def __init__(self, path, split,
cameras=None,
depth_type=None,
with_pose=False,
with_semantic=False,
back_context=0,
forward_context=0,
data_transform=None,
):
self.path = path
self.split = split
self.dataset_idx = 0
self.bwd = back_context
self.fwd = forward_context
self.has_context = back_context + forward_context > 0
self.num_cameras = len(cameras)
self.data_transform = data_transform
self.depth_type = depth_type
self.with_depth = depth_type is not None
self.with_pose = with_pose
self.with_semantic = with_semantic
self.dataset = SynchronizedSceneDataset(path,
split=split,
datum_names=cameras,
backward_context=back_context,
forward_context=forward_context,
requested_annotations=None,
only_annotated_datums=False,
)
def test_accumulation_in_scene(self):
# Load a scene without accumulation
scenes_dataset_json = os.path.join(self.DGP_TEST_DATASET_DIR,
"test_scene",
"scene_dataset_v1.0.json")
dataset = SynchronizedSceneDataset(
scenes_dataset_json,
split='train',
datum_names=['lidar'],
)
# Load the same scene with max context available accumulation context, this dataset has two scenes each with 3 samples
dataset_acc = SynchronizedSceneDataset(
scenes_dataset_json,
split='train',
datum_names=['lidar'],
requested_annotations=['bounding_box_3d'],
accumulation_context={'lidar': (2, 0)},
transform_accumulated_box_points=True)
# We should only have two samples (one per scene)
assert len(dataset_acc) == 2
# Verify that we have not lost any points by accumulating
num_points = 0
for i in range(3):
context = dataset[i]
num_points += len(context[0][-1]['point_cloud'])
context_acc = dataset_acc[0]
num_points_acc = len(context_acc[0][-1]['point_cloud'])
assert num_points == num_points_acc
def main():
parser = argparse.ArgumentParser(
description=__doc__,
formatter_class=argparse.RawDescriptionHelpFormatter)
parser.add_argument('--scene-dataset-json',
type=str,
required=True,
help='Path to local SceneDataset JSON.')
parser.add_argument('--split',
type=str,
default='train',
required=False,
help='Split [train, val, test].',
choices=['train', 'val', 'test'])
parser.add_argument('--verbose', action='store_true')
args = parser.parse_args()
# Verbose prints.
if args.verbose:
logging.getLogger().setLevel(level=logging.INFO)
# Load the dataset and build an index into the annotations requested.
# If previously loaded/initialized, load the pre-built dataset.
st = time.time()
dataset = SynchronizedSceneDataset(
scene_dataset_json=args.scene_dataset_json,
split=args.split,
datum_names=('lidar_02', ),
requested_annotations=('bounding_box_3d', ),
only_annotated_datums=True)
print('Loading dataset took {:.2f} s'.format(time.time() - st))
# Iterate through the dataset.
for _ in tqdm(dataset, desc='Loading samples from the dataset'):
pass
def get_ontology_kl(scene_dataset_json, annotation_type):
dataset = SynchronizedSceneDataset(scene_dataset_json,
split='train',
datum_names=['locator'],
backward_context=0,
requested_annotations=("key_line_2d", ))
return dataset.dataset_metadata.ontology_table.get(annotation_type, None)
def test_autolabels_custom_root(self):
"""Test that we can load autolabels using autolabel_root"""
scenes_dataset_json = os.path.join(self.DGP_TEST_DATASET_DIR,
"test_scene",
"scene_dataset_v1.0.json")
autolabel_model = 'test-model'
autolabel_annotation = 'bounding_box_3d'
requested_autolabels = (f'{autolabel_model}/{autolabel_annotation}', )
dataset_root = os.path.dirname(scenes_dataset_json)
autolabel_root = os.path.join(self.DGP_TEST_DATASET_DIR,
'autolabel_root')
clone_scene_as_autolabel(dataset_root, autolabel_root, autolabel_model,
autolabel_annotation)
dataset = SynchronizedSceneDataset(
scenes_dataset_json,
split='train',
datum_names=['LIDAR'],
forward_context=1,
backward_context=1,
requested_annotations=('bounding_box_3d', ),
requested_autolabels=requested_autolabels,
autolabel_root=autolabel_root,
use_diskcache=False,
)
assert len(dataset) == 2
for context in dataset:
for sample in context:
lidar = sample[0]
assert lidar['bounding_box_3d'] == lidar[
requested_autolabels[0]]
def _load_dataset(path, split):
