[Enhance] Support different colors for different classes in visualization (#2500)

* support different colors

* use tuple palette

mmdet3d/apis/inference.py

@@ -14,7 +14,7 @@ from mmengine.dataset import Compose, pseudo_collate
 from mmengine.registry import init_default_scope
 from mmengine.runner import load_checkpoint
 
-from mmdet3d.registry import MODELS
+from mmdet3d.registry import DATASETS, MODELS
 from mmdet3d.structures import Box3DMode, Det3DDataSample, get_box_type
 from mmdet3d.structures.det3d_data_sample import SampleList
 
@@ -38,6 +38,7 @@ def convert_SyncBN(config):
 def init_model(config: Union[str, Path, Config],
                checkpoint: Optional[str] = None,
                device: str = 'cuda:0',
+               palette: str = 'none',
                cfg_options: Optional[dict] = None):
     """Initialize a model from config file, which could be a 3D detector or a
     3D segmentor.
@@ -87,6 +88,20 @@ def init_model(config: Union[str, Path, Config],
             if 'PALETTE' in checkpoint.get('meta', {}):  # 3D Segmentor
                 model.dataset_meta['palette'] = checkpoint['meta']['PALETTE']
 
+        test_dataset_cfg = deepcopy(config.test_dataloader.dataset)
+        # lazy init. We only need the metainfo.
+        test_dataset_cfg['lazy_init'] = True
+        metainfo = DATASETS.build(test_dataset_cfg).metainfo
+        cfg_palette = metainfo.get('palette', None)
+        if cfg_palette is not None:
+            model.dataset_meta['palette'] = cfg_palette
+        else:
+            if 'palette' not in model.dataset_meta:
+                warnings.warn(
+                    'palette does not exist, random is used by default. '
+                    'You can also set the palette to customize.')
+                model.dataset_meta['palette'] = 'random'
+
     model.cfg = config  # save the config in the model for convenience
     if device != 'cpu':
         torch.cuda.set_device(device)

mmdet3d/datasets/det3d_dataset.py

@@ -139,20 +139,21 @@ class Det3DDataset(BaseDataset):
         self.metainfo['box_type_3d'] = box_type_3d
         self.metainfo['label_mapping'] = self.label_mapping
 
-        # used for showing variation of the number of instances before and
-        # after through the pipeline
-        self.show_ins_var = show_ins_var
-
-        # show statistics of this dataset
-        print_log('-' * 30, 'current')
-        print_log(f'The length of the dataset: {len(self)}', 'current')
-        content_show = [['category', 'number']]
-        for cat_name, num in self.num_ins_per_cat.items():
-            content_show.append([cat_name, num])
-        table = AsciiTable(content_show)
-        print_log(
-            f'The number of instances per category in the dataset:\n{table.table}',  # noqa: E501
-            'current')
+        if not kwargs.get('lazy_init', False):
+            # used for showing variation of the number of instances before and
+            # after through the pipeline
+            self.show_ins_var = show_ins_var
+
+            # show statistics of this dataset
+            print_log('-' * 30, 'current')
+            print_log(f'The length of the dataset: {len(self)}', 'current')
+            content_show = [['category', 'number']]
+            for cat_name, num in self.num_ins_per_cat.items():
+                content_show.append([cat_name, num])
+            table = AsciiTable(content_show)
+            print_log(
+                f'The number of instances per category in the dataset:\n{table.table}',  # noqa: E501
+                'current')
 
     def _remove_dontcare(self, ann_info: dict) -> dict:
         """Remove annotations that do not need to be cared.

