[Fix] enable visualization of the demo results online (#473)

demo/multi_modality_demo.py

@@ -17,6 +17,12 @@ def main():
         '--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
@@ -25,7 +31,13 @@ def main():
     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_result_meshlab(
+        data,
+        result,
+        args.out_dir,
+        args.score_thr,
+        show=args.show,
+        snapshot=args.snapshot)
 
 
 if __name__ == '__main__':

demo/pcd_demo.py

@@ -14,6 +14,12 @@ def main():
         '--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
@@ -21,7 +27,13 @@ def main():
     # test a single image
     result, data = inference_detector(model, args.pcd)
     # show the results
-    show_result_meshlab(data, result, args.out_dir, args.score_thr)
+    show_result_meshlab(
+        data,
+        result,
+        args.out_dir,
+        args.score_thr,
+        show=args.show,
+        snapshot=args.snapshot)
 
 
 if __name__ == '__main__':

mmdet3d/apis/inference.py

@@ -185,7 +185,12 @@ def inference_multi_modality_detector(model, pcd, image, ann_file):
     return result, data
 
 
-def show_result_meshlab(data, result, out_dir, score_thr=0.0):
+def show_result_meshlab(data,
+                        result,
+                        out_dir,
+                        score_thr=0.0,
+                        show=False,
+                        snapshot=False):
     """Show result by meshlab.
 
     Args:
@@ -193,6 +198,8 @@ def show_result_meshlab(data, result, out_dir, score_thr=0.0):
         result (dict): Predicted result from model.
         out_dir (str): Directory to save visualized result.
         score_thr (float): Minimum score of bboxes to be shown. Default: 0.0
+        show (bool): Visualize the results online. Defaults to False.
+        snapshot (bool): Whether to save the online results. Defaults to False.
     """
     points = data['points'][0][0].cpu().numpy()
     pts_filename = data['img_metas'][0][0]['pts_filename']
@@ -220,7 +227,14 @@ def show_result_meshlab(data, result, out_dir, score_thr=0.0):
         show_bboxes = Box3DMode.convert(pred_bboxes, box_mode, Box3DMode.DEPTH)
     else:
         show_bboxes = deepcopy(pred_bboxes)
-    show_result(points, None, show_bboxes, out_dir, file_name, show=False)
+    show_result(
+        points,
+        None,
+        show_bboxes,
+        out_dir,
+        file_name,
+        show=show,
+        snapshot=snapshot)
 
     if 'img' not in data.keys():
         return out_dir, file_name
@@ -242,7 +256,7 @@ def show_result_meshlab(data, result, out_dir, score_thr=0.0):
             data['img_metas'][0][0]['lidar2img'],
             out_dir,
             file_name,
-            show=False)
+            show=show)
     elif box_mode == Box3DMode.DEPTH:
         if 'calib' not in data.keys():
             raise NotImplementedError(
@@ -260,7 +274,7 @@ def show_result_meshlab(data, result, out_dir, score_thr=0.0):
             file_name,
             depth_bbox=True,
             img_metas=data['img_metas'][0][0],
-            show=False)
+            show=show)
     else:
         raise NotImplementedError(
             f'visualization of {box_mode} bbox is not supported')

mmdet3d/core/visualizer/show_result.py

@@ -70,7 +70,13 @@ def _write_oriented_bbox(scene_bbox, out_filename):
     return
 
 
-def show_result(points, gt_bboxes, pred_bboxes, out_dir, filename, show=True):
+def show_result(points,
+                gt_bboxes,
+                pred_bboxes,
+                out_dir,
+                filename,
+                show=False,
+                snapshot=False):
     """Convert results into format that is directly readable for meshlab.
 
     Args:
@@ -79,8 +85,12 @@ def show_result(points, gt_bboxes, pred_bboxes, out_dir, filename, show=True):
         pred_bboxes (np.ndarray): Predicted boxes.
         out_dir (str): Path of output directory
         filename (str): Filename of the current frame.
-        show (bool): Visualize the results online. Defaults to True.
+        show (bool): Visualize the results online. Defaults to False.
+        snapshot (bool): Whether to save the online results. Defaults to False.
     """
+    result_path = osp.join(out_dir, filename)
+    mmcv.mkdir_or_exist(result_path)
+
     if show:
         from .open3d_vis import Visualizer
 
@@ -89,10 +99,9 @@ def show_result(points, gt_bboxes, pred_bboxes, out_dir, filename, show=True):
             vis.add_bboxes(bbox3d=pred_bboxes)
         if gt_bboxes is not None:
             vis.add_bboxes(bbox3d=gt_bboxes, bbox_color=(0, 0, 1))
-        vis.show()
-
-    result_path = osp.join(out_dir, filename)
-    mmcv.mkdir_or_exist(result_path)
+        show_path = osp.join(result_path,
+                             f'{filename}_online.png') if snapshot else None
+        vis.show(show_path)
 
     if points is not None:
         _write_obj(points, osp.join(result_path, f'{filename}_points.obj'))
@@ -121,7 +130,8 @@ def show_seg_result(points,
                     filename,
                     palette,
                     ignore_index=None,
-                    show=False):
+                    show=False,
+                    snapshot=False):
     """Convert results into format that is directly readable for meshlab.
 
     Args:
@@ -134,6 +144,8 @@ def show_seg_result(points,
         ignore_index (int, optional): The label index to be ignored, e.g. \
             unannotated points. Defaults to None.
         show (bool, optional): Visualize the results online. Defaults to False.
+        snapshot (bool, optional): Whether to save the online results. \
+            Defaults to False.
     """
     # we need 3D coordinates to visualize segmentation mask
     if gt_seg is not None or pred_seg is not None:
@@ -156,6 +168,9 @@ def show_seg_result(points,
         pred_seg_color = np.concatenate([points[:, :3], pred_seg_color],
                                         axis=1)
 
+    result_path = osp.join(out_dir, filename)
+    mmcv.mkdir_or_exist(result_path)
+
     # online visualization of segmentation mask
     # we show three masks in a row, scene_points, gt_mask, pred_mask
     if show:
@@ -166,10 +181,9 @@ def show_seg_result(points,
             vis.add_seg_mask(gt_seg_color)
         if pred_seg is not None:
             vis.add_seg_mask(pred_seg_color)
-        vis.show()
-
-    result_path = osp.join(out_dir, filename)
-    mmcv.mkdir_or_exist(result_path)
+        show_path = osp.join(result_path,
+                             f'{filename}_online.png') if snapshot else None
+        vis.show(show_path)
 
     if points is not None:
         _write_obj(points, osp.join(result_path, f'{filename}_points.obj'))
@@ -190,7 +204,7 @@ def show_multi_modality_result(img,
                                filename,
                                depth_bbox=False,
                                img_metas=None,
-                               show=True,
+                               show=False,
                                gt_bbox_color=(61, 102, 255),
                                pred_bbox_color=(241, 101, 72)):
     """Convert multi-modality detection results into 2D results.
@@ -207,7 +221,7 @@ def show_multi_modality_result(img,
         filename (str): Filename of the current frame.
         depth_bbox (bool): Whether we are projecting camera bbox or lidar bbox.
         img_metas (dict): Used in projecting cameta bbox.
-        show (bool): Visualize the results online. Defaults to True.
+        show (bool): Visualize the results online. Defaults to False.
         gt_bbox_color (str or tuple(int)): Color of bbox lines.
            The tuple of color should be in BGR order. Default: (255, 102, 61)
         pred_bbox_color (str or tuple(int)): Color of bbox lines.