with exit code 1

[filaPro]

Please follow #27 .

[PengboLi1998]

could you please tell me how to modify it in detail?It was succeseful when I run the test.py,but when I run the pcd_demo.py,it throws the question. Thanks!

[joshiaLee]

modify inference.py
like below

def show_det_result_meshlab(data,
result,
out_dir,
score_thr=0.0,
show=False,
snapshot=False
):
"""Show 3D detection result by meshlab."""
points = data['points'][0][0].cpu().numpy()
pts_filename = data['img_metas'][0][0]['pts_filename']
file_name = osp.split(pts_filename)[-1].split('.')[0]

if 'pts_bbox' in result[0].keys():
    pred_bboxes = result[0]['pts_bbox']['boxes_3d'].tensor.numpy()
    pred_scores = result[0]['pts_bbox']['scores_3d'].numpy()
else:
    pred_bboxes = result[0]['boxes_3d'].corners.numpy() # without corners, when visualized the predicted bounding boxes are distorted.
    pred_scores = result[0]['scores_3d'].numpy()
    pred_labels = result[0]['labels_3d'].numpy() # pred_label is an essential parameter when executing _write_oriented_bbox.
# filter out low score bboxes for visualization
if score_thr > 0:
    
    inds = pred_scores > score_thr
    pred_bboxes = pred_bboxes[inds]
    
    

# for now we convert points into depth mode

box_mode = data['img_metas'][0][0]['box_mode_3d']
if box_mode != Box3DMode.DEPTH:
    points = points[..., [1, 0, 2]]
    points[..., 0] *= -1
    show_bboxes = Box3DMode.convert(pred_bboxes, box_mode, Box3DMode.DEPTH)
else:
    show_bboxes = deepcopy(pred_bboxes)

show_result(
    points,
    None,
    None, # extra None is essential
    show_bboxes,
    pred_labels, # pred_label is an essential parameter when executing _write_oriented_bbox
    out_dir,
    file_name,
    show=show,
    snapshot=snapshot)

return file_name