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kitti_dataset.py
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kitti_dataset.py
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import copy
import pickle
import numpy as np
from torch.utils.data import dataset
from skimage import io
import os
import os.path as osp
import MinkowskiEngine as ME
import torch
from . import kitti_utils
from ...ops.roiaware_pool3d import roiaware_pool3d_utils
from ...utils import box_utils, calibration_kitti, common_utils, object3d_kitti
from ..dataset import DatasetTemplate
def cart2hom(pts_3d):
n = pts_3d.shape[0]
pts_3d_hom = np.hstack((pts_3d, np.ones((n, 1), dtype=np.float32)))
return pts_3d_hom
def transform_points(pts_3d_ref, Tr):
pts_3d_ref = cart2hom(pts_3d_ref) # nx4
return np.dot(pts_3d_ref, np.transpose(Tr)).reshape(-1, 4)[:, 0:3]
class KittiDataset(DatasetTemplate):
def __init__(self, dataset_cfg, class_names, training=True, root_path=None, logger=None):
"""
Args:
root_path:
dataset_cfg:
class_names:
training:
logger:
"""
super().__init__(
dataset_cfg=dataset_cfg, class_names=class_names, training=training, root_path=root_path, logger=logger
)
self.split = self.dataset_cfg.DATA_SPLIT[self.mode]
self.root_split_path = self.root_path / ('training' if self.split != 'test' else 'testing')
split_dir = self.root_path / 'ImageSets' / (self.split + '.txt')
self.sample_id_list = [x.strip() for x in open(split_dir).readlines()] if split_dir.exists() else None
self.kitti_infos = []
self.include_kitti_data(self.mode)
self.load_ephemerality = None
self.load_history_path = None
self.load_background_sample = None
self.contant_reflex = dataset_cfg.get('CONSTANT_REFLEX', False)
if 'load_ephemerality' in dataset_cfg:
self.load_ephemerality = dataset_cfg.load_ephemerality
if 'load_background_sample' in dataset_cfg:
self.load_background_sample = dataset_cfg.load_background_sample
if 'LOAD_HISTORY' in dataset_cfg:
self.load_history = copy.deepcopy(dataset_cfg.LOAD_HISTORY)
self.history_cache_dir = None
if 'CACHE_ROOT' in self.load_history:
if self.load_history.get("HISTORY_AUG", False):
self.history_cache_dir = osp.join(
self.load_history.CACHE_ROOT,
f"raw_points_fwonly={self.load_history.FORWARD_ONLY}"
f"_history_scans_path={osp.basename(osp.normpath(self.load_history.DATA_PATH))}")
else:
self.history_cache_dir = osp.join(
self.load_history.CACHE_ROOT,
f"fwonly={self.load_history.FORWARD_ONLY}_vs={self.load_history.VOXEL_SIZE:02f}"
f"_history_scans_path={osp.basename(osp.normpath(self.load_history.DATA_PATH))}")
os.makedirs(self.history_cache_dir, exist_ok=True)
os.chmod(self.history_cache_dir, 0o777)
def include_kitti_data(self, mode):
if self.logger is not None:
self.logger.info('Loading KITTI dataset')
kitti_infos = []
for info_path in self.dataset_cfg.INFO_PATH[mode]:
info_path = self.root_path / info_path
if not info_path.exists():
continue
with open(info_path, 'rb') as f:
infos = pickle.load(f)
kitti_infos.extend(infos)
self.kitti_infos.extend(kitti_infos)
if self.logger is not None:
self.logger.info('Total samples for KITTI dataset: %d' % (len(kitti_infos)))
def set_split(self, split):
super().__init__(
dataset_cfg=self.dataset_cfg, class_names=self.class_names, training=self.training, root_path=self.root_path, logger=self.logger
)
self.split = split
self.root_split_path = self.root_path / ('training' if self.split != 'test' else 'testing')
