SYMBOL INDEX (235 symbols across 25 files)

FILE: config/config_parser.py
  class ConfigParser (line 16) | class ConfigParser():
    method __init__ (line 17) | def __init__(self, config_file: Union[str, PathLike, Path]) -> None:
    method parse (line 29) | def parse(self):
    method __str__ (line 101) | def __str__(self):

FILE: datasets/cityscapes.py
  class Cityscapes (line 15) | class Cityscapes(Dataset):
    method __init__ (line 16) | def __init__(
    method _get_filenames (line 53) | def _get_filenames(self, mode: str) -> List[Path]:
    method _divide_into_sequences (line 100) | def _divide_into_sequences(city_filenames: List[Path]) -> Dict[str, int]:
    method __getitem__ (line 125) | def __getitem__(self, index: int) -> Dict[Any, Tensor]:
    method _load_camera_calibration (line 186) | def _load_camera_calibration(
    method _load_relative_distance (line 213) | def _load_relative_distance(self, index: int) -> float:
    method _load_depth (line 228) | def _load_depth(
    method _preprocess (line 248) | def _preprocess(

FILE: datasets/config.py
  class Dataset (line 7) | class Dataset:

FILE: datasets/kitti.py
  class Kitti (line 21) | class Kitti(Dataset):
    method __init__ (line 22) | def __init__(
    method _get_filenames (line 110) | def _get_filenames(self, mode: str) -> List[Path]:
    method _load_timestamps (line 146) | def _load_timestamps(self) -> List[int]:
    method _load_global_poses (line 166) | def _load_global_poses(self) -> np.ndarray:
    method _load_relative_poses (line 179) | def _load_relative_poses(self) -> np.ndarray:
    method _compute_relative_distance (line 195) | def _compute_relative_distance(self) -> List[float]:
    method _filter_by_index (line 201) | def _filter_by_index(self, keep_indices: Union[np.ndarray, List[int]])...
    method _filter_by_distance (line 217) | def _filter_by_distance(self, min_distance: float) -> None:
    method __getitem__ (line 231) | def __getitem__(self, index: int) -> Dict[Any, Tensor]:
    method _load_relative_distance (line 319) | def _load_relative_distance(self, index: int) -> float:
    method _preprocess (line 333) | def _preprocess(
  function extract_raw_data (line 361) | def extract_raw_data(

FILE: datasets/robotcar.py
  class Robotcar (line 26) | class Robotcar(Dataset):
    method __init__ (line 27) | def __init__(
    method _get_filenames (line 90) | def _get_filenames(self, mode: str) -> List[Path]:
    method _load_velocity (line 111) | def _load_velocity(self) -> List[float]:
    method _load_camera_calibration (line 126) | def _load_camera_calibration(self) -> np.ndarray:
    method _load_global_poses (line 146) | def _load_global_poses(self) -> np.ndarray:
    method _load_relative_poses (line 170) | def _load_relative_poses(self) -> np.ndarray:
    method _compute_relative_distance (line 186) | def _compute_relative_distance(self) -> List[float]:
    method _filter_by_index (line 192) | def _filter_by_index(self, keep_indices: Union[np.ndarray, List[int]])...
    method _filter_by_distance (line 206) | def _filter_by_distance(self, min_distance: float) -> None:
    method __getitem__ (line 220) | def __getitem__(self, index: int) -> Dict[Any, Tensor]:
    method _load_relative_distance (line 272) | def _load_relative_distance(self, index: int) -> float:
    method _preprocess (line 283) | def _preprocess(
  function _xyzrpy_to_tmat (line 307) | def _xyzrpy_to_tmat(utm: np.ndarray, rpy: np.ndarray) -> np.ndarray:
  function _interpolate_poses (line 318) | def _interpolate_poses(pose_timestamps,
  function _so3_to_quaternion (line 434) | def _so3_to_quaternion(so3):
  function undistort_images (line 494) | def undistort_images(data_path_in: str, models_path: str) -> None:
  function _undistort (line 513) | def _undistort(image_file: Path, data_path_out: str, model):
  function _load_image (line 522) | def _load_image(image_path, model=None, debayer=True):
  class CameraModel (line 553) | class CameraModel:
    method __init__ (line 568) | def __init__(self, models_dir, images_dir):
    method project (line 584) | def project(self, xyz, image_size):
    method undistort (line 617) | def undistort(self, image):
    method __get_model_name (line 644) | def __get_model_name(self, images_dir):
    method __load_intrinsics (line 658) | def __load_intrinsics(self, models_dir, images_dir):
    method __load_lut (line 672) | def __load_lut(self, models_dir, images_dir):

