Visualize

.gitignore

@@ -127,3 +127,5 @@ exps/
 *.png
 /data/scannet/scans/
 /data/sunrgbd/OFFICIAL_SUNRGBD/
+*.obj
+*.ply

.isort.cfg

@@ -3,6 +3,6 @@ line_length = 79
 multi_line_output = 0
 known_standard_library = setuptools
 known_first_party = mmdet,mmdet3d
-known_third_party = cv2,load_scannet_data,matplotlib,mmcv,numba,numpy,nuscenes,plyfile,pycocotools,pyquaternion,pytest,scannet_utils,scipy,seaborn,shapely,skimage,sunrgbd_utils,terminaltables,torch,torchvision
+known_third_party = cv2,load_scannet_data,matplotlib,mmcv,numba,numpy,nuscenes,plyfile,pycocotools,pyquaternion,pytest,scannet_utils,scipy,seaborn,shapely,skimage,sunrgbd_utils,terminaltables,torch,torchvision,trimesh
 no_lines_before = STDLIB,LOCALFOLDER
 default_section = THIRDPARTY

docs/getting_started.md

@@ -88,9 +88,8 @@ python tools/test.py ${CONFIG_FILE} ${CHECKPOINT_FILE} [--out ${RESULT_FILE}] [-
 Optional arguments:
 - `RESULT_FILE`: Filename of the output results in pickle format. If not specified, the results will not be saved to a file.
 - `EVAL_METRICS`: Items to be evaluated on the results. Allowed values depend on the dataset, e.g., `proposal_fast`, `proposal`, `bbox`, `segm` are available for COCO, `mAP`, `recall` for PASCAL VOC. Cityscapes could be evaluated by `cityscapes` as well as all COCO metrics.
-- `--show`: If specified, detection results will be plotted on the images and shown in a new window. It is only applicable to single GPU testing and used for debugging and visualization. Please make sure that GUI is available in your environment, otherwise you may encounter the error like `cannot connect to X server`.
-- `--show-dir`: If specified, detection results will be plotted on the images and saved to the specified directory. It is only applicable to single GPU testing and used for debugging and visualization. You do NOT need a GUI available in your environment for using this option.
-- `--show-score-thr`: If specified, detections with score below this threshold will be removed.
+- `--show`: If specified, detection results will be plotted in the silient mode. It is only applicable to single GPU testing and used for debugging and visualization. This should be used with `--show-dir`.
+- `--show-dir`: If specified, detection results will be plotted on the `***_points.obj` and `***_pred.ply` files in the specified directory. It is only applicable to single GPU testing and used for debugging and visualization. You do NOT need a GUI available in your environment for using this option. 
 
 
 Examples:
@@ -157,6 +156,25 @@ You will get two json files `mask_rcnn_test-dev_results.bbox.json` and `mask_rcn
 
 The generated png and txt would be under `./mask_rcnn_cityscapes_test_results` directory.
 
+### Visualization
+
+To see the SUNRGBD, ScanNet or KITTI points and detection results, you can run the following command
+
+ ```bash
+ python tools/test.py ${CONFIG_FILE} ${CKPT_PATH} --show --show-dir ${SHOW_DIR}
+  ```
+
+Aftering running this command, plotted results ***_points.obj and ***_pred.ply files in `${SHOW_DIR}`.
+
+To see the points, detection results and ground truth of SUNRGBD, ScanNet or KITTI during evaluation time, you can run the following command
+```bash
+python tools/test.py ${CONFIG_FILE} ${CKPT_PATH} --eval 'mAP' --options "show=True" "out_dir=${SHOW_DIR}"
+```
+After running this command, you will obtain ***_points.ob, ***_pred.ply files and ***_gt.ply in `${SHOW_DIR}`.
+
+You can use 3D visualization software such as the [MeshLab](http://www.meshlab.net/) to open the these files under `${SHOW_DIR}` to see the 3D detection output. Specifically, open `***_points.obj` to see the input point cloud and open `***_pred.ply` to see the predicted 3D bounding boxes. This allows the inference and results generation be done in remote server and the users can open them on their host with GUI.
+
+**Notice**: The visualization API is a little unstable since we plan to refactor these parts together with MMDetection in the future.
 