split_dir = self.root_path / 'ImageSets' / (self.split + '.txt')
self.sample_id_list = [x.strip() for x in open(split_dir).readlines()] if split_dir.exists() else None
# print(split_dir)
# print(self.sample_id_list)
# raise
def get_history_raw(self, idx):
assert self.load_history is not None
history_scans = None
if self.history_cache_dir is not None and \
osp.exists(osp.join(self.history_cache_dir, f"{idx}.pth")):
try:
history_scans = torch.load(
osp.join(self.history_cache_dir, f"{idx}.pth"))
except:
print("reading error " + idx)
if history_scans is None:
history_scans = pickle.load(
open(osp.join(self.load_history.DATA_PATH, f"{idx}.pkl"), 'rb'))
history_scans = list(history_scans.values())
trans_mat = np.load(
osp.join(self.load_history.TRANS_MAT_PATH, f"{idx}.npy"))
history_scans = [transform_points(
x, np.linalg.inv(trans_mat)) for x in history_scans]
if self.load_history.FORWARD_ONLY:
history_scans = [x[x[:, 0] > 0, :] for x in history_scans]
if self.history_cache_dir is not None:
torch.save(
history_scans,
osp.join(self.history_cache_dir, f"{idx}.pth")
)
os.chmod(osp.join(self.history_cache_dir, f"{idx}.pth"), 0o777)
if self.training and self.load_history.get("RANDOM_DROPOUT", False):
num_scans = np.random.randint(1, high=len(history_scans)+1)
_scan_num_choice = np.random.choice(
len(history_scans), num_scans, replace=False)
history_scans = [history_scans[i]
for i in _scan_num_choice]
if self.load_history.LIMIT_NUM > 0 and len(history_scans) > self.load_history.LIMIT_NUM:
if self.training:
_scan_num_choice = np.random.choice(
len(history_scans), self.load_history.LIMIT_NUM, replace=False)
else:
_scan_num_choice = range(self.load_history.LIMIT_NUM)
history_scans = [history_scans[i]
for i in _scan_num_choice]
return history_scans
def get_history(self, idx):
assert self.load_history is not None
if self.history_cache_dir is not None and \
osp.exists(osp.join(self.history_cache_dir, f"{idx}.pth")):
history_coordinates = torch.load(
osp.join(self.history_cache_dir, f"{idx}.pth"))
else:
history_scans = pickle.load(
open(osp.join(self.load_history.DATA_PATH, f"{idx}.pkl"), 'rb'))
history_scans = list(history_scans.values())
trans_mat = np.load(
osp.join(self.load_history.TRANS_MAT_PATH, f"{idx}.npy"))
history_scans = [transform_points(
x, np.linalg.inv(trans_mat)) for x in history_scans]
if self.load_history.FORWARD_ONLY:
history_scans = [x[x[:, 0] > 0, :] for x in history_scans]
history_coordinates = [ME.utils.sparse_quantize(
x / self.load_history.VOXEL_SIZE) for x in history_scans]
if self.history_cache_dir is not None:
torch.save(
history_coordinates,
osp.join(self.history_cache_dir, f"{idx}.pth")
)
os.chmod(osp.join(self.history_cache_dir, f"{idx}.pth"), 0o777)
if self.load_history.LIMIT_NUM > 0 and len(history_coordinates) > self.load_history.LIMIT_NUM:
if self.training:
_scan_num_choice = np.random.choice(
len(history_coordinates), self.load_history.LIMIT_NUM, replace=False)
else:
_scan_num_choice = range(self.load_history.LIMIT_NUM)
history_coordinates = [history_coordinates[i] for i in _scan_num_choice]
history_features = [torch.ones((len(x), 1))
for x in history_coordinates]
return history_coordinates, history_features
def get_background_sample(self, idx):
return np.load(osp.join(self.load_background_sample, f"{idx}.npy"))
def get_lidar(self, idx):
