def gen_scene_splits(dataroot: str):
"""
Retruns all nuScenes scene splits by scene token, as specified in https://github.com/nutonomy/nuscenes-devkit/blob/master/python-sdk/nuscenes/utils/splits.py.
Arguments:
dataroot: Directory path of the nuScenes datasets, <str>.
"""
# Imports
from nuscenes.nuscenes import NuScenes
from nuscenes.utils.splits import create_splits_scenes
# Define
scene_splits = create_splits_scenes()
# Load trainval
version = 'v1.0-trainval'
nusc = NuScenes(version=version, dataroot=dataroot + version, verbose=False)
for split, scene_names in scene_splits.items():
if split == 'test':
continue
else:
scene_splits[split] = [nusc.field2token('scene', 'name', scene_name)[0] for scene_name in scene_names]
# Load test
version = 'v1.0-test'
nusc = NuScenes(version=version, dataroot=dataroot + version, verbose=False)
scene_splits['test'] = [nusc.field2token('scene', 'name', scene_name)[0] for scene_name in scene_splits['test']]
return scene_splits
def __init__(self,
nusc_root,
nusc_version,
split,
max_cam_sweeps=6,
max_lidar_sweeps=10,
max_radar_sweeps=6,
logging_level="INFO",
logger=None,
nusc=None):
"""
Image database object that holds the sample data tokens for the nuscenes
dataset.
:param root_path: location of the nuscenes dataset
:param nusc_version: the version of the dataset to use ('v1.0-trainval',
'v1.0-test', 'v1.0-mini')
:param max_cam_sweeps: number of sweep tokens to return for each camera
:param max_lidar_sweeps: number of sweep tokens to return for lidar
:param max_radar_sweeps: number of sweep tokens to return for each radar
"""
self.nusc_root = nusc_root
self.nusc_version = nusc_version
self.split = split
self.max_cam_sweeps = max_cam_sweeps
self.max_lidar_sweeps = max_lidar_sweeps
self.max_radar_sweeps = max_radar_sweeps
self.id_length = 8
self.db = {}
assert nusc_version in constants.NUSCENES_SPLITS.keys(), \
"Nuscenes version not valid."
assert split in constants.NUSCENES_SPLITS[nusc_version], \
"Nuscenes split ({}) is not valid for {}".format(split, nusc_version)
if logger is None:
self.logger = logging.initialize_logger('pynuscenes',
logging_level)
else:
self.logger = logger
if nusc is not None:
if self.nusc.version != nusc_version:
self.logger.info(
'Loading nuscenes {} dataset'.format(nusc_version))
self.nusc = NuScenes(version=nusc_version,
dataroot=self.nusc_root,
verbose=True)
else:
self.nusc = nusc
else:
self.logger.info(
'Loading nuscenes {} dataset'.format(nusc_version))
self.nusc = NuScenes(version=nusc_version,
dataroot=self.nusc_root,
verbose=True)
self.SENSOR_NAMES = [x['channel'] for x in self.nusc.sensor]
def main():
#Instantiate an object of the NuScenes dataset class
nusc = NuScenes(version='v1.0-mini', dataroot='/home/odysseas/thesis/data/sets/nuscenes_mini/', verbose=True)
#Load front_rad sample_data tokens
rad_sd_tokens = load_keyframe_rad_tokens(nusc)
#Load actual sample_data
radar_pointclouds = tokens_to_data(nusc, rad_sd_tokens)
depths_list = []
#Load x (front/depth) value of all radar data
depths_sum = 0.0
total_number_of_detections = 0
for pcl in range(len(radar_pointclouds)):
number_of_detections = len(radar_pointclouds[pcl].points)
total_number_of_detections += number_of_detections
for detection in range(len(radar_pointclouds[pcl].points)):
#radar_pointclouds[pcl].points = np.delete(radar_pointclouds[pcl].points, np.argwhere(radar_pointclouds[pcl].points[0] > 100))
depth = radar_pointclouds[pcl].points[detection][0]
depths_list.append(depth)
depths_sum += depth
depths_avg = depths_sum / total_number_of_detections
print('Total number of radar points detected across all scenes: {}'.format(total_number_of_detections))
print('Average depth for all radar detections across all scenes: {}'.format(depths_avg))
plt.hist(depths_list, bins = 100)
plt.title("Histogram of radar depths")
plt.ylabel("Number of detections")
plt.xlabel("Depth")
plt.xticks((np.arange(0, max(depths_list)+1, 20)))
plt.savefig('Radar_Depths_Histogram.png', bbox_inches='tight')
def main():
root_path = '/extssd/jiaxin/nuscenes/test'
nusc = NuScenes(version='v1.0-test', dataroot=root_path, verbose=True)
sensor = 'CAM_FRONT'
counter = 0
for i, scene in enumerate(nusc.scene):
scene_token = scene['token']
scene = nusc.get('scene', scene_token)
first_sample = nusc.get('sample', scene['first_sample_token'])
camera = nusc.get('sample_data', first_sample['data'][sensor])
img = np.array(