 ### Image demo
 

mmdet3d/apis/__init__.py

+from .test import single_gpu_test
+
+__all__ = ['single_gpu_test']

mmdet3d/apis/test.py

+import mmcv
+import torch
+
+
+def single_gpu_test(model, data_loader, show=False, out_dir=None):
+    model.eval()
+    results = []
+    dataset = data_loader.dataset
+    prog_bar = mmcv.ProgressBar(len(dataset))
+    for i, data in enumerate(data_loader):
+        with torch.no_grad():
+            result = model(return_loss=False, rescale=True, **data)
+
+        if show:
+            model.module.show_results(data, result, out_dir)
+
+        results.append(result)
+
+        batch_size = len(data['img_metas'][0].data)
+        for _ in range(batch_size):
+            prog_bar.update()
+    return results

mmdet3d/core/__init__.py

@@ -2,4 +2,5 @@ from .anchor import *  # noqa: F401, F403
 from .bbox import *  # noqa: F401, F403
 from .evaluation import *  # noqa: F401, F403
 from .post_processing import *  # noqa: F401, F403
+from .visualizer import *  # noqa: F401, F403
 from .voxel import *  # noqa: F401, F403

mmdet3d/core/visualizer/__init__.py

+from .show_result import show_result
+
+__all__ = ['show_result']

mmdet3d/core/visualizer/show_result.py

+import os.path as osp
+
+import mmcv
+import numpy as np
+import trimesh
+
+
+def _write_ply(points, out_filename):
+    N = points.shape[0]
+    fout = open(out_filename, 'w')
+    for i in range(N):
+        if points.shape[1] == 6:
+            c = points[i, 3:].astype(int)
+            fout.write(
+                'v %f %f %f %d %d %d\n' %
+                (points[i, 0], points[i, 1], points[i, 2], c[0], c[1], c[2]))
+
+        else:
+            fout.write('v %f %f %f\n' %
+                       (points[i, 0], points[i, 1], points[i, 2]))
+    fout.close()
+
+
+def _write_oriented_bbox(scene_bbox, out_filename):
+    """Export oriented (around Z axis) scene bbox to meshes
+
+    Args:
+        scene_bbox(list[ndarray] or ndarray): xyz pos of center and
+            3 lengths (dx,dy,dz) and heading angle around Z axis.
+            Y forward, X right, Z upward. heading angle of positive X is 0,
+            heading angle of positive Y is 90 degrees.
+        out_filename(str): Filename.
+    """
+
+    def heading2rotmat(heading_angle):
+        rotmat = np.zeros((3, 3))
+        rotmat[2, 2] = 1
+        cosval = np.cos(heading_angle)
+        sinval = np.sin(heading_angle)
+        rotmat[0:2, 0:2] = np.array([[cosval, -sinval], [sinval, cosval]])
+        return rotmat
+
+    def convert_oriented_box_to_trimesh_fmt(box):
+        ctr = box[:3]
+        lengths = box[3:6]
+        trns = np.eye(4)
+        trns[0:3, 3] = ctr
+        trns[3, 3] = 1.0
+        trns[0:3, 0:3] = heading2rotmat(box[6])
+        box_trimesh_fmt = trimesh.creation.box(lengths, trns)
+        return box_trimesh_fmt
+
+    if len(scene_bbox) == 0:
+        scene_bbox = np.zeros((1, 7))
+    scene = trimesh.scene.Scene()
+    for box in scene_bbox:
+        scene.add_geometry(convert_oriented_box_to_trimesh_fmt(box))
+
+    mesh_list = trimesh.util.concatenate(scene.dump())
+    # save to ply file
+    trimesh.io.export.export_mesh(mesh_list, out_filename, file_type='ply')
+
+    return
+
+
+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
+        _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
+        _write_oriented_bbox(pred_bboxes,
+                             osp.join(out_dir, f'{filename}_pred.ply'))

mmdet3d/datasets/custom_3d.py

@@ -168,7 +168,13 @@ class Custom3DDataset(Dataset):
         mmcv.dump(outputs, out)
         return outputs, tmp_dir
 
-    def evaluate(self, results, metric=None, iou_thr=(0.25, 0.5), logger=None):
+    def evaluate(self,
+                 results,
+                 metric=None,
+                 iou_thr=(0.25, 0.5),
+                 logger=None,
+                 show=False,
+                 out_dir=None):
         """Evaluate.
 