lidar_file = self.root_split_path / 'velodyne' / ('%s.bin' % idx)
assert lidar_file.exists()
points = np.fromfile(str(lidar_file), dtype=np.float32).reshape(-1, 4)
if self.contant_reflex:
points[:, 3] = self.contant_reflex
if self.load_ephemerality is not None:
# print(f"{idx} loading ephemerality")
if self.training:
load_ephemerality = self.load_ephemerality.train
else:
load_ephemerality = self.load_ephemerality.val
pts_intensity = np.load(os.path.join(
load_ephemerality, f"{idx}.npy")).astype(np.float32)
pts_intensity[pts_intensity < 0] = 0
# points[:, 3] = pts_intensity
if len(pts_intensity.shape) == 1:
pts_intensity = pts_intensity.reshape(-1,1)
points = np.concatenate((points[:, :3], pts_intensity), axis=1)
return points
def get_image(self, idx):
"""
Loads image for a sample
Args:
idx: int, Sample index
Returns:
image: (H, W, 3), RGB Image
"""
img_file = self.root_split_path / 'image_2' / ('%s.png' % idx)
assert img_file.exists()
image = io.imread(img_file)
image = image.astype(np.float32)
image /= 255.0
return image
def get_image_shape(self, idx):
img_file = self.root_split_path / 'image_2' / ('%s.png' % idx)
assert img_file.exists()
return np.array(io.imread(img_file).shape[:2], dtype=np.int32)
def get_label(self, idx):
label_file = self.root_split_path / 'label_2' / ('%s.txt' % idx)
assert label_file.exists()
return object3d_kitti.get_objects_from_label(label_file)
def get_depth_map(self, idx):
"""
Loads depth map for a sample
Args:
idx: str, Sample index
Returns:
depth: (H, W), Depth map
"""
depth_file = self.root_split_path / 'depth_2' / ('%s.png' % idx)
assert depth_file.exists()
depth = io.imread(depth_file)
depth = depth.astype(np.float32)
depth /= 256.0
return depth
def get_calib(self, idx):
calib_file = self.root_split_path / 'calib' / ('%s.txt' % idx)
assert calib_file.exists()
return calibration_kitti.Calibration(calib_file)
def get_road_plane(self, idx):
plane_file = self.root_split_path / 'planes' / ('%s.txt' % idx)
if not plane_file.exists():
return None
with open(plane_file, 'r') as f:
lines = f.readlines()
lines = [float(i) for i in lines[3].split()]
plane = np.asarray(lines)
# Ensure normal is always facing up, this is in the rectified camera coordinate
if plane[1] > 0:
plane = -plane
norm = np.linalg.norm(plane[0:3])
plane = plane / norm
return plane
@staticmethod
def get_fov_flag(pts_rect, img_shape, calib):
"""
Args:
pts_rect:
img_shape:
calib:
Returns:
"""
pts_img, pts_rect_depth = calib.rect_to_img(pts_rect)
val_flag_1 = np.logical_and(pts_img[:, 0] >= 0, pts_img[:, 0] < img_shape[1])
val_flag_2 = np.logical_and(pts_img[:, 1] >= 0, pts_img[:, 1] < img_shape[0])
val_flag_merge = np.logical_and(val_flag_1, val_flag_2)
pts_valid_flag = np.logical_and(val_flag_merge, pts_rect_depth >= 0)
return pts_valid_flag
def get_infos(self, num_workers=4, has_label=True, count_inside_pts=True, sample_id_list=None):
import concurrent.futures as futures
def process_single_scene(sample_idx):
print('%s sample_idx: %s' % (self.split, sample_idx))
info = {}
pc_info = {'num_features': 4, 'lidar_idx': sample_idx}
info['point_cloud'] = pc_info
image_info = {'image_idx': sample_idx, 'image_shape': self.get_image_shape(sample_idx)}
info['image'] = image_info
calib = self.get_calib(sample_idx)
P2 = np.concatenate([calib.P2, np.array([[0., 0., 0., 1.]])], axis=0)
R0_4x4 = np.zeros([4, 4], dtype=calib.R0.dtype)
R0_4x4[3, 3] = 1.
