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| # Copyright 2023 The human_scene_transformer Authors. | ||
| # | ||
| # Licensed under the Apache License, Version 2.0 (the "License"); | ||
| # you may not use this file except in compliance with the License. | ||
| # You may obtain a copy of the License at | ||
| # | ||
| # http://www.apache.org/licenses/LICENSE-2.0 | ||
| # | ||
| # Unless required by applicable law or agreed to in writing, software | ||
| # distributed under the License is distributed on an "AS IS" BASIS, | ||
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | ||
| # See the License for the specific language governing permissions and | ||
| # limitations under the License. | ||
|
|
||
| """Preprocesses the raw test split of JRDB. | ||
| """ | ||
|
|
||
| import os | ||
|
|
||
| from human_scene_transformer.data import utils | ||
| import numpy as np | ||
| import pandas as pd | ||
| import tensorflow as tf | ||
| import tqdm | ||
|
|
||
| INPUT_PATH = '<dataset_path>' | ||
| OUTPUT_PATH = '<output_path>' | ||
|
|
||
| POINTCLOUD = True | ||
| AGENT_KEYPOINTS = True | ||
| FROM_DETECTIONS = True | ||
|
|
||
|
|
||
| def list_test_scenes(input_path): | ||
| scenes = os.listdir(os.path.join(input_path, 'images', 'image_0')) | ||
| scenes.sort() | ||
| return scenes | ||
|
|
||
|
|
||
| def get_agents_features_df_with_box( | ||
| input_path, scene_id, max_distance_to_robot=10.0 | ||
| ): | ||
| """Returns agents features with bounding box from raw leaderboard data.""" | ||
| jrdb_header = [ | ||
| 'frame', | ||
| 'track id', | ||
| 'type', | ||
| 'truncated', | ||
| 'occluded', | ||
| 'alpha', | ||
| 'bb_left', | ||
| 'bb_top', | ||
| 'bb_width', | ||
| 'bb_height', | ||
| 'x', | ||
| 'y', | ||
| 'z', | ||
| 'height', | ||
| 'width', | ||
| 'length', | ||
| 'rotation_y', | ||
| 'score', | ||
| ] | ||
| scene_data_file = utils.get_file_handle( | ||
| os.path.join( | ||
| input_path, 'labels', 'raw_leaderboard', f'{scene_id:04}' + '.txt' | ||
| ) | ||
| ) | ||
| df = pd.read_csv(scene_data_file, sep=' ', names=jrdb_header) | ||
|
|
||
| def camera_to_lower_velodyne(p): | ||
| return np.stack( | ||
| [p[..., 2], -p[..., 0], -p[..., 1] + (0.742092 - 0.606982)], axis=-1 | ||
| ) | ||
|
|
||
| df = df[df['score'] >= 0.01] | ||
|
|
||
| df['p'] = df[['x', 'y', 'z']].apply( | ||
| lambda s: camera_to_lower_velodyne(s.to_numpy()), axis=1 | ||
| ) | ||
| df['distance'] = df['p'].apply(lambda s: np.linalg.norm(s, axis=-1)) | ||
| df['l'] = df['height'] | ||
| df['h'] = df['width'] | ||
| df['w'] = df['length'] | ||
| df['yaw'] = df['rotation_y'] | ||
|
|
||
| df['id'] = df['track id'].apply(lambda s: f'pedestrian:{s}') | ||
| df['timestep'] = df['frame'] | ||
|
|
||
| df = df.set_index(['timestep', 'id']) | ||
|
|
||
| df = df[df['distance'] <= max_distance_to_robot] | ||
|
|
||
| return df[['p', 'yaw', 'l', 'h', 'w']] | ||
|
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||
|
|
||
| def jrdb_preprocess_test(input_path, output_path): | ||
| scenes = list_test_scenes(os.path.join(input_path, 'test_dataset')) | ||
| subsample = 1 | ||
| for scene in tqdm.tqdm(scenes): | ||
| scene_save_name = scene + '_test' | ||
| agents_df = get_agents_features_df_with_box( | ||
| os.path.join(input_path, 'test_dataset'), | ||
| scenes.index(scene), | ||
| max_distance_to_robot=15.0, | ||
| ) | ||
|
|
||
| robot_odom = utils.get_robot( | ||
| os.path.join(input_path, 'processed', 'odometry_test'), scene | ||
| ) | ||
|
|
||
| if AGENT_KEYPOINTS: | ||
| keypoints = utils.get_agents_keypoints( | ||
| os.path.join( | ||
| input_path, 'processed', 'labels', 'labels_3d_keypoints_test' | ||
| ), | ||
| scene, | ||
| ) | ||
| keypoints_df = pd.DataFrame.from_dict( | ||
| keypoints, orient='index' | ||
| ).rename_axis(['timestep', 'id']) # pytype: disable=missing-parameter # pandas-drop-duplicates-overloads | ||
|
|
||
| agents_df = agents_df.join(keypoints_df) | ||
