def __init__(self):
# with open('./lib/datasets/dataloader_config.json', 'r') as f:
# json_obj = json.load(f)
# self.root = json_obj['TRAINING_DATA_PATH']
# self.num_pts = json_obj['NUM_POINTS']
# self.pc_path = os.path.join(self.root, 'point_cloud')
# self.label_path = os.path.join(self.root, 'labels')
# self.frames = [s.split('.')[0] for s in os.listdir(self.pc_path) if '.bin' in s ]
self.name = 'KITTI'
self.root = cfg.train_data_path
self.num_pts = cfg.num_points
self.pc_path = os.path.join(self.root, 'point_cloud')
self.label_path = os.path.join(self.root, 'labels')
self.frames = [s.split('.')[0] for s in os.listdir(self.pc_path) if '.bin' in s ]
self.num_classes = cfg.num_classes
if self.num_classes ==1:
self.num_classes = 0
self.num_features = cfg.num_features
self.num_target_attributes = cfg.num_target_attributes
self.split_ratio = cfg.split_ratio
self.num_samples = len(self.frames)
assert np.abs(np.sum(self.split_ratio) - 1.0) < 1e-5
train_split = int(self.num_samples * self.split_ratio[0])
val_split = int(self.num_samples * np.sum(self.split_ratio[:2]))
self.frames_indices = np.arange(len(self.frames))
# self.train_list = self.frames[:train_split]
# self.val_list = self.frames[train_split:val_split]
# self.test_list = self.frames[val_split:]
self.train_list = self.frames_indices[:train_split]
self.val_list = self.frames_indices[train_split:val_split]
self.test_list = self.frames_indices[val_split:]
self.train_list = DP.shuffle_list(self.train_list)
self.val_list = DP.shuffle_list(self.val_list)
def __init__(self, mode, test_id=None):
self.name = 'SemanticKITTI'
self.dataset_path = '/data/WQ/DataSet/semantic-kitti/dataset/sequences_0.06'
self.label_to_names = {0: 'unlabeled',
1: 'car',
2: 'bicycle',
3: 'motorcycle',
4: 'truck',
5: 'other-vehicle',
6: 'person',
7: 'bicyclist',
8: 'motorcyclist',
9: 'road',
10: 'parking',
11: 'sidewalk',
12: 'other-ground',
13: 'building',
14: 'fence',
15: 'vegetation',
16: 'trunk',
17: 'terrain',
18: 'pole',
19: 'traffic-sign'}
self.num_classes = len(self.label_to_names)
self.label_values = np.sort([k for k, v in self.label_to_names.items()])
self.label_to_idx = {l: i for i, l in enumerate(self.label_values)}
self.ignored_labels = np.sort([0])
self.seq_list = np.sort(os.listdir(self.dataset_path))
if mode == 'test':
self.test_scan_number = str(test_id)
self.mode = mode
train_list, val_list, test_list = DP.get_file_list(self.dataset_path, str(test_id))
if mode == 'training':
self.data_list = train_list
elif mode == 'validation':
self.data_list = val_list
elif mode == 'test':
self.data_list = test_list
# self.data_list = self.data_list[0:1]
self.data_list = DP.shuffle_list(self.data_list)
self.possibility = []
self.min_possibility = []
if mode == 'test':
path_list = self.data_list
for test_file_name in path_list:
points = np.load(test_file_name)
self.possibility += [np.random.rand(points.shape[0]) * 1e-3]
self.min_possibility += [float(np.min(self.possibility[-1]))]
cfg.ignored_label_inds = [self.label_to_idx[ign_label] for ign_label in self.ignored_labels]
cfg.class_weights = DP.get_class_weights('SemanticKITTI')
def __init__(self, test_id):
self.name = 'SemanticKITTI'
self.dataset_path = '/data/semantic_kitti/dataset/sequences_0.06'
self.label_to_names = {
0: 'unlabeled',
1: 'car',
2: 'bicycle',
3: 'motorcycle',
4: 'truck',
5: 'other-vehicle',
6: 'person',
7: 'bicyclist',
8: 'motorcyclist',
9: 'road',
10: 'parking',
11: 'sidewalk',
12: 'other-ground',
13: 'building',
14: 'fence',
15: 'vegetation',
16: 'trunk',
17: 'terrain',
18: 'pole',
19: 'traffic-sign'
}
self.num_classes = len(self.label_to_names)
self.label_values = np.sort(
[k for k, v in self.label_to_names.items()])
self.label_to_idx = {l: i for i, l in enumerate(self.label_values)}
self.ignored_labels = np.sort([0])
self.val_split = '08'
self.seq_list = np.sort(os.listdir(self.dataset_path))
self.test_scan_number = str(test_id)
self.train_list, self.val_list, self.test_list = DP.get_file_list(
self.dataset_path, self.test_scan_number)
self.train_list = DP.shuffle_list(self.train_list)
self.val_list = DP.shuffle_list(self.val_list)
self.possibility = []
self.min_possibility = []
def __init__(self, mode):
self.name = 'raildata_RandLA'
self.dataset_path = '/home/hwq/dataset/rail_randla_0.06'
self.label_to_names = {0: 'unlabeled', 1: 'rail', 2: 'pole'}
self.num_classes = len(self.label_to_names)
self.label_values = np.sort(
[k for k, v in self.label_to_names.items()]) # [0,1,2]
self.label_to_idx = {l: i
for i, l in enumerate(self.label_values)
} # dict {0:0,1:1,2:2}
self.ignored_labels = np.sort([0])
self.mode = mode
fns = sorted(os.listdir(join(self.dataset_path, 'velodyne')))
train_index = np.load('./utils/rail_index/trainindex.npy')
test_index = np.load('./utils/rail_index/testindex.npy')
alldatapath = []
for fn in fns:
alldatapath.append(os.path.join(self.dataset_path, fn))
# print(alldatapath,train_index)
self.data_list = []
if mode == 'training':
for index in train_index:
self.data_list.append(alldatapath[index])
elif mode == 'validation':
for index in test_index:
self.data_list.append(alldatapath[index])
elif mode == 'test':
for index in test_index:
self.data_list.append(alldatapath[index])
self.data_list = np.asarray(self.data_list)
self.data_list = DP.shuffle_list(self.data_list)
cfg.ignored_label_inds = [
self.label_to_idx[ign_label] for ign_label in self.ignored_labels
]
cfg.class_weights = DP.get_class_weights('Rail')