return SynchronizedSceneDataset(
# TODO: add a interactive checkbox to enable users to select datums.
scene_dataset_json=path,
split=split,
requested_annotations=("bounding_box_3d", ),
only_annotated_datums=True)
def get_ontology(scene_dataset_json, annotation_type):
dataset = SynchronizedSceneDataset(
scene_dataset_json,
split='train',
datum_names=['camera_01', 'lidar'],
backward_context=0,
requested_annotations=("bounding_box_2d", "bounding_box_3d"))
return dataset.dataset_metadata.ontology_table.get(annotation_type, None)
def setUp(self):
# Initialize synchronized dataset
scenes_dataset_json = os.path.join(self.DGP_TEST_DATASET_DIR, "test_scene", "scene_dataset_v1.0.json")
self.dataset = SynchronizedSceneDataset(
scenes_dataset_json,
split='train',
datum_names=['camera_01', 'lidar'],
backward_context=0,
requested_annotations=("bounding_box_2d", "bounding_box_3d")
)
def test_autolabels_missing_files(self):
"""Test that skip missing data can be used to skip missing autolabel scene dirs"""
scenes_dataset_json = os.path.join(self.DGP_TEST_DATASET_DIR,
"test_scene",
"scene_dataset_v1.0.json")
autolabel_model = 'test-model'
autolabel_annotation = 'bounding_box_3d'
requested_autolabels = (f'{autolabel_model}/{autolabel_annotation}', )
dataset_root = os.path.dirname(scenes_dataset_json)
autolabel_root = os.path.join(self.DGP_TEST_DATASET_DIR,
'autolabel_root')
autolabel_dirs = clone_scene_as_autolabel(dataset_root, autolabel_root,
autolabel_model,
autolabel_annotation)
# remove a scene dir and check we can still load the data
rmtree(autolabel_dirs[0])
# Test skip missing data allows us to load the dataset
dataset = SynchronizedSceneDataset(
scenes_dataset_json,
split='train',
datum_names=['LIDAR'],
forward_context=1,
backward_context=1,
requested_annotations=('bounding_box_3d', ),
requested_autolabels=requested_autolabels,
autolabel_root=autolabel_root,
skip_missing_data=True,
use_diskcache=False,
)
assert len(dataset) == 2
for context in dataset:
for sample in context:
lidar = sample[0]
autolab = lidar[requested_autolabels[0]]
assert autolab is None or lidar['bounding_box_3d'] == autolab
def test_only_annotated_datums(self):
"""Test that only annotated datums applies to autolabels also"""
scenes_dataset_json = os.path.join(self.DGP_TEST_DATASET_DIR,
"test_scene",
"scene_dataset_v1.0.json")
autolabel_model = 'test-model'
autolabel_annotation = 'bounding_box_3d'
requested_autolabels = (f'{autolabel_model}/{autolabel_annotation}', )
dataset_root = os.path.dirname(scenes_dataset_json)
autolabel_root = os.path.join(self.DGP_TEST_DATASET_DIR,
'autolabel_root')
autolabel_dirs = clone_scene_as_autolabel(dataset_root, autolabel_root,
autolabel_model,
autolabel_annotation)
# remove a scene dir and check we can still load the data
rmtree(autolabel_dirs[0])
# Test that only annotated datums works
dataset = SynchronizedSceneDataset(
scenes_dataset_json,
split='train',
datum_names=['LIDAR'],
forward_context=1,
backward_context=1,
requested_autolabels=requested_autolabels,
autolabel_root=autolabel_root,
only_annotated_datums=True,
skip_missing_data=True,
use_diskcache=False,
)
assert len(dataset) == 1
for context in dataset:
for sample in context:
lidar = sample[0]
assert lidar[requested_autolabels[0]] is not None
def test_accumulation(self):
"""Test accumulation"""
# Generate some samples
scenes_dataset_json = os.path.join(self.DGP_TEST_DATASET_DIR,
"test_scene",
"scene_dataset_v1.0.json")
dataset = SynchronizedSceneDataset(
scenes_dataset_json,
split='train',
datum_names=['lidar'],
)
assert len(dataset) >= 2
point_datums = []
for sample in dataset:
point_datums.append(sample[0][0])
p1, p2 = point_datums[0], point_datums[-1]
p1_and_p2_in_p1 = accumulate_points([p1, p2], p1)
assert len(p1_and_p2_in_p1['point_cloud']) == len(
p1['point_cloud']) + len(p2['point_cloud'])
p1_and_p2_in_p2 = accumulate_points([p1, p2], p2)
assert len(p1_and_p2_in_p2['point_cloud']) == len(
p1['point_cloud']) + len(p2['point_cloud'])
# If we move the accumulated p1 frame points to p2, we should recover the accumulated p2 points
p1_and_p2_in_p2_v2 = accumulate_points([p1_and_p2_in_p1], p2)
assert np.allclose(p1_and_p2_in_p2_v2['point_cloud'],
p1_and_p2_in_p2['point_cloud'])
# If we accumulate a single point nothing should happen
p1_v2 = accumulate_points([p1], p1)
assert np.allclose(p1_v2['point_cloud'], p1['point_cloud'])
def test_labeled_synchronized_scene_dataset(self):
"""Test synchronized scene dataset"""
expected_camera_fields = set([
'rgb',
'timestamp',
'datum_name',
'pose',
'intrinsics',
'extrinsics',
'bounding_box_2d',
'bounding_box_3d',
'depth',
'datum_type',
])
expected_lidar_fields = set([
'point_cloud',
'timestamp',
'datum_name',
'pose',
'extrinsics',
'bounding_box_2d',
'bounding_box_3d',
'extra_channels',
'datum_type',
])
expected_metadata_fields = set([
'scene_index', 'sample_index_in_scene', 'log_id', 'timestamp',
'scene_name', 'scene_description'
])
# Initialize synchronized dataset with 2 datums
scenes_dataset_json = os.path.join(self.DGP_TEST_DATASET_DIR,
"test_scene",
"scene_dataset_v1.0.json")
dataset = SynchronizedSceneDataset(
scenes_dataset_json,
split='train',
datum_names=['LIDAR', 'CAMERA_01'],
forward_context=1,
backward_context=1,
generate_depth_from_datum='LIDAR',
requested_annotations=("bounding_box_2d", "bounding_box_3d"))
