prepare_data.py

import os
import sys
import shutil
import numpy as np
import json
import pickle
import argparse

from cruw import CRUW
from cruw.annotation.init_json import init_meta_json
from cruw.mapping import ra2idx

from rodnet.core.confidence_map import generate_confmap, normalize_confmap, add_noise_channel
from rodnet.utils.load_configs import load_configs_from_file
from rodnet.utils.visualization import visualize_confmap

SPLITS_LIST = ['train', 'valid', 'test', 'demo']


def parse_args():
    parser = argparse.ArgumentParser(description='Prepare RODNet data.')
    parser.add_argument('--config', type=str, dest='config', help='configuration file path')
    parser.add_argument('--data_root', type=str, help='directory to the prepared data')
    parser.add_argument('--sensor_config', type=str, default='sensor_config_rod2021')
    parser.add_argument('--split', type=str, dest='split', default='',
                        help='choose from train, valid, test, supertest')
    parser.add_argument('--out_data_dir', type=str, default='./data',
                        help='data directory to save the prepared data')
    parser.add_argument('--overwrite', action="store_true", help="overwrite prepared data if exist")
    args = parser.parse_args()
    return args


def load_anno_txt(txt_path, n_frame, dataset):
    folder_name_dict = dict(
        cam_0='IMAGES_0',
        rad_h='RADAR_RA_H'
    )
    anno_dict = init_meta_json(n_frame, folder_name_dict)
    with open(txt_path, 'r') as f:
        data = f.readlines()
    for line in data:
        frame_id, r, a, class_name = line.rstrip().split()
        frame_id = int(frame_id)
        r = float(r)
        a = float(a)
        rid, aid = ra2idx(r, a, dataset.range_grid, dataset.angle_grid)
        anno_dict[frame_id]['rad_h']['n_objects'] += 1
        anno_dict[frame_id]['rad_h']['obj_info']['categories'].append(class_name)
        anno_dict[frame_id]['rad_h']['obj_info']['centers'].append([r, a])
        anno_dict[frame_id]['rad_h']['obj_info']['center_ids'].append([rid, aid])
        anno_dict[frame_id]['rad_h']['obj_info']['scores'].append(1.0)

    return anno_dict


def generate_confmaps(metadata_dict, n_class, viz):
    confmaps = []
    for metadata_frame in metadata_dict:
        n_obj = metadata_frame['rad_h']['n_objects']
        obj_info = metadata_frame['rad_h']['obj_info']
        if n_obj == 0:
            confmap_gt = np.zeros(
                (n_class + 1, radar_configs['ramap_rsize'], radar_configs['ramap_asize']),
                dtype=float)
            confmap_gt[-1, :, :] = 1.0  # initialize noise channal
        else:
            confmap_gt = generate_confmap(n_obj, obj_info, dataset, config_dict)
            confmap_gt = normalize_confmap(confmap_gt)
            confmap_gt = add_noise_channel(confmap_gt, dataset, config_dict)
        assert confmap_gt.shape == (
            n_class + 1, radar_configs['ramap_rsize'], radar_configs['ramap_asize'])
        if viz:
            visualize_confmap(confmap_gt)
        confmaps.append(confmap_gt)
    confmaps = np.array(confmaps)
    return confmaps


def prepare_data(dataset, config_dict, data_dir, split, save_dir, viz=False, overwrite=False):
    """
    Prepare pickle data for RODNet training and testing
    :param dataset: dataset object
    :param config_dict: rodnet configurations
    :param data_dir: output directory of the processed data
    :param split: train, valid, test, demo, etc.
    :param save_dir: output directory of the prepared data
    :param viz: whether visualize the prepared data
    :param overwrite: whether overwrite the existing prepared data
    :return:
    """
    camera_configs = dataset.sensor_cfg.camera_cfg
    radar_configs = dataset.sensor_cfg.radar_cfg
    n_chirp = radar_configs['n_chirps']
    n_class = dataset.object_cfg.n_class

    data_root = config_dict['dataset_cfg']['data_root']
    anno_root = config_dict['dataset_cfg']['anno_root']
    if split is None:
        set_cfg = {
            'subdir': '',
            'seqs': sorted(os.listdir(data_root))
        }
        sets_seqs = sorted(os.listdir(data_root))
    else:
        set_cfg = config_dict['dataset_cfg'][split]
        if 'seqs' not in set_cfg:
            sets_seqs = sorted(os.listdir(os.path.join(data_root, set_cfg['subdir'])))
        else:
            sets_seqs = set_cfg['seqs']

    if overwrite:
        if os.path.exists(os.path.join(data_dir, split)):
            shutil.rmtree(os.path.join(data_dir, split))
        os.makedirs(os.path.join(data_dir, split))

