Merge branch 'fix_visualize' into 'master'

Fix visualize

See merge request open-mmlab/mmdet.3d!93

mmdet3d/core/visualizer/show_result.py

@@ -81,13 +81,13 @@ def show_result(points, gt_bboxes, pred_bboxes, out_dir, filename):
     mmcv.mkdir_or_exist(out_dir)
 
     if gt_bboxes is not None:
-        gt_bboxes[:, -1] *= -1
+        gt_bboxes[:, 6] *= -1
         _write_oriented_bbox(gt_bboxes, osp.join(out_dir,
                                                  f'{filename}_gt.ply'))
     if points is not None:
         _write_ply(points, osp.join(out_dir, f'{filename}_points.obj'))
 
     if pred_bboxes is not None:
-        pred_bboxes[:, -1] *= -1
+        pred_bboxes[:, 6] *= -1
         _write_oriented_bbox(pred_bboxes,
                              osp.join(out_dir, f'{filename}_pred.ply'))

mmdet3d/datasets/kitti_dataset.py

@@ -555,13 +555,11 @@ class KittiDataset(Custom3DDataset):
     def show(self, results, out_dir):
         assert out_dir is not None, 'Expect out_dir, got none.'
         for i, result in enumerate(results):
+            example = self.prepare_test_data(i)
             data_info = self.data_infos[i]
             pts_path = data_info['point_cloud']['velodyne_path']
             file_name = osp.split(pts_path)[-1].split('.')[0]
-            points = np.fromfile(
-                osp.join(self.root_split, 'velodyne_reduced',
-                         f'{file_name}.bin'),
-                dtype=np.float32).reshape(-1, 4)
+            points = example['points'][0]._data.numpy()
             points = points[..., [1, 0, 2]]
             points[..., 0] *= -1
             gt_bboxes = self.get_ann_info(i)['gt_bboxes_3d'].tensor
@@ -573,4 +571,3 @@ class KittiDataset(Custom3DDataset):
                                             Box3DMode.DEPTH)
             pred_bboxes[..., 2] += pred_bboxes[..., 5] / 2
             show_result(points, gt_bboxes, pred_bboxes, out_dir, file_name)
-        print(results)

mmdet3d/datasets/nuscenes_dataset.py

@@ -401,22 +401,23 @@ class NuScenesDataset(Custom3DDataset):
 
     def show(self, results, out_dir):
         for i, result in enumerate(results):
+            example = self.prepare_test_data(i)
+            points = example['points'][0]._data.numpy()
             data_info = self.data_infos[i]
             pts_path = data_info['lidar_path']
             file_name = osp.split(pts_path)[-1].split('.')[0]
-            points = np.fromfile(pts_path, dtype=np.float32).reshape(-1, 4)
             points = points[..., [1, 0, 2]]
             points[..., 0] *= -1
+            inds = result['pts_bbox']['scores_3d'] > 0.1
             gt_bboxes = self.get_ann_info(i)['gt_bboxes_3d'].tensor
             gt_bboxes = Box3DMode.convert(gt_bboxes, Box3DMode.LIDAR,
                                           Box3DMode.DEPTH)
             gt_bboxes[..., 2] += gt_bboxes[..., 5] / 2
-            pred_bboxes = result['boxes_3d'].tensor.numpy()
+            pred_bboxes = result['pts_bbox']['boxes_3d'][inds].tensor.numpy()
             pred_bboxes = Box3DMode.convert(pred_bboxes, Box3DMode.LIDAR,
                                             Box3DMode.DEPTH)
             pred_bboxes[..., 2] += pred_bboxes[..., 5] / 2
             show_result(points, gt_bboxes, pred_bboxes, out_dir, file_name)
-        print(results)
 
 
 def output_to_nusc_box(detection):

mmdet3d/models/detectors/base.py

+import copy
 from os import path as osp
 
 from mmdet3d.core import Box3DMode, show_result
@@ -64,7 +65,7 @@ class Base3DDetector(BaseDetector):
 
         assert out_dir is not None, 'Expect out_dir, got none.'
 
-        pred_bboxes = result['pts_bbox']['boxes_3d'].tensor.numpy()
+        pred_bboxes = copy.deepcopy(result['boxes_3d'].tensor.numpy())
         # for now we convert points into depth mode
         if data['img_metas'][0]._data[0][0]['box_mode_3d'] != Box3DMode.DEPTH:
             points = points[..., [1, 0, 2]]