         Evaluation in indoor protocol.
@@ -177,7 +183,13 @@ class Custom3DDataset(Dataset):
             results (list[dict]): List of results.
             metric (str | list[str]): Metrics to be evaluated.
             iou_thr (list[float]): AP IoU thresholds.
+            show (bool): Whether to visualize.
+                Default: False.
+            out_dir (str): Path to save the visualization results.
+                Default: None.
 
+        Returns:
+            dict: Evaluation results.
         """
         from mmdet3d.core.evaluation import indoor_eval
         assert isinstance(
@@ -197,6 +209,8 @@ class Custom3DDataset(Dataset):
             logger=logger,
             box_type_3d=self.box_type_3d,
             box_mode_3d=self.box_mode_3d)
+        if show:
+            self.show(results, out_dir)
 
         return ret_dict
 

mmdet3d/datasets/kitti_dataset.py

@@ -9,6 +9,7 @@ import torch
 from mmcv.utils import print_log
 
 from mmdet.datasets import DATASETS
+from ..core import show_result
 from ..core.bbox import Box3DMode, CameraInstance3DBoxes, points_cam2img
 from .custom_3d import Custom3DDataset
 
@@ -181,7 +182,9 @@ class KittiDataset(Custom3DDataset):
                  metric=None,
                  logger=None,
                  pklfile_prefix=None,
-                 submission_prefix=None):
+                 submission_prefix=None,
+                 show=False,
+                 out_dir=None):
         """Evaluation in KITTI protocol.
 
         Args:
@@ -194,6 +197,10 @@ class KittiDataset(Custom3DDataset):
                 If not specified, a temp file will be created. Default: None.
             submission_prefix (str | None): The prefix of submission datas.
                 If not specified, the submission data will not be generated.
+            show (bool): Whether to visualize.
+                Default: False.
+            out_dir (str): Path to save the visualization results.
+                Default: None.
 
         Returns:
             dict[str: float]: results of each evaluation metric
@@ -230,6 +237,8 @@ class KittiDataset(Custom3DDataset):
 
         if tmp_dir is not None:
             tmp_dir.cleanup()
+        if show:
+            self.show(results, out_dir)
         return ap_dict
 
     def bbox2result_kitti(self,
@@ -508,3 +517,26 @@ class KittiDataset(Custom3DDataset):
                 label_preds=np.zeros([0, 4]),
                 sample_idx=sample_idx,
             )
+
+    def show(self, results, out_dir):
+        assert out_dir is not None, 'Expect out_dir, got none.'
+        for i, result in enumerate(results):
+            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 = points[..., [1, 0, 2]]
+            points[..., 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 = 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)

mmdet3d/datasets/nuscenes_dataset.py

@@ -7,7 +7,8 @@ import pyquaternion
 from nuscenes.utils.data_classes import Box as NuScenesBox
 
 from mmdet.datasets import DATASETS
-from ..core.bbox import LiDARInstance3DBoxes
+from ..core import show_result
+from ..core.bbox import Box3DMode, LiDARInstance3DBoxes
 from .custom_3d import Custom3DDataset
 
 
@@ -324,7 +325,9 @@ class NuScenesDataset(Custom3DDataset):
                  metric='bbox',
                  logger=None,
                  jsonfile_prefix=None,
-                 result_names=['pts_bbox']):
+                 result_names=['pts_bbox'],
+                 show=False,
+                 out_dir=None):
         """Evaluation in nuScenes protocol.
 
         Args:
@@ -335,6 +338,10 @@ class NuScenesDataset(Custom3DDataset):
             jsonfile_prefix (str | None): The prefix of json files. It includes
                 the file path and the prefix of filename, e.g., "a/b/prefix".
                 If not specified, a temp file will be created. Default: None.
+            show (bool): Whether to visualize.
+                Default: False.
+            out_dir (str): Path to save the visualization results.
+                Default: None.
 