mmdet3d/datasets/kitti_dataset.py

@@ -54,7 +54,9 @@ class KittiDataset(Det3DDataset):
     # TODO: use full classes of kitti
     METAINFO = {
         'classes': ('Pedestrian', 'Cyclist', 'Car', 'Van', 'Truck',
-                    'Person_sitting', 'Tram', 'Misc')
+                    'Person_sitting', 'Tram', 'Misc'),
+        'palette': [(106, 0, 228), (119, 11, 32), (165, 42, 42), (0, 0, 192),
+                    (197, 226, 255), (0, 60, 100), (0, 0, 142), (255, 77, 255)]
     }
 
     def __init__(self,

mmdet3d/datasets/lyft_dataset.py

@@ -44,7 +44,10 @@ class LyftDataset(Det3DDataset):
     METAINFO = {
         'classes':
         ('car', 'truck', 'bus', 'emergency_vehicle', 'other_vehicle',
-         'motorcycle', 'bicycle', 'pedestrian', 'animal')
+         'motorcycle', 'bicycle', 'pedestrian', 'animal'),
+        'palette': [(106, 0, 228), (119, 11, 32), (165, 42, 42), (0, 0, 192),
+                    (197, 226, 255), (0, 60, 100), (0, 0, 142), (255, 77, 255),
+                    (153, 69, 1)]
     }
 
     def __init__(self,

mmdet3d/datasets/nuscenes_dataset.py

@@ -60,7 +60,19 @@ class NuScenesDataset(Det3DDataset):
         ('car', 'truck', 'trailer', 'bus', 'construction_vehicle', 'bicycle',
          'motorcycle', 'pedestrian', 'traffic_cone', 'barrier'),
         'version':
-        'v1.0-trainval'
+        'v1.0-trainval',
+        'palette': [
+            (255, 158, 0),  # Orange
+            (255, 99, 71),  # Tomato
+            (255, 140, 0),  # Darkorange
+            (255, 127, 80),  # Coral
+            (233, 150, 70),  # Darksalmon
+            (220, 20, 60),  # Crimson
+            (255, 61, 99),  # Red
+            (0, 0, 230),  # Blue
+            (47, 79, 79),  # Darkslategrey
+            (112, 128, 144),  # Slategrey
+        ]
     }
 
     def __init__(self,

mmdet3d/datasets/s3dis_dataset.py

@@ -52,7 +52,10 @@ class S3DISDataset(Det3DDataset):
         'classes': ('table', 'chair', 'sofa', 'bookcase', 'board'),
         # the valid ids of segmentation annotations
         'seg_valid_class_ids': (7, 8, 9, 10, 11),
-        'seg_all_class_ids': tuple(range(1, 14))  # possibly with 'stair' class
+        'seg_all_class_ids':
+        tuple(range(1, 14)),  # possibly with 'stair' class
+        'palette': [(170, 120, 200), (255, 0, 0), (200, 100, 100),
+                    (10, 200, 100), (200, 200, 200)]
     }
 
     def __init__(self,

mmdet3d/datasets/scannet_dataset.py

@@ -57,7 +57,13 @@ class ScanNetDataset(Det3DDataset):
         'seg_valid_class_ids':
         (3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 14, 16, 24, 28, 33, 34, 36, 39),
         'seg_all_class_ids':
-        tuple(range(1, 41))
+        tuple(range(1, 41)),
+        'palette': [(31, 119, 180), (255, 187, 120), (188, 189, 34),
+                    (140, 86, 75), (255, 152, 150), (214, 39, 40),
+                    (197, 176, 213), (148, 103, 189), (196, 156, 148),
+                    (23, 190, 207), (247, 182, 210), (219, 219, 141),
+                    (255, 127, 14), (158, 218, 229), (44, 160, 44),
+                    (112, 128, 144), (227, 119, 194), (82, 84, 163)]
     }
 
     def __init__(self,

mmdet3d/datasets/seg3d_dataset.py

@@ -119,12 +119,13 @@ class Seg3DDataset(BaseDataset):
             **kwargs)
 
         self.metainfo['seg_label_mapping'] = self.seg_label_mapping
-        self.scene_idxs = self.get_scene_idxs(scene_idxs)
-        self.data_list = [self.data_list[i] for i in self.scene_idxs]
+        if not kwargs.get('lazy_init', False):
+            self.scene_idxs = self.get_scene_idxs(scene_idxs)
+            self.data_list = [self.data_list[i] for i in self.scene_idxs]
 