| agents_df.keypoints.fillna( | ||
| dict( | ||
| zip( | ||
| agents_df.index[agents_df['keypoints'].isnull()], | ||
| [np.ones((33, 3)) * np.nan] | ||
| * len( | ||
| agents_df.loc[ | ||
| agents_df['keypoints'].isnull(), 'keypoints' | ||
| ] | ||
| ), | ||
| ) | ||
| ), | ||
| inplace=True, | ||
| ) | ||
|
|
||
| robot_df = pd.DataFrame.from_dict(robot_odom, orient='index').rename_axis( # pytype: disable=missing-parameter # pandas-drop-duplicates-overloads | ||
| ['timestep'] | ||
| ) | ||
| # Remove extra data odometry datapoints | ||
| robot_df = robot_df.iloc[agents_df.index.levels[0]] | ||
|
|
||
| assert (agents_df.index.levels[0] == robot_df.index).all() | ||
|
|
||
| # Subsample | ||
| assert len(agents_df.index.levels[0]) == agents_df.index.levels[0].max() + 1 | ||
| agents_df_subsampled_index = agents_df.unstack('id').iloc[::subsample].index | ||
| agents_df = ( | ||
| agents_df.unstack('id') | ||
| .iloc[::subsample] | ||
| .reset_index(drop=True) | ||
| .stack('id', dropna=True) | ||
| ) | ||
|
|
||
| agents_in_odometry_df = utils.agents_to_odometry_frame( | ||
| agents_df, robot_df.iloc[::subsample].reset_index(drop=True) | ||
| ) | ||
|
|
||
| agents_pos_ragged_tensor = utils.agents_pos_to_ragged_tensor( | ||
| agents_in_odometry_df | ||
| ) | ||
| agents_yaw_ragged_tensor = utils.agents_yaw_to_ragged_tensor( | ||
| agents_in_odometry_df | ||
| ) | ||
| assert ( | ||
| agents_pos_ragged_tensor.shape[0] == agents_yaw_ragged_tensor.shape[0] | ||
| ) | ||
|
|
||
| tf.data.Dataset.from_tensors(agents_pos_ragged_tensor).save( | ||
| os.path.join(output_path, scene_save_name, 'agents', 'position') | ||
| ) | ||
| tf.data.Dataset.from_tensors(agents_yaw_ragged_tensor).save( | ||
| os.path.join(output_path, scene_save_name, 'agents', 'orientation') | ||
| ) | ||
|
|
||
| if AGENT_KEYPOINTS: | ||
| agents_keypoints_ragged_tensor = utils.agents_keypoints_to_ragged_tensor( | ||
| agents_in_odometry_df | ||
| ) | ||
| tf.data.Dataset.from_tensors(agents_keypoints_ragged_tensor).save( | ||
| os.path.join(output_path, scene_save_name, 'agents', 'keypoints') | ||
| ) | ||
|
|
||
| robot_in_odometry_df = utils.robot_to_odometry_frame(robot_df) | ||
| robot_pos = tf.convert_to_tensor( | ||
| np.stack(robot_in_odometry_df.iloc[::subsample]['p'].values).astype( | ||
| np.float32 | ||
| ) | ||
| ) | ||
| robot_orientation = tf.convert_to_tensor( | ||
| np.stack(robot_in_odometry_df.iloc[::subsample]['yaw'].values).astype( | ||
| np.float32 | ||
| ) | ||
| )[..., tf.newaxis] | ||
|
|
||
| tf.data.Dataset.from_tensors(robot_pos).save( | ||
| os.path.join(output_path, scene_save_name, 'robot', 'position') | ||
| ) | ||
| tf.data.Dataset.from_tensors(robot_orientation).save( | ||
| os.path.join(output_path, scene_save_name, 'robot', 'orientation') | ||
| ) | ||
|
|
||
| if POINTCLOUD: | ||
| scene_pointcloud_dict = utils.get_scene_poinclouds( | ||
| os.path.join(input_path, 'test_dataset'), scene, subsample=subsample | ||
| ) | ||
| # Remove extra timesteps | ||
| scene_pointcloud_dict = { | ||
| ts: scene_pointcloud_dict[ts] for ts in agents_df_subsampled_index | ||
| } | ||
|
|
||
| scene_pc_odometry = utils.pc_to_odometry_frame( | ||
| scene_pointcloud_dict, robot_df | ||
| ) | ||
|
|
||
| filtered_pc = utils.filter_agents_and_ground_from_point_cloud( | ||
| agents_in_odometry_df, scene_pc_odometry, robot_in_odometry_df | ||
| ) | ||
|
|
||
| scene_pc_ragged_tensor = tf.ragged.stack(filtered_pc) | ||
|
|
||
| assert ( | ||
| agents_pos_ragged_tensor.bounding_shape()[1] | ||
| == scene_pc_ragged_tensor.shape[0] | ||
| ) | ||
|
|
||
| tf.data.Dataset.from_tensors(scene_pc_ragged_tensor).save( | ||
| os.path.join(output_path, scene_save_name, 'scene', 'pc'), | ||
| compression='GZIP', | ||
| ) | ||
|
|
||
| if __name__ == '__main__': | ||
| jrdb_preprocess_test(INPUT_PATH, OUTPUT_PATH) |
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