# There are only 3 samples in the train and val split.
# With a forward and backward context of 1 each, the number of
# items in the dataset with the desired context frames is 1.
assert len(dataset) == 2
# Iterate through labeled dataset and check expected fields
assert dataset.calibration_table is not None
for idx, item in enumerate(dataset):
# Context size is 3 (forward + backward + reference)
assert_true(len(item) == 3)
# Check both datum and time-dimensions for expected fields
im_size = None
for t_item in item:
# Two selected datums
assert_true(len(t_item) == 2)
for datum in t_item:
if datum['datum_name'] == 'LIDAR':
# LIDAR should have point_cloud set
assert_true(set(datum.keys()) == expected_lidar_fields)
assert_true(isinstance(datum, OrderedDict))
elif datum['datum_name'].startswith('CAMERA_'):
# CAMERA_01 should have intrinsics/extrinsics set
assert_true(isinstance(datum, OrderedDict))
assert_true(datum['intrinsics'].shape == (3, 3))
assert_true(isinstance(datum['extrinsics'], Pose))
assert_true(isinstance(datum['pose'], Pose))
# Check image sizes for context frames
assert_true(
set(datum.keys()) == expected_camera_fields)
if im_size is None:
im_size = datum['rgb'].size
assert_true(datum['rgb'].size == im_size)
else:
raise RuntimeError('Unexpected datum_name {}'.format(
datum['datum_name']))
# Retrieve metadata about dataset item at index=idx
metadata = dataset.get_scene_metadata(idx)
assert_true(metadata.keys() == expected_metadata_fields)
class DGPDataset:
"""
DGP dataset class
Parameters
----------
path : str
Path to the dataset
split : str {'train', 'val', 'test'}
Which dataset split to use
cameras : list of str
Which cameras to get information from
depth_type : str
Which lidar will be used to generate ground-truth information
with_pose : bool
If enabled pose estimates are also returned
with_semantic : bool
If enabled semantic estimates are also returned
back_context : int
Size of the backward context
forward_context : int
Size of the forward context
data_transform : Function
Transformations applied to the sample
"""
def __init__(self, path, split,
cameras=None,
depth_type=None,
with_pose=False,
with_semantic=False,
back_context=0,
forward_context=0,
data_transform=None,
):
self.path = path
self.split = split
self.dataset_idx = 0
self.bwd = back_context
self.fwd = forward_context
self.has_context = back_context + forward_context > 0
self.num_cameras = len(cameras)
self.data_transform = data_transform
self.depth_type = depth_type
self.with_depth = depth_type is not None
self.with_pose = with_pose
self.with_semantic = with_semantic
self.dataset = SynchronizedSceneDataset(path,
split=split,
datum_names=cameras,
backward_context=back_context,
forward_context=forward_context,
requested_annotations=None,
only_annotated_datums=False,
)
def generate_depth_map(self, sample_idx, datum_idx, filename):
"""
Generates the depth map for a camera by projecting LiDAR information.
It also caches the depth map following DGP folder structure, so it's not recalculated
Parameters
----------
sample_idx : int
sample index
datum_idx : int
Datum index
filename :
Filename used for loading / saving
Returns
-------
depth : np.array [H, W]
Depth map for that datum in that sample
"""
# Generate depth filename
filename = '{}/{}.npz'.format(
os.path.dirname(self.path), filename.format('depth/{}'.format(self.depth_type)))
# Load and return if exists
if os.path.exists(filename):
return np.load(filename)['depth']
# Otherwise, create, save and return
else:
# Get pointcloud
scene_idx, sample_idx_in_scene, _ = self.dataset.dataset_item_index[sample_idx]
pc_datum_idx_in_sample = self.dataset.get_datum_index_for_datum_name(
scene_idx, sample_idx_in_scene, self.depth_type)
pc_datum_data = self.dataset.get_point_cloud_from_datum(
scene_idx, sample_idx_in_scene, pc_datum_idx_in_sample)
# Create camera
camera_rgb = self.get_current('rgb', datum_idx)
camera_pose = self.get_current('pose', datum_idx)
camera_intrinsics = self.get_current('intrinsics', datum_idx)
camera = Camera(K=camera_intrinsics, p_cw=camera_pose.inverse())
# Generate depth map
world_points = pc_datum_data['pose'] * pc_datum_data['point_cloud']
depth = generate_depth_map(camera, world_points, camera_rgb.size[::-1])
# Save depth map
os.makedirs(os.path.dirname(filename), exist_ok=True)
np.savez_compressed(filename, depth=depth)
# Return depth map
return depth
def get_current(self, key, sensor_idx):
"""Return current timestep of a key from a sensor"""
return self.sample_dgp[self.bwd][sensor_idx][key]
def get_backward(self, key, sensor_idx):
"""Return backward timesteps of a key from a sensor"""
return [] if self.bwd == 0 else \
[self.sample_dgp[i][sensor_idx][key] \
for i in range(0, self.bwd)]
def get_forward(self, key, sensor_idx):
"""Return forward timestep of a key from a sensor"""
return [] if self.fwd == 0 else \
[self.sample_dgp[i][sensor_idx][key] \
for i in range(self.bwd + 1, self.bwd + self.fwd + 1)]
def get_context(self, key, sensor_idx):
"""Get both backward and forward contexts"""
return self.get_backward(key, sensor_idx) + self.get_forward(key, sensor_idx)
def get_filename(self, sample_idx, datum_idx):
"""