Image.open(os.path.join(nusc.dataroot,
camera['filename'])).convert('L'))
H, W = img.shape[0], img.shape[1]
img_mean = np.mean(img.astype(np.float32))
white_mask = img > 150
white_area = np.sum(white_mask.astype(np.float32))
if img_mean < 110 and white_area < (H * W) * 0.1:
print('\'%s\',' % (scene_token))
counter += 1
plt.figure()
plt.gray()
plt.imshow(img)
plt.show()
print('%d night scenes' % counter)
def compile_data(version, dataroot, data_aug_conf, grid_conf, bsz, nworkers,
parser_name):
nusc = NuScenes(version='v1.0-{}'.format(version),
dataroot=os.path.join(dataroot, version),
verbose=False)
parser = {
'vizdata': VizData,
'segmentationdata': SegmentationData,
}[parser_name]
traindata = parser(nusc,
is_train=True,
data_aug_conf=data_aug_conf,
grid_conf=grid_conf)
valdata = parser(nusc,
is_train=False,
data_aug_conf=data_aug_conf,
grid_conf=grid_conf)
trainloader = torch.utils.data.DataLoader(traindata,
batch_size=bsz,
shuffle=True,
num_workers=nworkers,
drop_last=True,
worker_init_fn=worker_rnd_init)
valloader = torch.utils.data.DataLoader(valdata,
batch_size=bsz,
shuffle=False,
num_workers=nworkers)
return trainloader, valloader
def test_egoposes_on_map(self):
""" Test that all ego poses land on """
nusc = NuScenes(version=self.version,
dataroot=os.environ['NUSCENES'],
verbose=False)
whitelist = [
'scene-0499', 'scene-0501', 'scene-0502', 'scene-0515',
'scene-0517'
]
invalid_scenes = []
for scene in tqdm.tqdm(nusc.scene, leave=False):
if scene['name'] in whitelist:
continue
log = nusc.get('log', scene['log_token'])
map_name = log['location']
nusc_map = self.nusc_maps[map_name]
ratio_valid = get_egoposes_on_drivable_ratio(
nusc, nusc_map, scene['token'])
if ratio_valid != 1.0:
print(
'Error: Scene %s has a ratio of %f ego poses on the driveable area!'
% (scene['name'], ratio_valid))
invalid_scenes.append(scene['name'])
self.assertEqual(len(invalid_scenes), 0)
def merge_depth_sf(depth_meta_path,
sf_meta_path, save_path):
with open(depth_meta_path, 'r') as f:
depth_meta = json.load(f)
with open(sf_meta_path, 'r') as f:
sf_meta = json.load(f)
split = 'train'
data_path = 'data/nuscenes/'
nusc = NuScenes(
version=SPLITS[split], dataroot=data_path, verbose=True)
imgpath2paths = {}
for depth_info in depth_meta:
sample_token = depth_info['sample_token']
depth_path = depth_info['depth_path']
img_path = depth_info['img_path']
cam_name = img_path.split('/')[-2]
cam_token = nusc.get('sample', sample_token)['data'][cam_name]
sf_path = sf_meta[cam_token]['points_path']
img_path = sf_meta[cam_token]['img_path'] # use this version of img path
tmp = {'token': cam_token, 'depth_path': depth_path,
'cam_name': cam_name, 'sf_path': sf_path,
'img_path': img_path}
imgpath2paths[img_path] = tmp
with open(save_path, 'w') as f:
json.dump(imgpath2paths, f)
def __init__(self, set_name="mini_train"):
#assert statements
set_paths = [
'train', 'val', 'test', 'mini_train', 'mini_val', 'train_detect',
'train_track'
]
assert set_name in set_paths, "Incorrect set_name"
#Initialize data and Prediction Helper classes
self.data_path = DATA_PATH
self.nusc = NuScenes(version=DATA_VERSION,
dataroot=self.data_path,
verbose=True)
self.helper = PredictHelper(self.nusc)
#get all the scenes
self.scenes = create_splits_scenes()
#get all the scenes in the trainset
self.set_name = set_name
self.trainset = self.scenes[
self.set_name] #List of scenes as part of training set
self.prediction_scenes = json.load(
open(self.data_path + "maps/prediction_scenes.json", "r")
) #Dictionary containing list of instance and sample tokens for each scene
print("Number of samples in train set: %d" % (len(self.trainset)))
def __init__(self,
nuscenes_root,
version="v1.0-trainval",
max_scenes=None,
*,
read_radar: bool = True,
read_camera: bool = True,
read_semantics: bool = True,
read_bounding_boxes: bool = True):
self.nusc = NuScenes(version=version,
dataroot=nuscenes_root,
verbose=False)
self.root = pathlib.Path(nuscenes_root)
# global counter to sanity-check if we calculate the same number of points
# within boxes as the dataset authors
self.ns_lidar_pts_to_calculated_diff = 0
# flags defining the data entries to return from 'read'
self.read_radar = read_radar
self.read_camera = read_camera
self.read_semantics = read_semantics
self.read_bounding_boxes = read_bounding_boxes
if self.read_semantics and not hasattr(self.nusc, "lidarseg"):
raise RuntimeError("Error: nuScenes-lidarseg not installed!")