R0_4x4[:3, :3] = calib.R0
V2C_4x4 = np.concatenate([calib.V2C, np.array([[0., 0., 0., 1.]])], axis=0)
calib_info = {'P2': P2, 'R0_rect': R0_4x4, 'Tr_velo_to_cam': V2C_4x4}
info['calib'] = calib_info
if has_label:
obj_list = self.get_label(sample_idx)
annotations = {}
annotations['name'] = np.array([obj.cls_type for obj in obj_list])
annotations['truncated'] = np.array([obj.truncation for obj in obj_list])
annotations['occluded'] = np.array([obj.occlusion for obj in obj_list])
annotations['alpha'] = np.array([obj.alpha for obj in obj_list])
annotations['bbox'] = np.concatenate([obj.box2d.reshape(
1, 4) for obj in obj_list], axis=0) if len(obj_list) > 0 else np.array([]).reshape(0, 4)
annotations['dimensions'] = np.array(
[[obj.l, obj.h, obj.w] for obj in obj_list]).reshape(-1, 3) # lhw(camera) format
annotations['location'] = np.concatenate([obj.loc.reshape(
1, 3) for obj in obj_list], axis=0) if len(obj_list) > 0 else np.array([]).reshape(0, 3)
annotations['rotation_y'] = np.array([obj.ry for obj in obj_list])
annotations['score'] = np.array([obj.score for obj in obj_list])
annotations['difficulty'] = np.array([obj.level for obj in obj_list], np.int32)
num_objects = len([obj.cls_type for obj in obj_list if obj.cls_type != 'DontCare'])
num_gt = len(annotations['name'])
index = list(range(num_objects)) + [-1] * (num_gt - num_objects)
annotations['index'] = np.array(index, dtype=np.int32)
if len(obj_list) > 0:
loc = annotations['location'][:num_objects]
dims = annotations['dimensions'][:num_objects]
rots = annotations['rotation_y'][:num_objects]
loc_lidar = calib.rect_to_lidar(loc)
l, h, w = dims[:, 0:1], dims[:, 1:2], dims[:, 2:3]
loc_lidar[:, 2] += h[:, 0] / 2
gt_boxes_lidar = np.concatenate([loc_lidar, l, w, h, -(np.pi / 2 + rots[..., np.newaxis])], axis=1)
annotations['gt_boxes_lidar'] = gt_boxes_lidar
else:
annotations['gt_boxes_lidar'] = np.array([])
info['annos'] = annotations
if count_inside_pts:
if len(obj_list) > 0:
points = self.get_lidar(sample_idx)
calib = self.get_calib(sample_idx)
pts_rect = calib.lidar_to_rect(points[:, 0:3])
fov_flag = self.get_fov_flag(pts_rect, info['image']['image_shape'], calib)
pts_fov = points[fov_flag]
corners_lidar = box_utils.boxes_to_corners_3d(gt_boxes_lidar)
num_points_in_gt = -np.ones(num_gt, dtype=np.int32)
for k in range(num_objects):
flag = box_utils.in_hull(pts_fov[:, 0:3], corners_lidar[k])
num_points_in_gt[k] = flag.sum()
annotations['num_points_in_gt'] = num_points_in_gt
else:
annotations['num_points_in_gt'] = np.ones(0, dtype=np.int32)
return info
sample_id_list = sample_id_list if sample_id_list is not None else self.sample_id_list
with futures.ThreadPoolExecutor(num_workers) as executor:
infos = executor.map(process_single_scene, sample_id_list)
return list(infos)
def create_groundtruth_database(self, info_path=None, used_classes=None, split='train'):
import torch
database_save_path = Path(self.root_path) / ('gt_database' if split == 'train' else ('gt_database_%s' % split))
db_info_save_path = Path(self.root_path) / ('kitti_dbinfos_%s.pkl' % split)
database_save_path.mkdir(parents=True, exist_ok=True)
all_db_infos = {}
with open(info_path, 'rb') as f:
