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pcdet/datasets/waymo/waymo_utils.py Normal file
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# OpenPCDet PyTorch Dataloader and Evaluation Tools for Waymo Open Dataset
# Reference https://github.com/open-mmlab/OpenPCDet
# Written by Shaoshuai Shi, Chaoxu Guo
# All Rights Reserved 2019-2020.
import os
import pickle
import numpy as np
from ...utils import common_utils
import tensorflow as tf
from waymo_open_dataset.utils import frame_utils, transform_utils, range_image_utils
from waymo_open_dataset import dataset_pb2
try:
tf.enable_eager_execution()
except:
pass
WAYMO_CLASSES = ['unknown', 'Vehicle', 'Pedestrian', 'Sign', 'Cyclist']
def generate_labels(frame, pose):
obj_name, difficulty, dimensions, locations, heading_angles = [], [], [], [], []
tracking_difficulty, speeds, accelerations, obj_ids = [], [], [], []
num_points_in_gt = []
laser_labels = frame.laser_labels
for i in range(len(laser_labels)):
box = laser_labels[i].box
class_ind = laser_labels[i].type
loc = [box.center_x, box.center_y, box.center_z]
heading_angles.append(box.heading)
obj_name.append(WAYMO_CLASSES[class_ind])
difficulty.append(laser_labels[i].detection_difficulty_level)
tracking_difficulty.append(laser_labels[i].tracking_difficulty_level)
dimensions.append([box.length, box.width, box.height]) # lwh in unified coordinate of OpenPCDet
locations.append(loc)
obj_ids.append(laser_labels[i].id)
num_points_in_gt.append(laser_labels[i].num_lidar_points_in_box)
speeds.append([laser_labels[i].metadata.speed_x, laser_labels[i].metadata.speed_y])
accelerations.append([laser_labels[i].metadata.accel_x, laser_labels[i].metadata.accel_y])
annotations = {}
annotations['name'] = np.array(obj_name)
annotations['difficulty'] = np.array(difficulty)
annotations['dimensions'] = np.array(dimensions)
annotations['location'] = np.array(locations)
annotations['heading_angles'] = np.array(heading_angles)
annotations['obj_ids'] = np.array(obj_ids)
annotations['tracking_difficulty'] = np.array(tracking_difficulty)
annotations['num_points_in_gt'] = np.array(num_points_in_gt)
annotations['speed_global'] = np.array(speeds)
annotations['accel_global'] = np.array(accelerations)
annotations = common_utils.drop_info_with_name(annotations, name='unknown')
if annotations['name'].__len__() > 0:
global_speed = np.pad(annotations['speed_global'], ((0, 0), (0, 1)), mode='constant', constant_values=0) # (N, 3)
speed = np.dot(global_speed, np.linalg.inv(pose[:3, :3].T))
speed = speed[:, :2]
gt_boxes_lidar = np.concatenate([
annotations['location'], annotations['dimensions'], annotations['heading_angles'][..., np.newaxis], speed],
axis=1
)
else:
gt_boxes_lidar = np.zeros((0, 9))
annotations['gt_boxes_lidar'] = gt_boxes_lidar
return annotations
def convert_range_image_to_point_cloud(frame, range_images, camera_projections, range_image_top_pose, ri_index=(0, 1)):
"""
Modified from the codes of Waymo Open Dataset.
Convert range images to point cloud.
Args:
frame: open dataset frame
range_images: A dict of {laser_name, [range_image_first_return, range_image_second_return]}.
camera_projections: A dict of {laser_name,
[camera_projection_from_first_return, camera_projection_from_second_return]}.
range_image_top_pose: range image pixel pose for top lidar.
ri_index: 0 for the first return, 1 for the second return.
Returns:
points: {[N, 3]} list of 3d lidar points of length 5 (number of lidars).
cp_points: {[N, 6]} list of camera projections of length 5 (number of lidars).