FILE: datasets/utils.py
  class Dataset (line 17) | class Dataset(TorchDataset):
    method __init__ (line 18) | def __init__(
    method _load_image_filenames (line 74) | def _load_image_filenames(self) -> None:
    method _load_mask_filenames (line 82) | def _load_mask_filenames(self) -> None:
    method _len_frames (line 90) | def _len_frames(self) -> int:
    method __len__ (line 98) | def __len__(self) -> int:
    method _scale_camera_matrix (line 104) | def _scale_camera_matrix(self, camera_matrix: np.ndarray,
    method _pre_getitem (line 112) | def _pre_getitem(self, index: int) -> Tuple[List[Path], List[Path], in...
    method _post_getitem (line 154) | def _post_getitem(self, item: Dict[Any, Any], do_color_augmentation: b...
    method _load_from_cache (line 173) | def _load_from_cache(self, cache_name: str) -> Union[None, Any]:
    method _save_to_cache (line 185) | def _save_to_cache(self, cache_name: str, data: Any, replace: bool = F...
    method _get_filenames (line 197) | def _get_filenames(self, mode: str) -> List[Path]:
    method _preprocess (line 201) | def _preprocess(
    method __getitem__ (line 210) | def __getitem__(self, index: int) -> Dict[Any, Tensor]:
    method _to_tensor (line 214) | def _to_tensor(data) -> Tensor:
    method get_item_filenames (line 217) | def get_item_filenames(self, index: int):
  function get_random_color_jitter (line 236) | def get_random_color_jitter(
  function augment_data (line 265) | def augment_data(sample):
  function show_images (line 287) | def show_images(batch, scales=(0, 1, 2, 3), frames=(-1, 0, 1), augmented...

FILE: depth_pose_prediction/config.py
  class DepthPosePrediction (line 7) | class DepthPosePrediction:

FILE: depth_pose_prediction/depth_pose_prediction.py
  class DepthPosePrediction (line 38) | class DepthPosePrediction:
    method __init__ (line 39) | def __init__(self, dataset_config: DatasetConfig, config: Config, use_...
    method train (line 219) | def train(self,
    method adapt (line 291) | def adapt(self,
    method validate (line 321) | def validate(self) -> float:
    method compute_depth_error (line 344) | def compute_depth_error(
    method compute_pose_error (line 470) | def compute_pose_error(self, print_results: bool = True) -> Dict[str, ...
    method predict (line 530) | def predict(self, batch) -> Dict[Any, Tensor]:
    method predict_from_image (line 538) | def predict_from_image(self, image, as_numpy: bool = True):
    method predict_from_images (line 556) | def predict_from_images(
    method predict_pose (line 628) | def predict_pose(
    method save_model (line 669) | def save_model(self) -> None:
    method load_model (line 705) | def load_model(self, load_optimizer: bool = True) -> None:
    method load_online_model (line 751) | def load_online_model(self, load_optimizer: bool = True) -> None:
    method _set_train (line 797) | def _set_train(self) -> None:
    method _set_eval (line 802) | def _set_eval(self) -> None:
    method _set_adapt (line 807) | def _set_adapt(self, freeze_encoder: bool = True) -> None:
    method _create_dataloaders (line 829) | def _create_dataloaders(self, training: bool = True, validation: bool ...
    method _process_batch (line 906) | def _process_batch(
    method _predict_disparity (line 925) | def _predict_disparity(self,
    method _predict_poses (line 938) | def _predict_poses(self,
    method _reconstruct_images (line 976) | def _reconstruct_images(
    method _compute_loss (line 1019) | def _compute_loss(
    method _compute_velocity_loss (line 1125) | def _compute_velocity_loss(
    method _compute_smooth_loss (line 1149) | def _compute_smooth_loss(
    method _compute_reprojection_loss (line 1178) | def _compute_reprojection_loss(
    method save_prediction (line 1197) | def save_prediction(
    method _init_wandb (line 1246) | def _init_wandb(self):