# assert that the training targets range from 0 - (|mapping| - 1)
assert len(set(
NUSCENES_SEM_CLASSES.values())) == len(NUSCENES_SEM_CLASSES)
assert all(a == b
for a, b in zip(sorted(NUSCENES_SEM_CLASSES.values()),
range(len(NUSCENES_SEM_CLASSES))))
split_name = {
"v1.0-trainval": "nuscenes_default",
"v1.0-mini": "nuscenes_mini",
}.get(version, "nuscenes_{}".format(version))
# create split dict
self.split = {
"name": split_name,
"data": {k: []
for k in self.scene_split_lists.keys()},
}
for i, scene in enumerate(self.nusc.scene):
if max_scenes is not None and i > max_scenes:
break
name = scene["name"]
for k, v in self.scene_split_lists.items():
if name in v:
split_list = self.split["data"][k]
split_list.extend([
self.sample_id_template.format(name, i)
for i in range(0, scene["nbr_samples"])
])
break
else:
raise RuntimeError(
"Found scene that is not in a split: {}".format(name))
def main():
nusc = NuScenes(version='v1.0-mini',
dataroot='/home/odysseas/thesis/data/sets/nuscenes/',
verbose=True)
#Fix sensor.json file
sensor = nusc.sensor
sensor[:] = [
record for record in sensor if (record['channel'] == "CAM_FRONT") or (
record['channel'] == "RADAR_FRONT")
]
with open('./sensor.json', 'w') as fout:
json.dump(sensor, fout, indent=0)
cam_front_token = sensor[0]["token"]
radar_front_token = sensor[1]["token"]
#Fix calibrated_sensor.json file
calibrated_sensor = nusc.calibrated_sensor
calibrated_sensor[:] = [
record for record in calibrated_sensor
if (record['sensor_token'] == cam_front_token) or (
record['sensor_token'] == radar_front_token)
]
with open('./calibrated_sensor.json', 'w') as fout:
json.dump(calibrated_sensor, fout, indent=0)
def __init__(self,
nusc_kitti_dir: str = '/home/developer/nuscenes/nusc_kitti',
cam_name: str = 'CAM_FRONT',
lidar_name: str = 'LIDAR_TOP',
image_count: int = 10,
nusc_version: str = 'v1.0-mini',
split: str = 'mini_train'):
"""
:param nusc_kitti_dir: Where to write the KITTI-style annotations.
:param cam_name: Name of the camera to export. Note that only one camera is allowed in KITTI.
:param lidar_name: Name of the lidar sensor.
:param image_count: Number of images to convert.
:param nusc_version: nuScenes version to use.
:param split: Dataset split to use.
"""
self.nusc_kitti_dir = os.path.expanduser(nusc_kitti_dir)
self.cam_name = cam_name
self.lidar_name = lidar_name
self.image_count = image_count
self.nusc_version = nusc_version
self.split = split
# Create nusc_kitti_dir.
if not os.path.isdir(self.nusc_kitti_dir):
os.makedirs(self.nusc_kitti_dir)
# Select subset of the data to look at.
self.nusc = NuScenes(version=nusc_version,
dataroot='/home/developer/nuscenes')
def create_tf_record_train_as_val(fn_out, split, vis_results):
label_map_dict = label_map_util.get_label_map_dict(FLAGS.label_map)
writer = tf.python_io.TFRecordWriter(fn_out)
params = read_params(FLAGS.param)
logging.debug('Params: ' + str(params))
nusc = NuScenes(version='v1.0-trainval', dataroot=FLAGS.nuscenes, verbose=True)
sensor = 'LIDAR_TOP'
nu_to_kitti_lidar = Quaternion(axis=(0, 0, 1), angle=np.pi / 2).inverse
split_logs = create_splits_logs(split, nusc)
sample_tokens = split_to_samples(nusc, split_logs)
random.shuffle(sample_tokens)
print('Number of samples:', len(sample_tokens))
for sample_token in sample_tokens[1:100]:
sample = nusc.get('sample', sample_token)
lidar_top_data = nusc.get('sample_data', sample['data'][sensor])
if not lidar_top_data['prev']:
continue
lidar_top_data_prev = nusc.get('sample_data', lidar_top_data['prev'])
labels_corners, labels_center, labels_data = compute_labels_image(nusc, sample, sensor,
nu_to_kitti_lidar, params)
filename = os.path.splitext(os.path.splitext(lidar_top_data['filename'])[0])[0]
filename_prev = os.path.splitext(os.path.splitext(lidar_top_data_prev['filename'])[0])[0]
tf_example = dict_to_tf_example(labels_corners, labels_center, labels_data, params, label_map_dict,
FLAGS.data, FLAGS.data_beliefs, filename, filename_prev)
writer.write(tf_example.SerializeToString())
if (vis_results):
visualize_results(FLAGS.data, filename, labels_corners, os.path.join(FLAGS.output, 'Debug'))
def export_2d_annotation(root_path, info_path, version, mono3d=True):
"""Export 2d annotation from the info file and raw data.