         Returns:
             dict[str: float]
@@ -352,8 +359,30 @@ class NuScenesDataset(Custom3DDataset):
 
         if tmp_dir is not None:
             tmp_dir.cleanup()
+
+        if show:
+            self.show(results, out_dir)
         return results_dict
 
+    def show(self, results, out_dir):
+        for i, result in enumerate(results):
+            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
+            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 = 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):
     box3d = detection['boxes_3d']

mmdet3d/datasets/pipelines/formating.py

@@ -78,7 +78,8 @@ class Collect3D(object):
                             'pcd_horizontal_flip', 'pcd_vertical_flip',
                             'box_mode_3d', 'box_type_3d', 'img_norm_cfg',
                             'rect', 'Trv2c', 'P2', 'pcd_trans', 'sample_idx',
-                            'pcd_scale_factor', 'pcd_rotation')):
+                            'pcd_scale_factor', 'pcd_rotation',
+                            'pts_filename')):
         self.keys = keys
         self.meta_keys = meta_keys
 

mmdet3d/datasets/scannet_dataset.py

@@ -2,6 +2,7 @@ import os.path as osp
 
 import numpy as np
 
+from mmdet3d.core import show_result
 from mmdet3d.core.bbox import DepthInstance3DBoxes
 from mmdet.datasets import DATASETS
 from .custom_3d import Custom3DDataset
@@ -63,3 +64,18 @@ class ScanNetDataset(Custom3DDataset):
             pts_instance_mask_path=pts_instance_mask_path,
             pts_semantic_mask_path=pts_semantic_mask_path)
         return anns_results
+
+    def show(self, results, out_dir):
+        assert out_dir is not None, 'Expect out_dir, got none.'
+        for i, result in enumerate(results):
+            data_info = self.data_infos[i]
+            pts_path = data_info['pts_path']
+            file_name = osp.split(pts_path)[-1].split('.')[0]
+            points = np.fromfile(
+                osp.join(self.data_root, pts_path),
+                dtype=np.float32).reshape(-1, 6)
+            gt_bboxes = np.pad(data_info['annos']['gt_boxes_upright_depth'],
+                               ((0, 0), (0, 1)), 'constant')
+            pred_bboxes = result['boxes_3d'].tensor.numpy()
+            pred_bboxes[..., 2] += pred_bboxes[..., 5] / 2
+            show_result(points, gt_bboxes, pred_bboxes, out_dir, file_name)

mmdet3d/datasets/sunrgbd_dataset.py

+import os.path as osp
+
 import numpy as np
 
+from mmdet3d.core import show_result
 from mmdet3d.core.bbox import DepthInstance3DBoxes
 from mmdet.datasets import DATASETS
 from .custom_3d import Custom3DDataset
@@ -48,3 +51,21 @@ class SUNRGBDDataset(Custom3DDataset):
         anns_results = dict(
             gt_bboxes_3d=gt_bboxes_3d, gt_labels_3d=gt_labels_3d)
         return anns_results
+
+    def show(self, results, out_dir):
+        assert out_dir is not None, 'Expect out_dir, got none.'
+        for i, result in enumerate(results):
+            data_info = self.data_infos[i]
+            pts_path = data_info['pts_path']
+            file_name = osp.split(pts_path)[-1].split('.')[0]
+            points = np.fromfile(
+                osp.join(self.data_root, pts_path),
+                dtype=np.float32).reshape(-1, 6)
+            points[:, 3:] *= 255
+            if data_info['annos']['gt_num'] > 0:
+                gt_bboxes = data_info['annos']['gt_boxes_upright_depth']
+            else:
+                gt_bboxes = np.zeros((0, 7))
+            pred_bboxes = result['boxes_3d'].tensor.numpy()
+            pred_bboxes[..., 2] += pred_bboxes[..., 5] / 2
+            show_result(points, gt_bboxes, pred_bboxes, out_dir, file_name)

mmdet3d/models/detectors/base.py

+import os.path as osp
+
+from mmdet3d.core import Box3DMode, show_result
 from mmdet.models.detectors import BaseDetector
 