Returns the filename for an index, following DGP structure
Parameters
----------
sample_idx : int
Sample index
datum_idx : int
Datum index
Returns
-------
filename : str
Filename for the datum in that sample
"""
scene_idx, sample_idx_in_scene, datum_indices = self.dataset.dataset_item_index[sample_idx]
scene_dir = self.dataset.get_scene_directory(scene_idx)
filename = self.dataset.get_datum(
scene_idx, sample_idx_in_scene, datum_indices[datum_idx]).datum.image.filename
return os.path.splitext(os.path.join(os.path.basename(scene_dir),
filename.replace('rgb', '{}')))[0]
def __len__(self):
"""Length of dataset"""
return len(self.dataset)
def __getitem__(self, idx):
"""Get a dataset sample"""
# Get DGP sample (if single sensor, make it a list)
self.sample_dgp = self.dataset[idx]
self.sample_dgp = [make_list(sample) for sample in self.sample_dgp]
# Loop over all cameras
sample = []
for i in range(self.num_cameras):
data = {
'idx': idx,
'dataset_idx': self.dataset_idx,
'sensor_name': self.get_current('datum_name', i),
#
'filename': self.get_filename(idx, i),
'splitname': '%s_%010d' % (self.split, idx),
#
'rgb': self.get_current('rgb', i),
'intrinsics': self.get_current('intrinsics', i),
}
# If depth is returned
if self.with_depth:
data.update({
'depth': self.generate_depth_map(idx, i, data['filename'])
})
# If pose is returned
if self.with_pose:
data.update({
'extrinsics': self.get_current('extrinsics', i).matrix,
'pose': self.get_current('pose', i).matrix,
})
# If context is returned
if self.has_context:
data.update({
'rgb_context': self.get_context('rgb', i),
})
# If context pose is returned
if self.with_pose:
# Get original values to calculate relative motion
orig_extrinsics = Pose.from_matrix(data['extrinsics'])
orig_pose = Pose.from_matrix(data['pose'])
data.update({
'extrinsics_context':
[(orig_extrinsics.inverse() * extrinsics).matrix
for extrinsics in self.get_context('extrinsics', i)],
'pose_context':
[(orig_pose.inverse() * pose).matrix
for pose in self.get_context('pose', i)],
})
sample.append(data)
# Apply same data transformations for all sensors
if self.data_transform:
sample = [self.data_transform(smp) for smp in sample]
# Return sample (stacked if necessary)
return stack_sample(sample)
class DGPvaleoDataset:
"""
DGP dataset class
Parameters
----------
path : str
Path to the dataset
split : str {'train', 'val', 'test'}
Which dataset split to use
cameras : list of str
Which cameras to get information from
depth_type : str
Which lidar will be used to generate ground-truth information
with_pose : bool
If enabled pose estimates are also returned
with_semantic : bool
If enabled semantic estimates are also returned
back_context : int
Size of the backward context
forward_context : int
Size of the forward context
data_transform : Function
Transformations applied to the sample
"""
def __init__(
self,
path,
split,
cameras=None,
depth_type=None,
with_pose=False,
with_semantic=False,
back_context=0,
forward_context=0,
data_transform=None,
with_geometric_context=False,
):
self.path = path
self.split = split
self.dataset_idx = 0
self.bwd = back_context
self.fwd = forward_context
self.has_context = back_context + forward_context > 0
self.with_geometric_context = with_geometric_context
self.num_cameras = len(cameras)
self.data_transform = data_transform
self.depth_type = depth_type
self.with_depth = depth_type is not None
self.with_pose = with_pose
self.with_semantic = with_semantic
# arrange cameras alphabetically
cameras = sorted(cameras)
cameras_left = list(cameras)
cameras_right = list(cameras)
for i_cam in range(self.num_cameras):
replaced = False
for k in cam_left_dict:
if not replaced and k in cameras_left[i_cam]:
cameras_left[i_cam] = cameras_left[i_cam].replace(
k, cam_left_dict[k])
replaced = True
replaced = False
for k in cam_right_dict:
if not replaced and k in cameras_right[i_cam]:
cameras_right[i_cam] = cameras_right[i_cam].replace(
k, cam_right_dict[k])
replaced = True
print(cameras)
print(cameras_left)
print(cameras_right)
# arrange cameras left and right and extract sorting indices
self.cameras_left_sort_idxs = list(np.argsort(cameras_left))
self.cameras_right_sort_idxs = list(np.argsort(cameras_right))
cameras_left_sorted = sorted(cameras_left)
cameras_right_sorted = sorted(cameras_right)
self.dataset = SynchronizedSceneDataset(
path,
split=split,
datum_names=cameras,
backward_context=back_context,
forward_context=forward_context,
requested_annotations=None,
only_annotated_datums=False,
)
if self.with_geometric_context:
self.dataset_left = SynchronizedSceneDataset(
path,
split=split,
datum_names=cameras_left_sorted,
backward_context=back_context,
forward_context=forward_context,
requested_annotations=None,
only_annotated_datums=False,
)
self.dataset_right = SynchronizedSceneDataset(
path,
split=split,
datum_names=cameras_right_sorted,
backward_context=back_context,
forward_context=forward_context,
requested_annotations=None,
only_annotated_datums=False,
)
@staticmethod
def _get_base_folder(image_file):
"""The base folder"""
return '/'.join(image_file.split('/')[:-4])
@staticmethod
def _get_sequence_name(image_file):
"""Returns a sequence name like '20180227_185324'."""