Args:
root_path (str): Root path of the raw data.
info_path (str): Path of the info file.
version (str): Dataset version.
mono3d (bool): Whether to export mono3d annotation. Default: True.
"""
# get bbox annotations for camera
camera_types = [
'CAM_FRONT',
'CAM_FRONT_RIGHT',
'CAM_FRONT_LEFT',
'CAM_BACK',
'CAM_BACK_LEFT',
'CAM_BACK_RIGHT',
]
nusc_infos = mmcv.load(info_path)['infos']
nusc = NuScenes(version=version, dataroot=root_path, verbose=True)
# info_2d_list = []
cat2Ids = [
dict(id=nus_categories.index(cat_name), name=cat_name)
for cat_name in nus_categories
]
coco_ann_id = 0
coco_2d_dict = dict(annotations=[], images=[], categories=cat2Ids)
for info in mmcv.track_iter_progress(nusc_infos):
for cam in camera_types:
cam_info = info['cams'][cam]
coco_infos = get_2d_boxes(nusc,
cam_info['sample_data_token'],
visibilities=['', '1', '2', '3', '4'],
mono3d=mono3d)
(height, width, _) = mmcv.imread(cam_info['data_path']).shape
coco_2d_dict['images'].append(
dict(file_name=cam_info['data_path'].split('data/nuscenes/')
[-1],
id=cam_info['sample_data_token'],
token=info['token'],
cam2ego_rotation=cam_info['sensor2ego_rotation'],
cam2ego_translation=cam_info['sensor2ego_translation'],
ego2global_rotation=info['ego2global_rotation'],
ego2global_translation=info['ego2global_translation'],
cam_intrinsic=cam_info['cam_intrinsic'],
width=width,
height=height))
for coco_info in coco_infos:
if coco_info is None:
continue
# add an empty key for coco format
coco_info['segmentation'] = []
coco_info['id'] = coco_ann_id
coco_2d_dict['annotations'].append(coco_info)
coco_ann_id += 1
if mono3d:
json_prefix = f'{info_path[:-4]}_mono3d'
else:
json_prefix = f'{info_path[:-4]}'
mmcv.dump(coco_2d_dict, f'{json_prefix}.coco.json')
def factory(dataset, dir_nuscenes):
"""Define dataset type and split training and validation"""
assert dataset in ['nuscenes', 'nuscenes_mini', 'nuscenes_teaser']
if dataset == 'nuscenes_mini':
version = 'v1.0-mini'
else:
version = 'v1.0-trainval'
nusc = NuScenes(version=version, dataroot=dir_nuscenes, verbose=True)
scenes = nusc.scene
if dataset == 'nuscenes_teaser':
with open("splits/nuscenes_teaser_scenes.txt", "r") as file:
teaser_scenes = file.read().splitlines()
scenes = [scene for scene in scenes if scene['token'] in teaser_scenes]
with open("splits/split_nuscenes_teaser.json", "r") as file:
dic_split = json.load(file)
split_train = [
scene['name'] for scene in scenes
if scene['token'] in dic_split['train']
]
split_val = [
scene['name'] for scene in scenes
if scene['token'] in dic_split['val']
]
else:
split_scenes = splits.create_splits_scenes()
split_train, split_val = split_scenes['train'], split_scenes['val']
return nusc, scenes, split_train, split_val
def prep_list_of_sessions(self):
if self.phase in ['train', 'validation']:
version = 'v1.0-trainval'
elif self.phase == 'test':
version = 'v1.0-test'
self.NuScenes_data = NuScenes(version=version,
dataroot=DATASET_ROOT,
verbose=True)
self.num_sessions = len(self.NuScenes_data.scene)
self.cloud_tokens = []
self.session_lengths = []
self.session_locations = []
self.session_names = []
for session_ind in range(self.num_sessions):
record = self.NuScenes_data.scene[session_ind]
session_token = record['token']
self.session_names.append(record['name'])
location = self.NuScenes_data.get('log',
record['log_token'])['location']
self.session_locations.append(location)
sample_token = record["first_sample_token"]
sample = self.NuScenes_data.get("sample", sample_token)
lidar_token = sample["data"]["LIDAR_TOP"]
cur_lidar_tokens = []
while len(lidar_token) > 0:
cur_lidar_tokens.append(lidar_token)
lidar_data = self.NuScenes_data.get("sample_data", lidar_token)
lidar_token = lidar_data["next"]
self.cloud_tokens.append(cur_lidar_tokens)
self.session_lengths.append(len(cur_lidar_tokens))
def __init__(self, root, mode, opt: options.Options):