infos = pickle.load(f)
for k in range(len(infos)):
print('gt_database sample: %d/%d' % (k + 1, len(infos)))
info = infos[k]
sample_idx = info['point_cloud']['lidar_idx']
points = self.get_lidar(sample_idx)
annos = info['annos']
names = annos['name']
difficulty = annos['difficulty']
bbox = annos['bbox']
gt_boxes = annos['gt_boxes_lidar']
num_obj = gt_boxes.shape[0]
if num_obj == 0:
continue
point_indices = roiaware_pool3d_utils.points_in_boxes_cpu(
torch.from_numpy(points[:, 0:3]), torch.from_numpy(gt_boxes)
).numpy() # (nboxes, npoints)
for i in range(num_obj):
filename = '%s_%s_%d.bin' % (sample_idx, names[i], i)
filepath = database_save_path / filename
gt_points = points[point_indices[i] > 0]
gt_points[:, :3] -= gt_boxes[i, :3]
# print(filepath, gt_points.shape)
with open(filepath, 'w') as f:
gt_points.tofile(f)
if (used_classes is None) or names[i] in used_classes:
db_path = str(filepath.relative_to(self.root_path)) # gt_database/xxxxx.bin
db_info = {'name': names[i], 'path': db_path, 'image_idx': sample_idx, 'gt_idx': i,
'box3d_lidar': gt_boxes[i], 'num_points_in_gt': gt_points.shape[0],
'difficulty': difficulty[i], 'bbox': bbox[i], 'score': annos['score'][i]}
if names[i] in all_db_infos:
all_db_infos[names[i]].append(db_info)
else:
all_db_infos[names[i]] = [db_info]
for k, v in all_db_infos.items():
print('Database %s: %d' % (k, len(v)))
with open(db_info_save_path, 'wb') as f:
pickle.dump(all_db_infos, f)
@staticmethod
def generate_prediction_dicts(batch_dict, pred_dicts, class_names, output_path=None):
"""
Args:
batch_dict:
frame_id:
pred_dicts: list of pred_dicts
pred_boxes: (N, 7), Tensor
pred_scores: (N), Tensor
pred_labels: (N), Tensor
class_names:
output_path:
Returns:
"""
def get_template_prediction(num_samples):
ret_dict = {
'name': np.zeros(num_samples), 'truncated': np.zeros(num_samples),
'occluded': np.zeros(num_samples), 'alpha': np.zeros(num_samples),
'bbox': np.zeros([num_samples, 4]), 'dimensions': np.zeros([num_samples, 3]),
'location': np.zeros([num_samples, 3]), 'rotation_y': np.zeros(num_samples),
'score': np.zeros(num_samples), 'boxes_lidar': np.zeros([num_samples, 7])
}
return ret_dict
def generate_single_sample_dict(batch_index, box_dict):
pred_scores = box_dict['pred_scores'].cpu().numpy()
pred_boxes = box_dict['pred_boxes'].cpu().numpy()
pred_labels = box_dict['pred_labels'].cpu().numpy()
pred_dict = get_template_prediction(pred_scores.shape[0])
if pred_scores.shape[0] == 0:
return pred_dict
calib = batch_dict['calib'][batch_index]
image_shape = batch_dict['image_shape'][batch_index].cpu().numpy()
pred_boxes_camera = box_utils.boxes3d_lidar_to_kitti_camera(pred_boxes, calib)
pred_boxes_img = box_utils.boxes3d_kitti_camera_to_imageboxes(
pred_boxes_camera, calib, image_shape=image_shape
)
pred_dict['name'] = np.array(class_names)[pred_labels - 1]
pred_dict['alpha'] = -np.arctan2(-pred_boxes[:, 1], pred_boxes[:, 0]) + pred_boxes_camera[:, 6]
pred_dict['bbox'] = pred_boxes_img.reshape(-1, 4)
pred_dict['dimensions'] = pred_boxes_camera[:, 3:6].reshape(
-1, 3)
pred_dict['location'] = pred_boxes_camera[:, 0:3].reshape(
-1, 3)
pred_dict['rotation_y'] = pred_boxes_camera[:, 6]
pred_dict['score'] = pred_scores