return image_file.split('/')[-4]
@staticmethod
def _get_camera_name(image_file):
"""Returns 'cam_i', i between 0 and 4"""
return image_file.split('/')[-2]
def _get_path_to_ego_mask(self, image_file):
"""Get the current folder from image_file."""
return os.path.join(self._get_base_folder(image_file),
self._get_sequence_name(image_file),
'semantic_masks',
self._get_camera_name(image_file) + '.npy')
def generate_depth_map(self, sample_idx, datum_idx, filename):
"""
Generates the depth map for a camera by projecting LiDAR information.
It also caches the depth map following DGP folder structure, so it's not recalculated
Parameters
----------
sample_idx : int
sample index
datum_idx : int
Datum index
filename :
Filename used for loading / saving
Returns
-------
depth : np.array [H, W]
Depth map for that datum in that sample
"""
# Generate depth filename
filename = '{}/{}.npz'.format(
os.path.dirname(self.path),
filename.format('depth/{}'.format(self.depth_type)))
# Load and return if exists
if os.path.exists(filename):
return np.load(filename, allow_pickle=True)['depth']
# Otherwise, create, save and return
else:
# Get pointcloud
scene_idx, sample_idx_in_scene, _ = self.dataset.dataset_item_index[
sample_idx]
pc_datum_idx_in_sample = self.dataset.get_datum_index_for_datum_name(
scene_idx, sample_idx_in_scene, self.depth_type)
pc_datum_data = self.dataset.get_point_cloud_from_datum(
scene_idx, sample_idx_in_scene, pc_datum_idx_in_sample)
# Create camera
camera_rgb = self.get_current('rgb', datum_idx)
camera_pose = self.get_current('pose', datum_idx)
camera_intrinsics = self.get_current('intrinsics', datum_idx)
camera = Camera(K=camera_intrinsics, p_cw=camera_pose.inverse())
# Generate depth map
world_points = pc_datum_data['pose'] * pc_datum_data['point_cloud']
depth = generate_depth_map(camera, world_points,
camera_rgb.size[::-1])
# Save depth map
os.makedirs(os.path.dirname(filename), exist_ok=True)
np.savez_compressed(filename, depth=depth)
# Return depth map
return depth
def get_current(self, key, sensor_idx):
"""Return current timestep of a key from a sensor"""
return self.sample_dgp[self.bwd][sensor_idx][key]
def get_current_left(self, key, sensor_idx):
"""Return current timestep of a key from a sensor"""
return self.sample_dgp_left[self.bwd][sensor_idx][key]
def get_current_right(self, key, sensor_idx):
"""Return current timestep of a key from a sensor"""
return self.sample_dgp_right[self.bwd][sensor_idx][key]
def get_backward(self, key, sensor_idx):
"""Return backward timesteps of a key from a sensor"""
return [] if self.bwd == 0 else \
[self.sample_dgp[i][sensor_idx][key] \
for i in range(0, self.bwd)]
def get_backward_left(self, key, sensor_idx):
"""Return backward timesteps of a key from a sensor"""
return [] if self.bwd == 0 else \
[self.sample_dgp_left[i][sensor_idx][key] \
for i in range(0, self.bwd)]
def get_backward_right(self, key, sensor_idx):
"""Return backward timesteps of a key from a sensor"""
return [] if self.bwd == 0 else \
[self.sample_dgp_right[i][sensor_idx][key] \
for i in range(0, self.bwd)]
def get_forward(self, key, sensor_idx):
"""Return forward timestep of a key from a sensor"""
return [] if self.fwd == 0 else \
[self.sample_dgp[i][sensor_idx][key] \
for i in range(self.bwd + 1, self.bwd + self.fwd + 1)]
def get_forward_left(self, key, sensor_idx):
"""Return forward timestep of a key from a sensor"""
return [] if self.fwd == 0 else \
[self.sample_dgp_left[i][sensor_idx][key] \
for i in range(self.bwd + 1, self.bwd + self.fwd + 1)]
def get_forward_right(self, key, sensor_idx):
"""Return forward timestep of a key from a sensor"""
return [] if self.fwd == 0 else \
[self.sample_dgp_right[i][sensor_idx][key] \
for i in range(self.bwd + 1, self.bwd + self.fwd + 1)]
def get_context(self, key, sensor_idx):
"""Get both backward and forward contexts"""
return self.get_backward(key, sensor_idx) + self.get_forward(
key, sensor_idx)
def get_context_left(self, key, sensor_idx):
"""Get both backward and forward contexts"""
return self.get_backward_left(key, sensor_idx) + self.get_forward_left(
key, sensor_idx)
def get_context_right(self, key, sensor_idx):
"""Get both backward and forward contexts"""
return self.get_backward_right(
key, sensor_idx) + self.get_forward_right(key, sensor_idx)
def get_filename(self, sample_idx, datum_idx):
"""
Returns the filename for an index, following DGP structure
Parameters
----------
sample_idx : int
Sample index
datum_idx : int
Datum index