super(nuScenesLoader, self).__init__()
self.root = root
self.opt = opt
self.mode = mode
# farthest point sample
self.farthest_sampler = FarthestSampler(dim=3)
# list of (traversal, pc_timestamp, pc_timestamp_idx, traversal_pc_num)
if mode == 'train':
self.nuscenes_path = os.path.join(root, 'trainval')
version = 'v1.0-trainval'
else:
self.nuscenes_path = os.path.join(root, 'test')
version = 'v1.0-test'
self.dataset = make_nuscenes_dataset(self.nuscenes_path)
self.nusc = NuScenes(version=version,
dataroot=self.nuscenes_path,
verbose=True)
self.camera_name_list = [
'CAM_FRONT', 'CAM_FRONT_LEFT', 'CAM_FRONT_RIGHT', 'CAM_BACK',
'CAM_BACK_LEFT', 'CAM_BACK_RIGHT'
]
def evaluation(self, det_annos, class_names, **kwargs):
import json
from nuscenes.nuscenes import NuScenes
from . import nuscenes_utils
nusc = NuScenes(version=self.dataset_cfg.VERSION,
dataroot=str(self.root_path),
verbose=True)
nusc_annos = nuscenes_utils.transform_det_annos_to_nusc_annos(
det_annos, nusc)
nusc_annos['meta'] = {
'use_camera': False,
'use_lidar': True,
'use_radar': False,
'use_map': False,
'use_external': False,
}
output_path = Path(kwargs['output_path'])
output_path.mkdir(exist_ok=True, parents=True)
res_path = str(output_path / 'results_nusc.json')
with open(res_path, 'w') as f:
json.dump(nusc_annos, f)
self.logger.info(
f'The predictions of NuScenes have been saved to {res_path}')
if self.dataset_cfg.VERSION == 'v1.0-test':
return 'No ground-truth annotations for evaluation', {}
from nuscenes.eval.detection.config import config_factory
from nuscenes.eval.detection.evaluate import NuScenesEval
eval_set_map = {
'v1.0-mini': 'mini_val',
'v1.0-trainval': 'val',
'v1.0-test': 'test'
}
try:
eval_version = 'detection_cvpr_2019'
eval_config = config_factory(eval_version)
except:
eval_version = 'cvpr_2019'
eval_config = config_factory(eval_version)
nusc_eval = NuScenesEval(
nusc,
config=eval_config,
result_path=res_path,
eval_set=eval_set_map[self.dataset_cfg.VERSION],
output_dir=str(output_path),
verbose=True,
)
metrics_summary = nusc_eval.main(plot_examples=0, render_curves=False)
with open(output_path / 'metrics_summary.json', 'r') as f:
metrics = json.load(f)
result_str, result_dict = nuscenes_utils.format_nuscene_results(
metrics, self.class_names, version=eval_version)
return result_str, result_dict
def quick_test(dataroot='/data/nuscenes', gpuid=0, nworkers=10):
"""Evaluate detections with PKL.
"""
nusc = NuScenes(version='v1.0-mini', dataroot=dataroot, verbose=True)
nusc_maps = get_nusc_maps(dataroot)
cfg = config_factory('detection_cvpr_2019')
device = torch.device(f'cuda:{gpuid}') if gpuid >= 0\
else torch.device('cpu')
print(f'using device: {device}')
get_example_submission()
nusc_eval = DetectionEval(nusc,
config=cfg,
result_path='./example_submission.json',
eval_set='mini_train',
output_dir='./res',
verbose=True)
info = calculate_pkl(nusc_eval.gt_boxes,
nusc_eval.pred_boxes,
nusc_eval.sample_tokens,
nusc_eval.nusc,
nusc_maps,
device,
nworkers,
bsz=128,
plot_kextremes=5,
verbose=True)
print({k: v for k, v in info.items() if k != 'full'})
def create_nuscenes_info(version, data_path, save_path, max_sweeps=10):
from nuscenes.nuscenes import NuScenes
from nuscenes.utils import splits
from . import nuscenes_utils
data_path = data_path / version / 'v1.0-trainval_meta' # change data path
save_path = save_path / version
assert version in ['v1.0-trainval', 'v1.0-test', 'v1.0-mini']
if version == 'v1.0-trainval':
train_scenes = splits.train
val_scenes = splits.val
elif version == 'v1.0-test':
train_scenes = splits.test
val_scenes = []
elif version == 'v1.0-mini':
train_scenes = splits.mini_train
val_scenes = splits.mini_val
else:
raise NotImplementedError
nusc = NuScenes(version=version, dataroot=data_path, verbose=True)
available_scenes = nuscenes_utils.get_available_scenes(nusc)
available_scene_names = [s['name'] for s in available_scenes]