"""
calibrations = sorted(frame.context.laser_calibrations, key=lambda c: c.name)
points = []
cp_points = []
points_NLZ = []
points_intensity = []
points_elongation = []
frame_pose = tf.convert_to_tensor(np.reshape(np.array(frame.pose.transform), [4, 4]))
# [H, W, 6]
range_image_top_pose_tensor = tf.reshape(
tf.convert_to_tensor(range_image_top_pose.data), range_image_top_pose.shape.dims
)
# [H, W, 3, 3]
range_image_top_pose_tensor_rotation = transform_utils.get_rotation_matrix(
range_image_top_pose_tensor[..., 0], range_image_top_pose_tensor[..., 1],
range_image_top_pose_tensor[..., 2])
range_image_top_pose_tensor_translation = range_image_top_pose_tensor[..., 3:]
range_image_top_pose_tensor = transform_utils.get_transform(
range_image_top_pose_tensor_rotation,
range_image_top_pose_tensor_translation)
for c in calibrations:
points_single, cp_points_single, points_NLZ_single, points_intensity_single, points_elongation_single \
= [], [], [], [], []
for cur_ri_index in ri_index:
range_image = range_images[c.name][cur_ri_index]
if len(c.beam_inclinations) == 0: # pylint: disable=g-explicit-length-test
beam_inclinations = range_image_utils.compute_inclination(
tf.constant([c.beam_inclination_min, c.beam_inclination_max]),
height=range_image.shape.dims[0])
else:
beam_inclinations = tf.constant(c.beam_inclinations)
beam_inclinations = tf.reverse(beam_inclinations, axis=[-1])
extrinsic = np.reshape(np.array(c.extrinsic.transform), [4, 4])
range_image_tensor = tf.reshape(
tf.convert_to_tensor(range_image.data), range_image.shape.dims)
pixel_pose_local = None
frame_pose_local = None
if c.name == dataset_pb2.LaserName.TOP:
pixel_pose_local = range_image_top_pose_tensor
pixel_pose_local = tf.expand_dims(pixel_pose_local, axis=0)
frame_pose_local = tf.expand_dims(frame_pose, axis=0)
range_image_mask = range_image_tensor[..., 0] > 0
range_image_NLZ = range_image_tensor[..., 3]
range_image_intensity = range_image_tensor[..., 1]
range_image_elongation = range_image_tensor[..., 2]
range_image_cartesian = range_image_utils.extract_point_cloud_from_range_image(
tf.expand_dims(range_image_tensor[..., 0], axis=0),
tf.expand_dims(extrinsic, axis=0),
tf.expand_dims(tf.convert_to_tensor(beam_inclinations), axis=0),
pixel_pose=pixel_pose_local,
frame_pose=frame_pose_local)
range_image_cartesian = tf.squeeze(range_image_cartesian, axis=0)
points_tensor = tf.gather_nd(range_image_cartesian,
tf.where(range_image_mask))
points_NLZ_tensor = tf.gather_nd(range_image_NLZ, tf.compat.v1.where(range_image_mask))
points_intensity_tensor = tf.gather_nd(range_image_intensity, tf.compat.v1.where(range_image_mask))
points_elongation_tensor = tf.gather_nd(range_image_elongation, tf.compat.v1.where(range_image_mask))
cp = camera_projections[c.name][0]
cp_tensor = tf.reshape(tf.convert_to_tensor(cp.data), cp.shape.dims)
cp_points_tensor = tf.gather_nd(cp_tensor, tf.where(range_image_mask))
points_single.append(points_tensor.numpy())
cp_points_single.append(cp_points_tensor.numpy())
points_NLZ_single.append(points_NLZ_tensor.numpy())
points_intensity_single.append(points_intensity_tensor.numpy())
points_elongation_single.append(points_elongation_tensor.numpy())
points.append(np.concatenate(points_single, axis=0))
cp_points.append(np.concatenate(cp_points_single, axis=0))
points_NLZ.append(np.concatenate(points_NLZ_single, axis=0))
points_intensity.append(np.concatenate(points_intensity_single, axis=0))
points_elongation.append(np.concatenate(points_elongation_single, axis=0))
return points, cp_points, points_NLZ, points_intensity, points_elongation
def save_lidar_points(frame, cur_save_path, use_two_returns=True):
ret_outputs = frame_utils.parse_range_image_and_camera_projection(frame)
if len(ret_outputs) == 4:
range_images, camera_projections, seg_labels, range_image_top_pose = ret_outputs
else:
assert len(ret_outputs) == 3
range_images, camera_projections, range_image_top_pose = ret_outputs
points, cp_points, points_in_NLZ_flag, points_intensity, points_elongation = convert_range_image_to_point_cloud(
frame, range_images, camera_projections, range_image_top_pose, ri_index=(0, 1) if use_two_returns else (0,)
)