Returns
-------
filename : str
Filename for the datum in that sample
"""
scene_idx, sample_idx_in_scene, datum_indices = self.dataset.dataset_item_index[
sample_idx]
scene_dir = self.dataset.get_scene_directory(scene_idx)
filename = self.dataset.get_datum(
scene_idx, sample_idx_in_scene,
datum_indices[datum_idx]).datum.image.filename
return os.path.splitext(
os.path.join(os.path.basename(scene_dir),
filename.replace('rgb', '{}')))[0]
def get_filename_left(self, sample_idx, datum_idx):
"""
Returns the filename for an index, following DGP structure
Parameters
----------
sample_idx : int
Sample index
datum_idx : int
Datum index
Returns
-------
filename : str
Filename for the datum in that sample
"""
scene_idx, sample_idx_in_scene, datum_indices = self.dataset_left.dataset_item_index[
sample_idx]
scene_dir = self.dataset_left.get_scene_directory(scene_idx)
filename = self.dataset_left.get_datum(
scene_idx, sample_idx_in_scene,
datum_indices[datum_idx]).datum.image.filename
return os.path.splitext(
os.path.join(os.path.basename(scene_dir),
filename.replace('rgb', '{}')))[0]
def get_filename_right(self, sample_idx, datum_idx):
"""
Returns the filename for an index, following DGP structure
Parameters
----------
sample_idx : int
Sample index
datum_idx : int
Datum index
Returns
-------
filename : str
Filename for the datum in that sample
"""
scene_idx, sample_idx_in_scene, datum_indices = self.dataset_right.dataset_item_index[
sample_idx]
scene_dir = self.dataset_right.get_scene_directory(scene_idx)
filename = self.dataset_right.get_datum(
scene_idx, sample_idx_in_scene,
datum_indices[datum_idx]).datum.image.filename
return os.path.splitext(
os.path.join(os.path.basename(scene_dir),
filename.replace('rgb', '{}')))[0]
def get_camera_idx_left(self, camera_idx):
return self.cameras_left_sort_idxs[camera_idx]
def get_camera_idx_right(self, camera_idx):
return self.cameras_right_sort_idxs[camera_idx]
def __len__(self):
"""Length of dataset"""
return len(self.dataset)
def __getitem__(self, idx):
"""Get a dataset sample"""
# Get DGP sample (if single sensor, make it a list)
self.sample_dgp = self.dataset[idx]
self.sample_dgp = [make_list(sample) for sample in self.sample_dgp]
if self.with_geometric_context:
self.sample_dgp_left = self.dataset_left[idx]
self.sample_dgp_left = [
make_list(sample) for sample in self.sample_dgp_left
]
self.sample_dgp_right = self.dataset_right[idx]
self.sample_dgp_right = [
make_list(sample) for sample in self.sample_dgp_right
]
# print('self.sample_dgp :')
# print(self.sample_dgp)
# print('self.sample_dgp_left :')
# print(self.sample_dgp_left)
# print('self.sample_dgp_right :')
# print(self.sample_dgp_right)
# Loop over all cameras
sample = []
for i in range(self.num_cameras):
i_left = self.get_camera_idx_left(i)
i_right = self.get_camera_idx_right(i)
# print(self.get_current('datum_name', i))
# print(self.get_filename(idx, i))
# print(self.get_current('intrinsics', i))
# print(self.with_depth)
data = {
'idx':
idx,
'dataset_idx':
self.dataset_idx,
'sensor_name':
self.get_current('datum_name', i),
#
'filename':
self.get_filename(idx, i),
'splitname':
'%s_%010d' % (self.split, idx),
#
'rgb':
self.get_current('rgb', i),
'intrinsics':
self.get_current('intrinsics', i),
'extrinsics':
self.get_current('extrinsics', i).matrix,
'path_to_ego_mask':
os.path.join(
os.path.dirname(self.path),
self._get_path_to_ego_mask(self.get_filename(idx, i))),
}
# If depth is returned
if self.with_depth:
data.update({
'depth':
self.generate_depth_map(idx, i, data['filename'])
})
# If pose is returned
if self.with_pose:
data.update({
'pose': self.get_current('pose', i).matrix,
})
if self.has_context:
orig_extrinsics = Pose.from_matrix(data['extrinsics'])
data.update({
'rgb_context':
self.get_context('rgb', i),
'intrinsics_context':
self.get_context('intrinsics', i),
'extrinsics_context':
[(extrinsics.inverse() * orig_extrinsics).matrix
for extrinsics in self.get_context('extrinsics', i)],
})
data.update({
'path_to_ego_mask_context': [
os.path.join(
os.path.dirname(self.path),
self._get_path_to_ego_mask(
self.get_filename(idx, i)))
for _ in range(len(data['rgb_context']))
],
})
data.update({
'context_type': [],
})
for _ in range(self.bwd):
data['context_type'].append('backward')
for _ in range(self.fwd):
data['context_type'].append('forward')
# If context pose is returned
if self.with_pose:
# Get original values to calculate relative motion
orig_pose = Pose.from_matrix(data['pose'])
data.update({