    for seq in sets_seqs:
        seq_path = os.path.join(data_root, set_cfg['subdir'], seq)
        seq_anno_path = os.path.join(anno_root, set_cfg['subdir'], seq + config_dict['dataset_cfg']['anno_ext'])
        save_path = os.path.join(save_dir, seq + '.pkl')
        print("Sequence %s saving to %s" % (seq_path, save_path))

        try:
            if not overwrite and os.path.exists(save_path):
                print("%s already exists, skip" % save_path)
                continue
            image_dir = os.path.join(seq_path, camera_configs['image_folder'])
            image_paths = sorted([os.path.join(image_dir, name) for name in os.listdir(image_dir) if
                                  name.endswith(camera_configs['ext'])])
            n_frame = len(image_paths)

            radar_dir = os.path.join(seq_path, dataset.sensor_cfg.radar_cfg['chirp_folder'])
            if radar_configs['data_type'] == 'RI' or radar_configs['data_type'] == 'AP':
                radar_paths = sorted([os.path.join(radar_dir, name) for name in os.listdir(radar_dir) if
                                      name.endswith(dataset.sensor_cfg.radar_cfg['ext'])])
                n_radar_frame = len(radar_paths)
                assert n_frame == n_radar_frame
            elif radar_configs['data_type'] == 'RISEP' or radar_configs['data_type'] == 'APSEP':
                radar_paths_chirp = []
                for chirp_id in range(n_chirp):
                    chirp_dir = os.path.join(radar_dir, '%04d' % chirp_id)
                    paths = sorted([os.path.join(chirp_dir, name) for name in os.listdir(chirp_dir) if
                                    name.endswith(config_dict['dataset_cfg']['radar_cfg']['ext'])])
                    n_radar_frame = len(paths)
                    assert n_frame == n_radar_frame
                    radar_paths_chirp.append(paths)
                radar_paths = []
                for frame_id in range(n_frame):
                    frame_paths = []
                    for chirp_id in range(n_chirp):
                        frame_paths.append(radar_paths_chirp[chirp_id][frame_id])
                    radar_paths.append(frame_paths)
            elif radar_configs['data_type'] == 'ROD2021':
                assert len(os.listdir(radar_dir)) == n_frame * len(radar_configs['chirp_ids'])
                radar_paths = []
                for frame_id in range(n_frame):
                    chirp_paths = []
                    for chirp_id in radar_configs['chirp_ids']:
                        path = os.path.join(radar_dir, '%06d_%04d.' % (frame_id, chirp_id) +
                                            dataset.sensor_cfg.radar_cfg['ext'])
                        chirp_paths.append(path)
                    radar_paths.append(chirp_paths)
            else:
                raise ValueError

            data_dict = dict(
                data_root=data_root,
                data_path=seq_path,
                seq_name=seq,
                n_frame=n_frame,
                image_paths=image_paths,
                radar_paths=radar_paths,
                anno=None,
            )

            if split == 'demo':
                # no labels need to be saved
                pickle.dump(data_dict, open(save_path, 'wb'))
                continue
            else:
                anno_obj = {}
                if config_dict['dataset_cfg']['anno_ext'] == '.txt':
                    anno_obj['metadata'] = load_anno_txt(seq_anno_path, n_frame, dataset)

                elif config_dict['dataset_cfg']['anno_ext'] == '.json':
                    with open(os.path.join(seq_anno_path), 'r') as f:
                        anno = json.load(f)
                    anno_obj['metadata'] = anno['metadata']
                else:
                    raise

                anno_obj['confmaps'] = generate_confmaps(anno_obj['metadata'], n_class, viz)
                data_dict['anno'] = anno_obj
                # save pkl files
                pickle.dump(data_dict, open(save_path, 'wb'))
            # end frames loop

        except Exception as e:
            print("Error while preparing %s: %s" % (seq_path, e))


if __name__ == "__main__":
    args = parse_args()
    data_root = args.data_root
    if args.split == '':
        splits = None
    else:
        splits = args.split.split(',')
    out_data_dir = args.out_data_dir
    os.makedirs(out_data_dir, exist_ok=True)
    overwrite = args.overwrite

    dataset = CRUW(data_root=data_root, sensor_config_name=args.sensor_config)
    config_dict = load_configs_from_file(args.config)
    radar_configs = dataset.sensor_cfg.radar_cfg

    if splits == None:
        prepare_data(dataset, config_dict, out_data_dir, split=None, save_dir=out_data_dir, viz=False,
                     overwrite=overwrite)
    else:
        for split in splits:
            if split not in SPLITS_LIST:
                raise TypeError("split %s cannot be recognized" % split)

        for split in splits:
            save_dir = os.path.join(out_data_dir, split)
            if not os.path.exists(save_dir):
                os.makedirs(save_dir)

            print('Preparing %s sets ...' % split)
            prepare_data(dataset, config_dict, out_data_dir, split, save_dir, viz=False, overwrite=overwrite)