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pre_process_kitti.py
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pre_process_kitti.py
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import argparse
import pdb
import cv2
import numpy as np
import os
from tqdm import tqdm
import sys
CUR = os.path.dirname(os.path.abspath(__file__))
sys.path.append(CUR)
from utils import read_points, write_points, read_calib, read_label, \
write_pickle, remove_outside_points, get_points_num_in_bbox, \
points_in_bboxes_v2
def judge_difficulty(annotation_dict):
truncated = annotation_dict['truncated']
occluded = annotation_dict['occluded']
bbox = annotation_dict['bbox']
height = bbox[:, 3] - bbox[:, 1]
MIN_HEIGHTS = [40, 25, 25]
MAX_OCCLUSION = [0, 1, 2]
MAX_TRUNCATION = [0.15, 0.30, 0.50]
difficultys = []
for h, o, t in zip(height, occluded, truncated):
difficulty = -1
for i in range(2, -1, -1):
if h > MIN_HEIGHTS[i] and o <= MAX_OCCLUSION[i] and t <= MAX_TRUNCATION[i]:
difficulty = i
difficultys.append(difficulty)
return np.array(difficultys, dtype=np.int)
def create_data_info_pkl(data_root, data_type, prefix, label=True, db=False):
sep = os.path.sep
print(f"Processing {data_type} data..")
ids_file = os.path.join(CUR, 'dataset', 'ImageSets', f'{data_type}.txt')
with open(ids_file, 'r') as f:
ids = [id.strip() for id in f.readlines()]
split = 'training' if label else 'testing'
kitti_infos_dict = {}
if db:
kitti_dbinfos_train = {}
db_points_saved_path = os.path.join(data_root, f'{prefix}_gt_database')
os.makedirs(db_points_saved_path, exist_ok=True)
for id in tqdm(ids):
cur_info_dict={}
img_path = os.path.join(data_root, split, 'image_2', f'{id}.png')
lidar_path = os.path.join(data_root, split, 'velodyne', f'{id}.bin')
calib_path = os.path.join(data_root, split, 'calib', f'{id}.txt')
cur_info_dict['velodyne_path'] = sep.join(lidar_path.split(sep)[-3:])
img = cv2.imread(img_path)
image_shape = img.shape[:2]
cur_info_dict['image'] = {
'image_shape': image_shape,
'image_path': sep.join(img_path.split(sep)[-3:]),
'image_idx': int(id),
}
calib_dict = read_calib(calib_path)
cur_info_dict['calib'] = calib_dict
lidar_points = read_points(lidar_path)
reduced_lidar_points = remove_outside_points(
points=lidar_points,
r0_rect=calib_dict['R0_rect'],
tr_velo_to_cam=calib_dict['Tr_velo_to_cam'],
P2=calib_dict['P2'],
image_shape=image_shape)
saved_reduced_path = os.path.join(data_root, split, 'velodyne_reduced')
os.makedirs(saved_reduced_path, exist_ok=True)
saved_reduced_points_name = os.path.join(saved_reduced_path, f'{id}.bin')
write_points(reduced_lidar_points, saved_reduced_points_name)
if label:
label_path = os.path.join(data_root, split, 'label_2', f'{id}.txt')
annotation_dict = read_label(label_path)
annotation_dict['difficulty'] = judge_difficulty(annotation_dict)
annotation_dict['num_points_in_gt'] = get_points_num_in_bbox(
points=reduced_lidar_points,
r0_rect=calib_dict['R0_rect'],
tr_velo_to_cam=calib_dict['Tr_velo_to_cam'],
dimensions=annotation_dict['dimensions'],
location=annotation_dict['location'],
rotation_y=annotation_dict['rotation_y'],
name=annotation_dict['name'])
cur_info_dict['annos'] = annotation_dict
if db:
indices, n_total_bbox, n_valid_bbox, bboxes_lidar, name = \
points_in_bboxes_v2(
points=lidar_points,
r0_rect=calib_dict['R0_rect'].astype(np.float32),
tr_velo_to_cam=calib_dict['Tr_velo_to_cam'].astype(np.float32),
dimensions=annotation_dict['dimensions'].astype(np.float32),
location=annotation_dict['location'].astype(np.float32),
rotation_y=annotation_dict['rotation_y'].astype(np.float32),
name=annotation_dict['name']
)
for j in range(n_valid_bbox):
db_points = lidar_points[indices[:, j]]
db_points[:, :3] -= bboxes_lidar[j, :3]
db_points_saved_name = os.path.join(db_points_saved_path, f'{int(id)}_{name[j]}_{j}.bin')
write_points(db_points, db_points_saved_name)
db_info={
'name': name[j],
'path': os.path.join(os.path.basename(db_points_saved_path), f'{int(id)}_{name[j]}_{j}.bin'),
'box3d_lidar': bboxes_lidar[j],
'difficulty': annotation_dict['difficulty'][j],
'num_points_in_gt': len(db_points),
}
if name[j] not in kitti_dbinfos_train:
kitti_dbinfos_train[name[j]] = [db_info]
else:
kitti_dbinfos_train[name[j]].append(db_info)
kitti_infos_dict[int(id)] = cur_info_dict
saved_path = os.path.join(data_root, f'{prefix}_infos_{data_type}.pkl')
write_pickle(kitti_infos_dict, saved_path)
if db:
saved_db_path = os.path.join(data_root, f'{prefix}_dbinfos_train.pkl')
write_pickle(kitti_dbinfos_train, saved_db_path)
return kitti_infos_dict
def main(args):
data_root = args.data_root
prefix = args.prefix
## 1. train: create data infomation pkl file && create reduced point clouds
## && create database(points in gt bbox) for data aumentation
kitti_train_infos_dict = create_data_info_pkl(data_root, 'train', prefix, db=True)
## 2. val: create data infomation pkl file && create reduced point clouds
kitti_val_infos_dict = create_data_info_pkl(data_root, 'val', prefix)
## 3. trainval: create data infomation pkl file
kitti_trainval_infos_dict = {**kitti_train_infos_dict, **kitti_val_infos_dict}
saved_path = os.path.join(data_root, f'{prefix}_infos_trainval.pkl')
write_pickle(kitti_trainval_infos_dict, saved_path)
## 4. test: create data infomation pkl file && create reduced point clouds
kitti_test_infos_dict = create_data_info_pkl(data_root, 'test', prefix, label=False)
if __name__ == '__main__':
parser = argparse.ArgumentParser(description='Dataset infomation')
parser.add_argument('--data_root', default='/mnt/ssd1/lifa_rdata/det/kitti',
help='your data root for kitti')
parser.add_argument('--prefix', default='kitti',
help='the prefix name for the saved .pkl file')
args = parser.parse_args()
main(args)