-        # set group flag for the sampler
-        if not self.test_mode:
-            self._set_group_flag()
+            # set group flag for the sampler
+            if not self.test_mode:
+                self._set_group_flag()
 
     def get_label_mapping(self,
                           new_classes: Optional[Sequence] = None) -> tuple:

mmdet3d/datasets/sunrgbd_dataset.py

@@ -47,7 +47,11 @@ class SUNRGBDDataset(Det3DDataset):
     """
     METAINFO = {
         'classes': ('bed', 'table', 'sofa', 'chair', 'toilet', 'desk',
-                    'dresser', 'night_stand', 'bookshelf', 'bathtub')
+                    'dresser', 'night_stand', 'bookshelf', 'bathtub'),
+        'palette': [(255, 187, 120), (255, 152, 150), (140, 86, 75),
+                    (188, 189, 34), (44, 160, 44), (247, 182, 210),
+                    (196, 156, 148), (23, 190, 207), (148, 103, 189),
+                    (227, 119, 194)]
     }
 
     def __init__(self,

mmdet3d/datasets/waymo_dataset.py

@@ -68,7 +68,14 @@ class WaymoDataset(KittiDataset):
         load_interval (int): load frame interval. Defaults to 1.
         max_sweeps (int): max sweep for each frame. Defaults to 0.
     """
-    METAINFO = {'classes': ('Car', 'Pedestrian', 'Cyclist')}
+    METAINFO = {
+        'classes': ('Car', 'Pedestrian', 'Cyclist'),
+        'palette': [
+            (0, 120, 255),  # Waymo Blue
+            (0, 232, 157),  # Waymo Green
+            (255, 205, 85)  # Amber
+        ]
+    }
 
     def __init__(self,
                  data_root: str,

mmdet3d/visualization/local_visualizer.py

@@ -10,11 +10,12 @@ import numpy as np
 from matplotlib.collections import PatchCollection
 from matplotlib.patches import PathPatch
 from matplotlib.path import Path
-from mmdet.visualization import DetLocalVisualizer
+from mmdet.visualization import DetLocalVisualizer, get_palette
 from mmengine.dist import master_only
 from mmengine.structures import InstanceData
 from mmengine.visualization import Visualizer as MMENGINE_Visualizer
-from mmengine.visualization.utils import check_type, tensor2ndarray
+from mmengine.visualization.utils import (check_type, color_val_matplotlib,
+                                          tensor2ndarray)
 from torch import Tensor
 
 from mmdet3d.registry import VISUALIZERS
@@ -173,7 +174,7 @@ class Det3DLocalVisualizer(DetLocalVisualizer):
                    pcd_mode: int = 0,
                    vis_mode: str = 'replace',
                    frame_cfg: dict = dict(size=1, origin=[0, 0, 0]),
-                   points_color: Tuple[float] = (1, 1, 1),
+                   points_color: Tuple[float] = (0.8, 0.8, 0.8),
                    points_size: int = 2,
                    mode: str = 'xyz') -> None:
         """Set the point cloud to draw.
@@ -295,7 +296,7 @@ class Det3DLocalVisualizer(DetLocalVisualizer):
 
             line_set = geometry.LineSet.create_from_oriented_bounding_box(
                 box3d)
-            line_set.paint_uniform_color(bbox_color)
+            line_set.paint_uniform_color(np.array(bbox_color[i]) / 255.)
             # draw bboxes on visualizer
             self.o3d_vis.add_geometry(line_set)
 
@@ -509,6 +510,8 @@ class Det3DLocalVisualizer(DetLocalVisualizer):
         else:
             raise NotImplementedError('unsupported box type!')
 
+        edge_colors_norm = color_val_matplotlib(edge_colors)
+
         corners_2d = proj_bbox3d_to_img(bboxes_3d, input_meta)
         if img_size is not None:
             # Filter out the bbox where half of stuff is outside the image.
@@ -518,6 +521,14 @@ class Det3DLocalVisualizer(DetLocalVisualizer):
                         (corners_2d[..., 1] >= 0) & (corners_2d[..., 1] <= img_size[1])  # noqa: E501
             valid_bbox_idx = valid_point_idx.sum(axis=-1) >= 4
             corners_2d = corners_2d[valid_bbox_idx]
+            filter_edge_colors = []
+            filter_edge_colors_norm = []
+            for i, color in enumerate(edge_colors):
+                if valid_bbox_idx[i]:
+                    filter_edge_colors.append(color)
+                    filter_edge_colors_norm.append(edge_colors_norm[i])
+            edge_colors = filter_edge_colors
+            edge_colors_norm = filter_edge_colors_norm
 