FILE: depth_pose_prediction/networks/depth_decoder.py
  class DepthDecoder (line 14) | class DepthDecoder(nn.Module):
    method __init__ (line 15) | def __init__(
    method forward (line 51) | def forward(self, input_features: Tensor) -> Dict[Tuple[str, int], Ten...

FILE: depth_pose_prediction/networks/layers.py
  class ConvBlock (line 9) | class ConvBlock(nn.Module):
    method __init__ (line 12) | def __init__(
    method forward (line 22) | def forward(self, x: Tensor) -> Tensor:
  class Conv3x3 (line 28) | class Conv3x3(nn.Module):
    method __init__ (line 31) | def __init__(
    method forward (line 45) | def forward(self, x: Tensor) -> Tensor:
  class BackprojectDepth (line 51) | class BackprojectDepth(nn.Module):
    method __init__ (line 54) | def __init__(self, batch_size: int, height: int, width: int) -> None:
    method forward (line 74) | def forward(self, depth, inv_K):
  class Project3D (line 82) | class Project3D(nn.Module):
    method __init__ (line 85) | def __init__(self, batch_size: int, height: int, width: int, eps: floa...
    method forward (line 93) | def forward(self, points, K, T):
  class SSIM (line 107) | class SSIM(nn.Module):
    method __init__ (line 110) | def __init__(self) -> None:
    method forward (line 123) | def forward(self, x: Tensor, y: Tensor) -> Tensor:

FILE: depth_pose_prediction/networks/pose_decoder.py
  class PoseDecoder (line 11) | class PoseDecoder(nn.Module):
    method __init__ (line 12) | def __init__(
    method forward (line 37) | def forward(self, input_features: Tensor) -> Tuple[Tensor, Tensor]:

FILE: depth_pose_prediction/networks/resnet_encoder.py
  class ResNetMultiImageInput (line 13) | class ResNetMultiImageInput(models.ResNet):
    method __init__ (line 17) | def __init__(
  function resnet_multiimage_input (line 47) | def resnet_multiimage_input(
  class ResnetEncoder (line 79) | class ResnetEncoder(nn.Module):
    method __init__ (line 86) | def __init__(
    method forward (line 115) | def forward(self, x: Tensor) -> List[Tensor]:

FILE: depth_pose_prediction/pytorch3d.py
  function quaternion_to_axis_angle (line 8) | def quaternion_to_axis_angle(quaternions: torch.Tensor) -> torch.Tensor:
  function matrix_to_quaternion (line 37) | def matrix_to_quaternion(matrix: torch.Tensor) -> torch.Tensor:
  function _sqrt_positive_part (line 89) | def _sqrt_positive_part(x: torch.Tensor) -> torch.Tensor:

FILE: depth_pose_prediction/utils.py
  function parameters_from_transformation (line 14) | def parameters_from_transformation(
  function transformation_from_parameters (line 34) | def transformation_from_parameters(
  function get_translation_matrix (line 58) | def get_translation_matrix(translation_vector: Tensor) -> Tensor:
  function rot_from_axisangle (line 74) | def rot_from_axisangle(axis_angle: Tensor) -> Tensor:
  function disp_to_depth (line 120) | def disp_to_depth(
  function h_concat_images (line 148) | def h_concat_images(im1: Image, im2: Image) -> Image:

FILE: loop_closure_detection/config.py
  class LoopClosureDetection (line 6) | class LoopClosureDetection:

FILE: loop_closure_detection/encoder.py
  class FeatureEncoder (line 7) | class FeatureEncoder:
    method __init__ (line 8) | def __init__(self, device: torch.device):
    method __call__ (line 28) | def __call__(self, image: Tensor) -> Tensor:

FILE: loop_closure_detection/loop_closure_detection.py
  class LoopClosureDetection (line 15) | class LoopClosureDetection:
    method __init__ (line 16) | def __init__(
    method add (line 41) | def add(self, image_id: int, image: Tensor) -> None:
    method search (line 53) | def search(self, image_id: int) -> Tuple[List[int], List[float]]:
    method predict (line 78) | def predict(self, image_0: Tensor, image_1: Tensor) -> float:
    method display_matches (line 86) | def display_matches(image_0, image_1, image_id_0, image_id_1, transfor...

FILE: loop_closure_detection/utils.py
  function plot_image_matches (line 6) | def plot_image_matches(

FILE: slam/config.py
  class Slam (line 6) | class Slam:
  class ReplayBuffer (line 19) | class ReplayBuffer:

FILE: slam/meshlab.py
  class MeshlabInf (line 14) | class MeshlabInf:
    method show_multi_layer (line 16) | def show_multi_layer(**kwargs):
    method plot3d (line 32) | def plot3d(pts, false_color=False):
    method get_colormap (line 38) | def get_colormap(sz, cmap_name="jet"):
    method __init__ (line 42) | def __init__(self, global_transformation=np.eye(4)):
    method clear (line 48) | def clear(self):
    method add_camera (line 51) | def add_camera(self, p, color=DEFAULT_COLOR, scale=0.1, rotation=np.ey...
    method add_line (line 72) | def add_line(self, p1, p2, c=None):
    method add_mesh (line 79) | def add_mesh(self, xyz, c=None):
    method add_points (line 116) | def add_points(self, xyz, color=None):
    method add_pgon (line 141) | def add_pgon(self, xyz, color=None):
    method add_faces (line 147) | def add_faces(self, verts):
    method read (line 150) | def read(self, fname):
    method show (line 153) | def show(self, false_color=False):
    method write (line 159) | def write(self, fname, false_color=False, verbose=True):
  function norm_range_01 (line 209) | def norm_range_01(v: np.ndarray, prcnt: tuple = None) -> np.ndarray:
  function rotation_matrix_from_to (line 232) | def rotation_matrix_from_to(v_from: Union[List, Tuple, np.ndarray],

FILE: slam/pose_graph_optimization.py
  class PoseGraphOptimization (line 7) | class PoseGraphOptimization(g2o.SparseOptimizer):
    method __init__ (line 8) | def __init__(self):
    method __str__ (line 23) | def __str__(self):
    method vertex_ids (line 30) | def vertex_ids(self):
    method optimize (line 33) | def optimize(self, max_iterations=1000, verbose=False):
    method add_vertex (line 38) | def add_vertex(self, vertex_id, pose, fixed=False):
    method add_vertex_point (line 45) | def add_vertex_point(self, vertex_id, point, fixed=False):
    method add_edge (line 52) | def add_edge(self,
    method add_edge_pose_point (line 75) | def add_edge_pose_point(self,
    method get_pose (line 91) | def get_pose(self, vertex_id):
    method get_all_poses (line 94) | def get_all_poses(self):
    method get_transform (line 97) | def get_transform(self, vertex_id_src, vertex_id_dst):
    method does_edge_exists (line 103) | def does_edge_exists(self, vertex_id_a, vertex_id_b):
    method is_vertex_in_any_edge (line 108) | def is_vertex_in_any_edge(self, vertex_id):
    method does_vertex_have_only_global_edges (line 115) | def does_vertex_have_only_global_edges(self, vertex_id):
    method visualize_in_meshlab (line 124) | def visualize_in_meshlab(self, filename, meshlab=None, verbose=True):