train_scenes = list(
filter(lambda x: x in available_scene_names, train_scenes))
val_scenes = list(filter(lambda x: x in available_scene_names, val_scenes))
train_scenes = set([
available_scenes[available_scene_names.index(s)]['token']
for s in train_scenes
])
val_scenes = set([
available_scenes[available_scene_names.index(s)]['token']
for s in val_scenes
])
print('%s: train scene(%d), val scene(%d)' %
(version, len(train_scenes), len(val_scenes)))
train_nusc_infos, val_nusc_infos = nuscenes_utils.fill_trainval_infos(
data_path=data_path,
nusc=nusc,
train_scenes=train_scenes,
val_scenes=val_scenes,
test='test' in version,
max_sweeps=max_sweeps)
if version == 'v1.0-test':
print('test sample: %d' % len(train_nusc_infos))
with open(save_path / f'nuscenes_infos_{max_sweeps}sweeps_test.pkl',
'wb') as f:
pickle.dump(train_nusc_infos, f)
else:
print('train sample: %d, val sample: %d' %
(len(train_nusc_infos), len(val_nusc_infos)))
with open(save_path / f'nuscenes_infos_{max_sweeps}sweeps_train.pkl',
'wb') as f:
pickle.dump(train_nusc_infos, f)
with open(save_path / f'nuscenes_infos_{max_sweeps}sweeps_val.pkl',
'wb') as f:
pickle.dump(val_nusc_infos, f)
def get_pose_intrinsic(
save_path='/public/MARS/datasets/nuScenes-SF/meta/cam_pose_intrinsic.json'
):
split = 'train'
data_path = 'data/nuscenes/'
nusc = NuScenes(version=SPLITS[split], dataroot=data_path, verbose=True)
samples = nusc.sample
cam_token2cam_intext = {}
for sample in tqdm(samples):
for cam_name in CamNames:
cam_token = sample['data'][cam_name]
cam_data = nusc.get('sample_data', cam_token)
ego_pose = nusc.get('ego_pose', cam_data['ego_pose_token'])
cam_cs = nusc.get('calibrated_sensor',
cam_data['calibrated_sensor_token'])
# used to transform from ego to global
pose_matrix = quat_trans2matrix(ego_pose['rotation'],
ego_pose['translation'])
# used to transform from cameral to ego
cam_pose = quat_trans2matrix(cam_cs['rotation'],
cam_cs['translation'])
cam_pose_world = np.matmul(pose_matrix, cam_pose)
ret = {'pose': cam_pose_world.tolist()}
ret['intrinsic'] = cam_cs['camera_intrinsic']
cam_token2cam_intext[cam_token] = ret
with open(save_path, 'w') as f:
json.dump(cam_token2cam_intext, f)
def get_boxes2d(sample_data_token: str, image_annotations_token2ind = {}, image_annotations = []):
nusc = NuScenes(version='v1.0-mini', dataroot='dataset/nuScenes/v1.0-mini', verbose=True)
# Retrieve sensor & pose records
sd_record = nusc.get('sample_data', sample_data_token)
curr_sample_record = nusc.get('sample', sd_record['sample_token'])
#curr_sample_record['image_anns']
boxes2d = []
if curr_sample_record['prev'] == "" or sd_record['is_key_frame']:
# If no previous annotations available, or if sample_data is keyframe just return the current ones.
for i,x in enumerate(curr_sample_record['anns']):
record = nusc.get('sample_annotation', x)
instance_token = record['instance_token']
record2d = image_annotations[image_annotations_token2ind[instance_token]]
box2d = Box2d(record2d['bbox_corners'], name=record2d['category_name'], token=record2d['sample_annotation_token'],
visibility=record2d['visibility_token'],filename=record2d['filename'])
boxes2d.append(box2d)
else:
prev_sample_record = nusc.get('sample', curr_sample_record['prev'])
curr_ann_recs = [nusc.get('sample_annotation', token) for token in curr_sample_record['anns']]
prev_ann_recs = [nusc.get('sample_annotation', token) for token in prev_sample_record['anns']]
# Maps instance tokens to prev_ann records
prev_inst_map = {entry['instance_token']: entry for entry in prev_ann_recs}
t0 = prev_sample_record['timestamp']
t1 = curr_sample_record['timestamp']
t = sd_record['timestamp']
# There are rare situations where the timestamps in the DB are off so ensure that t0 < t < t1.
t = max(t0, min(t1, t))
boxes = []
for curr_ann_rec in curr_ann_recs:
if curr_ann_rec['instance_token'] in prev_inst_map:
# If the annotated instance existed in the previous frame, interpolate center & orientation.
prev_ann_rec = prev_inst_map[curr_ann_rec['instance_token']]
# Interpolate center.
center = [np.interp(t, [t0, t1], [c0, c1]) for c0, c1 in zip(prev_ann_rec['translation'],
curr_ann_rec['translation'])]
# Interpolate orientation.
rotation = Quaternion.slerp(q0=Quaternion(prev_ann_rec['rotation']),
q1=Quaternion(curr_ann_rec['rotation']),
amount=(t - t0) / (t1 - t0))
box = Box(center, curr_ann_rec['size'], rotation, name=curr_ann_rec['category_name'],
token=curr_ann_rec['token'])
else:
# If not, simply grab the current annotation.
box = self.get_box(curr_ann_rec['token'])
boxes.append(box)
return boxes2d
def init_nu():
""" -------------------------------------------------------------------------------------------------------------
Initialize nuScene
return: [tuple] NuScenes instance, NuScenesCanBus instance
------------------------------------------------------------------------------------------------------------- """
nu = NuScenes( version=nuscenes_ver, dataroot=dir_orig, verbose=verbose ) # NuScenes database
nc = NuScenesCanBus( dataroot=dir_orig ) # NuScenes CAN bus
return nu, nc
def __init__(self,
data_root,
filenames,
height,
width,
frame_idxs,
num_scales,
version='v1.0-mini',
sensor='CAM_FRONT',
is_train=False,
img_ext='.jpg'):
super(NuscDataset, self).__init__()
self.data_path = data_root
self.data_path = '/share/nuscenes'
self.filenames = filenames
self.height = height
self.width = width
self.num_scales = num_scales
self.interp = Image.ANTIALIAS
self.frame_idxs = frame_idxs
self.is_train = is_train
self.img_ext = img_ext
self.loader = pil_loader
self.to_tensor = transforms.ToTensor()
self.nusc = NuScenes(version=version,
dataroot=self.data_path,
verbose=True)
self.sensor = sensor
self.data_root = '/share/nuscenes'
self.full_res_shape = (1600, 640)
# We need to specify augmentations differently in newer versions of torchvision.
# We first try the newer tuple version; if this fails we fall back to scalars
try:
self.brightness = (0.8, 1.2)
self.contrast = (0.8, 1.2)
self.saturation = (0.8, 1.2)
self.hue = (-0.1, 0.1)
transforms.ColorJitter.get_params(self.brightness, self.contrast,
self.saturation, self.hue)
except TypeError:
self.brightness = 0.2
self.contrast = 0.2
self.saturation = 0.2
self.hue = 0.1
self.resize = {}
for i in range(self.num_scales):
s = 2**i
self.resize[i] = transforms.Resize(
(self.height // s, self.width // s), interpolation=self.interp)
self.load_depth = self.check_depth()
def create_nuscenes(root: str, version: str = "v1.0-trainval") -> NuScenes:
"""
Creates nuScenes object that can be later passed to NuscenesBEVDataset.
Warning, it takes up a considerable abound of RAM space.
:param root: path to folder with nuscenes dataset
:param version: version of the dataset
:return: created NuScenes object
"""
return NuScenes(dataroot=root, version=version)
def __init__(self):
self.nusc = NuScenes(version='v1.0-mini',
dataroot='../data/sets/nuscenes',
verbose=False)
self.sceneID = 0
self.scene = self.nusc.scene[self.sceneID]
self.current_sample = self.nusc.get('sample',
self.scene['first_sample_token'])
print('Data Reader Initialized')
def __init__(self, config={}):
self.config = {
'version': 'v1.0-mini',
'dataroot': os.path.join(os.environ['PKG_PATH'],'data')
}
self.config.update(config)
self.nusc = NuScenes(version=self.config['version'], dataroot=self.config['dataroot'], verbose=True)
self.nusc_can = NuScenesCanBus(dataroot=self.config['dataroot'])
self.utils = Utils(self.nusc, self.nusc_can)
def _test_visual(idx=0):
split = 'train'
data_path = 'data/nuscenes/'
nusc = NuScenes(version=SPLITS[split], dataroot=data_path, verbose=True)
samples = nusc.sample
sample = samples[idx]
sample_token = sample['token']
#tp_parse_sample(sample_token, nusc)
visualize_sample(sample_token, nusc)
embed()
def export_2d_annotation(root_path, info_path, version):
"""Export 2d annotation from the info file and raw data.
Args:
root_path (str): Root path of the raw data.
info_path (str): Path of the info file.
version (str): Dataset version.