'pose_context':
[(orig_pose.inverse() * pose).matrix
for pose in self.get_context('pose', i)],
})
if self.with_geometric_context:
orig_extrinsics = data['extrinsics']
#orig_extrinsics[:3,3] = -np.dot(orig_extrinsics[:3,:3].transpose(), orig_extrinsics[:3,3])
orig_extrinsics_left = self.get_current_left(
'extrinsics', i_left).matrix
orig_extrinsics_right = self.get_current_right(
'extrinsics', i_right).matrix
#orig_extrinsics_left[:3,3] = -np.dot(orig_extrinsics_left[:3,:3].transpose(), orig_extrinsics_left[:3,3])
#orig_extrinsics_right[:3,3] = -np.dot(orig_extrinsics_right[:3,:3].transpose(), orig_extrinsics_right[:3,3])
orig_extrinsics = Pose.from_matrix(orig_extrinsics)
orig_extrinsics_left = Pose.from_matrix(orig_extrinsics_left)
orig_extrinsics_right = Pose.from_matrix(orig_extrinsics_right)
data['rgb_context'].append(self.get_current_left(
'rgb', i_left))
data['rgb_context'].append(
self.get_current_right('rgb', i_right))
data['intrinsics_context'].append(
self.get_current_left('intrinsics', i_left))
data['intrinsics_context'].append(
self.get_current_right('intrinsics', i_right))
data['extrinsics_context'].append(
(orig_extrinsics_left.inverse() * orig_extrinsics).matrix)
data['extrinsics_context'].append(
(orig_extrinsics_right.inverse() * orig_extrinsics).matrix)
#data['extrinsics_context'].append((orig_extrinsics.inverse() * orig_extrinsics_left).matrix)
#data['extrinsics_context'].append((orig_extrinsics.inverse() * orig_extrinsics_right).matrix)
data['path_to_ego_mask_context'].append(
os.path.join(
os.path.dirname(self.path),
self._get_path_to_ego_mask(
self.get_filename_left(idx, i_left))))
data['path_to_ego_mask_context'].append(
os.path.join(
os.path.dirname(self.path),
self._get_path_to_ego_mask(
self.get_filename_right(idx, i_right))))
data['context_type'].append('left')
data['context_type'].append('right')
data.update({
'sensor_name_left':
self.get_current_left('datum_name', i_left),
'sensor_name_right':
self.get_current_right('datum_name', i_right),
#
'filename_left':
self.get_filename_left(idx, i_left),
'filename_right':
self.get_filename_right(idx, i_right),
#
#'rgb_left': self.get_current_left('rgb', i),
#'rgb_right': self.get_current_right('rgb', i),
#'intrinsics_left': self.get_current_left('intrinsics', i),
#'intrinsics_right': self.get_current_right('intrinsics', i),
#'extrinsics_left': self.get_current_left('extrinsics', i).matrix,
#'extrinsics_right': self.get_current_right('extrinsics', i).matrix,
#'path_to_ego_mask_left': self._get_path_to_ego_mask(self.get_filename_left(idx, i)),
#'path_to_ego_mask_right': self._get_path_to_ego_mask(self.get_filename_right(idx, i)),
})
# data.update({
# 'extrinsics_context_left':
# [(orig_extrinsics_left.inverse() * extrinsics_left).matrix
# for extrinsics_left in self.get_context_left('extrinsics', i)],
# 'extrinsics_context_right':
# [(orig_extrinsics_right.inverse() * extrinsics_right).matrix
# for extrinsics_right in self.get_context_right('extrinsics', i)],
# 'intrinsics_context_left': self.get_context_left('intrinsics', i),
# 'intrinsics_context_right': self.get_context_right('intrinsics', i),
# })
sample.append(data)
# Apply same data transformations for all sensors
if self.data_transform:
sample = [self.data_transform(smp) for smp in sample]
# Return sample (stacked if necessary)
return stack_sample(sample)
def __init__(
self,
path,
split,
cameras=None,
depth_type=None,
with_pose=False,
with_semantic=False,
back_context=0,
forward_context=0,
data_transform=None,
with_geometric_context=False,
):
self.path = path
self.split = split
self.dataset_idx = 0
self.bwd = back_context
self.fwd = forward_context
self.has_context = back_context + forward_context > 0
self.with_geometric_context = with_geometric_context
self.num_cameras = len(cameras)
self.data_transform = data_transform
self.depth_type = depth_type
self.with_depth = depth_type is not None
self.with_pose = with_pose
self.with_semantic = with_semantic
# arrange cameras alphabetically
cameras = sorted(cameras)
cameras_left = list(cameras)
cameras_right = list(cameras)
for i_cam in range(self.num_cameras):
replaced = False
for k in cam_left_dict:
if not replaced and k in cameras_left[i_cam]:
cameras_left[i_cam] = cameras_left[i_cam].replace(
k, cam_left_dict[k])
replaced = True
replaced = False
for k in cam_right_dict:
if not replaced and k in cameras_right[i_cam]:
cameras_right[i_cam] = cameras_right[i_cam].replace(
k, cam_right_dict[k])
replaced = True
print(cameras)
