DATASET: 'PandasetDataset'
DATA_PATH: '../data/pandaset'

POINT_CLOUD_RANGE: [-70, -40, -3, 70, 40, 1] # xmin, ymin, zmin, xmax, ymax, zmax

DATA_SPLIT: {
    'train': train,
    'test': val
}

SEQUENCES: {
    'train': ['014', '050', '079', '048', '093', '091', '063', '104', '100', '092', '012', '047', '018', '006', '099', '085', '035', '041', '052', '105', '030', '113', '002', '084', '028', '119', '044', '005', '102', '034', '077', '064', '067', '058', '019', '015', '037', '095', '120', '066', '023', '071', '117', '098', '139', '038', '116', '046', '088', '089', '040', '033', '016', '024', '122', '039', '158', '069', '124', '123', '106'], # ~60% of the sequences, randomly chosen
    'val': ['045', '059', '055', '051', '020', '097', '073', '043', '003', '101', '027', '056', '011', '078', '080', '109', '042', '021', '094', '057'], # ~20% of the sequences, randomly chosen
    'test': ['074', '004', '086', '062', '068', '008', '001', '110', '053', '115', '054', '065', '017', '103', '072', '013', '029', '090', '112', '149', '070', '032'] # ~20% of the sequences, randomly chosen
}

# Acquisition device to consider when loading the data
# Pandaset contains data from:
# - a pandar64 spinning lidar
# - a pandarGT forward facing lidar
# To use data from:
# - the pandar64 lidar only (default), set LIDAR_DEVICE to 0,
# - the pandarGT lidar onlu, set it to 1
# - both devices, set it to -1
LIDAR_DEVICE: 0


INFO_PATH: {
    'train': [pandaset_infos_train.pkl],
    'test': [pandaset_infos_val.pkl],
}

TRAINING_CATEGORIES: {
# This maps raw dataset categories with the corresponding categories used in training
# This map can be incomplete. In case a category is not present, the category
# for training is the same as the raw dataset category
  'Car': 'Car',
  'Pickup Truck': 'Car',
  'Medium-sized Truck': 'Truck',
  'Semi-truck': 'Truck',
  'Towed Object': 'Other Vehicle',
  'Motorcycle': 'Motorcycle',
  'Other Vehicle - Construction Vehicle': 'Other Vehicle',
  'Other Vehicle - Uncommon': 'Other Vehicle',
  'Other Vehicle - Pedicab': 'Other Vehicle',
  'Emergency Vehicle': 'Other Vehicle',
  'Bus': 'Bus',
  'Bicycle': 'Bicycle',
  'Pedestrian': 'Pedestrian',
  'Pedestrian with Object': 'Pedestrian',
  'Animals - Other': 'Animal'
}


FOV_POINTS_ONLY: False


DATA_AUGMENTOR:
    DISABLE_AUG_LIST: ['placeholder']
    AUG_CONFIG_LIST:
# gt sampling not working at the moment
        - NAME: gt_sampling
          USE_ROAD_PLANE: False
          DB_INFO_PATH:
              - pandaset_dbinfos_train.pkl
          PREPARE: {
             filter_by_min_points: ['Car:5', 'Pedestrian:5', 'Bicycle:5'],
             filter_by_difficulty: [-1],
          }

          SAMPLE_GROUPS: ['Car:20','Pedestrian:15', 'Bicycle:15']
          NUM_POINT_FEATURES: 4
          DATABASE_WITH_FAKELIDAR: False
          REMOVE_EXTRA_WIDTH: [0.0, 0.0, 0.0]
          LIMIT_WHOLE_SCENE: True

        - NAME: random_world_flip
          ALONG_AXIS_LIST: ['x', 'y']

        - NAME: random_world_rotation
          WORLD_ROT_ANGLE: [-3.14159265, 3.114159265]

        - NAME: random_world_scaling
          WORLD_SCALE_RANGE: [0.95, 1.05]


POINT_FEATURE_ENCODING: {
    encoding_type: absolute_coordinates_encoding,
    used_feature_list: ['x', 'y', 'z', 'intensity'],
    src_feature_list: ['x', 'y', 'z', 'intensity'],
}


DATA_PROCESSOR:
    - NAME: mask_points_and_boxes_outside_range
      REMOVE_OUTSIDE_BOXES: True

    - NAME: shuffle_points
      SHUFFLE_ENABLED: {
        'train': True,
        'test': False
      }

    - NAME: transform_points_to_voxels
      VOXEL_SIZE: [0.05, 0.05, 0.1]
      MAX_POINTS_PER_VOXEL: 5
      MAX_NUMBER_OF_VOXELS: {
        'train': 16000,
        'test': 40000
      }