 
@@ -52,3 +55,23 @@ class Base3DDetector(BaseDetector):
             return self.forward_train(**kwargs)
         else:
             return self.forward_test(**kwargs)
+
+    def show_results(self, data, result, out_dir):
+        points = data['points'][0]._data[0][0].numpy()
+        pts_filename = data['img_metas'][0]._data[0][0]['pts_filename']
+        file_name = osp.split(pts_filename)[-1].split('.')[0]
+
+        assert out_dir is not None, 'Expect out_dir, got none.'
+
+        pred_bboxes = result['pts_bbox']['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]]
+            points[..., 0] *= -1
+            pred_bboxes = Box3DMode.convert(
+                pred_bboxes, data['img_metas'][0]._data[0][0]['box_mode_3d'],
+                Box3DMode.DEPTH)
+            pred_bboxes[..., 2] += pred_bboxes[..., 5] / 2
+        else:
+            pred_bboxes[..., 2] += pred_bboxes[..., 5] / 2
+        show_result(points, None, pred_bboxes, out_dir, file_name)

mmdet3d/models/detectors/mvx_two_stage.py

+import os.path as osp
+
 import torch
 import torch.nn as nn
 import torch.nn.functional as F
 
-from mmdet3d.core import bbox3d2result, merge_aug_bboxes_3d
+from mmdet3d.core import (Box3DMode, bbox3d2result, merge_aug_bboxes_3d,
+                          show_result)
 from mmdet3d.ops import Voxelization
 from mmdet.core import multi_apply
 from mmdet.models import DETECTORS
@@ -349,3 +352,23 @@ class MVXTwoStageDetector(Base3DDetector):
         merged_bboxes = merge_aug_bboxes_3d(aug_bboxes, img_metas,
                                             self.pts_bbox_head.test_cfg)
         return merged_bboxes
+
+    def show_results(self, data, result, out_dir):
+        points = data['points'][0]._data[0][0].numpy()
+        pts_filename = data['img_metas'][0]._data[0][0]['pts_filename']
+        file_name = osp.split(pts_filename)[-1].split('.')[0]
+
+        assert out_dir is not None, 'Expect out_dir, got none.'
+        inds = result['pts_bbox']['scores_3d'] > 0.1
+        pred_bboxes = result['pts_bbox']['boxes_3d'][inds].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]]
+            points[..., 0] *= -1
+            pred_bboxes = Box3DMode.convert(
+                pred_bboxes, data['img_metas'][0]._data[0][0]['box_mode_3d'],
+                Box3DMode.DEPTH)
+            pred_bboxes[..., 2] += pred_bboxes[..., 5] / 2
+        else:
+            pred_bboxes[..., 2] += pred_bboxes[..., 5] / 2
+        show_result(points, None, pred_bboxes, out_dir, file_name)

requirements/runtime.txt

+networkx>=2.2,<2.3
+trimesh>=2.35.39,<2.35.40
 matplotlib
 mmcv>=0.6.0
 numba==0.48.0
@@ -5,6 +7,7 @@ numpy
 nuscenes-devkit==1.0.5
 # need older pillow until torchvision is fixed
 Pillow<=6.2.2
+plyfile
 six
 terminaltables
 torch>=1.3

tools/test.py

@@ -8,9 +8,10 @@ from mmcv.parallel import MMDataParallel, MMDistributedDataParallel
 from mmcv.runner import get_dist_info, init_dist, load_checkpoint
 from tools.fuse_conv_bn import fuse_module
 
+from mmdet3d.apis import single_gpu_test
 from mmdet3d.datasets import build_dataloader, build_dataset
 from mmdet3d.models import build_detector
-from mmdet.apis import multi_gpu_test, set_random_seed, single_gpu_test
+from mmdet.apis import multi_gpu_test, set_random_seed
 from mmdet.core import wrap_fp16_model
 
 
@@ -38,6 +39,8 @@ def parse_args():
         help='evaluation metrics, which depends on the dataset, e.g., "bbox",'
         ' "segm", "proposal" for COCO, and "mAP", "recall" for PASCAL VOC')
     parser.add_argument('--show', action='store_true', help='show results')
+    parser.add_argument(
+        '--show-dir', help='directory where results will be saved')
     parser.add_argument(
         '--gpu-collect',
         action='store_true',
@@ -125,7 +128,7 @@ def main():
 
     if not distributed:
         model = MMDataParallel(model, device_ids=[0])
-        outputs = single_gpu_test(model, data_loader, args.show)
+        outputs = single_gpu_test(model, data_loader, args.show, args.show_dir)
     else:
         model = MMDistributedDataParallel(
             model.cuda(),