         lines_verts_idx = [0, 1, 2, 3, 7, 6, 5, 4, 0, 3, 7, 4, 5, 1, 2, 6]
         lines_verts = corners_2d[:, lines_verts_idx, :]
@@ -533,7 +544,7 @@ class Det3DLocalVisualizer(DetLocalVisualizer):
         p = PatchCollection(
             pathpatches,
             facecolors='none',
-            edgecolors=edge_colors,
+            edgecolors=edge_colors_norm,
             linewidths=line_widths,
             linestyles=line_styles)
 
@@ -547,7 +558,7 @@ class Det3DLocalVisualizer(DetLocalVisualizer):
             edge_colors=edge_colors,
             line_styles=line_styles,
             line_widths=line_widths,
-            face_colors=face_colors)
+            face_colors=edge_colors)
 
     @master_only
     def draw_seg_mask(self, seg_mask_colors: np.ndarray) -> None:
@@ -598,6 +609,7 @@ class Det3DLocalVisualizer(DetLocalVisualizer):
             return None
 
         bboxes_3d = instances.bboxes_3d  # BaseInstance3DBoxes
+        labels_3d = instances.labels_3d
 
         data_3d = dict()
 
@@ -612,8 +624,14 @@ class Det3DLocalVisualizer(DetLocalVisualizer):
             else:
                 bboxes_3d_depth = bboxes_3d.clone()
 
+            max_label = int(max(labels_3d) if len(labels_3d) > 0 else 0)
+            bbox_color = palette if self.bbox_color is None \
+                else self.bbox_color
+            bbox_palette = get_palette(bbox_color, max_label + 1)
+            colors = [bbox_palette[label] for label in labels_3d]
+
             self.set_points(points, pcd_mode=2)
-            self.draw_bboxes_3d(bboxes_3d_depth)
+            self.draw_bboxes_3d(bboxes_3d_depth, bbox_color=colors)
 
             data_3d['bboxes_3d'] = tensor2ndarray(bboxes_3d_depth.tensor)
             data_3d['points'] = points
@@ -646,10 +664,19 @@ class Det3DLocalVisualizer(DetLocalVisualizer):
                             single_img_meta[key] = meta[i]
                         else:
                             single_img_meta[key] = meta
+
+                    max_label = int(
+                        max(labels_3d) if len(labels_3d) > 0 else 0)
+                    bbox_color = palette if self.bbox_color is None \
+                        else self.bbox_color
+                    bbox_palette = get_palette(bbox_color, max_label + 1)
+                    colors = [bbox_palette[label] for label in labels_3d]
+
                     self.draw_proj_bboxes_3d(
                         bboxes_3d,
                         single_img_meta,
-                        img_size=single_img.shape[:2][::-1])
+                        img_size=single_img.shape[:2][::-1],
+                        edge_colors=colors)
                     if vis_task == 'mono_det' and hasattr(
                             instances, 'centers_2d'):
                         centers_2d = instances.centers_2d
@@ -668,7 +695,15 @@ class Det3DLocalVisualizer(DetLocalVisualizer):
                     img = img.permute(1, 2, 0).numpy()
                     img = img[..., [2, 1, 0]]  # bgr to rgb
                 self.set_image(img)
-                self.draw_proj_bboxes_3d(bboxes_3d, input_meta)
+
+                max_label = int(max(labels_3d) if len(labels_3d) > 0 else 0)
+                bbox_color = palette if self.bbox_color is None \
+                    else self.bbox_color
+                bbox_palette = get_palette(bbox_color, max_label + 1)
+                colors = [bbox_palette[label] for label in labels_3d]
+
+                self.draw_proj_bboxes_3d(
+                    bboxes_3d, input_meta, edge_colors=colors)
                 if vis_task == 'mono_det' and hasattr(instances, 'centers_2d'):
                     centers_2d = instances.centers_2d
                     self.draw_points(centers_2d)