# 3d points in vehicle frame.
points_all = np.concatenate(points, axis=0)
points_in_NLZ_flag = np.concatenate(points_in_NLZ_flag, axis=0).reshape(-1, 1)
points_intensity = np.concatenate(points_intensity, axis=0).reshape(-1, 1)
points_elongation = np.concatenate(points_elongation, axis=0).reshape(-1, 1)
num_points_of_each_lidar = [point.shape[0] for point in points]
save_points = np.concatenate([
points_all, points_intensity, points_elongation, points_in_NLZ_flag
], axis=-1).astype(np.float32)
np.save(cur_save_path, save_points)
# print('saving to ', cur_save_path)
return num_points_of_each_lidar
def process_single_sequence(sequence_file, save_path, sampled_interval, has_label=True, use_two_returns=True, update_info_only=False):
sequence_name = os.path.splitext(os.path.basename(sequence_file))[0]
# print('Load record (sampled_interval=%d): %s' % (sampled_interval, sequence_name))
if not sequence_file.exists():
print('NotFoundError: %s' % sequence_file)
return []
dataset = tf.data.TFRecordDataset(str(sequence_file), compression_type='')
cur_save_dir = save_path / sequence_name
cur_save_dir.mkdir(parents=True, exist_ok=True)
pkl_file = cur_save_dir / ('%s.pkl' % sequence_name)
sequence_infos = []
if pkl_file.exists():
sequence_infos = pickle.load(open(pkl_file, 'rb'))
sequence_infos_old = None
if not update_info_only:
print('Skip sequence since it has been processed before: %s' % pkl_file)
return sequence_infos
else:
sequence_infos_old = sequence_infos
sequence_infos = []
for cnt, data in enumerate(dataset):
if cnt % sampled_interval != 0:
continue
# print(sequence_name, cnt)
frame = dataset_pb2.Frame()
frame.ParseFromString(bytearray(data.numpy()))
info = {}
pc_info = {'num_features': 5, 'lidar_sequence': sequence_name, 'sample_idx': cnt}
info['point_cloud'] = pc_info
info['frame_id'] = sequence_name + ('_%03d' % cnt)
info['metadata'] = {
'context_name': frame.context.name,
'timestamp_micros': frame.timestamp_micros
}
image_info = {}
for j in range(5):
width = frame.context.camera_calibrations[j].width
height = frame.context.camera_calibrations[j].height
image_info.update({'image_shape_%d' % j: (height, width)})
info['image'] = image_info
pose = np.array(frame.pose.transform, dtype=np.float32).reshape(4, 4)
info['pose'] = pose
if has_label:
annotations = generate_labels(frame, pose=pose)
info['annos'] = annotations
if update_info_only and sequence_infos_old is not None:
assert info['frame_id'] == sequence_infos_old[cnt]['frame_id']
num_points_of_each_lidar = sequence_infos_old[cnt]['num_points_of_each_lidar']
else:
num_points_of_each_lidar = save_lidar_points(
frame, cur_save_dir / ('%04d.npy' % cnt), use_two_returns=use_two_returns
)
info['num_points_of_each_lidar'] = num_points_of_each_lidar
sequence_infos.append(info)
with open(pkl_file, 'wb') as f:
pickle.dump(sequence_infos, f)
print('Infos are saved to (sampled_interval=%d): %s' % (sampled_interval, pkl_file))
return sequence_infos