FILE: slam/replay_buffer.py
  class ReplayBuffer (line 19) | class ReplayBuffer(TorchDataset):
    method __init__ (line 20) | def __init__(
    method add (line 82) | def add(self, sample: Dict[str, Any], sample_filenames: Dict[str, Any],
    method get (line 186) | def get(self, sample: Dict[str, Any], image_features: Optional[Tensor]...
    method save_state (line 237) | def save_state(self):
    method load_state (line 246) | def load_state(self, state_path: Path):
    method __getitem__ (line 257) | def __getitem__(self, index: int) -> Dict[Any, Tensor]:
    method __len__ (line 260) | def __len__(self):
    method _get (line 263) | def _get(self, filename, include_batch=True):
    method _reset_storage_dir (line 293) | def _reset_storage_dir(self):

FILE: slam/slam.py
  class Slam (line 18) | class Slam:
    method __init__ (line 19) | def __init__(self, config):
    method __len__ (line 134) | def __len__(self):
    method step (line 137) | def step(self):
    method save_metrics (line 283) | def save_metrics(self) -> None:
    method save_model (line 295) | def save_model(self) -> None:
    method _cat_dict (line 301) | def _cat_dict(dict_1, dict_2):
    method _pose_graph_to_2d_trajectory (line 312) | def _pose_graph_to_2d_trajectory(pose_graph):
    method plot_trajectory (line 318) | def plot_trajectory(self):
    method plot_metrics (line 336) | def plot_metrics(self, filename: str = 'metrics.png'):

FILE: slam/transform.py
  function print_tmat (line 5) | def print_tmat(tmat, note=''):
  function print_array (line 9) | def print_array(array, note=''):
  function print_sixdof (line 13) | def print_sixdof(sixdof, note=''):
  function string_tmat (line 17) | def string_tmat(tmat, note=''):
  function string_sixdof (line 21) | def string_sixdof(sixdof, note=''):
  function create_empty_sixdof (line 28) | def create_empty_sixdof():
  function tmat2sixdof (line 33) | def tmat2sixdof(tmat):
  function sixdof2tmat (line 46) | def sixdof2tmat(sixdof):
  function tmat2array (line 55) | def tmat2array(tmat):
  function array2tmat (line 67) | def array2tmat(array):
  function array2sixdof (line 71) | def array2sixdof(array):
  function apply_transformation (line 84) | def apply_transformation(transformation: np.ndarray, input_data: np.ndar...

FILE: slam/utils.py
  function save_data (line 14) | def save_data(fname, obj):
  function load_data (line 19) | def load_data(fname):
  function depth_to_pcl (line 25) | def depth_to_pcl(backproject_depth: BackprojectDepth,
  function pcl_to_image (line 41) | def pcl_to_image(
  function save_point_cloud (line 61) | def save_point_cloud(
  function accumulate_pcl (line 76) | def accumulate_pcl(pcl_list: List[np.ndarray], global_pose_list: np.ndar...
  function generate_figure (line 85) | def generate_figure(
  function scale_optimization (line 129) | def scale_optimization(pred_poses, gt_poses):
  function scale_lse_solver (line 151) | def scale_lse_solver(X, Y):
  function trajectory_distances (line 164) | def trajectory_distances(poses):
  function last_frame_from_segment_length (line 175) | def last_frame_from_segment_length(dist, first_frame, length):
  function rotation_error (line 191) | def rotation_error(pose_error):
  function translation_error (line 206) | def translation_error(pose_error):
  function calc_sequence_errors (line 220) | def calc_sequence_errors(pred_poses, gt_poses):
  function compute_segment_error (line 253) | def compute_segment_error(seq_errs):
  function compute_overall_err (line 292) | def compute_overall_err(seq_err):
  function compute_ATE (line 317) | def compute_ATE(pred_poses, gt_poses):
  function compute_RPE (line 331) | def compute_RPE(pred_poses, gt_poses):
  function calc_error (line 357) | def calc_error(pred_poses, gt_poses, optimize_scale: bool = False) -> str:
  function calc_depth_error (line 389) | def calc_depth_error(