pred_dict['boxes_lidar'] = pred_boxes
return pred_dict
annos = []
for index, box_dict in enumerate(pred_dicts):
frame_id = batch_dict['frame_id'][index]
single_pred_dict = generate_single_sample_dict(index, box_dict)
single_pred_dict['frame_id'] = frame_id
annos.append(single_pred_dict)
if output_path is not None:
cur_det_file = output_path / ('%s.txt' % frame_id)
with open(cur_det_file, 'w') as f:
bbox = single_pred_dict['bbox']
loc = single_pred_dict['location']
dims = single_pred_dict['dimensions'] # lhw -> hwl
for idx in range(len(bbox)):
print('%s -1 -1 %.4f %.4f %.4f %.4f %.4f %.4f %.4f %.4f %.4f %.4f %.4f %.4f %.4f'
% (single_pred_dict['name'][idx], single_pred_dict['alpha'][idx],
bbox[idx][0], bbox[idx][1], bbox[idx][2], bbox[idx][3],
dims[idx][1], dims[idx][2], dims[idx][0], loc[idx][0],
loc[idx][1], loc[idx][2], single_pred_dict['rotation_y'][idx],
single_pred_dict['score'][idx]), file=f)
return annos
def evaluation(self, det_annos, class_names, range_eval=True, ranges=(0, 30, 50, 80), **kwargs):
if 'annos' not in self.kitti_infos[0].keys():
return None, {}
from .kitti_object_eval_python import eval as kitti_eval
eval_det_annos = copy.deepcopy(det_annos)
eval_gt_annos = [copy.deepcopy(info['annos']) for info in self.kitti_infos]
# np.concatenate([a["bbox"] for a in eval_det_annos], 0)
# print(id(eval_det_annos), "evaluation")
if range_eval:
ap_result_str, ap_dict = kitti_eval.get_range_eval_result(
eval_gt_annos, eval_det_annos, class_names, ranges=ranges)
else:
ap_result_str, ap_dict = kitti_eval.get_official_eval_result(eval_gt_annos, eval_det_annos, class_names)
return ap_result_str, ap_dict
def __len__(self):
if self._merge_all_iters_to_one_epoch:
return len(self.kitti_infos) * self.total_epochs
return len(self.kitti_infos)
def __getitem__(self, index):
# index = 4
if self._merge_all_iters_to_one_epoch:
index = index % len(self.kitti_infos)
info = copy.deepcopy(self.kitti_infos[index])
sample_idx = info['point_cloud']['lidar_idx']
img_shape = info['image']['image_shape']
calib = self.get_calib(sample_idx)
get_item_list = self.dataset_cfg.get('GET_ITEM_LIST', ['points'])
input_dict = {
'frame_id': sample_idx,
'calib': calib,
}
if 'annos' in info:
annos = info['annos']
annos = common_utils.drop_info_with_name(annos, name='DontCare')
loc, dims, rots = annos['location'], annos['dimensions'], annos['rotation_y']
if len(annos['name']) > 0:
gt_names = annos['name']
gt_boxes_camera = np.concatenate([loc, dims, rots[..., np.newaxis]], axis=1).astype(np.float32)
gt_boxes_lidar = box_utils.boxes3d_kitti_camera_to_lidar(gt_boxes_camera, calib)
input_dict.update({
'gt_names': gt_names,
'gt_boxes': gt_boxes_lidar
})
else:
input_dict.update({
'gt_names': annos['name'],
'gt_boxes': np.zeros((0, 7), dtype=float)
})
if "gt_boxes2d" in get_item_list:
input_dict['gt_boxes2d'] = annos["bbox"]
road_plane = self.get_road_plane(sample_idx)
if road_plane is not None:
input_dict['road_plane'] = road_plane
if "points" in get_item_list:
points = self.get_lidar(sample_idx)
if self.dataset_cfg.FOV_POINTS_ONLY:
pts_rect = calib.lidar_to_rect(points[:, 0:3])
fov_flag = self.get_fov_flag(pts_rect, img_shape, calib)
