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# Copyright (c) OpenMMLab. All rights reserved.
import unittest
import numpy as np
import torch
from mmengine.testing import assert_allclose
from mmdet3d.datasets import ScanNetDataset, ScanNetSegDataset
from mmdet3d.structures import DepthInstance3DBoxes
from mmdet3d.utils import register_all_modules
def _generate_scannet_seg_dataset_config():
data_root = './tests/data/scannet/'
ann_file = 'scannet_infos.pkl'
classes = ('wall', 'floor', 'cabinet', 'bed', 'chair', 'sofa', 'table',
'door', 'window', 'bookshelf', 'picture', 'counter', 'desk',
'curtain', 'refrigerator', 'showercurtrain', 'toilet', 'sink',
'bathtub', 'otherfurniture')
palette = [
[174, 199, 232],
[152, 223, 138],
[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],
]
scene_idxs = [0]
modality = dict(use_lidar=True, use_camera=False)
pipeline = [
dict(
type='LoadPointsFromFile',
coord_type='DEPTH',
shift_height=False,
use_color=True,
load_dim=6,
use_dim=[0, 1, 2, 3, 4, 5]),
dict(
type='LoadAnnotations3D',
with_bbox_3d=False,
with_label_3d=False,
with_mask_3d=False,
with_seg_3d=True),
dict(type='PointSegClassMapping'),
dict(
type='IndoorPatchPointSample',
num_points=5,
block_size=1.5,
ignore_index=len(classes),
use_normalized_coord=True,
enlarge_size=0.2,
min_unique_num=None),
dict(type='NormalizePointsColor', color_mean=None),
dict(type='Pack3DDetInputs', keys=['points', 'pts_semantic_mask'])
]
data_prefix = dict(
pts='points',
pts_instance_mask='instance_mask',
pts_semantic_mask='semantic_mask')
return (data_root, ann_file, classes, palette, scene_idxs, data_prefix,
pipeline, modality)
def _generate_scannet_dataset_config():
data_root = 'tests/data/scannet'
ann_file = 'scannet_infos.pkl'
classes = ('cabinet', 'bed', 'chair', 'sofa', 'table', 'door', 'window',
'bookshelf', 'picture', 'counter', 'desk', 'curtain',
'refrigerator', 'showercurtrain', 'toilet', 'sink', 'bathtub',
'garbagebin')
modality = dict(use_lidar=True, use_camera=False)
pipeline = [
dict(
type='LoadPointsFromFile',
coord_type='DEPTH',
shift_height=True,
load_dim=6,
use_dim=[0, 1, 2]),
dict(
type='LoadAnnotations3D',
with_bbox_3d=True,
with_label_3d=True,
with_mask_3d=True,
with_seg_3d=True),
dict(type='GlobalAlignment', rotation_axis=2),
dict(type='PointSegClassMapping'),
dict(type='PointSample', num_points=5),
dict(
type='RandomFlip3D',
sync_2d=False,
flip_ratio_bev_horizontal=1.0,
flip_ratio_bev_vertical=1.0),
dict(
type='GlobalRotScaleTrans',
rot_range=[-0.087266, 0.087266],
scale_ratio_range=[1.0, 1.0],
shift_height=True),
dict(
type='Pack3DDetInputs',
keys=[
'points', 'pts_semantic_mask', 'gt_bboxes_3d', 'gt_labels_3d',
'pts_instance_mask'
])
]
data_prefix = dict(
pts='points',
pts_instance_mask='instance_mask',
pts_semantic_mask='semantic_mask')
return data_root, ann_file, classes, data_prefix, pipeline, modality
class TestScanNetDataset(unittest.TestCase):
def test_scannet(self):
np.random.seed(0)
data_root, ann_file, classes, data_prefix, \
pipeline, modality, = _generate_scannet_dataset_config()
register_all_modules()
scannet_dataset = ScanNetDataset(
data_root,
ann_file,
data_prefix=data_prefix,
pipeline=pipeline,
metainfo=dict(classes=classes),
modality=modality)
scannet_dataset.prepare_data(0)
input_dict = scannet_dataset.get_data_info(0)
scannet_dataset[0]
# assert the the path should contains data_prefix and data_root
self.assertIn(data_prefix['pts'],
input_dict['lidar_points']['lidar_path'])
self.assertIn(data_root, input_dict['lidar_points']['lidar_path'])
ann_info = scannet_dataset.parse_ann_info(input_dict)
# assert the keys in ann_info and the type
except_label = np.array([
6, 6, 4, 9, 11, 11, 10, 0, 15, 17, 17, 17, 3, 12, 4, 4, 14, 1, 0,
0, 0, 0, 0, 0, 5, 5, 5
])
self.assertEqual(ann_info['gt_labels_3d'].dtype, np.int64)
assert_allclose(ann_info['gt_labels_3d'], except_label)
self.assertIsInstance(ann_info['gt_bboxes_3d'], DepthInstance3DBoxes)
assert len(ann_info['gt_bboxes_3d']) == 27
assert torch.allclose(ann_info['gt_bboxes_3d'].tensor.sum(),
torch.tensor([107.7353]))
no_class_scannet_dataset = ScanNetDataset(
data_root, ann_file, metainfo=dict(classes=['cabinet']))
input_dict = no_class_scannet_dataset.get_data_info(0)
ann_info = no_class_scannet_dataset.parse_ann_info(input_dict)
# assert the keys in ann_info and the type
self.assertIn('gt_labels_3d', ann_info)
# assert mapping to -1 or 1
assert (ann_info['gt_labels_3d'] <= 0).all()
self.assertEqual(ann_info['gt_labels_3d'].dtype, np.int64)
# all instance have been filtered by classes
self.assertEqual(len(ann_info['gt_labels_3d']), 27)
self.assertEqual(len(no_class_scannet_dataset.metainfo['classes']), 1)
def test_scannet_seg(self):
data_root, ann_file, classes, palette, scene_idxs, data_prefix, \
pipeline, modality, = _generate_scannet_seg_dataset_config()
register_all_modules()
np.random.seed(0)
scannet_seg_dataset = ScanNetSegDataset(
data_root,
ann_file,
metainfo=dict(classes=classes, palette=palette),
data_prefix=data_prefix,
pipeline=pipeline,
modality=modality,
scene_idxs=scene_idxs)
input_dict = scannet_seg_dataset.prepare_data(0)
points = input_dict['inputs']['points']
data_sample = input_dict['data_samples']
pts_semantic_mask = data_sample.gt_pts_seg.pts_semantic_mask
expected_points = torch.tensor([[
0.0000, 0.0000, 1.2427, 0.6118, 0.5529, 0.4471, -0.6462, -1.0046,
0.4280
],
[
0.1553, -0.0074, 1.6077, 0.5882,
0.6157, 0.5569, -0.6001, -1.0068,
0.5537
],
[
0.1518, 0.6016, 0.6548, 0.1490,
0.1059, 0.0431, -0.6012, -0.8309,
0.2255
],
[
-0.7494, 0.1033, 0.6756, 0.5216,
0.4353, 0.3333, -0.8687, -0.9748,
0.2327
],
[
-0.6836, -0.0203, 0.5884, 0.5765,
0.5020, 0.4510, -0.8491, -1.0105,
0.2027
]])
expected_pts_semantic_mask = np.array([13, 13, 12, 2, 0])
assert torch.allclose(points, expected_points, 1e-2)
self.assertTrue(
(pts_semantic_mask.numpy() == expected_pts_semantic_mask).all())
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