"""
# get bbox annotations for camera
camera_types = [
"CAM_FRONT",
"CAM_FRONT_RIGHT",
"CAM_FRONT_LEFT",
"CAM_BACK",
"CAM_BACK_LEFT",
"CAM_BACK_RIGHT",
]
nusc_infos = mmcv.load(info_path)["infos"]
nusc = NuScenes(version=version, dataroot=root_path, verbose=True)
# info_2d_list = []
cat2Ids = [
dict(id=nus_categories.index(cat_name), name=cat_name)
for cat_name in nus_categories
]
coco_ann_id = 0
coco_2d_dict = dict(annotations=[], images=[], categories=cat2Ids)
for info in mmcv.track_iter_progress(nusc_infos):
for cam in camera_types:
cam_info = info["cams"][cam]
coco_infos = get_2d_boxes(
nusc,
cam_info["sample_data_token"],
visibilities=["", "1", "2", "3", "4"],
)
(height, width, _) = mmcv.imread(cam_info["data_path"]).shape
coco_2d_dict["images"].append(
dict(
file_name=cam_info["data_path"],
id=cam_info["sample_data_token"],
width=width,
height=height,
)
)
for coco_info in coco_infos:
if coco_info is None:
continue
# add an empty key for coco format
coco_info["segmentation"] = []
coco_info["id"] = coco_ann_id
coco_2d_dict["annotations"].append(coco_info)
coco_ann_id += 1
mmcv.dump(coco_2d_dict, f"{info_path[:-4]}.coco.json")
def create_nuscenes_infos(root_path, version="v1.0-trainval"):
from nuscenes.nuscenes import NuScenes
nusc = NuScenes(version=version, dataroot=root_path, verbose=True)
from nuscenes.utils import splits
available_vers = ["v1.0-trainval", "v1.0-test", "v1.0-mini"]
assert version in available_vers
if version == "v1.0-trainval":
train_scenes = splits.train
val_scenes = splits.val
elif version == "v1.0-test":
train_scenes = splits.test
val_scenes = []
elif version == "v1.0-mini":
train_scenes = splits.mini_train
val_scenes = splits.mini_val
else:
raise ValueError("unknown")
test = "test" in version
root_path = Path(root_path)
# filter exist scenes. you may only download part of dataset.
available_scenes = _get_available_scenes(nusc)
available_scene_names = [s["name"] for s in available_scenes]
train_scenes = list(
filter(lambda x: x in available_scene_names, train_scenes))
val_scenes = list(filter(lambda x: x in available_scene_names, val_scenes))
train_scenes = set([
available_scenes[available_scene_names.index(s)]["token"]
for s in train_scenes
])
val_scenes = set([
available_scenes[available_scene_names.index(s)]["token"]
for s in val_scenes
])
if test:
print(f"test scene: {len(train_scenes)}")
else:
print(
f"train scene: {len(train_scenes)}, val scene: {len(val_scenes)}")
train_nusc_infos, val_nusc_infos = _fill_trainval_infos(
nusc, train_scenes, val_scenes, test)
if test:
print(f"test sample: {len(train_nusc_infos)}")
with open(root_path / "infos_test.pkl", 'wb') as f:
pickle.dump(train_nusc_infos, f)
else:
print(
f"train sample: {len(train_nusc_infos)}, val sample: {len(val_nusc_infos)}"
)
with open(root_path / "infos_train.pkl", 'wb') as f:
pickle.dump(train_nusc_infos, f)
with open(root_path / "infos_val.pkl", 'wb') as f:
pickle.dump(val_nusc_infos, f)
def convert(split='val',
data_path='data/nuscenes/',
save_path='data/nuscenes/depth_maps'):
nusc = NuScenes(version=SPLITS[split], dataroot=data_path, verbose=True)
nusc_exp = NuScenesExplorer(nusc)
save_dir = os.path.join(save_path, split)
if not os.path.isdir(save_dir):
pass
ret = []
for sample in nusc.sample:
sample_token = sample['token']
print(sample_token, len(ret))
lidar_token = sample['data'][LidarName]
for cam_name in CamNames:
cam_token = sample['data'][cam_name]
depth_map_path = sample_token + cam_name + '.pts'
depth_map_path = os.path.join(save_dir, 'depth_data',
depth_map_path)
img_path = nusc.get_sample_data_path(cam_token)
data_info = {}
data_info['sample_token'] = sample_token
data_info['lidar_token'] = lidar_token
data_info['depth_path'] = depth_map_path
data_info['img_path'] = img_path
ret.append(data_info)
continue
points, coloring, im = nusc_exp.map_pointcloud_to_image(
lidar_token, cam_token)
float_x_cords = points[0] # < 1600
float_y_cords = points[1] # < 900
float_depth = coloring #
point_with_depth = np.stack(
[float_x_cords, float_y_cords, float_depth], axis=-1)
np.save(depth_map_path, point_with_depth)
#nusc.render_pointcloud_in_image(sample_token, camera_channel='CAM_FRONT', out_path='./render.png', verbose=False)
meta_file_path = os.path.join(save_dir, 'meta.json')
with open(meta_file_path, 'w') as f:
json.dump(ret, f)