points = points[fov_flag]
input_dict['points'] = points
if "images" in get_item_list:
input_dict['images'] = self.get_image(sample_idx)
if "depth_maps" in get_item_list:
input_dict['depth_maps'] = self.get_depth_map(sample_idx)
if "calib_matricies" in get_item_list:
input_dict["trans_lidar_to_cam"], input_dict["trans_cam_to_img"] = kitti_utils.calib_to_matricies(calib)
if "history_scans" in get_item_list:
if self.load_history.get("HISTORY_AUG", False):
input_dict["history_scans"] = self.get_history_raw(
sample_idx)
else:
input_dict["history_coordinates"], input_dict["history_features"] = self.get_history(
sample_idx)
if "background_sample" in get_item_list and self.training:
input_dict["background_sample"] = self.get_background_sample(
sample_idx)
data_dict = self.prepare_data(data_dict=input_dict)
data_dict['image_shape'] = img_shape
return data_dict
def create_kitti_infos(dataset_cfg, class_names, data_path, save_path, workers=4):
dataset = KittiDataset(dataset_cfg=dataset_cfg, class_names=class_names, root_path=data_path, training=False)
train_split, val_split = 'train', 'val'
train_filename = save_path / ('kitti_infos_%s.pkl' % train_split)
val_filename = save_path / ('kitti_infos_%s.pkl' % val_split)
trainval_filename = save_path / 'kitti_infos_trainval.pkl'
test_filename = save_path / 'kitti_infos_test.pkl'
print('---------------Start to generate data infos---------------')
dataset.set_split(train_split)
kitti_infos_train = dataset.get_infos(num_workers=workers, has_label=True, count_inside_pts=True)
with open(train_filename, 'wb') as f:
pickle.dump(kitti_infos_train, f)
print('Kitti info train file is saved to %s' % train_filename)
dataset.set_split(val_split)
kitti_infos_val = dataset.get_infos(num_workers=workers, has_label=True, count_inside_pts=True)
with open(val_filename, 'wb') as f:
pickle.dump(kitti_infos_val, f)
print('Kitti info val file is saved to %s' % val_filename)
with open(trainval_filename, 'wb') as f:
pickle.dump(kitti_infos_train + kitti_infos_val, f)
print('Kitti info trainval file is saved to %s' % trainval_filename)
# dataset.set_split('test')
# kitti_infos_test = dataset.get_infos(num_workers=workers, has_label=False, count_inside_pts=False)
# with open(test_filename, 'wb') as f:
# pickle.dump(kitti_infos_test, f)
# print('Kitti info test file is saved to %s' % test_filename)
print('---------------Start create groundtruth database for data augmentation---------------')
dataset.set_split(train_split)
dataset.create_groundtruth_database(train_filename, split=train_split)
print('---------------Data preparation Done---------------')
if __name__ == '__main__':
import sys
if sys.argv.__len__() > 1 and sys.argv[1] == 'create_kitti_infos':
import yaml
from pathlib import Path
from easydict import EasyDict
dataset_cfg = EasyDict(yaml.load(open(sys.argv[2])))
ROOT_DIR = (Path(__file__).resolve().parent / '../../../').resolve()
create_kitti_infos(
dataset_cfg=dataset_cfg,
class_names=['Car', 'Pedestrian', 'Cyclist'],
# data_path=ROOT_DIR / 'data' / 'kitti',
# save_path=ROOT_DIR / 'data' / 'kitti'
data_path=ROOT_DIR / 'tools' / dataset_cfg.DATA_PATH,
save_path=ROOT_DIR / 'tools' / dataset_cfg.DATA_PATH
)