print(cameras_left)
print(cameras_right)
# arrange cameras left and right and extract sorting indices
self.cameras_left_sort_idxs = list(np.argsort(cameras_left))
self.cameras_right_sort_idxs = list(np.argsort(cameras_right))
cameras_left_sorted = sorted(cameras_left)
cameras_right_sorted = sorted(cameras_right)
self.dataset = SynchronizedSceneDataset(
path,
split=split,
datum_names=cameras,
backward_context=back_context,
forward_context=forward_context,
requested_annotations=None,
only_annotated_datums=False,
)
if self.with_geometric_context:
self.dataset_left = SynchronizedSceneDataset(
path,
split=split,
datum_names=cameras_left_sorted,
backward_context=back_context,
forward_context=forward_context,
requested_annotations=None,
only_annotated_datums=False,
)
self.dataset_right = SynchronizedSceneDataset(
path,
split=split,
datum_names=cameras_right_sorted,
backward_context=back_context,
forward_context=forward_context,
requested_annotations=None,
only_annotated_datums=False,
)
def test_labeled_synchronized_scene_dataset(self):
"""Test synchronized scene dataset"""
expected_camera_fields = set([
'rgb', 'timestamp', 'datum_name', 'pose', 'intrinsics',
'extrinsics', 'bounding_box_2d', 'bounding_box_3d', 'class_ids',
'instance_ids', 'depth'
])
expected_lidar_fields = set([
'point_cloud', 'timestamp', 'datum_name', 'pose', 'extrinsics',
'bounding_box_3d', 'class_ids', 'instance_ids', 'extra_channels'
])
expected_metadata_fields = set([
'scene_index', 'sample_index_in_scene', 'log_id', 'timestamp',
'scene_name', 'scene_description'
])
# Initialize synchronized dataset with 2 datums
scenes_dataset_json = os.path.join(self.DGP_TEST_DATASET_DIR,
"test_scene",
"scene_dataset_v1.0.json")
dataset = SynchronizedSceneDataset(
scenes_dataset_json,
split='train',
forward_context=1,
backward_context=1,
generate_depth_from_datum='LIDAR',
requested_annotations=("bounding_box_2d", "bounding_box_3d"))
dataset.select_datums(['LIDAR', 'CAMERA_01'])
dataset.prefetch()
# There are only 3 samples in the train and val split.
# With a forward and backward context of 1 each, the number of
# items in the dataset with the desired context frames is 1.
assert len(dataset) == 2
# Iterate through labeled dataset and check expected fields
assert dataset.calibration_table is not None
for idx, item in enumerate(dataset):
# Context size is 3 (forward + backward + reference)
assert_true(len(item) == 3)
# Two selected datums
for t_item in item:
assert_true(len(t_item) == 2)
# LIDAR should have point_cloud set
for t_item in item:
assert_true(set(t_item[0].keys()) == expected_lidar_fields)
assert_true(isinstance(t_item[0], OrderedDict))
# CAMERA_01 should have intrinsics/extrinsics set
im_size = None
for t_item in item:
assert_true(isinstance(t_item[1], OrderedDict))
assert_true(t_item[1]['intrinsics'].shape == (3, 3))
assert_true(isinstance(t_item[1]['extrinsics'], Pose))
assert_true(isinstance(t_item[1]['pose'], Pose))
# Check image sizes for context frames
assert_true(set(t_item[1].keys()) == expected_camera_fields)
if im_size is None:
im_size = t_item[1]['rgb'].size
assert_true(t_item[1]['rgb'].size == im_size)
# Retrieve metadata about dataset item at index=idx
metadata = dataset.get_scene_metadata(idx)
assert_true(metadata.keys() == expected_metadata_fields)
# Make sure you cannot select unavailable datums
with assert_raises(AssertionError) as _:
dataset.select_datums(['FAKE_LIDAR_NAME'])
# Synchronized dataset with all available datums within a sample
dataset_args = dict(backward_context=0,
forward_context=0,
requested_annotations=("bounding_box_3d",
"bounding_box_2d"))
if args.dataset_json:
logging.info('dataset-json mode: Using split {}'.format(args.split))
dataset = SynchronizedDataset(args.dataset_json,
split=args.split,
**dataset_args)
elif args.scene_dataset_json:
logging.info('scene-dataset-json mode: Using split {}'.format(
args.split))
dataset = SynchronizedSceneDataset(args.scene_dataset_json,
split=args.split,
**dataset_args)
elif args.scene_json:
logging.info('scene-json mode: Split value ignored')
# Fetch scene from S3 to cache if remote scene JSON provided
if args.scene_json.startswith('s3://'):
args.scene_json = fetch_remote_scene(args.scene_json)
dataset = SynchronizedScene(args.scene_json, **dataset_args)
else:
raise ValueError('Provide either --dataset-json or --scene-json')
if args.point_cloud_only:
dataset.select_datums(datum_names=['LIDAR'])
logging.info('Dataset: {}'.format(len(dataset)))
# 2D visualization